Citation |

- Permanent Link:
- http://ufdc.ufl.edu/UFE0041943/00001
## Material Information- Title:
- Factors Influencing Success in Online High School Algebra
- Creator:
- Liu, Feng
- Place of Publication:
- [Gainesville, Fla.]
- Publisher:
- University of Florida
- Publication Date:
- 2010
- Language:
- english
- Physical Description:
- 1 online resource (195 p.)
## Thesis/Dissertation Information- Degree:
- Doctorate ( Ph.D.)
- Degree Grantor:
- University of Florida
- Degree Disciplines:
- Curriculum and Instruction (ISC)
Teaching and Learning - Committee Chair:
- Cavanaugh, Catherine S.
- Committee Members:
- Dawson, Kara M.
Jacobbe, Timothy Algina, James J. - Graduation Date:
- 8/7/2010
## Subjects- Subjects / Keywords:
- Academic achievement ( jstor )
Algebra ( jstor ) Distance education ( jstor ) Educational environment ( jstor ) Learning ( jstor ) Mathematics ( jstor ) Online learning ( jstor ) Schools ( jstor ) Students ( jstor ) Teachers ( jstor ) Teaching and Learning -- Dissertations, Academic -- UF - Genre:
- Electronic Thesis or Dissertation
bibliography ( marcgt ) theses ( marcgt ) government publication (state, provincial, terriorial, dependent) ( marcgt ) Curriculum and Instruction (ISC) thesis, Ph.D.
## Notes- Abstract:
- At present, an increasing number of students at the K-12 level in the U.S. are taking courses online via virtual schools, which have been in existence since the end of the 20th century. Virtual schooling is becoming a mainstream option alongside traditional face-to-face learning environments. Even with its increasing popularity, very few empirical studies have been conducted to provide practical guidance for teaching, learning, research, and policy making in K-12 virtual schooling. Some leading virtual school organizations, such as the Southern Regional Educational Board and the International Association for K-12 Online Learning, have produced standards in these fields. However, many of the standards lack empirical support based on research conducted in virtual learning environments. Math has been identified as a very important force to push a society forward since it is considered a foundational subject. Many countries emphasize the improvement of math knowledge and they develop policies to attract more people to the field. The examination of success factors in the math field in general and Algebra in specific in virtual learning environments can provide better implementation strategies in virtual schools to improve student math and science achievement and increase the Science, Technology, Engineering, and Mathematics (STEM) workforce in U.S. The purpose of this study is to examine the factors including LMS utilization, teacher comment/feedback and student demographic information that can influence the success of Algebra courses in K-12 virtual learning environments. Students who completed Algebra and took the end-of-course (EOC) test and students who took one state standardized mathematics test at grade 6-8 level in a state virtual school in the Midwestern U.S region during 2008-2009 participated in this study. Student scores on these tests were collected by this virtual school. Hierarchical linear modeling (HLM) technique was used for data analysis to account for the influence of school characteristics on student scores. The results show these factors have different influences on student performance on the state standardized mathematics test and the Algebra EOC test. These findings have implications for quality online teaching, instructional design, and the policy-making process in virtual learning environments. Further research is proposed based on the results and limitations of this study. ( en )
- General Note:
- In the series University of Florida Digital Collections.
- General Note:
- Includes vita.
- Bibliography:
- Includes bibliographical references.
- Source of Description:
- Description based on online resource; title from PDF title page.
- Source of Description:
- This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
- Thesis:
- Thesis (Ph.D.)--University of Florida, 2010.
- Local:
- Adviser: Cavanaugh, Catherine S.
- Statement of Responsibility:
- by Feng Liu.
## Record Information- Source Institution:
- UFRGP
- Rights Management:
- Applicable rights reserved.
- Embargo Date:
- 10/8/2010
- Resource Identifier:
- 004979704 ( ALEPH )
705932761 ( OCLC ) - Classification:
- LD1780 2010 ( lcc )
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Algebra I 8. What is the value of the numerical expression below? V/16 + 24- 23 3 A. 4 8. 6 C. 8 D. 10 9. Aaron listed the ages of all of his family members as shown below. 10,10, 10,10,10, 12, 14,14, 15,16,50,50,51, 53,80 What is the mean age of his family members? A. 10 B. 14 C. 27 D. 70 Copyright t) 2008 by the Missouri State Department of Elementary and Secondary Education. 126 Table 4-2: Standardized test takers demographics GRADE 6: 35(47.3%), 7: 28(37.8%), 8: 6(8.1%), 9: 4(5.4%), 10: 1(1.4%) RACE White: 60(81.1%), Other Minority: 14(18.9%) Standardized test Grade 6 FRL 0: 6(8.1%), 1: 68(91.9%) IEP 0: 5(6.8%), 1:69(93.2%) PT/FT 0: 61(82.4%), 1: 13(17.6%) GRADE 7: 29(39.7%), 8: 32(43.8%), 9: 9(12.3%), 10: 2(2.7%), missing: 1 (1.4%) Standardized test Grade RACE White: 59(80.8%), Other Minority: 14(19.2%) 7 FRL 0: 16(21.9%), 1: 57(78.1%) IEP 0: 19(26.0%), 1: 54(74.0%) PT/FT 0: 68(93.2%), 1: 5(6.8%) GRADE 8: 63(58.9%), 9: 39(36.4%), 10: 4(3.7%), missing: 1(0.9%) RACE White: 83(77.6%), Other Minority: 24(22.4%) Standardized test Grade 8 FRL 0: 20(18.7%), 1: 87(81.3%) IEP 0: 20(18.7%), 1: 87(81.3%) PT/FT 0: 89(83.2%), 1: 18(16.8%) 9.31, p=0.254) between these two ethnicity groups for the grade 8 standardized test, with the same direction as it for the grade 7 standardized test. Student's grade level in his/her physical school has no significant effect (-0.17, p= 0.083) on score in the grade 7 mathematics standardized test, with students in lower grade levels tending to achieve higher scores (see Table4-3). The non significant effect of student grade level (1.71, p=0.623) has also been found for the grade 8 mathematics standardized test (see Table 4-3). However, the direction is different for the grade 7 standardized test. Table 4-3 shows there is a strong and significant effect of student learning ability (-41.90, p=0.001) on student test score in the grade 7 standardized test, with students without individual educational plans performing better than those students with educational plans in the virtual school. The significant effect of student learning ability (21.92, p=0.022) was also observed for the grade 8 mathematics standardized test. Interestingly, the direction tells us students with individual educational plans achieved better scores. Table 4-3 demonstrates the non significant effect of student status in virtual school on student score for the grade 7 standardized test (4.97, p=0.614), grade 8 standardized test (-11.98, p=0.146). However, the two directions are different, with the direction for the grade 7 test showing full-time students tending to perform better than part-time students and the direction for grade 8 test showing part- time students tending to perform better. Shown in table 4-4, the participation in free or reduced lunch programs has no significant effect (-26.54, p=0.496) on student score in the grade 6 mathematics standardized test, with students who did not participate in these programs tending to achieve better scores than their counterparts who participated in these programs. There FACTORS INFLUENCING SUCCESS IN ONLINE HIGH SCHOOL ALGEBRA By FENG LIU A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2010 Algebra I 22. What is true about the slope and y-intercept of the two equations below? 4x + 3y = 12 -8x + 6y = 6 A. same slope, same y-intercept B. same slope, different y-intercept C. different slope, same y-intercept D. different slope, different y-intercept 23. The diagram shows the outcomes of flipping a coin and rolling a die. H T 1 2 3 4 5 6 1 2 3 4 5 6 H1 H2 H3 H4 H5 H6 T1 T2 T3 T4 T5 T6 Which statement regarding the diagram isfalse? A. The probability of obtaining "H6" is 2 out of 12. B. There are 12 possible outcomes in the sample space. C. The chance of flipping "heads" and rolling a "5" is 1 in 12. D. Flipping 'tails" and rolling a "2" represents about 8% of the possible outcomes of the sample space. Go Oan Copyright 2008 by the Missouri State Dcparntlet of Elementary and Socondary Eduation. 135 APPENDIX F STATE STANDARDS FOR MATHEMATICS AT GRADE LEVEL 6 Number and Operations N.1.A.06 compare and order integers, positive rationals and percent, including finding their approximate location on a number line N.1.B.06 recognize and generate equivalent forms of fractions, decimals and percent N.1.C.06 recognize equivalent representations for the same number and generate them by decomposing and composing numbers, including expanded notation N.1.D.06 use factors and multiples to describe relationships between and among numbers, including whole number common factors and common multiples N.2.B.06 describe the effects of addition and subtraction on fractions and decimals N.3.C.06 add and subtract positive rational numbers N.3.D.06 estimate and justify the results of addition and subtraction of positive rational numbers N.3.E.06 solve problems using equivalent ratios Algebraic Relationships A. 1.B.06 represent and describe patterns with tables, graphs, pictures, symbolic rules or words A.1 .C.06 compare various forms of representations to identify a pattern A.1.D.06 identify functions as linear or nonlinear from a table or graph A.2.A.06 use variables to represent unknown quantities in expressions A.2.B.06 recognize equivalent forms for simple Algebraic expressions including associative and distributive properties 167 investigated systematically in one model regarding their effects on student success in K- 12 virtual learning environments. In a document about nation wide college student transcripts, Adelman (1995) reported that math courses detained the top 7 places in the percentage of grades that were withdrawals, incomplete, or no credit repeats. The first six were pre-college math courses and the seventh was college Algebra. Clearly, math is a difficult subject for many students including secondary level and higher education level. School Algebra is therefore a key subject during the school reform discussion (Chazon & Yerushalmy, 2003). It has been critical for filtering the educational opportunities for high school students to further study in college (Moses, 1994; Moses et al., 1989). Algebra/mathematics is also a very important momentum to push a society to move forward. Many career options are only open to students with advanced mathematics skills in the job market (House, 1993). Stanic and Hart (1995) believe mastering mathematics knowledge and being able to apply mathematics ideas are critical for each member in a society to participate in the democratic processes and have more career opportunities. The possession of more mathematical literacy for everyone in the society is also the need for full participation in military service and shifts in US and the worlds' economic systems (Secada, 1992). The purpose of this study is to examine the factors including LMS utilization, teacher comment/feedback and student demographic information that can influence the success of Algebra courses in K-12 virtual learning environments. This study can help discover certain characteristics and good practices in online learning and help incorporate them into the instructional model of the K-12 virtual learning environment. It starting point for more studies utilizing both qualitative and quantitative methods to help the development of one success model to improve student academic achievement in virtual schooling. 116 school during online course design for the implementation of certain strategies such as peer support and online tutoring, or flexible timelines and multiple paths to help higher grade students in Algebra I courses to achieve better performance. Online teachers also should provide individual assistance based on the needs of different students. Broad Implications for Online Course Design and Online Teaching In September 2007, International Association for K-12 Online Learning (iNACOL) endorsed the National Standards of Quality for Online Courses based on the Southern Regional Education Board (SREB) Standards for Quality Online Courses. In February 2008, iNACOL released National Standards for Quality Online Teaching based on SREB's Standards for Quality Online Teaching and Online Teaching Evaluation for State Virtual Schools. The SREB's two sets of standards have been widely used by the 16 states in the southern United States. iNACOL' National Standards of Quality for Online Courses standards were designed to "provide states, districts, online programs, and other organizations with a set of quality guidelines for online course content, instructional design, technology, student assessment, and course management." (iNACOL, 2006, p.1). There are 6 categories in iNACOL standards: 1. Content 2. Instructional Design 3. Student Assessment 4. Technology 5. Course Evaluation and Management 6. 21st Century Skills. Under each category there are a set of standards. National Standards for Quality Online Teaching is designed to "provide states, districts, online programs, and other organizations with a set of quality guidelines for online teaching and instructional design." (iNACOL, 2008, p.1). There are 13 categories in these standards: 111 Algebra I 24. The population of a type of bacteria triples every minute. The chart below represents the population of bacteria after t minutes. t 0 1 2 3 4 5 Bacteria Population 1 3 9 27 81 243 Which type of function represents the data? A. linear B. quadratic C. exponential D. absolute value 25. What are the slope, m, and the y-intercept, b, of a line that passes through the points (-3, 1) and (7, -5)? 3 -4 A. m= and b = -5 B. m = and b = -4 3 C. m 5 and b - 5 Copyright t) 2008 by the Misouri State Department of Elementary and Secondary Education. 136 learning process, and student and faculty attitude and satisfaction with the learning experience (Gallagher & McCormick, 1999). Cavanaugh (2001) conducted one meta-analysis study to examine the effectiveness of interactive distance education in the K-12 learning environment. She reviewed 19 experimental and quasi-experimental studies selected with strict criteria including the focus of study, publication date, research design, and calculated effect sizes to assess the effects of some technologies including video-conferencing and online telecommunications on student achievement and to investigate the success factors for effective distance education. All these studies covered a wide range of subject areas and grade levels. The overall effect size for the 19 studies, 0.147, indicated the small positive effect of distance education over traditional education. No significant differences were found in grade levels, ability levels, content areas, technology use, and achievement measure. The author concluded distance education can be at least as effective as traditional education to help students achieve academic goals and that offering distance courses at the secondary level could enrich the course curriculum and students' knowledge structure. Sherry, Jess, and Billig (2002) conducted one action research study to evaluate the effectiveness of online learning in improving student media literacy and multimedia techniques. They collected data quantitatively using surveys and qualitatively using interview and focus groups from students and instructor. The results showed technologies that are integrated into online learning can help students acquire a variety of skills such as creating multimedia projects, editing digital artifacts, designing web pages, and promoting student learning motivation. They concluded that the technology- Marjoribanks, K. (1988): Perceptions of family environments, educational and occupational outcomes: Social-status differences. Perceptual and Motor Skills, 66, 3-9. Marsh, H.W., & Yeung, A.S. (1997). Causal effects of academic self-concept on academic achievement: Structural equation models of longitudinal data. Journal of Educational Psychology, 89, 41-54. Mayes, R. L. (1995). The application of a computer algebra system as a tool in college algebra. School Science and Mathematics, 95(2), 61-67. Mayzer, R., & Dejong, C. (2003). Student satisfaction with distance education in a criminal justice graduate course. Journal of Criminal Justice Education 14(1): 37- 52. McCleary, I. D., & Egan, M. W. (1989). Program design and evaluation: two-way interactive television. The American Journal of Distance Education, 3(1), 50-60. McCollum, H. (1990). A review of research on effective instructional strategies and classroom management approaches. In M. S. Knapp& P. M. Shields (Eds.), Better schooling for the children of poverty: Alternatives to conventional wisdom (Vol. 2: Commissioned papers and literature review Chapter 12. Study of Academic Instruction for Disadvantaged Students, U.S. Department of Education contract no. LC88054001). Washington, DC: Government Printing Office. McFarland, D. & Hamilton, D. (2006). Factors affecting student performance and satisfaction: Online versus traditional course delivery. Journal of Computer Information Systems, 46(2), 25-32. McLeod, S., Hughes, J. E., Brown, R., Choi, J., & Maeda, Y. (2005). Algebra achievement in virtual and traditional schools. Naperville, IL: Learning Point Associates. McLoyd, V. (1998). Socioeconomic disadvantage and child development. American Psychologist, 53, 185-204. Means, B., Toyama, Y., Murphy, R., Bakia, M., Jones, K. (2009). Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies. Washington, D.C. Available at: http://www.ed. gov/rschstat/evalltech/evidence-based-practices/finalreport. pdf Missouri Department of Elementary and Secondary Education (MDESE) (2009a). End- of-Course Assessments. Available at: http://dese.mo.gov/divimprove/assess/eoc.html 184 online students and the predictors for their success, Wang and Newlin (2000) found out students who participate in online activities at a high level tend to perform well in the online course. They concluded the total online course activity is a predictor of students' final grades. Compared to the students in traditional classrooms, online students spend more time in the virtual learning environments on interacting with one another on academic topics (O'Dwyer et al., 2007). The peer-to-peer interaction, in turn, could help improve online students' learning outcomes (Cavanaugh, 2007). In the present study, the numbers of times students logged into the LMS and how long they stayed in the LMS were considered the indication of student participation level on online academic activities. The number of times students logged into the LMS also has been identified as a strong predictor of student academic performance in online learning (Dietz, 2002; Dickson, 2005). Compared to traditional classroom instructors, online instructors lack of the regular set of cues about students' confusion or frustration during the learning process such as their facial expression and body positions. The measure of time students spent in the online academic activities can provide online instructors the information about students' understanding of content materials. A lower level of involvement in online activities in the course at the beginning of the semester could be an early warning sign of failure later during the learning process. Therefore, online instructors should closely monitor these behaviors via LMS login data to prevent students who show warning signs at the beginning from failure. The influence of time students spent in the LMS was found to be positive for the five groups and significant for Algebra I (2). These findings are aligned with the 101 Table A-5. Problem Solving Standard for Grades 6-8 Instructional programs from prekindergarten through grade 12 should enable all students to- build new mathematical knowledge through problem solving; solve problems that arise in mathematics and in other contexts; apply and adapt a variety of appropriate strategies to solve problems; monitor and reflect on the process of mathematical problem solving. Table A-6. Reasoning and Proof Standard for Grades 6-8 Instructional programs from prekindergarten through grade 12 should enable all students to- recognize reasoning and proof as fundamental aspects of mathematics; make and investigate mathematical conjectures; develop and evaluate mathematical arguments and proofs; select and use various types of reasoning and methods of proof. Table A-7. Communication Standard for Grades 6-8 Instructional programs from prekindergarten through grade 12 should enable all students to- organize and consolidate their mathematical thinking through communication; communicate their mathematical thinking coherently and clearly to peers, teachers, and others; analyze and evaluate the mathematical thinking and strategies of others; use the language of mathematics to express mathematical ideas precisely. Table A-8. Connections Standard for Grades 6-8 Instructional programs from prekindergarten through grade 12 should enable all students to- recognize and use connections among mathematical ideas; understand how mathematical ideas interconnect and build on one another to produce a coherent whole; recognize and apply mathematics in contexts outside of mathematics. Table A-9. Representation Standard for Grades 6-8 Instructional programs from prekindergarten through grade 12 should enable all students to- create and use representations to organize, record, and communicate mathematical ideas; select, apply, and translate among mathematical representations to solve problems; use representations to model and interpret physical, social, and mathematical phenomena. 152 Success Factors in Online Learning Given the fact of extraordinary development of online education in the last two decades, little research has been conducted to examine success factors in the online learning environment as compared to the traditional learning environment. With the high early dropout and failure rates in the online learning environment (Carr, 2000a; Roblyer & Elbaum, 2000), there is an urgent need for more research on success factors to prevent students from dropping out of virtual or physical school and ensure their success in this learning environment (Barbour & Reeves, 2009; Bernard et al., 2004a; Butz, 2004; Dickson, 2005; McLeod et al., 2005). At the K-12 level, there is great concern about the readiness for students to take online courses and succeed academically because they are not socially and emotionally mature as compared to students in higher education (Picciano & Seaman, 2007). The review of studies examining success factors in online learning environments as well as traditional learning environments can better guide the practice of K-12 online education. Schrum and Hong (2001) administered a survey with 70 institutions and found several factors can influence student success in e-learning environment: learning styles, prior technology experience, personal disposition, study habits, and tools accessibility. Brown and Liedholm (2002) conducted a comparative study between online education and traditional education and found student's personal effort on learning tasks could make a difference in academic performance. Swan (2002) investigated the correlation between 22 course design factors and student academic achievement and satisfaction with learning experience. She found three factors: transparent interface/clarity and consistency in course design, instructor feedback/instructor-student interaction, and dynamic online discussion are associated with the success of online learning. These also will add to the knowledge of the effectiveness of online/distance education in helping improve student academic achievement in K-12 virtual schooling and provide valuable guidance for better implementation and practice. The investigation of success factors will provide a deeper understanding of success in the K-12 virtual learning environment specifically in online Algebra/math courses to guide management of virtual schools for maximizing their effectiveness to provide better assistance and supplementation to the traditional learning environment. Review of Literature The review of literature in this chapter covers the effectiveness of online/distance education, Algebra/mathematics education, and online success factors. The review of literature on effectiveness of online/distance education presents the evidence for the conduct of increasing research in online/distance education. It also provides the rationale for the investigation of online success factors in this study. The review of Algebra/mathematics education can grant the support for the selection of specific courses in which the present study is conducted and demonstrates the relationships between a traditional teaching format and online education. The review of online success factors grounds the present work in the related studies and provides the support for the selection of factors in the present study. Effectiveness of Online/Distance Education Well designed distance education courses/programs can provide effective learning with innovative pedagogy, rich experience, and deep understanding of knowledge (Cavanaugh, 2001). Many studies have been done to examine the effectiveness of distance education. Research on distance education effectiveness has mainly focused on several aspects: student learning outcomes, student-instructor interaction during the * Does teacher comment or feedback influence Algebra/mathematics performance in online education? * Do student demographic information such as race/ethnicity, grade level, status in virtual school, whether have individual educational plan (IEP), and participation in free/reduced lunch programs influence Algebra/mathematics performance in online education? Significance of the Study Even after more than 10 years of extraordinary growth in K-12 online learning, little research has been done as compared to post-secondary education (Cavanaugh 2007; Cooze & Barbour, 2005; Means et al., 2009; Picciano & Seaman, 2007; Picciano & Seaman, 2009; Ronsisvalle & Watkins, 2005). The amount of evidence-based research or empirical study applicable to guide educators' instruction and policy makers' decision relevancies is slight (O'Dwyer, Carey, & Kleiman 2007). The dearth of studies on academic achievement in K-12 virtual learning environments in comparison with that in traditional learning environments (Cooze & Barbour, 2005; Means et al., 2009; Picciano & Seaman, 2007; Picciano & Seaman, 2009; Smith, Clark, & Blomeyer, 2005; Watson, 2007) form the rationale for this study. This study can help discover certain characteristics and good practices in online learning and incorporate them into the instructional model of the K-12 virtual learning environment. This study could add to the knowledge of effectiveness of online/distance education in helping improve student academic achievement in the K-12 virtual learning environment. This will provide valuable guidance for the better implementation and practice of K-12 virtual schooling. Given the dearth of research on the factors of academic success in K-12 virtual learning environments, this study could be beneficial to educators, course designers, researchers, online program leaders, policy makers, and society at large. The investigation of success factors in this study will provide a deeper understanding of Definition of the Terms Distance education has been practiced in various forms since its emergence in the early 1900s, evolving from correspondence to broadcasting including radio and television, to online education today (Moore & Kearsley, 1996). It has experienced an extraordinary development in the 20th century and its practice will continue to grow in the 21st century. Many distance education related terms have appeared: cybershool, distance education, distance learning, e-learning, online education, online learning, virtual school, and web-based learning. There are also multiple definitions for each of these terms. In this study, the authors are using the definitions that have been broadly cited though by no means are they the most accurate ones. Distance education, defined by Keegan (1996), has four main components: (1) quasi-permanent separation of teacher and learner, (2) the use of technical support to bridge the distance, (3) two-way communication during the process, and (4) possible non presence of learning groups. It is probably the most cited definition of DE in the literature. Another very comprehensive definition of distance education is in a published monograph by The Association for Education Communications and Technology (Schlosser & Simonson, 2002): "Institution-based, formal education where the learning group is separated, and where interactive telecommunications systems are used to connect learners, resources, and instructors" (p. 1). Distance learning, defined by Allen et al. (2004), is a course where the students and instructor will not be physically in the same location during the teaching/learning process. Distance learning can be conducted asynchronously using communication techniques such as e-mail, audio/video recording, mail correspondence, and synchronously using techniques such as television, radio, internet chat room, and telephone (Allen et al, 2004). Brophy, J. E. (1990). Effective schooling for disadvantaged students. In M. S. Knapp & P. M. Shields (Eds.), Better schooling for the children of poverty: Alternatives to conventional wisdom (Vol. 2: Commissioned papers and literature review. Chapter LX. Study of Academic Instruction for Disadvantaged Students, U.S. Department of Education contract no. LC88054001). Washington, DC: Government Printing Office. Brown, B. W., & Liedholm, C. E. (2002). Can Web courses replace the classroom in principles of microeconomics? American Economic Review, 92(2), 1-12. Brown, S. W. & Kulikowich, J. M. (2004). Teaching statistics from a distance: What have we learned? International Journal of Instructional Media, 31:1-17. Butz, C. (2004). Parent and student satisfaction with online education at the elementary and secondary levels. Unpublished dissertation. Las Vegas, NV: University of Nevada at Las Vegas. Campbell, M., Floyd, J., & Sheridan, J. B. (2002). Assessment of student performance and attitudes for courses taught online versus onsite. The Journal of Applied Business Research, 18(2), 45-51. Carr, S. (2000a). As distance education comes of age, the challenge is keeping the students. Chronicle of Higher Education [Online serial]. Available: http://chronicle.com. Carr, S. (2000b). Psych students learn more through distance ed, but are less satisfied. The Chronicle of Higher Education. Carswell, L., Thomas, P., Petre, M., Price, B., & Richards, M. (2000) Distance education via the Internet: the student experience. British Journal of Education Technology, 29-46 Catsambis, S. (1995). The path to math: Gender and racial-ethnic differences in mathematics participation from middle school to high school. Sociology of Education, 67, 199-215. Cavanaugh, C. (2009). Getting students more learning time online: Distance education in support of expanded learning time in k-12 schools. Washington, DC: Center for American Progress. Cavanaugh, C. (2007). Students Achievement in Elementary and High school. In Handbook of Distance Education, ed. M. G. Moore, 157-168. Mahwah, NJ: Lawrence Erlbaum Associates. 173 Algebra I 5. Given the following fractions: 3 18 24 3 12 4' 29' 39' 5' 18 Which group below has the fractions in order from least to greatest? A. 3 24 18 12 3 5' 39' 29' 18' 4 3 3 18 24 12 4' 5' 29' 39' 18 S3 12 24 3 18 5' 18' 39' 29 3D. 3 12 18 24 4' 5' 18' 29' 39 6. The automobile repair shop uses the following chart to determine labor costs for each job. Automobile Repair Shop Costs Hours Labor Cost 1 $25 2 $40 3 $55 4 $70 Which function should the automobile repair shop use to determine the labor cost, C, for a job that takes h hours? A. C- 15h B. C- 15h I 10 C. C 25 1 15h D. C-25h+15h Copyright t) 2008 by the Missouri State Department of Elementary and Secondary Education. 124 Bernard, R.M., Abrami, P.C., Lou, Y., & Borokhovski, E. (2004a). A methodological morass? How we can improve the quality of quantitative research in distance education. Distance Education, 25 (2), 176-198. Bernard, R. M., Lou, Y., Abrami, P. C., Wozney, L., Borokhovski, E., Wallet, P., Wade, A., & Fiset, M. (2004b). How does distance education compare with classroom instruction? A meta-analysis of the empirical literature. Review of Educational Research 74(3): 379-439. Black, E. (2009). An evaluation of familial involvements' influence on student achievement in K--12 virtual schooling. Ph.D. dissertation, University of Florida, United States -- Florida. Retrieved May 25, 2010, from Dissertations & Theses @ University of Florida FCLA. (Publication No. AAT 3367406). Blaylock, T. H., & Newman, W. J. (2005). The impact of computer-based secondary education. Education 125 (3): 373-384. Blomeyer, R. (2002). Online learning for K-12 students: What do we know now? North Central Regional Educational Laboratory. Available at: http://www.ncrel.org/tech/elearn/synthesis.pdf Boaz, M. (1999). Effective methods of communication and student collaboration. In Teaching at a distance: A handbook for instructors, 41-48. Mission Viejo, CA: Archipelago Productions and League for Innovation in the Community College. Bogden, J. (2003). Cyber charter schools: A new breed in the education corral. Available at: http://www.ed.gov/about/offices/list/os/technology/plan/2004/site/bb/files/post092 _1.pdf Bothun, G. D. (1998). Distance education: Effective learning or content-free credits? Cause/Effect, 21(2), 28-31, 36-37. Bransford, D. J., Brown, L. A., & Cocking, R. R. (1999). How people learn: Brain, Mind, Experience, and School. National Academy Press. Washing, D. C. Braun, H., Jenkins, F., & Grigg, W. (2006). Comparing private schools and public schools using hierarchical linear modeling (No. 2006-461). Washington, DC: National Center for Education Statistics. 172 student mathematics achievement (Catsambis, 1995; Ercikan, McCreith, & Lapointe, 2005; Ho et al., 2000). Some affective variables such as self-concepts, attitudes toward mathematics, self-confidence mathematics learning ability, motivation, locus of control, and perceptions of the usefulness of mathematics have been found to relate to student academic achievement in mathematics (Bassarear, 1991; Duranczyk, 1997; House, 1995; House, 1993; Marsh & Yeung, 1997; Reyes, 1984). Increased study time and the taking of advanced coursework also can positively affect students' mathematics achievement (Secada, 1992). Student English language proficiency is another factor for student mathematics achievement in U.S. (Jacobson, 2000; Secada, 1992). Bilingual students whose native language is not English will be likely to achieve higher performance in mathematics if they receive the instruction in their native language (Secada, 1992). Ercikan et al. (2005) conducted one exploratory research study examining factors that might affect students' achievement in mathematics and their participation in advanced mathematics courses in three countries: Canada, Norway, and the US. They found students' personal and home environment variables strongly affect their achievement in mathematics and participation in advanced mathematics courses in these three countries. These researchers specifically confirmed the relationship between attitude toward mathematics and participation in advanced mathematics courses in these countries and the relationship between SES related variables and achievement in mathematics in the US. Higbee and Tomas (1999) conducted one research study to examine the relationship between non-cognitive variables including math anxiety, perceived skills and attitudes; and experience and prior knowledge about course content. Learning environment characteristics include technology support, course content area, and accessibility to Internet. At present, no clear set of characteristics have been identified to predict success in virtual learning environments, and no conclusive model has been created to apply in online learning practice (Roblyer & Davis, 2008; Tallent-Runnels et al., 2006). There is a gap regarding the establishment of one online success model to help improve student academic achievement considering the quick development of virtual schooling in the US. Learner characteristic variables including personal effort/participation in academic activities, student learning ability/whether has individual educational plan, race/ethnicity, and family background/participation in free or reduced lunch programs, and learning environment variables including teacher comment /instructor-student interaction and school type (private or public school) have been proved in some studies to correlate to student academic achievement. However, these variables have not been investigated systematically in one model regarding their effects on student success in K-12 virtual learning environments. Math knowledge is important for a citizen to fully participate in society. Math is the most widely used subject among all the fields and almost every career uses math at different levels (Saint Paul Public Schools, 2007). During the May 2003 commencement address, the president of Society for Industrial and Applied Mathematics (SIAM), Professor Doug Arnold mentioned math is the foundation to understand the world around us and math knowledge can influence other sciences as well such as economics, business, and sociology. He predicted that math will have huge impacts in the 21st century, the digitalized and data-enriched century. Test Fixed Effect Coefficient GRADE -3.89 RACE 0.93 FRL -0.04 IEP -1.37 PT/FT 1.78 TEACHERCOM -0.04 TOTALLOG -0.03 TOTALMIN 0.0005 GRADE -3.36 RACE -2.53 FRL -4.36 IEP -2.26 PTFT 6.49 TEACHERCOM 0.01 TOTALLOG -0.02 TOTALMIN 0.0004 Table 4-4: Least-squares estimates of fixed effects (with robust standard errors) Standard Error 1.76 4.22 3.93 6.19 5.20 0.06 0.03 0.00 1.14 3.37 3.15 3.83 3.85 0.09 0.01 0.00 Algebra I (1) Algebra I (2) T-ratio -2.21 0.22 -0.01 -0.22 0.34 -0.76 -1.21 1.04 -2.96 -0.75 -1.38 -0.59 1.69 0.11 -2.90 2.74 d.f. 92 92 92 92 92 92 92 92 66 66 66 66 66 66 66 66 P-value 0.030 0.826 0.992 0.825 0.733 0.450 0.230 0.304 0.005 0.455 0.172 0.557 0.096 0.912 0.006 0.008 skills. They randomly assigned the participants into experimental groups with the implementation of intervention: online learning and control group without the intervention. They analyzed the knowledge and skill growth measured by two knowledge questionnaires and charts quantitatively and the posts and emails qualitatively. The results showed the intervention had positive effects on knowledge growth and the quality of practice for these physicians. The researchers confirmed the promise of the broad implementation of online education in general. Williams (2006) reviewed 25 comparative studies from 1990 to 2003 on distance education in allied health science education to examine the learning effectiveness on student achievement and the instructional design (ID) components contributing to the effectiveness. The overall effect size, 0.15, with confidence interval from 0.07 to 0.23, showed distance education was slightly more effective than traditional education with respect to improved student achievement. The results also showed the integration of ID components in distance courses had a positive effect on achievement. The researcher suggested the effective distance education courses should incorporate various ID components. The study was concluded with the promotion of distance education courses/programs and a call for more research on the effect of different aspects such as educational level on the effectiveness of distance education. In 2009 US Department of Education (DOE) released a report about a meta- analysis of empirical studies from 1996 to 2008 to evaluate the effectiveness of online learning practice. The studies included in this meta-analysis were selected based on the criteria: rigorous research design including random assignment or controlled quasi- experimental design to contrast online to traditional education, objective learning LIST OF TABLES Table page 3-1 Coding of the independent variables .............. ..... ... .......... ...... ..... 67 4-1 EOC test takers demographics....................... ..... ........................... 85 4-2 Standardized test takers demographics .............. ..... ................... .... 86 4-3 Overview of RA model for different datasets ...... ..... ..................................... 87 4-4 Least-squares estimates of fixed effects (with robust standard errors)............ 88 4-5 Least-squares estimates of fixed effects (with robust standard errors)............ 89 4-6 Ordinary Least-squares estimates of fixed effects .................... ........ ........... 90 4-7 Descriptive statistics for EO C test takers................................. ..................... 91 4-8 Descriptive statistics for standardized test takers ..................... ....... ............ 92 4-9 Standardized coefficients for EOC test takers...... ..... .................................... 93 4-10 Standardized coefficients for standardized test takers ............... ........... 94 4-11 A adjusted R -squares .......... ......... .......... .............. ............... .............. 95 5-1 Significance and Direction of the Effect of Factors.................................. 117 5-2 Alignment with National Standards in Quality Online Course........................ 118 A-1 Number and Operations Standard for Grades 6-8 Expectations ..... ........... 147 A-2 Geometry Standard for Grades 6-8 Expectations................................... 149 A-3 Measurement Standard for Grades 6-8 Expectations............................. 150 A-4 Data Analysis and Probability Standard for Grades 6-8 Expectations......... 151 A-5 Problem Solving Standard for Grades 6-8 ...... ..... ...................................... 152 A-6 Reasoning and Proof Standard for Grades 6-8 ........................................... 152 A-7 Communication Standard for Grades 6-8 ............................................... 152 A-8 Connections Standard for Grades 6-8 .................................................. 152 A-9 Representation Standard for Grades 6-8 ............ ................................... 152 BIOGRAPHICAL SKETCH Feng Liu was born in 1973 in Liaocheng City, Shandong Province, China. As the second child of one three-child family, he attended No.1 High School, Zhongyuan Oil Field in Puyang City, Henan Province, China. Feng Liu graduated from Nanjing Normal University's Computer Science Department with a Bachelor of Science in computer science education in 1995. He has taught computer science courses at postsecondary level including Nanjing Material Polytechnic School and Nanjing University of Finance & Economics for eight and half years. Feng Liu came to United States at January 2004 to further his education at Georgia College & State University where he earned a Master of Education in educational technology in May 2006. In August of 2006, Feng Liu enrolled as a doctoral fellow in the Educational Technology program in School of Teaching and Learning at the University of Florida (UF). During his study at UF, Feng Liu has focused on research in the learning technologies. His research interests include the investigation of online learning success and the effectiveness of virtual schooling, the employment of advanced research methods and statistical approaches in educational research, and the use of e- game/simulation for knowledge gain, attitude change and motivation in areas such as science and second language acquisition. He has several publications in these areas. 195 basic mathematics skills, and divide of whole class activities into group and individual work. Considerable research has been conducted on the approaches to Algebra learning. Bednarz, Kieran, and Lee (1996) reported the four approaches that have been focused on at an international colloquium on Algebra in early 1990, including generalization of numerical and geometric patterns and laws regarding Algebraic relationships, functional situations, modeling of mathematical phenomena, and problem solving. Drijvers (2003) described the similar approaches for Algebra learning in more detail: * Problem-solving approach: view Algebra as a way to solve problems that can be expressed in equations. * Functional approach: view Algebra as a way to investigate the functions and relations among different variables. * Generalization approach: mainly focus on the examination of patterns or models and configurations, and focus on the generalization of relations among different variables. * Language approach: view Algebra as a way to convey mathematics ideas in which Algebra is merely a representation structure composed of symbols without specific context attached meaning. Drijvers also identified some aspects that make learning Algebra difficult. These include: 1. The difficulty for students to relate the formal algorithmic procedures with informal while meaningful methods 2. The abstract characters of Algebra problem solving approaches that students can't connect them with the concrete situations 3. The Algebraic language includes particular symbols and rules that are difficult for students to grasp American Students and Minority Students. Other categorical variables were coded accordingly during data analysis. Table 3-1 shows the coding information. Students who took the two Algebra EOC tests were from grades 8 to 12. Students who took the standardized mathematics test grade 6, 7, or 8 were from grade 6 to 10. These two sets of groups were overlap to some degree. Therefore, the analysis was conducted for these two sets of groups separately. Some of the participants in state standardized test groups will take Algebra course in this virtual school. The analysis of these groups can add to the knowledge of success factors in Algebra. This virtual school student body included students statewide from bricks-and- mortar public and private schools as well as home school students. The physical schools as well as the home schools that students attend could affect student academic performance through school culture, technical support, and resources available for students. Student test scores within the same physical school are not independent of one another. Therefore, any evaluation of the influence of student level factors such as grade level, race, and teacher comment on these scores must account for the influence of school characteristics. To investigate the Algebra/mathematics success factors in the K-12 online learning environment, Hierarchical Linear Modeling (HLM) technique was used to account for the clustering of student score within one school caused by school characteristics. HLM was carried out by the software program HLM 6.06 for data analysis in this study. The fully unconditional or Random ANOVA (RA) model was estimated at the beginning in order to partition the variance into within-school (Sigma Square) and between-school (Tau) components. After that, all independent variables Table 5-3. Continued Findings, derivative outcomes, or implications of the present study Online teachers should provide individual assistance based on the needs of different students. Aligned standards in iNACOL National Standards for Quality Online Teaching Provides activities, modified as necessary, that are relevant to the needs of all students. (G) Personalizes feedback (support, growth and encouragement). (D) Provides evidence of effective learning strategies that worked for the individual student and details specific changes in future instruction based upon assessment results and research study (data-driven and research- based). (J The teacher demonstrates competencies in using data and findings from assessments and other data sources to modify instructional methods and content and to guide student learning.) 120 Racial gaps in student test score have been proved in many other studies conducted in traditional learning environments (Bali & Alvarez, 2004; Barth, 2001; Hall et al., 2000; Lockhead et al., 1985). The student body in online K-12 schools represents the community that is served by traditional school system (Ronsisvalle & Watkins, 2005). The findings about the racial gaps in student achievement in other studies could apply to the present study as well. The significant racial difference for standardized test grade 7 and nearly significant difference for standardized test grade 6 provides the evidence for the findings in other studies. The directions of the difference in the two groups show white American students perform better than other minority groups as a whole. However, the finding that the significant racial difference was only found for 1 out 5 groups could be due to the coding system that combined different minority groups into one category potentially masking important information regarding the differences in student academic achievement among different racial groups. Future study could be conducted to investigate these differences with bigger sample size. The effect of student grade level in physical school was found to be negative and significant for two Algebra I groups, with students from lower grade levels performing better than those from higher grade levels. Students taking the standardized tests are from lower levels (grade 6-8) compared with the students who took the Algebra I courses and Algebra I EOC tests (most of them from grade 9-12). Algebra I is a required course for high school graduation. Many students in higher grades such as grade 11, 12 take Algebra I courses in this virtual school as credit recovery or remediation to make up failing grades in their physical schools to meet the graduation requirement. It could be the explanation for the negative and significant effect of this factor for the two Algebra I groups. The 109 student cognitive factors such as locus of control and learning styles; prior technology skills and attitudes; and experience and prior knowledge about course content while learning environment characteristics include technology support, course content area, and accessibility to the Internet. At present, no clear set of characteristics have been identified to predict the success of the virtual learning environment, and no conclusive model has been created to apply in online learning practice (Roblyer & Davis, 2008; Tallent-Runnels et al., 2006). Other learner characteristics including personal effort/participation in academic activities, whether has individual educational plan, race/ethnicity, and family background/participation in free/reduced lunch programs, and learning environment characteristics including teacher comment/feedback/instructor- student interaction and school type (private or public school) also have been proved in some studies to correlate to student academic achievement. However, these factors' influences have not been investigated systematically. The review of these factors in light of the relationship with student academic achievement in other studies can provide deeper understanding of success in online learning in general and the K-12 virtual school environment in specific and shed light on the establishment of a model to predict online learning success in general and online Algebra/mathematics learning in specific. Teacher comments/teacher-student interaction Teacher comments and student-teacher interaction is a critical component in academic learning (Boaz, 1999; Laurillard, 1997; Parker, 1999; Schaffer & Hannafin, 1993; Summer, 1991; Swan, 2003; Williams, 2006). It can affect learning in traditional f2f learning environments (Christophel, 1990; Kelly & Gorham, 1988; Rodriguez, Plax & Kearney, 1996) and online learning environments (Blomeyer, 2002; Jiang & Ting, 2000; Johnson et al., 2000; Swan, et. al., 2000; Swan, 2002; Swan, 2003; Tallent-Runnels et Table A-1. Continued Instructional programs from prekindergarten through grade 12 should enable all students to- Compute fluently and make reasonable estimates In grades 6-8 all students should- * select appropriate methods and tools for computing with fractions and decimals from among mental computation, estimation, calculators or computers, and paper and pencil, depending on the situation, and apply the selected methods; * develop and analyze algorithms for computing with fractions, decimals, and integers and develop fluency in their use; * develop and use strategies to estimate the results of rational-number computations and judge the reasonableness of the results; * develop, analyze, and explain methods for solving problems involving proportions, such as scaling and finding equivalent ratios. 148 Ledman, E. R. (2008). COMPARING STUDENT LEARNING IN ONLINE AND CLASSROOM FORMATS OF THE SAME COURSE. Developments in Business Simulation and Experiential Learning, 35: 351-352. Available at: http://sbaweb.wayne.edu/~absel/bkl/vol35/35bq.pdf Liao, Y. C. (1998). Effects of hypermedia versus traditional instruction on students' achievement: A meta-analysis. Journal of Research on Computing in Education, 30(4), 341-359. Liaw, S., & Huang, H. (2000). Enhancing interactivity in web-based instruction: A review of the literature. Educational Technology, 40(3), 41-45. Lim, J., Kim, M., Chen, S. S., & Ryder, E. C. (2008). An Empirical Investigation of Student Achievement and Satisfaction in Different Learning Environments. Journal of Instructional Psychology, 35(2), 113-119. Lin, C.S. (2001). Implementation of the Virtual School: Best Cyber Academy, in Advances in Multimedia Information Processing PCM 2001, (Eds.) Shum, Heung-Fu, Liao, Mark, & Chang, Shih-Fu, IEEE Lecture Notes in Computer Science, p.p. 316-323. Springer-Verlag, Berlin, Germany. Lipman A. J., Sade R. M., Glotzbach A. L., Lancaster C. J., Marshall M. F. (2001). The incremental value of Internet-based instruction as an adjunct to classroom instruction: a prospective randomized study. Academy of Medicine. 76:1060 - 1064. Liu, F., & Cavanaugh, C. (2010). High Enrollment Course Success Factors in Virtual School: Factors influencing student academic achievement. International Journal on E-Learning. Lockee, B. B., Burton, J. K., & Cross, L. H. (1999). No Comparison: Distance Education Finds a New Use for "No Significant Difference". Educational Technology Research and Development, 7(3), 33-42. Lockhead, M., Thorpe, M., Brooks-Gunn, J., Casserly, P., & McAloon, A. (1985). Sex and ethnic differences in middle school mathematics, science, and computer science: What do we know? Princeton, NJ: Educational Testing Service. Lubienski, S. T., & Lubienski, C. (2005). A new look at public and private schools: Student background and mathematics achievement. Phi Delta Kappan, 86, 696- 699. Maki R. H., Maki W. S., Patterson M, Whittaker P. D. (2000). Evaluation of a Web- based introductory psychology course: I. Learning and satisfaction in on-line versus lecture courses. Behavior Research Methods, Instruments, and Computers. 32:230 239. 183 Allen and Seaman (2006) defined three types of online courses. Online is the course where most or all of the content is delivered online. At least 80% of the traditional face to face (f2f) classroom meeting time is replaced by online activity. Blended/Hybrid is the course that combines online and traditional f2f delivery methods. A considerable proportion (30 to 79%) of the content is delivered online. Web-facilitated is the course where web-based technologies are used to facilitate learning. A proportion (1 to 29%) of the content is delivered online. Virtual school, defined by Clark (2000), is "a state approved and/or regionally accredited school that offers secondary credit courses through distance learning methods that include Internet-based delivery" (p. i). Russell (2004) defined virtual school as "a form of schooling that uses online computers to provide some or all of a student's education" (p. 2). Greenway and Vanourek (2006) described virtual schools as "a hybrid of public, charter, and home schooling, with ample dashes of tutoring and independent study thrown in, all turbocharged by Internet technology" (p. 36). A more recent study conducted by Barbour and Reeves (2009), defined virtual school as "an entity, which has been approved or accredited by a state or governing body within the state, that offers secondary-level courses through distance delivery most commonly using the Internet." (p. 412). This study examined the practice of virtual school following Clark's and Barbour and Reeves' definition. Organization of the Study The remainder of the study is organized into five chapters and appendices including some released test items and national and state Algebra and mathematics standards. Chapter two is the review of the related literature regarding mathematics courses specifically Algebra success factors and online learning success factors. Freedman, G., Darrow, R., Watson, J., & Lorenzo, G. (2002). California virtual school report: A national survey of virtual education practice and policy with recommendations for the State of California. Clarence Center, NY: Lorenzo Associates Inc. Available at: http://web.archive.org/web/20061207213241/http://www.edpath.com/imagesNH SReport.pdf Gallagher, P., & McCormick, K. (1999). Student satisfaction with two-way interactive distance education for delivery of early childhood special education coursework. Journal of Special Education Technology 14(1) 32 47. Gallagher, W. (2009). Rapt: Attention and the focused life. New York: Penguin. Garrison, D. R. (2003). Cognitive presence for effective asynchronous online learning: the role of reflective inquiry, self-direction and metacognition. In J. Bourne & J. C. Moore (eds.), Elements of Quality Online Education: Practice and Direction, 47- 58. Needham, MA: Sloan-C. Glass, V. G. (2009). The realities of K-12 virtual education. Boulder and Tempe: Education and the Public Interest Center & Education Policy Research Unit. Available at: http://epicpolicy.org/publication/realities-K-12-virtual-education Glenn, A. S. (2001). A comparison of distance learning and traditional learning environments. (ERIC Document Reproduction Service No. ED 457 778. Glenn, L. M., Jones, C. G., & Hoyt, J. E. (2003). The effect of interaction levels on student performance: A comparative analysis of web-mediated versus traditional delivery. Journal of Interactive Learning Research, 14, 285-299. Goodwin, B. N. Miklich, B. A., and Overall, J. U. (1993). Perceptions and attitudes of faculty and students in two distance learning modes of delivery: online computer and telecourse. (ERIC Document Reproduction Services No. ED371 708). Goos, M., Galbraith, P., Renshaw, P., & Geiger, V. (2003). Perspectives on technology mediated learning in secondary school mathematics classrooms. Journal of Mathematical Behavior, 22, 73-89. Greenway, R. & Vanourek, G. (2006). The Virtual Revolution: Understanding Online Schools. Education Next. P 35-41. Greer, B. (1996). Theories of Mathematics Education: The Role of Cognitive Analyses. In L. P. Steffe, P. Nesher, P, Cobb, G. A. Goldin, & B. Greer (Eds) Theories of Mathematical Learning. Lawrence Erlbaum Associates, Inc. NJ, 179-196. Grouws, D. (Ed.). (1992). Handbook of research on mathematics teaching and learning. New York: Macmillan 178 14 Study the figures labeled A and B. i * i Figure B shows Figure A after 1 transformation. Which transformation was used-a flip, a slide, or a turn? Write your answer on the line. In the figures above, draw the flip line, slide arrow, or turn point on the figures for the transformation you chose. Go On Page9 (. F -T'.I, .' _' t -1.- r i., .. r, I .'... r of Elementary and Secondary Education 158 A.3.A.06 model and solve problems, using multiple representations such as graphs, tables, expressions and equations A.4.A.06 compare situations with constant or varying rates of change Geometric and Spatial Relationships G.1 .A.06 identify the properties of one-, two- and three-dimensional shapes using the appropriate geometric vocabulary G.1.B.06 describe relationships between the corresponding angles and the length of corresponding sides of similar triangles (whole number scale factors) G.2.A.06 use coordinate geometry to construct geometric shapes G.3.A.06 describe the transformation from a given pre-image to its image using the terms reflection/ flip, rotation/ turn and translation/ slide G.3.C.06 create polygons and designs with rotational symmetry G.4.A.06 use spatial visualization to identify isometric representations of mat plans G.4.B.06 draw or use visual models to represent and solve problems Measurement M. 1.A.06 identify and justify an angle as acute, obtuse, straight or right M.1.C.06 solve problems involving elapsed time (hours and minutes) M.2.A.06 estimate a measurement using either standard or non-standard unit of measurement M.2.B.06 select and use benchmarks to estimate measurements of 0-, 45-, 90-, 180-, 360- degree angles 168 Ritchie, H., & Newby, T. J. (1989). Classroom lecture/discussion vs. Live televised instruction. A comparison of effects on student performance, attitude, and interaction. The American Journal of Distance Education 3 (3). 36-45. Rivera, J. C., Rice, M. L. (2002). A comparison of student outcomes & satisfaction between traditional & web based course offerings. Online Journal of Distance Learning Administration, 5(3). Robinson, W. B. (1970). The effects of two semester of secondary school calculus on students' first and second quarter calculus grades at the University of Utah. Journal of Research in Mathematics Education, 1, 57-60. Roblyer, M. D., & Davis, L. (2008). Predicting success for virtual school students: Putting research-based models into practice. Online Journal of Distance Learning Administration, XI (IV). Roblyer, M. D., Davis, L., Mills, S., Marshall, J., & Pape, L. (2008). Toward practical procedures for predicting and promoting success in virtual school students. The American Journal of Distance Education, 22(2), 90-109. Roblyer, M. D., & Elbaum, B. (2000). Virtual learning? Research on virtual high schools. Learning & Leading with Technology, 27(4), 58-61. Roblyer, M.D., Freeman, J., Stabler, M., & Schneidmiller, J. (2007). External evaluation of the Alabama ACCESS Initiative: Phase 3 report. Eugene, OR: International Society for Technology in Education. Roblyer, M. D., & Marshall, J. C. (2003). Predicting the success of virtual high school students: Preliminary results from an educational success prediction instrument. Journal of Research on Technology in Education 35(2): 241-256. Rocha, E. (2007). Choosing more time for students: The what, why, and how of expanded learning. Washington: Center for American Progress. Rodriguez, J. L., Plax, T. G. & Kearney, P. (1996). Clarifying the relationship between teacher nonverbal immediacy and student cognitive learning: affective learning as the central causal mediator. Communication Education, 45, 293-305. Ronsisvalle, T., & Watkins, R. (2005). Student success in online K-12 education. Quarterly Review of Distance Education, 6(2), 117-124. Rose, R.M., & Blomeyer, R.L. (2007). Access and Equity in Online Classes and Virtual Schools. North American Council for Online Learning. Retrieved Aug 31, 2009, from http://www.nacol.org/docs/NACOL_EquityAccess.pdf 188 APPENDIX E MAP GRADE 6 RELEASED ITEMS SPRING 06 1 The circle graph below shows sales of different kinds of bikes at Bill's Bikes. BILL'S BIKES Other Hybrid Road SKind Bike Bike g jChild's Bike Mountain Bike Which kind of bike had approximately twice the sales as hybrid bikes? s O road bike O child's bike O mountain bike O other kind of bike 2 Ms. Williams is making a design using tiles, as shown below. 2iI1II1 Which statement describes the pattern? O 1 large tile above and 2 small tiles below O 1 large tile above and 3 small tiles below O 2 large tiles above and 3 small tiles below O 2 large tiles above and 4 small tiles below Page 2 I( .1I. the Mw l-rTi Sla De paiTTIrnl of Rlriernity =nd SecO:cndiry Rdic-Lraim 153 More research has been called for that focuses on students' academic performance, particularly on the factors influencing the success of students in K-12 virtual learning environments (Smith, Clark, & Blomeyer, 2005). The question of whether the factors that affect students' achievement in the traditional school learning environment play the same role in the virtual school learning environment remain to be answered. Academic performance is considered as the single greatest indicator of school completion (Battin- Pearson, Newcomb, & Abbott, 2000), and lowering the school dropout rate is a national priority. The investigation of the factors that influence student academic performance in virtual schools is of critical interest to educators, researchers, virtual school program administrators, and policy makers. Algebra I 1 .A line passes through the points (3, 5) and (-2, 7). On the line below, write an equation of the line and graph it on the coordinate grid. Equation: Copyright j 208 by the Missouri State DpTsutmncn of Elmantary and Sccondary Eduation 143 -x lunch programs (3 = -18.52), whether have IEP (3 = -36.89) and student status (3 = 17.59) were most important factors. For standardized test grade 7, whether have IEP (3 = -44.41) and teacher comment (3 = 35.70) were most important factors while for standardized test grade 8, participation in school free or reduced lunch programs (3 = - 57.44), whether have IEP (3 = 21.92) and student status (3 = -11.49) were most important factors. These findings show that the same factors can influence student test score differently for different online Algebra courses. The adjusted R-square (Rc2) was also calculated according to the formula: Rc2= 1- VAR eNAR t to show the reduction of test score variance from the RA model with the addition of the factors. Table 4-10 shows that the same set of factors accounted for student score variance at different degree for different tests. All these findings demonstrated the complexity of the investigation of factors influencing success in online Algebra. The following Table 5-1 shows the summary of the significance and direction of the factor effect on student academic performance in the five tests. The "+" sign indicates the positive direction of the factor effect and the "-" sign indicates the negative direction of the factor effect. The "X" sign indicates the factor had a significant effect on student academic performance in the corresponding test. Research Question 1 Does the level of LMS utilization influence Algebra/mathematics performance in online education? The time spent in academic activities has been identified as a very important factor that has strong effect on success in online education (Cavanaugh, 2007), face-to-face instruction (Rocha, 2007), and blended programs (Cavanaugh, 2009). Based on a study for the investigation of the cognitive-motivational and demographic characteristics of 100 Edge and Friedberg (1984) conducted one study to evaluate the effect of student ACT scores, high school prior knowledge in calculus, gender, family size, and high school size on student academic achievement in the first college calculus course. They found the long-term perseverance/self-control ability and student pre experience/knowledge in Algebra can significantly affect student achievement in the first semester of calculus for freshman. Schiefele and Csikzentmihalyi (1995) conducted one research study using 108 high school freshmen and sophomores to examine the relationships between interest, learning motivation, prior mathematics knowledge/mathematic ability, student mathematics learning experience, and academic achievement in mathematics. The researchers found mathematics ability is a significant predictor of academic achievement, and the predictability of interest for achievement is different for students at different grade levels. At 9th or 10th grade level, interest is a good predictor of achievement. Belcheir (2002) reported a research study on the exploration of variables that can predict success in math courses. The sample of participants was 734 college students enrolled in one intermediate Math course. This study included learner variables such as student math learning attitude and dispositions, study skills, and student commitments. This study did not find time on task as a good predictor of course success as expected. However, the researcher further explained that some valuable information could be missing in this study because the researcher did not collect information about how students spent time studying and whether students felt the amount of time they could use on the course was sufficient. Student motivation and commitment were found to be the most significant predictors of success for the Algebra courses. The researcher also statement Wang and Newlin (2000) made in their study mentioned above that students participating in online academic activities at a higher level achieve better performance in online learning. They echo the call for sustained time on task for cognitive learning (Gallagher, 2009) and provide support for the emphasis of expanded learning time, including with online courses, to improve academic achievement (Cavanaugh, 2009). These findings could be explained by the call for changes in instructional practices in mathematics education by many educational reformers such as the implementation of standards for mathematics instruction from the National Council of Teachers of Mathematics (NCTM, 1989, 1991) and the active involvement in academic activities is one of their arguments (Forman, 1996). This also confirmed the value of increased participation in learning activities in mathematics education emphasized in Forman's article. However, it's surprising to the researcher that this factor only had a significant effect for Algebra I (2) in this study considering many other studies already showed the importance of time on task for the improvement of student achievement. Many of the students taking Algebra I (2) course are from higher grade levels for credit recovery or to make up failing grades in their physical schools. The increased engaged time on task could be more effective with respect to the improvement of academic achievement than the other four groups. Nevertheless, the significant effect of time spent in the LMS for 1 out of 5 groups calls for more studies on activities that engage students during their stay in the LMS as an explanation for the findings. The effect of the number of times students logged into the LMS on student academic achievement is negative for the 4 out 5 groups and negative and significant for Algebra I (2). To some degree, this is contradictory to the belief that the number of 102 TOTALMIN is significant for Algebra I (2) (0.0004, p=0.008). The direction of the effect tells us students who stayed in the LMS longer performed better. State Standardized Test Table 4-3 shows TOTALLOG has no significant effect (0.02, p=0.725) on student score in the grade 7 mathematics standardized test. The direction shows students who logged into the LMS more tending to perform better than those students who logged into the LMS less. The effect of TOTALLOG is also not significant (-0.07, p=0.117) for the grade 8 mathematics standardized test, with students who logged into the LMS less achieving higher scores. Table 4-4 shows TOTALLOG also has no significant effect (- 0.04, p=0.414) for the grade 6 mathematics standardized test, with the same direction as it in grade 8. Table 4-3 shows there is a weak and non significant effect of TOTALMIN (0.0004, p=0.680) on student score in the grade 7 mathematics standardized test, with students who stayed in the LMS longer tending to achieve higher scores. The effect of TOTALMIN is nearly significant (0.001, p=0.057) for grade 8. The direction of the effect tells us students who stayed in the LMS longer tended to perform better. Table 4-4 shows there is a weak and non significant effect (0.0005, p=0.544) of TOTALMIN for the grade 6 mathematics standardized test, with students who spent more time in the LMS tending to achieve higher scores. Research Question 2 Does teacher comment or feedback predict Algebra/mathematics performance in online education? Bransford et al. (1999) emphasized the importance of frequent feedback from the instructors for students to monitor their learning process and evaluate their understanding levels and the learning strategies during the learning process. Based on Table A-2. Geometry Standard for Grades 6-8 Expectations Instructional programs from prekindergarten through grade 12 should enable all students to- Analyze characteristics and properties of two- and three-dimensional geometric shapes and develop mathematical arguments about geometric relationships In grades 6-8 all students should- * precisely describe, classify, and understand relationships among types of two- and three-dimensional objects using their defining properties; * understand relationships among the angles, side lengths, perimeters, areas, and volumes of similar objects; * create and critique inductive and deductive arguments concerning geometric ideas and relationships, such as congruence, similarity, and the Pythagorean relationship. Specify locations and describe spatial use coordinate geometry to represent relationships using coordinate geometry and examine the properties of geometric and other representational systems shapes; use coordinate geometry to examine special geometric shapes, such as regular polygons or those with pairs of parallel or perpendicular sides. Apply transformations and use symmetry describe sizes, positions, and to analyze mathematical situations orientations of shapes under informal transformations such as flips, turns, slides, and scaling; examine the congruence, similarity, and line or rotational symmetry of objects using transformations. Use visualization, spatial reasoning, and draw geometric objects with specified geometric modeling to solve problems properties, such as side lengths or angle measures; use two-dimensional representations of three-dimensional objects to visualize and solve problems such as those involving surface area and volume; use visual tools such as networks to represent and solve problems; use geometric models to represent and explain numerical and Algebraic relationships; recognize and apply geometric ideas and relationships in areas outside the mathematics classroom, such as art, science, and everyday life. 149 Skills, 2006; Watson & Ryan, 2006). Virtual school gives students who failed a course in a traditional classroom the chance for remediation (Barker & Wendel, 2001; Freedman et al., 2002; Glass, 2009; Newman, Stein, & Trask, 2003) and students who want advanced courses an enriched curriculum such as advanced placement courses in different fields including mathematics and science (Barker & Wendel, 2001; Butz, 2004; Newman, Stein, & Trask, 2003; Watson, Gemin, & Ryan, 2008). Virtual school also can benefit home school students through offering more educational opportunities that they otherwise wouldn't have due to reasons such as their parents' lack of knowledge or family resource limitation (Butz, 2004; Watson, Gemin, & Ryan, 2008). Research in Online/Distance Education and Significance of This Study Along with the growth of online education in the US, considerable research has been conducted on online/distance education effectiveness with respect to improved student academic performance and most of the studies have confirmed its effectiveness. However, little research has been done to examine success factors in K- 12 online learning environments. In recent years, two lines of research emerged to address online success factors: one focuses on learner characteristics and another one focuses on learning environment characteristics (Roblyer et al., 2008). However, no clear set of characteristics have been identified as online success factors and no conclusive model has been created to apply in online learning practice (Roblyer & Davis, 2008; Tallent-Runnels et al., 2006). Learner characteristics including participation in academic activities, whether have IEP and learning environment characteristics including teacher comment and school type (private or public school) have been proved to correlate to student academic achievement. However, these variables have not been LIST OF REFERENCES Adelman, C. (1995). The New College Course Map and Transcript Files: Changes in Course-Taking and Achievement, 1972-1993. Washington, DC: U.S. Department of Education, National Center for Education Statistics. Aivazidis, C., Lazaridou, M., & Hellden, G. F. (2006). A Comparison Between a Traditional and Online Environmental Educational Program. The Journal of Environmental Education, 45-54. Allen, E., & Seaman, J. (2007). Online nation: Five years of growth in online learning. Needham, MA: Sloan Consortium. Allen, E., & Seaman, J. (2006). Making the grade: Online learning in the United States, 2006. Needham, MA: The Sloan Consortium. Allen, M., Bourihis, J., Burrell, N., & Mabry, E. (2002). Comparing student satisfaction with distance education to traditional classrooms in higher education: A meta- analysis. The American Journal of Distance Education, 16(2), 83-97. Allen, M., Burrell, N., Timmerman, E., Bourhis, J., & Mabry, E. (2007). Literature of Satisfaction. In Handbook of Distance Education, ed. M. G. Moore, 149-156. Mahwah, NJ: Lawrence Erlbaum Associates. Allen, M., Mabry, E., Mattrey, M., Bourhis, J., Titsworth, S., & Burrell, N. (2004). Evaluating the effectiveness of distance learning: A comparison using meta- analysis. Journal of Communication, 54, 402-420. Anderson, T., & Kuskis, A. (2007). Interaction. In Handbook of Distance Education. Edited by Moore, M. G. TF-LEA. Anglin, G., & Morrison, G. (2000). An analysis of distance education research: Implications for the instructional technologist. Quarterly Review of Distance Education, 1(3), 180194. Aragon, S. R., Johnson, S. D., & Shaik, N. (2002). The influence of learning style preferences on student success in online vs. face-to-face environments. American Journal of Distance Education, 16(4): 27-243. Asuncion, J., Fichten, C., Wolforth, J., Hewlett, M., Klomp, R., & Barile, M. (2006). Accessibility of eLearning in postsecondary education: Student and faculty perspectives. Retrieved from California State University at Northridge, Center on Disabilities, Technology and Persons with Disabilities Conference Web site: http://www.csun.edu/cod/conf/2006/proceedings/3061.htm 170 tasks that require students to work through different problems, arguments, or require extended writing (see Appendix B for some released samples). These EOC tests are intended to measure students' skills in number and operations, Algebraic relationships, and data and probability. This state has its standards for Algebra (see Appendix C for the state Algebra standards). The Appendices A-E provide the evidence of alignment between the Algebra I EOC test and state Algebra standards. State Standardized Test Students who took one state standardized mathematics test grade 6, 7, or 8 after they finished one year of study in this virtual school during 2008-09 academic year participated in this study. This standardized mathematics test is aligned with the state Show-Me Standards which are the educational standards of this state. For mathematics, the Show-Me standards require students in state public schools to obtain knowledge of (MDESE, 2008) 1. Addition, subtraction, multiplication and division; other number sense, including numeration and estimation; and the application of these operations and concepts in the workplace and other situations 2. Geometric and spatial sense involving measurement (including length, area, volume), trigonometry, and similarity and transformations of shapes 3. Data analysis, probability and statistics 4. Patterns and relationships within and among functions and Algebraic, geometric and trigonometric concepts 5. Mathematical systems (including real numbers, whole numbers, integers, fractions), geometry, and number theory (including primes, factors, multiples) 6. Discrete mathematics (such as graph theory, counting techniques, matrices) This grade level state standardized mathematics test is a standards-based test designed to measure the skills for each grade of students in the state where this virtual Clark, T., & Berge, Z. (2005). Perspectives on Virtual Schools. In Z. L. Berge & T. Clark (Eds.), Virtual schools: Planning for success (pp. 9-19). New York, NY: Teachers College Press. Cobb, P., Yackel, E., & Wood, T. (1992). Interaction and learning in mathematics classroom situations. Educational Studies in Mathematics, 23, 99-122. Cohen, J. (1987). Parents as educational models and definers. Journal of Marriage and the Family, 49, 339-349. Coleman, S. J. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, S95-S120. Coleman, S. J., Hoffer, T. (1987). Public and Private High Schools: The Impact of Communities. New York: Basic Books. Coleman, S. J., Hoffer, T, & Kilgore, S. (1982). High School Achievement: Public, Catholic, and Private Schools Compared. New York: Basic Books. Coma Del Corral, M. J., Guevara, J. G., Luquin, P. A., Pena, H. J., & Otero, J. J. M. (2006). Usefulness o fan Internet-based thematic learning network: Comparison of effectiveness with traditional teaching. Medical Informatics and the Internet in Medicine, 31(1), 59-66. Coombs, N., & Banks, R. (2000). Distance learning and students with disabilities: Easy tips for teachers. Retrieved from California State University at Northridge, Center on Disabilities. Technology and Persons with Disabilities Conference Web site http://www.csun.edu/cod/conf/2000/proceedings/0119Coombs.htm Cooze, M., & Barbour, M. (2005). Learning Styles: A Focus on E-Learning Practices and Pedagogy and their Implications for Designing E-Learning for Secondary School Students in Newfoundland and Labrador. Malaysian Online Journal of Instructional Technology. 2(1). ISSN: 1823-1144. Cornford, J., & Pollock, N. (2003). Putting the university online: Information, technology and organizational change, Philadelphia: Open University Press. Cox, R. D. (2005). Online education as institutional myth: Rituals and realities at community colleges. Teachers College Record. 107(8),1754-1787. Crotty, M. (1998). The foundations of social research: meaning and perspective in the research process. London: Sage Publications. D'Amico, E. (2008). Searching for the Right Formula. Chemical Week. 170(28). P: 31- 36. 175 and students who worked on problems on their own (active learning strategy) more often tended to achieve higher performance. Students who can associate their mathematics knowledge with their daily lives tended to achieve lower test score. More research is needed on the effect of learning strategies (group work or independent study) and the triangulation of a variety of academic measurements during the study of Algebra learning factors. Elington (2003) investigated the effects of calculators including basic, scientific, and graphing on students' achievement and attitude levels through the examination of 54 studies, 26 of which targeted high school students. She found the use of calculators in the testing system and instruction can increase students' strategic skills, computational and conceptual skills, and problem-solving skills and promote students' positive attitude toward learning mathematics. Based on the empirical studies, Hollar and Norwood (1999) and Shoaf-Grubbs (1993) found the use of graphing calculators can increase students' overall mathematics ability including the understanding of function, the ability for modeling, interpreting, and translating. In additional to students' increased ability in mathematics problem solving, the integration of technology during the mathematics learning process can also promote collaboration among students during group interactions and class discussions (Goos et al., 2003). Wheland et al. (2003) examined two types of factors that affect student academic performance in an intermediate Algebra course: instructor characteristics- English speaking status (non-native English speaker), teaching assistant or adjunct faculty, and student characteristics: attendance. The effect size was also calculated in the study. The researchers found the instructor characteristics variables: English speaking status, Hughes et al. (2005) believed that teachers' individual feedback can increase communication opportunities for students who are shy and may not participate in academic activities, and these opportunities are helpful to develop closer relationships between the instructor and students. Constructive and timely feedback from instructor is one of the success factors for the practice of an effective virtual learning course/program (Cavanaugh, 2004). Frequent and open communication between students and instructor is identified as an important component to build a virtual community during online learning (Lin 2001; Murphy, Mahoney & Harvell, 2000). The development of a learning community in an online K-12 course is considered an important factor for students' better academic performance (Lin 2001; Oren, et al., 2002; Ronsisvalle & Watkins 2005; Wang & Newlin, 2000). Wang and Newlin (2000) argued the social support provided by the learning community could improve students' academic achievement as well as their involvement and interest in online academic activities. Oren, et al. (2002) believed teachers should act as a moderator to facilitate and scaffold students learning and encourage various interactions especially peer-to-peer interaction to let students learn from each other. They emphasized teachers' supportive feedback to encourage student-to-student social interactions for the formation of the social groups during the leaning process and beyond. O'Dwyer et al. (2007) conducted a quasi-experimental study to examine the impact of one Algebra I online initiative on students' learning outcomes and found that online students themselves also highly value the student-instructor interaction during the learning process. Findings In the present study, RA model was analyzed at the beginning to partition the total variance of student score into within-school and between-school components. The intra- class correlation coefficient was calculated for the five groups and it was Algebra I (1) - .84, Algebra II (2) -.70, Standardized test Grade 6 -.98, Standardized test Grade 7 - .72, and Standardized test Grade 8 .72 respectively. This shows the between-school variance was big in comparison with the within-school variance for all these five groups especially for the Standardized test Grade 6 group. Partially, it could be attributed to the small number of students per school. The big ICC indicated students from different schools are different from each other with respect to their academic achievement. This finding confirmed the gap between private and public schools in student academic achievement found in other studies (Braun, Jenkins, Grigg, Tirre, Spellings, Whitehurst, & Schneider, 2006; Demircioglu & Norman, 1999; Lubienski & Lubienski, 2005). It also could indicate that it will take time for the standardized testing criterion to be well implemented in the school system of this state. In the present study, standardized coefficient (3) was calculated according to the formula: Pk =bk Sxk Sy (bk is the unstandardized coefficient, Sxk is the standard deviation of the corresponding independent variable, and Sy is the standard deviation of the dependent variable). It can be used to compare among different factors with respect to the importance in determining test score. Table 4-9 shows that for Algebra I (1) group, student status (3 = 2.42) and teacher comment (3 = -2.47) were most important factors, and for Algebra I (2) group, participation in school free or reduced lunch programs (3 = -5.24) and student status (3 = 9.85) were most important factors. Table 4- 10 shows that for standardized test grade 6, participation in school free or reduced Gubernick, L., & Ebeling, A. (1997). I got my degree through e-mail. Forbes, 159(12), 84-92. Gunawardena, C. N. & Mclsaac, M. S. (2004). Distance Education. In D. H. Jonassen (Ed.), Handbook of research for educational communications and technology (2nd edition). (pp. 355-396). Mahwah, NJ: LEA. Hall, C.W., Davis, N.B., Bolen, L.M. and Chia, R. (2000) Gender and racial differences in mathematics performance. Journal of Social Psychology, 139(6), 677-689. Hassell, B. C., & Terrell, M. G. (2004). How can virtual schools be a vibrant part of meeting the choice provisions of the No Child Left Behind Act? Virtual School Report. Available at: http://www.doe.virginia.gov/VDOE/Instruction/title1/Hassel- Terrell-VirtualSchools.pdf Heines, R. A., & Hulse, D. B. (1996). Two-way interactive television: An emerging technology for university level business school instruction. Journal of Education for Business, 71(2), 74-76. Helphinstine, L. (1995). Technology and distance education. Vocational Education Journal, 70(2), 47-48. Hernandez, F. J. (2005). Equity and access: The promise of virtual schools. In Z. L. Berge & T. Clark (Eds.), Virtual schools: Planning for success (pp. 20-34). New York, NY: Teachers College Press. Hiebert, J., Grouws, A. D. (2007). The effects of classroom mathematics teaching on students' learning. In Second Handbook of Research on Mathematics Teaching and Learning, edited by Frank K. Lester, Jr., pp. 371-404. Charlotte, N.C.: Information Age Publishing; Reston, Va. National Council of Teachers of Mathematics, 2007. Hill, J. R. (1997). Distance learning environments via World Wide Web. In B.H. Khan (Ed.). Web-based instruction (pp. 75-80). Englewood Cliffs, NJ: Educational Technology Publications. Hollar,J. C., & Norwood, K. (1999). The effects of a graphing approach intermediate algebra curriculum on students' understanding of function. Journal for Research in Mathematics Education, 30, 220-226. Ho, H.Z., Senturk, D., Lam, A., Zimmer, J. M., Hong, S., Okamoto, Y., Chiu, S. Y., Nakazawa, Y., & Wang, C. P. (2000). The affective and cognitive dimensions of math anxiety: A cross-national study. Journal for Research in Mathematics Education, 31, 541-557. 179 Table 4-5: Least-squares estimates of fixed Test Fixed Effect Coefficient Standardized test Grade 7 Standardized test Grade 8 GRADE -0.17 RACE -21.28 FRL -0.89 IEP -41.90 PT/FT 4.97 TEACHERCOM 0.46 TOTALLOG 0.02 TOTALMIN 0.0004 GRADE 1.71 RACE -9.31 FRL -61.40 IEP 21.92 PT/FT -11.98 TEACHERCOM 0.002 TOTALLOG -0.07 TOTALMIN 0.001 effects (with robust standard errors) Standard Error 0.10 10.50 11.03 11.93 9.81 0.22 0.06 0.00 3.47 8.11 9.57 9.39 8.17 0.16 0.04 0.00 T-ratio -1.76 -2.03 -0.08 -3.51 0.51 2.09 0.35 0.41 0.49 -1.15 -6.42 2.33 -1.47 0.01 -1.58 1.92 d.f. 64 64 64 64 64 64 64 64 98 98 98 98 98 98 98 98 P-value 0.083 0.046 0.936 0.001 0.614 0.041 0.725 0.680 0.623 0.254 0.000 0.022 0.146 0.990 0.117 0.057 teaching assistant or adjunct faculties do not have significant effect on student performance while student attendance can significantly affect academic performance. Schoen et al. (2003) also analyzed teacher variables related to student achievement within one reform-based project, the Core-Plus Mathematics Project involving 40 teachers and their 1466 students in 2 schools. They found teaching behaviors such as following the guidance and recommendations of standards and aligning the instruction with the high mathematics expectations are related to higher academic achievement. Algebra teaching and learning A significant amount of research has been conducted on Algebra teaching and learning considering its importance as the momentum to push society to move forward. The focus of Algebra teaching and learning research has been shifted from students' understanding of Algebra activities to the way students construct meaning of Algebra procedures and objects (Kieran, 2007). Based on these studies, recommendations and suggestions have been provided to help improve Algebra teaching and learning quality. Smith, diSessa, and Roschelle (1993) believe school Algebra instruction should build upon the strengths and the resources within the perceptions students have based on their own experience in relation to Algebra concepts. Students should grasp the ability to solve ill-defined tasks that are more closely connected with the questions they may have in the real life rather than the well-defined ones within the school settings (Resnick, 1987). Based on the review of literature on Algebra teaching and learning for students with different backgrounds, Secada (1992) recommended some instructional strategies including increased school time, more mathematics course taking, use of students' native language for instruction, direct instruction for structured curriculum and including insufficiently trained special education teachers and inadequate support services for them to use these technologies (NCES, 2000). This could lead to the academic gap between students with disabilities and students without these disabilities in online learning. Students' learning ability could affect other academic performance in addition to achievement such as academic engagement. Kersting (1997) interviewed 10 deaf students to examine their learning experience and found these students had lower academic engagement in learning activities unless they got sufficient support during their learning process. Richardson, Long, and Foster (2004) compared deaf students and their peers without hearing loss regarding academic engagement in distance learning. There were 267 students with a hearing loss and 178 students without this disability in an open university who participated in this study. The results showed students with hearing loss could not perform well on communication and some other tasks in comparison to students without this disability, which could affect the academic achievement negatively for these disabled students. School type The gap in student achievement between private and public schools has been documented in many studies (Chubb & Moe, 1990; Coleman & Hoffer, 1987; Coleman, Hoffer, & Kilgore, 1982). In 2006, the US Department of Education's National Center for Education Statistics (NCES) released a report about the academic achievement differences in reading and math at grade levels 4 and 8 between private and public schools (Braun, Jenkins, & Grigg, 2006). This report showed on average the private school mean score was higher than the public school's. Students in private schools achieved at higher levels academically than those in public schools. However, many relationship with student academic performance at the school level. Participation in a school lunch program was also frequently used as the measurement of student's family Social Economics Status (SES) in the literature on student academic achievement (Sirin, 2005). The level of the family support including the resources provided for students and education values can influence student academic achievement (Hiebert & Grouws, 2007). Higher SES families provide students more resources at home and social capital, both of which can improve chances for their academic success (Coleman, 1988). A considerable body of research has been done on the relationship between SES and student academic performance. The magnitude of this relationship was found to be strong in two meta-analytic studies conducted more than twenty years apart from each other: 0.343 in White's (1982) meta-analysis and 0.299 in Sirin's (2005) meta- analysis. In K-12 online learning environment, participation in school reduced lunch program/family SES could also be associated with student academic success. Learning ability/presence of individual educational plan Student learning ability is a factor that can influence student academic success during the learning process (Keeler & Homey, 2007). The virtual school student body is a diverse population including students with different learning disabilities (Dickson, 2005; Ferdig, Papanastasiou, & DiPietro, 2005). Virtual school offers individual education plans for these students during the learning process. Therefore, whether or not a student has an individual education plan could be a sign of the level of learning abilities. The review of studies on the relationship between learning ability and academic achievement could shed light on the decision making process to provide more opportunities for students with special needs to succeed in the K-12 online learning environment. al., 2006). Interaction, well described by Cavanaugh, is the "core of teaching" (2001, p. 3) and "at the heart of online learning" (2007, p. 6). The presence of interactivity is vital for the quality education in distance learning (Blomeyer, 2002; Flottemesch, 2000; NACOL, 2006; Parker, 1999; Zhao et al., 2004). It can help students evaluate their learning progress and adjust the instructional strategies if necessary to improve the learning outcome which will lead to a deeper understanding of knowledge (Hiebert & Grouws, 2007; Parker, 1999; Schoenfeld-Tacher, McConnell, & Graham, 2001). Student-instructor interaction can provide the social support for students during the learning process, which is conducive to higher academic achievement and the development of social skills (Parker, 1999). It also has a positive relationship with students' satisfaction with their learning experience (Liaw & Huang, 2000; Swan, 2002; Usun, 2004). The educators' active facilitation in the form of teacher comment and feedback in online learning is an important factor that influences students' academic performance (Cavanaugh et al., 2005; Dickson, 2005; Ferdig, Papanastasiou, & DiPietro, 2005; Hughes et al., 2005; Karp & Woods, 2003; Lin 2001; Peters 1999; Phipps & Merisotis, 2000; Smouse, 2005; Zucker, 2005). Jiang and Ting (2000) conducted one study to examine instructor activity in online learning and found the student's perceived learning is correlated with the number of feedback comments per student that the instructor made. This relationship also has been confirmed by Swan et al. (2000) and Swan (2002). Anderson and Kuskis (2007) argued many of the pedagogical benefits brought by instructor feedback/student-teacher interaction such as those related to motivation are relevant to distance education as well as the conventional classroom education. B ALGEBRA I PERFORMANCE EVENT RELEASED SAMPLES ........................ 142 C STATE ALGEBRA STANDARDS ................................................ 145 D NATIONAL COUNCIL OF TEACHERS OF MATHEMATICS MATHEMATICS STANDARDS FOR GRADES 6-8................................................. 147 E MAP GRADE 6 RELEASED ITEMS SPRING 06........................................... 153 F STATE STANDARDS FOR MATHEMATICS AT GRADE LEVEL 6..................... 167 LIST OF REFERENCES ........... ..... ............ ........................... ............... 170 BIOGRAPHICAL SKETCH ........................... ............................ 195 the feedback, students could revise their thinking and enrich their knowledge structure as they move along. Teacher feedback or teacher comment on student assignments, papers, and projects has been identified as a critical factor that can influence student academic performance in online education (Cavanaugh et al., 2005; Dickson, 2005; Ferdig, Papanastasiou, & DiPietro, 2005; Hughes et al., 2005; Peters, 1999; Zucker, 2005). Phipps and Merisotis (2000) believed that student-teacher interaction and the timely and constructive teachers' feedback to students' assignments and questions are critical characteristics of the teaching/learning benchmarks for the quality of online learning. Watson and Ryan (2006) showed there are big differences in students' experiences between virtual classrooms with minimal teacher involvement and those with greater student-teacher interactions via different means such as e-mail, online message, online discussion forum, phone, etc. Based on a quasi-experimental study on the impact of one state-wide Algebra I online initiative on students' learning outcomes, O'Dwyer et al. (2007) found that online students highly value the student-instructor interaction during the learning process. The critical value of teacher feedback and teacher comment for success in online learning is also applicable to students with special needs. Based on a study of students with learning disabilities (SLD) and students with attention deficit hyperactivity disorder (ADHD), Smouse (2005) found communication with and feedback from instructors was the most valuable aspect of online courses. To investigate the effect of teacher comment or teacher feedback on student achievement in mathematics and Algebra in particular in online learning environments, the number of teacher comments was analyzed using HLM along with other factors in one single equation. perform at the highest level in math, followed by Caucasian students, and then Hispanic. All the three groups perform better than African American students. Hall et al. (2000) also found significant differences in student math achievement among different ethnicities in a study on gender and racial differences in mathematics performance among 5th and 8th grade students in the United States. These differences continue at the higher level. The math skills of most African American in 12th grade, as Barth (2001) described, are only equivalent to the skills of Caucasian students in the eighth grade. U.S. DOE released a report in 2004 about the gaps in academic achievement in different content areas such as reading, math, and science among different racial and ethnic groups based on the data collected since the mid-1980s. At 4th grade level, 41% of whites and 38% of Asians were proficient readers while the number for African Americans, Hispanics, and Native Americans was 13%, 15%, and 16%, respectively. In mathematics, 48% of Asians and 43% of Whites achieved at proficient level while only 10% of African Americans, 16% of Hispanics, and 17% of Native Americans achieved at this level. Participation in school free lunch/family SES Participation in school free or reduced programs has a correlation with student academic achievement, and the magnitude of correlation is weaker as grade level rises (McLoyd, 1998). Klein et al. (2000) conducted a research study on the relationship between students' participation in free or reduced lunch programs and school test score using the data about 2000 Texas 5th graders in reading and math. They found the percentage of students participating in the free or reduced lunch program in a school can affect the school's mean test score. The researchers believed participation in these programs could be considered a sign of the level of poverty which has a strong A. The teacher meets the professional teaching standards established by a state- licensing agency or the teacher has academic credentials in the field in which he or she is teaching. B. The teacher has the prerequisite technology skills to teach online. C. The teacher plans, designs and incorporates strategies to encourage active learning, interaction, participation and collaboration in the online environment. D. The teacher provides online leadership in a manner that promotes student success through regular feedback, prompt response and clear expectations. E. The teacher models, guides and encourages legal, ethnical, safe and healthy behavior related to technology use. F. The teacher has experienced online learning from the perspective of a student. G. The teacher understands and is responsive to students with special needs in the online classroom. H. The teacher demonstrates competencies in creating and implementing assessments in online learning environments in ways that assure validity and reliability of instruments and procedures. I. The teacher develops and delivers assessments, projects, and assignments that meet standards-based learning goals and assesses learning progress by measuring student achievement of learning goals. J. The teacher demonstrates competencies in using data and findings from assessments and other data sources to modify instructional methods and content and to guide student learning. K. The teacher demonstrates frequent and effective strategies that enable both teacher and students to complete self- and pre- assessments. L. The teacher collaborates with colleagues. M. The teacher arranges media and content to help students and teachers transfer knowledge most effectively in the online environment. (Instructional Design) Under each category there are a set of standards. Many of the findings, derivative outcomes, or implications in the present study align with the two sets of standards. The following two tables show these alignments. 112 is a nearly significant difference (-21.48, p=0.068) in student score between Caucasian American students and the minority students for the grade 6 standardized test. The direction shows Caucasian American students tended to perform better than the minority students. Student grade level has no significant effect (-8.13, p=0.106) on student score in the grade 6 standardized test, with students who are in the lower grade levels tending to achieve better performance. A non significant while strong effect was observed for student learning ability (-40.10, p=0.348) in the grade 6 mathematics standardized test (see Table 4-4). The direction tells us students without individual educational plans tended to perform better than their counterparts who had educational plans. Table 4-4 demonstrates the non significant effect (11.62, p=0.344) of student status in the virtual school for the grade 6 standardized test, with full-time students tending to achieve better scores than part-time students. Summary of Findings The purpose of this study is to examine the factors including LMS utilization, teacher comment/feedback and student demographic information that can influence the success of Algebra courses in K-12 virtual learning environments. The three research questions formulated sought to (1) discover the influence of student participation in online academic activities on student mathematics achievement in virtual learning environments; (2) explore whether teacher comment or feedback can predict student academic achievement in online mathematics courses; and (3) investigate the differences in online mathematics achievement among students with different demographic information. The results of question one showed the influence of participation in online academic activities on achievement could be different based on mathematics levels. Table 5-1: Significance and Direction of the Effect of Factors Grade Race Free or IEP Student Teacher Number Time Level Reduced Status Comment of Spent ourse\Factor Lunch Times in the Course\Factor Logged LMS into the LMS Algebra 1st half -X + + + I 2nd half -X + + -X +X Grade 6 + + MAP Grade 7 -X -X + + X + + Grade 8 + -X + X + + TOTAL of 5 2 1 1 2 0 1 1 1 school is located (MDESE, c). It also has a national norm-referenced test that can be used to compare students in this state with students across the country. This component helps align the state standardized test with the National Council of Teachers of Mathematics (NCTM) standards. See Appendix D for NCTM mathematics standards for grades 6-8. There are three types of questions in this grade level standardized mathematics test: 1. multiple choice items that are developed specifically for students in this state or the questions in the national norm-referenced survey; 2. constructed response items that require students to provide the response rather than selecting the options among different choices; 3. performance events items as described above in EOC test that are longer, and focusing on more challenging tasks that require students to work through different problems, arguments, or require extended writing (MDESE, c). See Appendix E for released items of this standardized test (spring 2006) at grade level 6. This state has standards for mathematics at different grade levels. See Appendix F for the state standards for mathematics at grade level 6 (due to the space limit, the author did not attach released items of this standardized test (spring 2006) at grade 7 and 8 and state standards for mathematics at grade level 7 and 8). The Appendices D-F provide the evidence of alignment between this state standardized mathematics test and NCTM mathematics standards and state mathematics standards. Data Analysis Due to the very small sample size of minority groups including Asian American, Hispanic American, Indian American, and African American in this study, these four groups were combined as one category during data analysis in this study: Minority. There are only two categories in the categorical variable: Racial/Ethnicity, Caucasian 7 Study Figure A and Figure B below. Figure A --Figure Figure B In the table below, fill in the correct number of faces, vertices, and edges for Figure A and Figure B. I F Number of Edges Number Number of Faces of Vertices Figure A Figure B 8 Karen baked cookies for 2 hours and 25 minutes. She finished baking cookies at 4:15 P.M. What time did she begin baking cookies? O 1:50 P.M. O 2:40 P.M. O 2:50 P.M. O 6:40 P.M. ! -I Go On 1 Page 5 '. the Misouri State Department of Elementary and Secondary Education 156 Table A-4. Data Analysis and Probability Standard for Grades 6-8 Expectations Instructional programs from In grades 6-8 all students should- prekindergarten through grade 12 should enable all students to- Formulate questions that can be formulate questions, design studies, and addressed with data and collect, collect data about a characteristic organize, and display relevant data to shared by two populations or different answer them characteristics within one population; select, create, and use appropriate graphical representations of data, including histograms, box plots, and scatterplots. Select and use appropriate statistical find, use, and interpret measures of methods to analyze data center and spread, including mean and interquartile range; discuss and understand the correspondence between data sets and their graphical representations, especially histograms, stem-and-leaf plots, box plots, and scatterplots. Develop and evaluate inferences and use observations about differences predictions that are based on data between two or more samples to make conjectures about the populations from which the samples were taken; make conjectures about possible relationships between two characteristics of a sample on the basis of scatterplots of the data and approximate lines of fit; use conjectures to formulate new questions and plan new studies to answer them. Understand and apply basic concepts of understand and use appropriate probability terminology to describe complementary and mutually exclusive events; use proportionality and a basic understanding of probability to make and test conjectures about the results of experiments and simulations; compute probabilities for simple compound events, using such methods as organized lists, tree diagrams, and area models. 151 Algebra I 20. What is the mode of the data set displayed below? Stem Leaf 1 00344444 2 2249 3 1123 4 678888899 5 001257 19 8 Key 1 13 = 13 A. 14 8. 48 C. 4and8 D. 14 and 48 21. Which number line below shows the set of numbers graphed correctly? j3, -7 1 -2, -1 2'2' A. 4 3 -2 -1 0 1 2 3 4 B. -4 -3 -2 -1 0 1 2 3 4 C. -4 -3 -2 -1 0 1 2 3 4 D. -4 -3 -2 -1 0 1 2 3 4 Copyright t 2008 by the Misouri State Department of Elementary and Secondary Education. 134 Cavanaugh et al. (2004) reviewed 14 studies to examine the effectiveness of K-12 distance education. The studies selected were related to distance education published between 1999 and 2004 under the following criteria: type of publication, K-12 focus, quantitative experimental or quasi-experimental studies, and enough statistical information for the calculation of effect size. They specifically looked at the effects of distance education on student academic achievement and the effects of different features of distance education including content area, duration and frequency of distance education, student grade level, school type, interaction, and instructor role on academic achievement. The overall effect size, zero, showed distance education is as effective as traditional education. The wide range of effect size (-1.158, 0.597) indicated some distance education courses/programs were much better than traditional education while others were much worse. Publication and methodological variables such as year and type of publication, measurement employed in the study and statistical power, and distance education experience variables such as duration and frequency of distance education, instructor role, and type of interactions had no significant influence on effect sizes. However, instructional and program variables such as student grade level, school type, and content area did influence effect sizes significantly. The researchers concluded with the promotion of implementation of K-12 distance education with close collaboration among different stakeholders including teachers, researchers, policymakers, developers, and parents, and more rigorous research in this field to guide the practice and implementation of K-12 distance education. Stewart et al. (2005) evaluated the effectiveness of one online case-based continuing education program for family physicians in improving their knowledge and Algebra I 3. Daniel made a box-and-whisker plot of the ages of his cousins. 12 14 16 18 20 22 24 26 28 30 What is the median age of his cousins? A. 24 B. 25 C. 27 D. 28 4. Given y = x2, how would the graph of y = x2 2 differ? A. It shifts 2 units up. B. It shifts 2 units down. C. It shifts 2 units left. D. It shifts 2 units nght. Go On > Copyright 2008 by the Missouri State Dcpartwieat of Elementary and Secondary Educaton. 123 Algebra I 33. 1rn The length of a rectangle is 4 times its width. If the length of the rectangle is cut in half, the new perimeter is which percent of the original perimeter? A. 25% B. 50% C. 60% D. 100% 34. What is the simplified form of the expression? 4x3y3 A. Y B. 2Y B. x2 C. 2x'y D. 2x"y 35. What is the solution for the system of equations? y = 2x- 3 4x 3y = 31 A. ( 11, 25) B. (-11, -19) C. (11, 19) D. (14, 25) STOP Copyright V 2008 by the Missouri State Deparntret of Elementary and Secondary Eduation. 141 APPENDIX D NATIONAL COUNCIL OF TEACHERS OF MATHEMATICS MATHEMATICS STANDARDS FOR GRADES 6-8 Table A-1. Number and Operations Standard for Grades 6-8 Expectations Instructional programs from prekindergarten through grade 12 should enable all students to- Understand numbers, ways of representing numbers, relationships among numbers, and number systems Understand meanings of operations and how they relate to one another In grades 6-8 all students should- * work flexibly with fractions, decimals, and percent to solve problems; * compare and order fractions, decimals, and percent efficiently and find their approximate locations on a number line; * develop meaning for percent greater than 100 and less than 1; * understand and use ratios and proportions to represent quantitative relationships; * develop an understanding of large numbers and recognize and appropriately use exponential, scientific, and calculator notation; * use factors, multiples, prime factorization, and relatively prime numbers to solve problems; * develop meaning for integers and represent and compare quantities with them. * understand the meaning and effects of arithmetic operations with fractions, decimals, and integers; * use the associative and commutative properties of addition and multiplication and the distributive property of multiplication over addition to simplify computations with integers, fractions, and decimals; * understand and use the inverse relationships of addition and subtraction, multiplication and division, and squaring and finding square roots to simplify computations and solve problems. 147 Algebra I 12. A survey was taken asking participants their age and the number of minutes they exercise per week. The results of the survey are shown in the scatterplot below. Minutes of Exercise per Week 140 120 3 C 100 ^-N s so ( 60 a 2 40 LU w . * * 9 a a a . ,... .. ,- . '. .. @@ i* U 10 3 0 10 20 30 40 50 60 Age, in years The data for people who are 30 to 39 years of age are not displayed. Based on the scatterplot, how many minutes would a 30- to 39-year-old person be expected to exercise? A. 40-60 minutes B. 60-80 minutes C. 80-100 minutes D. 100-120 minutes Go On > Copyright 2008 by the Missouri State Dcparntroet of Elemntary and Secondary Educaton. 129 I Jiang, M. & Ting, E. (2000). A study of factors influencing students' perceived learning in a web-based course environment. International Journal of Educational Telecommunications, 6 (4), 317-338. Johnson, C. D. (1986). Practical Answers to Concerns about Teaching the Handicapped. Technology Teacher, 45 (8) (May-June): 11-13. Johnson, S. D., Aragon, S. R., Shaik, N., & Palma-Rivas, N. (2000). Comparative analysis of learner satisfaction and learning outcomes in online and face-to-face learning environments. Journal of Interactive Learning Research, 11(1), 29-49. Jones, T. (1992). Students' evaluation questionnaire for the fall semester of 1991. A summary and report. (ERIC Document Reproduction Services No. ED 345 716). Joy, E.H. & Garcia, F.E. (2000). Measuring Learning Effectiveness: A New Look at No- Significant-Difference Findings. Journal of Asynchronous Learning Networks, 4 (1); 33-39, Available at: http://www.sloan-c.org/publications/jaln/v4n 1/pdf/v4n 1 joygarcia.pdf Karp, G. G., Woods, L. M. (2003). Wellness NutriFit Online Learning in Physical Education for High School Students. The Journal of Interactive Online Learning, 2(2). ISSN: 1541-4914. Karr, C. L., Weck, B., Sunal, D. W., & Cook, T. M. (2003). Analysis of the effectiveness of online learning in a graduate engineering math course. Journal of Interactive Online Learning, 1(3). Keegan, D. (1996). Foundations of distance education (3rd ed.). London: Routledge. Keeler, C. G., & Homey, M. (2007). Online course designs: Are special needs being met? American Journal of Distance Education, 21(2), 61-75. Keeler, C. G., Richter, J., Anderson-lnman, L., Homey, A. M., & Ditson, M. (2007). Exceptional Learners: Differentiated Instructional Online. In C. Cavanaugh & R. Blomeyer (Eds.), What Works in K-12 Online Learning (pp. 125-141). International Society for Technology in Education (ISTE). Kelley, D. & Gorham, J. (1988) Effects of immediacy on recall of information. Communication Education, 37 (2), 198-207. Kellogg, L., & Politoski, K. (2002). Virtual schools across America: Trends in K-12 online education. Los Angeles, CA: Peak Group Research Corporation. Kerka, S. (1996). Distance learning, the internet, and the worldwide web. Columbus, OH: ERIC Clearinghouse. 181 comments, grade level, and race on student test score must account for the influence of school characteristics on this dependent variable. The Hierarchical linear modeling (HLM) technique was carried out by the software program HLM 6.06 for data analysis to account for the clustering of students' scores within one school caused by the school characteristics. The fully unconditional or Random ANOVA (RA) model was utilized to partition the total variance of student test score into within-school (Sigma Square) and between-school (Tau) components at the beginning during the analysis. After that, all the independent variables were added into the model. Generalized estimating equation was then applied for the estimation of coefficients of the different variables. RA Model The RA model was estimated for each dataset to partition the variance of student test score into within-school (Sigma Square) and between-school (Tau) components. Level-1 Model Y = BO + R Level-2 Model BO = GOO + UO The Intra-Class Correlation (ICC) was calculated according to the formula: Tau/ (Tau+ Sigma Square) for each dataset. Results for the RA model are presented in Table 4-1. Table 4-1 demonstrates the ICC for all the five datasets is equal to or above 0.7. This finding shows the between-school variance was large in comparison with the within-school variance for the five groups of students. This tells us the students within the same school are similar with respect to their academic achievement in mathematics and in Algebra particularly in comparison with the students from different schools. CHAPTER 2 REVIEW OF LITERATURE Introduction Online/distance education has greatly contributed to the American education system with its broad advantages covering a variety of aspects including economic, political, demographic, and pedagogical (Dede, 1990). Distance learning, especially online learning, can decrease instructor and student travel costs and possibly increase instructors' productivity (Bartley & Golek, 2004; Cavanaugh, 2001; Cornford & Pollock, 2003; Evans & Haase, 2001; Gallagher & McCormick, 1999; Glenn, 2001; Paulsen et al., 1998). The communities benefit from online/distance learning because it can increase educational opportunities that otherwise might be restricted by geographic barriers or resource restriction and provide the flexibility for students at different levels (Bogden, 2003; Helphinstine, 1995; Kerka, 1996; Parsad & Lewis, 2008; Patrick, 2004; Shachar & Neumann, 2003). The online delivery method via learning management systems can help the decision making process in regard to instruction and administration issues to improve teaching and learning effectiveness through providing data-enriched environments (NACOL & Partnership for 21st Century Skills, 2006). Well designed online instruction can promote collaboration among peers and between learners and instructors and co-construct their knowledge structure (Bartley & Golek, 2004; Blomeyer, 2002; Hassell & Terrell, 2004; Hill, 1997; Summers, Waigandt, & Whittaker, 2005; Webster & Hackey, 1997), leading to the enhancement of higher-order thinking skills and cognitive abilities (Blomeyer, 2002; Garrison, 2003) as well as motivation for students with different learning styles (Butz, 2004; Hassell & Terrell, 2004). Online courses have worked well with a variety of learners including at-risk times students login to the LMS is a strong and positive predictor of success in online learning (Dietz, 2002; Dickson, 2005). It is possible that if students are logging into the course environment more often, they are staying and working for shorter time periods, negatively impacting their concentration on their studies. This also calls for more research on the investigation of LMS utilization with bigger sample size and diversified mathematics tests. Implications Related to Research Question 1 Several implications for research, policy and practice can be drawn from the outcomes associated with research question one even though the results found in this study are mixed. These implications provide guidance for future studies to investigate the effect of time on task and the form of activities students engaged in when they stay in the LMS on academic achievement in virtual learning environments. The positive and significant effect of the time spent in LMS for Algebra I (2) shows students who spent more time in the LMS performed better than students spending less time in the LMS. It is plausible that each log in session of high-performing students was longer than the session length for lower-performing students, showing that high-performing students may benefit from sustained time on task rather than more frequent but shorter time on task. This explanation would support flexible online courses that allow students to stay in the course for extended periods of time while working on complex and abstract content. The positive influence of time spent in the LMS provides the support for the improvement of many LMSs to make them more user-friendly with attractive interfaces that motivate students to spend more time in the system engaging in academic activities delivered during the learning process, as well as teaching practices that foster 103 enriched learning environment in general and online education in specific has a positive influence on student achievement. Allen et al. (2002) reviewed 25 empirical studies comparing student satisfaction between distance education and traditional classrooms. The criteria used to select the studies included the presence of comparison group and sufficient statistical information that effect sizes can be calculated. The results showed overall there was no significant difference in satisfaction level though students showed a slight preference toward traditional education over distance education. The researchers also examined the effects of communication and interaction on student satisfaction level and found that there was virtually no effect on communication methods (video, audio, written). They supported the implementation of distance education by providing the evidence that distance education will not reduce student satisfaction with the learning experience. Aragon, Johnson, and Shaik (2002) examined the impact of learning style preference on student academic success between online and traditional learning environments to investigate the effectiveness of online learning. Thirty-eight students taking a graduate-level instructional design course, with nineteen in a traditional classroom and nineteen in an online course, taught by the same instructor in a Midwestern university participated in this study. The two groups of students were equivalent with respect to their demographic information such as age, undergraduate GPA, and year of baccalaureate graduation. The researchers found that there were differences in learning style between these two groups, though these differences were not significant when student success was controlled. It indicated online education can be as effective as traditional f2f education in helping students succeed academically were added into the model. Generalized estimating equation was applied for the estimation of correlation coefficients. Limitations Limitations of this study include: 1. The small sample size could affect the power for statistic claims. 2. The coding strategy for race/ethnicity variable could mask influential information. 3. Many home school students and the very small number of students from many different physical schools (some only had one student) may cause data analysis difficulty. Conclusions This dissertation examined the impact of some variables including students' demographic information, teacher comments, and student utilization of the LMS on academic performance in Algebra EOC tests and state standard mathematics tests using a sample of students from a state led virtual school in the Midwestern U.S region. The results show different variables affect student Algebra/mathematics achievement in different ways. No single factor investigated in the present study has been found to be significant for all five groups. It could be due to the limitations mentioned in Chapter 3: Methodology. It also indicated that some other factors such as instructional strategies utilized, teacher experience and student prior subject knowledge could have been missed in the present study. They should be investigated in the future studies on success factors in the virtual schooling. Outcomes of this study have some specific implications for researchers, practitioners, and policy makers. The results show the time student spent in the LMS has positive influence on student academic achievement. This provides the support for the online instructional designers or LMS developers to utilize more advanced technologies such as some educational games and refine the course delivery system to motivate students learn the content and spend more time engaging the academic activities. It also can lend relevance to online instructors for the implementation of instructional strategies to encourage students to focus on the learning tasks during their stay in the course delivery systems. The results of data analysis in this study show the influences of many factors are mixed. Some are positive, and others are negative. Even for the same factor, the influence could be in different directions for different tests. This indicates that the investigation of success factors of online learning is a complex process in which 113 studies did not control other important variables such as student SES, or grade level when examining the difference between public and private schools and many of them have been done more than two decades ago, so the data could be already outdated. Therefore, more studies with new data and new methodology are encouraged to be conducted. A study (Lubienski & Lubienski, 2005) supported by the National Assessment of Educational Progress (NAEP) compared student achievement in mathematics between public and private schools using a student sample of 4th and 8th graders. There were over 13000 4th grade students from 607 schools, 385 of which were public schools and 222 were private schools, and over 15000 8th grade students from 740 schools, 383 of which were public schools and 357 were private schools, who participated in this study. The results showed overall students in private schools outperformed their counterparts in public schools; however, after controlling for student's SES, public schools outperformed private schools. The larger proportion of high-SES students in private schools accounted for their overall outperformance. The researchers called for more research on the examination of effectiveness of public and private schools. The review of success factors grounds the present work in the related literature. It helps the establishment of the model in the present study with respect to the selection of independent variables. For example, the family SES has been proved to relate to student academic performance in many studies, which provides the support for the inclusion of the participation in school reduced or free lunch programs which could be the indicator of family SES in the model. Many studies in this literature are quantitative 25 Sela uses one type of bread and one type of filling to make each sandwich. Filling ham tuna cheese peanut butter Which diagram shows all the possible types of sandwiches Sela can make? Bread Filling ham wheat ha tuna cheese -white -" peanut butter O Bread Filling wheat peanut butter ham-cheese white tuna O Bread Filling wheat ham- cheese white tuna wheat peanut butter O Bread Filling ham tuna wheat t cheese peanut butter ham S. tuna white tuna cheese peanut butter 0 Page 16 : '. i i. the Misouri Stte Depaitment of Elementary and Secondary Education 164 Bread wheat white Table 4-7 Descriptive statistics for EOC test takers Test Algebra I (1) Algebra I (2) Variables FINAL GRADE GRADE RACE FRL IEP PT/FT TEACHERCOM TOTALLOG TOTALMIN FINAL GRADE GRADE RACE FRL IEP PT/FT TEACHERCOM TOTALLOG TOTALMIN N 101 101 101 101 101 101 101 101 101 75 75 75 75 75 75 75 75 75 Mean 70.82 9.92 .19 .33 .07 .14 20.78 215.36 10783.93 79.00 9.45 .17 .29 .07 .17 15.31 461.04 27425.76 Std. Deviation 15.598 1.026 .393 .471 .255 .347 21.390 125.476 6418.637 13.650 1.119 .381 .458 .251 .381 21.526 288.174 17097.182 ACKNOWLEDGMENTS I offer a special thank you to my family my parents, sister, and brother their unconditional love has brought me to where I am now. I would not have finished this long journey without their support. I want to give a special thank you to my mom, who has always been there to encourage me to move forward especially when I was feeling down. My whole family provides the momentum for me to conquer all the difficulties that I have been through during this long, and sometimes lonely, journey. I thank Dr. Cathy Cavanaugh, my major advisor and committee chair. As my primary mentor, she inspired me with her dedication to the research and practice in virtual schooling and her motivating work spirit. As a leading researcher in K-12 virtual schooling, she gave me confidence and provided me invaluable guidance to position myself in this field. It is such an honor to be a mentee under her direction. I thank Dr. Kara Dawson, Dr. James Algina, and Dr. Tim Jacobbe, my committee member, for their help and support during this journey. I thank Dr. Dawson for introducing me to the educational technology program, the ever-changing and exciting field. She is my role model to balance life between family and the professional world. I thank Dr. Algina, for his guidance on my minor: research, evaluation, and methodology. His strictness in educational research truly helped me build the foundation to conduct rigorous research in the field of education. I thank Dr. Jacobbe for his patience and insightful questions that inspire me to advance my knowledge in math education. In addition to my committee, I would like to thank my cohort and support group of doctoral students who helped me go through this long process. I thank them for their encouragement and support and all the inspiring stories they shared with me. All these people have made my life much easier during the process. other requirements. A single LMS was utilized by this virtual school to manage course content and deliver instruction at the secondary level. The students needed to take the EOC test at the end of each semester during 2008-09 academic year after they completed the course. Some students also took one state standardized mathematics test at the end of academic year. Students who completed the four Algebra courses: Algebra I (1st half), Algebra I (2nd half), Algebra II (1st half), and Algebra II (2nd half) and took the EOC tests and the students who took the state standardized mathematics test grade 6, 7, or 8 participated in this study (Due to lack of information about students who took the standardized test grade 3, 4, or 5, they were dropped from the study). The number of students who took the four Algebra EOC tests was: 101, 75, 26, and 36 respectively. Due to the insufficient power for data analysis caused by the small sample size of Algebra II (1st half) and Algebra II (2nd half), 26 and 36, these two groups were dropped from the study. Within the two Algebra I groups, 64 out of 101 students in Algebra I (1st half) (63.4%) and 59 out of 75 students in Algebra I (2nd half) (78.7%) were second year students in this virtual school. Students who took the two Algebra EOC tests were from grades 8 to 12. Students who took the standardized mathematics test grade 6, 7, or 8 were from grade 6 to 10 and the number was: 74 (grade 6), 73 (grade 7), and 107 (grade 8). All of these participants were first year students in this virtual school. Instrument Success in an online course can be measured by academic achievement including the grades students earn and their performance on advanced placement exams (Ronsisvalle & Watkins, 2005; Tallent-Runnels et al, 2006). Dickson (2005) used student final score as the measure of student performance in online courses during the Table 4-8 Descriptive statistics for standardized test takers Test Variables N MAP SCALE SCORE 74 GRADE 74 RACE 74 MAP FRL 74 Standardized test Grade IEP STUDENT 74 6TFT 74 PTFT 74 Standardized test Grade 7 TEACHERCOM TOTALLOG TOTALMIN MAP SCALE SCORE GRADE RACE MAPFRL IEPSTUDENT PTFT TEACHERCOM TOTALLOG TOTALMIN MAP SCALE SCORE 107 696.23 38.020 GRADE 107 8.36 .994 RACE 107 .22 .419 MAP FRL 107 .81 .392 Standardized test Grade 107 81 392 IEP STUDENT 107 .81 .392 8 PTFT 107 .17 .376 TEACHERCOM 107 11.28 18.569 TOTALLOG 107 293.93 202.395 TOTALMIN 107 18067.10 14548.311 Mean 664.07 6.76 .19 .92 .93 .18 11.32 354.65 23006.26 676.18 9.03 .19 .78 .74 .07 11.15 253.25 16720.51 Std. Deviation 41.827 .919 .394 .275 .253 .383 18.144 181.272 13370.676 41.913 10.704 .396 .417 .442 .254 19.715 141.193 9324.034 Table 4-9 Standardized coefficients for EOC test takers Tests Factors Standardized Coefficients GRADE -0.26 RACE 0.36 Algebra I (1) FRL -0.05 IEP -0.74 PT/FT 2.42 TEACHERCOM -2.47 TOTALLOG -0.18 TOTALMIN 0.03 GRADE -0.28 RACE -0.86 FRL -5.24 IEP -1.24 Algebra I (2) PTT 9. PTIFT 9.85 TEACHERCOM 0.56 TOTALLOG -0.27 TOTALMIN 0.02 26 Each of two snack stands at the soccer field collected data to compare sales. Which of these does not show the number of hot dogs sold at each stand? SNACK STANDS 25 20 Hot Dogs Soda Water Popcorn Snacks KEY O Stand 1 I Stand 2 0 SNACK STANDS Hot longs Soda E 1 Stand 1 Popcorn Stand 2 Popcorn Water SNACK STANDS 5 0* Hot Dogs Soda Water Popcorn Snacks KEY -0- Stand 1 "-- Stand 2 SNACK STANDS Snacks Stand 1 Stand 2 Hot Dogs 1I 19 Soda IS 21 Watel I 5 I, Pop,.,,r r, 19 t U a ! a Go On I) Page 17 . i. the Misouri State Depaitment of Elementary and Secondary Education 165 Algebra I * What is the y-intercept of the line? * What is the slope of the line? * Write a new equation with the same slope and a different y-intercept on the line below. Equation: * On the lines below, explain how the graph of this line relates to the original line. * Write a new equation with the original y-intercept and a different slope on the line below. Equation: * On the lines below, explain how the graph of this line relates to the original line. STOP Copyright V 2008 by the Missouri State Dcpartleat of Elementary and Secondary Eduation. 144 Algebra I 13. Ben bought 8 notebooks for $24.50. Some of the notebooks were $2.50 each, and the others were $3.25 each. If x represents the number of least expensive notebooks, which equation can be used to find the number of least expensive notebooks purchased? A. $5.75(8 x) $24.50 B. $2.50(x 8) 1 $3.25x C. $2.50x + $3.25(8 x) D. $2.50x + $3.25(x 8) $24.50 $24.50 $24.50 14. The number 18 is 24% of which number? A. 4.32 B. 75 C. 1331 3 D. 432 Copyright 0 2008 by the Missouri State Department of Elementary and Secondary Education. 130 Swan, K., Shea, P., Fredericksen, E., Pickett, A, Pelz, W. & Maher, G. (2000). Building knowledge building communities: consistency, contact and communication in the virtual classroom. Journal of Educational Computing Research, 23 (4), 389-413. Tallent-Runnels, M. K., Thomas, J. A., Lan, W. Y., Cooper, S., Ahern, T. C., Shaw, S. M., & Liu, X. (2006). Teaching courses online: A review of the research. Review of Educational Research, 76(1), 93-135. Thompson, M.S., Alexander, K.L., & Entwisle, D.R. (1988). Household composition, parental expectations, and school achievement. Social Forces, 67, 424-451. Tucker, B. (2007). Laboratories of reform: Virtual high schools and innovation in public education. Education Sector Reports. Retrieved March 10, 2009 from: http://www.educationsector.org/usr_docNirtual_Schools.pdf Tucker, S. (2001). Distance education: Better, worse, or as good as traditional education? Journal of Distance Learning Administration, 4 (4). Available at: http://www.westga.edu/~distance/ojdla/winter44/tucker44.html Ungerleider, C, & Burns, T. (2003). A systematic review of the effectiveness and efficiency of networked ICT in education: A state of the art report to the Council of Ministers Canada and Industry Canada. Ottawa: Industry Canada. U.S. Department of Education, Office of Educational Technology. (2004) Toward a New Golden Age in American Education: How the Internet, the Law and Today's Students Are Revolutionizing Expectations. Washington, D. C. U.S. Department of Education (DOE), National Center for Education Statistics (NCES) (2008). Digest of Education Statistics, Chapter 6 (NCES 2008-022). Usun, S. (2004). Interaction in Turkish distance education system. AACE Journal, 12(2), 123-140. Valenta, A., Therriault, D., Dieter, M. & Mrtek, R. (2001). Identifying student attitudes and learning styles in distance education. Journal of Asynchronous Learning Networks, 5(2), 111-127. Voigt, J. (1994). Negotiation of mathematical meaning and learning mathematics. Educational Studies in Mathematics, 26, 275-298. Voigt, J. (1996). Negotiation of Mathematical Meaning in Classroom Process: Social Interaction and Learning Mathematics. In L. P. Steffe, P. Nesher, P, Cobb, G. A. Goldin, & B. Greer (Eds) Theories of Mathematical Learning. Lawrence Erlbaum Associates, Inc. NJ, 21-50. 192 Coefficients for the Variables After estimating the RA model, all the independent variables were added into the model at the level 1 (student level). With the exception of the data on the standardized test in grade 6, the generalized estimating equation (GEE) procedure was then used to estimate the coefficients of these variables. For the data on the standardized test in grade 6, ordinary least square was used. The results are presented in Tables 4-2 to 4-4. Summarization of the results is presented following Table 4-4. Level-1 Model Y = Y = BO + B1*(GRADE) + B2*(RACE) + B3*(FRL) + B4*(IEP) + B5*(PT/FT) + B6*(TEACHERCOM) + B7*(TOTALLOG) + B8*(TOTALMIN) + R Level-2 Model BO = GOO + UO B1 = G10 B2 = G20 B3 = G30 B4 = G40 B5 = G50 B6 = G60 B7 = G70 B8 = G80 The results of the GEE are presented in Tables. data analysis in a study conducted to investigate the variability of student performance in online courses. Full-time online schools assessed student achievement in the same way as all public schools (Watson, Gemin & Ryan, 2008). Student achievement in many supplemental online programs is also assessed by course grade or EOC test score (Watson, Gemin, & Ryan, 2008). Indeed, some virtual schools and their teachers are paid on the basis of successful students, defined as those passing their courses. Algebra EOC Test Students who took the Algebra I EOC test in the virtual school participated in this study. The Algebra EOC tests were tests administered at the end of each semester in this virtual school. According to the virtual school administration, they have high correspondence to the state's Algebra and mathematics content standards. The purpose of the EOC test, as described by the state's department of education (2009), is to: * Measuring student achievement and progress toward postsecondary readiness * Identifying students' strengths and weaknesses * Communicating expectations for all students * Meeting state and national accountability requirements * Evaluating programs The Algebra I EOC test includes one session of multiple choice items and one session of performance events (Missouri Department of Elementary and Secondary Education (MDESE), 2009a). The items in the multiple choice session are developed specifically for students in this state (see Appendix A for some released samples). The items in the performance events session are longer, and focusing on more challenging Algebra I 10. What is the product of the following expression? 2x(x2 + x 5) A. 2x I x 5 B. 2x+ + 2x 10 C. 2xd + 2x? 5x D. 2x3 + 2x2 1x Go On > Copyright V 2008 by the Missouri State Dcpartlmet of Elementary and Secondary Education. 127 Table 4-3: Overview of RA model for different datasets Test Variables df Sigma Square Tau ICC Algebra I (1) None 79 41.79 225.26 0.84 Algebra II (2) None 56 63.25 146.04 0.70 Standardized test Grade 6 None 69 43.33 1797.90 0.98 Standardized test Grade 7 None 63 468.66 1189.96 0.72 Standardized test Grade 8 None 93 401.44 1052.68 0.72 Davis, D. J. (1984). Evaluation and comparison of teleconference training with face- to-face training and the effects on attitude and learning. Dissertation Abstracts International, 46, (No. AAC850579). Dede, C. (1990). The evolution of distance learning: Technology-mediated interactive learning. Journal of Research on Computing in Education, 22, 247-265. Dees, C. S. (1994). An investigation of distance education versus traditional course delivery using comparisons of student achievement scores. Northern Illinois University. Unpublished dissertation. Demircioglu, H., & Norman, N. (1999). Effects of some variables on chemistry achievements and chemistry-related attitudes of high school students. Hacettepe Universitesi Egitim Fak/ltesi Dergisi 16-17: 40- 44 [1999] Detwiler, E. J. (2008). Comparing Student Performance in Online and Blended Sections of a GIS Programming Class. Transactions in GIS, 12(1): 131-144. Dickson, W. P. (2005). Toward a deeper understanding of student performance in virtual high school courses: Using quantitative analyses and data visualization to inform decision making. Naperville, IL: Learning Point Associates. Dietz, T. L. (2002). Predictors of success in large enrollment introductory courses: An examination of the impact of learning communities and virtual learning resources on student success in an introductory level sociology course. Teaching Sociology 30, 80-88. Doing the Math and Making an Impact. Retrieved from: http://www.ima.umn.edu/newsltrs/updates/summer03/ Dominguez, P. S., & Ridley, D. R. (2001). Assessing distance education courses and discipline differences in their effectiveness. Journal of Instructional Psychology, 28(1), 15-19. Donlevy, J. (2003). Teachers, technology, and training: Online learning in virtual high school. International Journal of Instructional Media, 30(2): 117-121. Drijvers P.H.M. (2003). Learning algebra in a computer algebra environment. Design research on the understanding of the concept of parameter, Dissertation. Utrecht: CD-b press. Duranczyk, I. M. (1997). Unanticipated outcomes of developmental math education-A qualitative study. Selected Conference Papers, National Association for Developmental Education, 3, 19-21. 176 CHAPTER 4 RESULTS Introduction As stated in Chapter 1, the study reported here examined the success factors including student demographic information, teacher comments, and student participation level in online academic activities in K-12 virtual learning environments. This chapter is organized based on the three research questions posed in Chapter 1. It first describes the sample of this study. It then reports the effects of LMS utilization, teacher comment, and student demographic information such as race/ethnicity and whether have IEP on Algebra/mathematic achievement in virtual learning environments. Sample The data were collected during the 2008-09 academic year by a consulting company that works with one state-led virtual school in the Midwestern US region. This consulting company collected student demographic information and their performance on two types of tests: EOC test and state standardized test. The researcher obtained the data from this consulting company. The criteria for the participation in this study were: (1). students who completed Algebra online courses during the 2008-09 year and took the EOC test at the end of each semester; or (2)students in this virtual school who took one state standardized mathematics test at the end of the 2008-09 academic year. EOC Tests Taker This virtual school offered four Algebra courses: Algebra I (1st half), Algebra I (2nd half), Algebra II (1st half), and Algebra II (2nd half) during the 2008-09 academic year. The number of students who completed these four Algebra courses and took the EOC tests at the end of each semester during that academic year was: 101, 75, 26, and 36 |

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PAGE 1 1 FACTORS INFLUENCING SUCCESS IN ONLINE HIGH SCHOOL ALGEBRA By FENG LIU A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2010 PAGE 2 2 2010 Feng Liu PAGE 3 3 To my family PAGE 4 4 ACKNOWLEDGMENTS I offer a special thank you to my family my parents, sister, and brother their unconditional love has brought me to where I am now. I would not have fini shed this long journey without their support. I want to give a special thank you to my mom, who has always been there to encourage me to move forward especially when I was feeling down. My whole family provides the momentum for me to conquer all the diffic ulties that I have been through during this long, and sometimes lonely, journey. I thank Dr. Cathy Cavanaugh, my major advisor and committee chair. As my primary mentor, she inspired me with her dedication to the research and practice in virtual schooling and her motivating work spirit. As a leading researcher in K 12 virtual schooling, she gave me confidence and provided me invaluable guidance to position myself in this field. It is such an honor to be a mentee under her direction. I thank Dr. Kara Dawson, Dr. James Algina, and Dr. Tim Jacobbe, my committee member, for their help and support during this journey. I thank Dr. Dawson for introducing me to the educational technology program, the ever changing and exciting field. She is my role model to balance life between family and the professional world. I thank Dr. Algina, for his guidance on my minor: research, evaluation, and methodology. His strictness in educational research truly helped me build the foundation to conduct rigorous research in the field of education. I thank Dr. Jacobbe for his patience and insightful questions that inspire me to advance my knowledge in math education. In addition to my committee, I would like to thank my cohort and support group of doctoral students who helped me go through this long process. I thank them for their encouragement and support and all the inspiring stories they shared with me. All these people have made my life much easier during the process. PAGE 5 5 Finally, I thank all the faculty and staff in the College of Educa tion at University of Florida who have shaped me in many ways and helped me reach this significant milestone. I thank the University of Florida Graduate Alumni Fellowship Program, which provided me with generous funding for four years. I would not have finished this journey without its assistance. PAGE 6 6 TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................................................................................. 4 LIST OF TABLES ............................................................................................................ 9 LIST OF FIGURES ........................................................................................................ 10 ABSTRACT ................................................................................................................... 11 CHAPTER 1 INTRODUCTION .................................................................................................... 13 Background ............................................................................................................. 13 Problem Statement ................................................................................................. 14 Purpose Statement ................................................................................................. 16 Resear ch Questions ............................................................................................... 16 Significance of the Study ........................................................................................ 17 Delimitations ........................................................................................................... 18 De finition of the Terms ............................................................................................ 19 Organization of the Study ....................................................................................... 20 2 REVIEW OF LITERATURE .................................................................................... 23 Introduction ............................................................................................................. 23 Research in Online/Distance Education and Significance of This Study ................ 25 Review of Literature ................................................................................................ 27 Effectiveness of Online/Distance Education ..................................................... 27 Algebra/Mathematics Education ....................................................................... 35 Learner characteristics variables ............................................................... 35 Learning environment variables ................................................................. 39 Algebra teaching and learning ................................................................... 41 Success Factors in Online Learning ................................................................. 44 Teacher comments/teacher student interaction ......................................... 46 Participation in online academic activities .................................................. 49 Race/ethnicity ............................................................................................ 49 Participation in school free lunch/family SES ............................................. 50 Learning ability/presence of i ndividual educational plan ............................ 51 School type ................................................................................................ 54 Conclusion .............................................................................................................. 56 3 METHODOLOGY ................................................................................................... 59 Introduction ............................................................................................................. 59 Researc h Design .................................................................................................... 59 PAGE 7 7 Participants and Data Collection ............................................................................. 60 Instrument ............................................................................................................... 61 Al gebra EOC Test ............................................................................................ 62 State Standardized Test ................................................................................... 63 Data Analysis .......................................................................................................... 64 Li mitations ............................................................................................................... 66 4 RESULTS ............................................................................................................... 68 Introduction ............................................................................................................. 68 Sample .................................................................................................................... 68 EOC Tests Taker .............................................................................................. 68 State Standardized Test Taker ......................................................................... 69 RA Model ................................................................................................................ 70 Coefficients for the Variables .................................................................................. 71 EOC Test .......................................................................................................... 72 State Standardized Mathematics Test .............................................................. 72 Descriptive Statistics, Standardized Coefficient, and Reduction of Variance .......... 72 Research Question 1 .............................................................................................. 73 EOC Test .......................................................................................................... 74 State Standardized Test ................................................................................... 75 Research Question 2 .............................................................................................. 75 EOC Test .......................................................................................................... 77 State Standardized Test ................................................................................... 77 Research Question 3 .............................................................................................. 77 EOC Test .......................................................................................................... 79 State Standardized Test ................................................................................... 80 Summary of Findings .............................................................................................. 82 5 DISCUSSION AND IMPLICATIONS ....................................................................... 96 Introduction ............................................................................................................. 96 Summary of Study .................................................................................................. 96 Overview of the Problem .................................................................................. 96 Purpose Statement and Research Questions .................................................. 97 Review of t he Methodology .............................................................................. 98 Findings .................................................................................................................. 99 Research Question 1 ............................................................................................ 100 Research Q uestion 2 ............................................................................................ 104 Research Question 3 ............................................................................................ 107 Broad Implications for Online Course Design and Online Teaching ..................... 111 Conclusions .......................................................................................................... 113 APPENDIX A ALGEBRA I MULTIPLE CHOICE RELEASED SAMPLES .................................... 121 PAGE 8 8 B ALGEBRA I PERFORMANCE EVENT RELEASED SAMPLES ........................... 142 C STATE ALGEBRA STANDARDS ......................................................................... 145 D NATIONAL COUNCIL OF TEACHERS OF MATHEMATICS MATHEMATICS S TANDARDS FOR GRADES 6 8 ......................................................................... 147 E MAP GRADE 6 RELEASED ITEMS SPRING 06 .................................................. 153 F STATE STANDARDS FOR MATHEMATICS AT GRADE LEVEL 6 ..................... 167 LIST OF REFERENCES ............................................................................................. 170 BIOGRAPHICAL SKETCH .......................................................................................... 195 PAGE 9 9 LIST OF TABLES Table page 3 1 Coding of the independent variables .................................................................. 67 4 1 EOC test takers demographics ........................................................................... 85 4 2 Standardized test takers demographics ............................................................. 86 4 3 Overview of RA model for different datasets ...................................................... 87 4 4 Least squares estimates of f ixed effects (with robust standard errors) ............... 88 4 5 Least squares estimates of fixed effects (with robust standard errors) ............... 89 4 6 Ordinary Least squares estimates of fixed effects .............................................. 90 4 7 Descriptive statistics for EOC test takers ............................................................ 91 4 8 Descriptive statistic s for standardized test takers ............................................... 92 4 9 Standardized coefficients for EOC test takers .................................................... 93 4 10 Standardized coefficients for standardized test takers ....................................... 94 4 11 Adjusted R squares ............................................................................................ 95 5 1 Significance and Direction of the Effect of Factors ........................................... 117 5 2 Alignment with National Standards in Quality Online Course ........................... 118 A 1 Number and Operations Standard for Grades 6 8 Expectations ...................... 147 A 2 Geometry Standard for Grades 6 8 Expectations ............................................ 149 A 3 Measurement Standard for Grades 6 8 Expectations ...................................... 150 A 4 Data Analysis and Probability Standard for Grades 6 8 Expectations ............. 151 A 5 Problem Solving Standard for Grades 6 8 ....................................................... 152 A 6 Reasoning and Proof Standard for Grades 6 8 ................................................ 152 A 7 Communication Standard for Grades 6 8 ........................................................ 152 A 8 Connections Standard for Grades 6 8 ............................................................. 152 A 9 Representation Standard for Grades 6 8 ......................................................... 152 PAGE 10 10 LIST OF FIGURE S Figure page 1 1 Online Students at K 12 Level ............................................................................ 22 PAGE 11 11 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy FACTORS INFLUENCING SUCCESS IN ONLINE HIGH SCHOOL ALGEBRA By Feng Liu August 2010 Chair: Cathy Cavanaugh Major: Curriculum and Instruction At present, an increasing number of students at the K 12 level in the U.S. are taking courses online via virtual schools, which have been in existence since the end of the 20th century. Virtual schooling is becoming a mainstream option alongside traditional face to face learning environments. Even with its increasing popularity, ver y few empirical studies have been conducted to provide practical guidance for teaching, learning, research, and policy making in K 12 virtual schooling. Some leading virtual school organizations, such as the Southern Regional Educational Board and the Inte rnational Association for K 12 Online Learning, have produced standards in these fields. However, many of the standards lack empirical support based on research conducted in virtual learning environments. Math has been identified as a very important force to push a society forward since it is considered a foundational subject. Many countries emphasize the improvement of math knowledge and they develop policies to attract more people to the field. The examination of success factors in the math field in general and Algebra in specific in virtual learning environments can provide better implementation strategies in PAGE 12 12 virtual schools to improve student math and science achievement and increase the Science, Technology, Engineering, and Mathematics (STEM) workforce in U.S The purpose of this study is to examine the factors including LMS utilization, teacher comment /feedback and s tudent demographic information that can influence the success of Algebra courses in K 12 virtual learning environments. Students who compl eted Algebra and took the endof course (EOC) test and students who took one state standardized mathematics test at grade 68 level in a state virtual school in the Midwestern U.S region during 20082009 participated in this study. Student scores on these tests were collected by this virtual school. Hierarchical linear modeling (HLM) technique was used for data analysis to account for the influence of school characteristics on student scores. The results show these factors have different influences on stude nt performance on the state standardized mathematics test and the Algebra EOC test. These findings have implications for quality online teaching, instructional design, and the policy making process in virtual learning environments. Further research is pro posed based on the results and limitations of this study. PAGE 13 13 CHAPTER 1 INTRODUCTION Background The United States has experienced an extraordinary growth in online education at the K 12 level since its emergence in the late 1990s: from single online course of ferings to large virtual schools today. Thousands of students were attracted to online education because of the advantages it brings such as flexible and longer school time, more educational opportunities, and increased access to resources (Cavanaugh, et al., 2004). Several surveys have showed that at least one third of high school students had online learning experience (Allen & Seaman, 2006; Setzer & Lewis, 2005). Figure 1 1 shows the dramatic increase of K 12 online enrollment between 2001 and 2008 (Clark 2001; Glass, 2009; Newman, Stein, & Trask, 2003; Peak Group 2002; Picciano & Seaman, 2009; Picciano & Seaman, 2007; Setzer & Lewis, 2005; Tucker 2007; Zandberg, Lewis, & Greene, 2008). By 2016, this number is anticipated to reach 56 million and will ke ep growing in the future (Picciano & Seaman, 2009). Only public school students were included in this figure; the number will be higher if all other students are included, such as those in private schools and homeschools (Picciano & Seaman, 2009). The vi rtual school movement in the US is the outgrowth of independent study high schools in many ways (Clark 2003). The first two virtual schools in the US, the Virtual High School (VHS) and Florida Virtual School (FLVS), were both created in 1997 (Barbour & Reeves, 2009). By 2001, about 14 states had established statewide virtual schools and 40,000 to 50,000 students enrolled in courses offered by these schools (Clark, 2001). In July 2005, 21 states offered statewide online programs ( Watson & Kalmon, 2005) By September 2006, 38 states have either stateled online learning PAGE 14 14 programs or online education policies, and 24 states have stateled online learning programs (Watson & Ryan, 2006). As of September 2007, 42 states offered significant hybrid learning programs (students in physical schools taking some courses online), pure online programs (students in physical schools or at home taking most or all of course online) or both ( Watson & Ryan 2007) By fall, 2008, 44 states have online offering for students and 34 s tates offer state led online programs or initiatives (Watson, Gemin, & Ryan, 2008). Response to a survey administered during 200708 showed about 75% of public K 12 school districts were offering full or partial online courses (Glass, 2009; Picciano & Seam an, 2009), approximately 10% increase since 200506 academic year (Picciano & Seaman, 2009). Additionally, another 15% were planning to have online offerings within the next three years (Picciano & Seaman, 2009). Currently all states offer online courses a t school or district level (Cavanaugh 2007). Online education is not seen as separate entity any more but one kind of educational approach which can strengthen the public education and benefit the society at large (Watson & Ryan, 2006). There is a need for deeper understanding of the success in virtual learning environments for the better utilization of this education format to help improve student academic achievement. Problem Statement Along with the extraordinary growth of online education in the US., s ome research has been conducted to examine success factors in online learning environments. According to Roblyer et al., (2008), there are two lines of research emerged to address success factors in online learning: studies focusing on learner characterist ics and studies focusing on learning environment characteristics. Learner characteristics include student cognitive factors such as locus of control and learning styles; prior technology PAGE 15 15 skills and attitudes; and experience and prior knowledge about course content Learning environment characteristics include technology support, course content area, and accessibility to Internet. At present, no clear set of characteristics have been identified to predict success in virtual learning environments, and no conc lusive model has been created to apply in online learning practice (Roblyer & Davis, 2008; Tallent Runnels et al., 2006). There is a gap regarding the establishment of one online success model to help improve student academic achievement considering the q uick development of virtual schooling in the US. Learner characteristic variables including personal effort/participation in academic activities, student learning ability/whether has individual educational plan, race/ethnicity, and family background/partic ipation in free or reduced lunch programs, and learning environment variables including teacher comment /instructor student interaction and school type (private or public school) have been proved in some studies to correlate to student academic achievement However, these variables have not been investigated systematically in one model regarding their effects on student success in K 12 virtual learning environments. Math knowledge is important for a citizen to fully participate in society. Math is the most widely used subject among all the fields and almost every career uses math at different levels (Saint Paul Public Schools, 2007) During the May 2003 commencement address, the president of Society for Industrial and Applied Mathematics (SIAM), Professor D oug Arnold mentioned math is the foundation to understand the world around us and math knowledge can influence other sciences as well such as economics, business, and sociology. He predicted that math will have huge impacts in the 21st century, the digital ized and dataenriched century. PAGE 16 16 Math has been considered a very important force to push a society forward. Many countries emphasize the improvement of math knowledge and they develop policies to attract more people into this field. The underachievement of students in math subjects at the K 12 level could lead to the lack of preparation for students to pursue advanced degrees in these fields. This will cause a shortage in the workforce in math and other sciences fields, which, in turn, could weaken the mome ntum for a country to move forward in many aspects. The National Association of Manufacturers (NAM) believed the shortage of workforce in Science, Technology, Engineering, and Mathematics (STEM) fields can weaken manufacturers abilities to ensure quality, productivity efficiency, and customers' satisfaction (DAmico, 2008) T he quality of Algebra courses is essential in building the number of students who are ready for advanced degrees in STEM and career success in these fields. Growing number of students take math courses online at K 12 level so there is a need to examine the quality of online math courses and build one online success model to help improve student academic achievement in general and Algebra/mathematics in specific The present study was d esigned to fill this gap. Purpose Statement Based on the lack of models for predicting success in high school Algebra courses and the clear need for increased Algebra achievement, this study examines the problem of identifying factors that influence onlin e high school Algebra performance. Research Questions The research questions in this study are: Does the level of LMS utilization influence Algebra/mathematics performance in online education? PAGE 17 17 Does teacher comment or feedback influence Algebra/mathemati cs performance in online education? Do student demographic information such as race/ethnicity, grade level, status in virtual school, whether have individual educational plan (IEP) and participation in free/reduced lunch programs influence Algebra/mathem atics performance in online education? Significance of t he S tudy Even after more than 10 years of extraordinary growth in K 12 online learning, little research has been done as compared to post secondary education (Cavanaugh 2007; Cooze & Barbour, 2005; M eans et al., 2009; Picciano & Seaman, 2007; Picciano & Seaman, 2009; Ronsisvalle & Watkins, 2005). The amount of evidencebased research or empirical study applicable to guide educators instruction and policy makers decision relevancies is slight ( ODwy er, Carey, & Kleiman 2007) The dearth of studies on academic achievement in K 12 virtual learning environments in comparison with that in traditional learning environments ( Cooze & Barbour, 2005; Means et al., 2009; Picciano & Seaman, 2007; Picciano & Sea man, 2009; Smith, Clark, & Blomeyer, 2005; Watson, 2007) form the rationale for this study. T his study can help discover certain characteristics and good practices in online learning and incorporate them into the instructional model of the K 12 virtual learning environment. This study could add to the knowledge of effectiveness of online/distance education in helping improve student academic achievement in the K 12 virtual learning environment. This will provide valuable guidance for the better implementati on and practice of K 12 virtual schooling Given the dearth of research on the factors of academic success in K 12 virtual learning environments, this study could be beneficial to educators, course designers, researchers, online program leaders, policy m akers, and society at large. The investigation of success factors in this study will provide a deeper understanding of PAGE 18 18 success in online learning in general and in the K 12 virtual learning environment in specific. It has implications for the decision maki ng process for virtual schools with respect to the development of more efficient online courses in general and online mathematics courses in specific. It also could help identify the success factors that should be considered in the virtual learning process and guide management of virtual schools to maximize their effectiveness to provide better assistance and supplement to the traditional learning environment. Delimitations The study was conducted from Oct 2009 to June 2010. One state virtual school in the Midwestern US region was chosen as the location in which the data w ere collected. This virtual school has a big student population and large Algebra course enrollment from which the researcher can draw the sample. This virtual school also has a comprehensive data system enabling the use of advanced statistic model during data analysis. This virtual school can represent the virtual school as a whole in US in many respects such as the design of courses according to state and national standards the utiliz ation of one single LMS to deliver the course materials, and the flexible timeline for students to finish courses. However, this virtual also has its own characteristics that may not be common in other virtual schools such as it recruits both full time and part time students and it moves paced courses all along. Students statewide from bricks andmortar public and private schools as well as home school students were eligible to enroll in this virtual school. Students in this virtual school who completed Alg ebra courses during 200809 and took the EOC test and students who took one state standardized mathematics test at grade level 68 during 20082009 participated in this study PAGE 19 19 Definition of the T erms Distance education has been practiced in various forms since its emergence in the early 1900s, evolving from correspondence to broadcasting including radio and television, to online education today (Moore & Kearsley, 1996). It has experienced an extraordinary development in the 20th century and its practice wi ll continue to grow in the 21st century. Many distance education related terms have appeared: cybershool, distance education, distance learning, elearning, online education, online learning, virtual school and web based learning. There are also multiple definitions for each of these terms. In this study, the authors are using the definitions that have been broadly cited though by no means are they the most accurate ones. Distance education, defined by Keegan (1996), has four main components: (1) quasi permanent separation of teacher and learner, (2) the use of technical support to bridge the distance, (3) twoway communication during the process, and (4) possible non presence of learning groups. I t i s probably the most cited definition of DE in the litera ture. Another very comprehensive definition of distance education is in a published monograph by The Association for Education Communications and Technology (Schlosser & Simonson, 2002): Institutionbased, formal education where the learning group is separated, and where interactive telecommunications systems are used to connect learners, resources, and instructors (p. 1). Distance learning, defined by Allen et al. (2004), is a course where the students and instructor will not be physically in the same lo cation during the teaching/learning process. Distance learning can be conducted asynchronously using communication techniques such as email, audio/video recording, mail correspondence, and synchronously using techniques such as television, radio, internet chat room, and telephone (Allen et al, 2004). PAGE 20 20 Allen and Seaman (2006) defined three types of online courses. Online is the course where most or all of the content is delivered online. At least 80% of the traditional face to face (f2f) classroom meeting t ime is replaced by online activity. Blended/Hybrid is the course that combines online and traditional f2f delivery methods. A considerable proportion (30 to 79%) of the content is delivered online. Webfacilitated is the course where webbased technologies are used to facilitate learning. A proportion (1 to 29%) of the content is delivered online. Virtual school, defined by Clark (2000), is a state approved and/or regionally accredited school that offers secondary credit courses through distance learning methods that include Internet based delivery (p. i). Russell (2004) defined virtual school as a form of schooling that uses online computers to provide some or all of a students education (p. 2). Greenway and Vanourek (2006) described virtual schools as a hybrid of public, charter, and home schooling, with ample dashes of tutoring and independent study thrown in, all turbocharged by Internet technology (p. 36). A more recent study conducted by Barbour and Reeves (2009), defined virtual school as an entity, which has been approved or accredited by a state or governing body within the state, that offers secondary level courses through distance delivery most commonly using the Internet. (p. 412). This study examined the practice of virtual school following Clarks and Barbour and Reeves definition. Organization of the S tudy The remainder of the study is organized into five chapters and appendices including some released test items and national and state Algebra and mathematics standards Chapter t wo is the review of the related literature regarding mathematics courses specifically Algebra success factors and online learning success factors. PAGE 21 21 Chapter three presents the research design and methodology of the study. The population and sampling technique, instruments that were used for data collection, and the procedure of data analysis are described. Chapter four presents the results of the data analysis and the findings based on the analysis. Chapter five contains the summary, discussions and implicati ons of the results, recommendations based on the results, and conclusions. The study concludes with a bibliography and appendices. PAGE 22 22 0.04 0.18 0.3 0.5 0.7 1 0 0.2 0.4 0.6 0.8 1 1.2 2000-01 2001-02 2002-03 2004-05 2005-06 2007-08 Academic Year Enrollment Unit: Million Figure 1 1 Online Students at K 12 Level PAGE 23 23 CHAPTER 2 REVIEW OF LITERATURE Introduction Online/distance education has greatly contributed to the American education system with its broad advantages covering a variety of aspects including economic, political, demographic, and pedagogical (Dede, 1990). Distance learning, especially online learning, can decrease instructor and student travel costs and possibly increase instructors productivity (Bartley & Golek, 2004; Cavanaugh, 2001; Cornford & Pollock, 2003; Evans & Haase, 2001; Gallagher & McCormick, 1999; Glenn, 2001; Paulsen et al., 1998). The communities benefit from online/distance learning because it can increase educational opportunities that otherwise might be restricted by geographic barriers or resource restriction and provide the flexibility for students at different levels (Bogden, 2003; Helphinstine, 1995; Kerka, 1996; Parsad & Lewis, 2008; Patrick, 2004; Shachar & Neumann, 2003). The online delivery method via learning management systems can help the decision making process in regard to instruction and administration issues to improve teaching and learning ef fectiveness through providing dataenriched environments (NACOL & Partnership for 21st Century Skills, 2006). Well designed online instruction can promote collaboration among peers and between learners and instructors and coconstruct their knowledge struc ture (Bartley & Golek, 2004; Blomeyer, 2002; Hassell & Terrell, 2004; Hill, 1997; Summers, Waigandt, & Whittaker, 2005; Webster & Hackey, 1997), leading to the enhancement of higher order thinking skills and cognitive abilities (Blomeyer, 2002; Garrison, 2003) as well as motivation for students with different learning styles (Butz, 2004; Hassell & Terrell, 2004). Online courses have worked well with a variety of learners including at risk PAGE 24 24 students, students with some disabilities, and students with limited English proficiency (Keeler et al., 2007; NACOL, 2009; Wat s on, Gemin, & Ryan, 2008). The advocates of online learning believe it will transform teaching, learning, and schooling as a whole (Cox, 2005). As a comparatively new form of online education, virtual schooling has experienced significant development since its emergence in the late 1990s and has been accepted by more and more educators and students because of its great benefits. With its help, small schools and rural schools especially can offer a wi de range of high quality courses that otherwise they can't offer (Donlevy, 2003). Virtual schooling gives students more options for obtaining education (Butz, 2004; Clark & Berge, 2005; Newman, Stein, & Trask, 2003; NACOL & the Partnership for 21st Century Skills, 2006). Virtual school could help solve the inequality of educational opportunities caused by a variety of reasons such as family income, geographical location, and school resources (Blaylock & Newman, 2005; Cavanaugh, 2001; Clark & Berge, 2005; Hernandez, 2005; Kellogg & Politoski, 2002; Newman, Stein, & Trask, 2003; Roblyer et al., 2007; Rose & Blomeyer, 2007; Setzer & Lewis, 2005; Watson & Ryan, 2007). Virtual school can help improve student learning outcomes and skills (Clark & Berge, 2005) through offering individual instruction and flexibility to meet the specific needs of students (Keeler et al., 2007; Kellogg & Politoski, 2002; Newman, Stein, & Trask, 2003). With the support of advanced technologies and rigorous curriculum, virtual schooling can help students master 21st century skills including global awareness, self directed learning, information and communications technology literacy, problem solving, and time management and responsibility (NACOL & the Partnership for 21st Century PAGE 25 25 Skills, 2006; Watson & Ryan, 2006). Virtual school gives students who failed a course in a traditional classroom the chance for remediation (Barker & Wendel, 2001; Freedman et al., 2002; Glass, 2009; Newman, Stein, & Trask, 2003) and students who want advanced courses an enriched curriculum such as advanced placement courses in different fields including mathematics and science (Barker & Wendel, 2001; Butz, 2004; Newman, Stein, & Trask, 2003; Watson, Gemin, & Ryan, 2008). Virtual school also can benefit home school students through offering more educational opportunities that they otherwise wouldnt have due to reasons such as their parents lack of knowledge or family resource limitation (Butz, 2004; Watson, Gemin, & Ryan, 2008). Research in O nline/D istance E duc ation and S ignificance of T his S tudy Along with the growth of online education in the US, considerable research has been conducted on online/distance education effectiveness with respect to improved student academic performance and most of the studies have confirmed its effectiveness. However, little research has been done to examine success factors in K 12 online learning environments. In recent years, two lines of research emerged to address online success factors: one focuses on learner characteristics and another one focuses on learning environment characteristics (Roblyer et al., 2008). However, no clear set of characteristics have been identified as online success factors and no conclusive model has been created to apply in online learning practice (Roblyer & Davis, 2008; Tallent Runnels et al., 2006). Learner characteristics including participation in academic activities, whether have IEP and learning environment characteristics including teacher comment and school type (private or public school) have been proved to correlate to student academic achievement. However, these variables have not been PAGE 26 26 investigated systematically in one model regarding their effects on student success in K 12 virtual learning environments. In a document about nation wide coll ege student transcripts, Adelman (1995) reported that math courses detained the top 7 places in the percentage of grades that were withdrawals, incompletes, or no credit repeats. The first six were precollege math courses and the seventh was college Algeb ra Clearly, math is a difficult subject for many students including secondary level and higher education level. School Algebra is therefore a key subject during the school reform discussion (Chazon & Yerushalmy, 2003). It has been critical for filtering the educational opportunities for high school students to further study in college (Moses, 1994; Moses et al., 1989). Algebra/mathematics is also a very important momentum to push a society to move forward. Many career options are only open to students wit h advanced mathematics skills in the job market (House, 1993). Stanic and Hart (1995) believe mastering mathematics knowledge and being able to apply mathematics ideas are critical for each member in a society to participate in the democratic processes and have more career opportunities. The possession of more mathematical literacy for everyone in the society is also the need for full participation in military service and shifts in US and the worlds economic systems (Secada, 1992). The purpose of this stu dy is to examine the factors including LMS utilization, teacher comment /feedback and s tudent demographic information that can influence the success of Algebra courses in K 12 virtual learning environments. This study can help discover certain characteristi cs and good practices in online learning and help incorporate them into the instructional model of the K 12 virtual learning environment. It PAGE 27 27 also will add to the knowledge of the effectiveness of online/distance education in helping improve student academi c achievement in K 12 virtual schooling and provide valuable guidance for better implementation and practice. The investigation of success factors will provide a deeper understanding of success in the K 12 virtual learning environment specifically in online Algebra/math courses to guide management of virtual schools for maximizing their effectiveness to provide better assistance and supplementation to the traditional learning environment. Review of L iterature The review of literature in this chapter covers the effectiveness of online/distance education, Algebra/mathematics education, and online success factors. The review of literature on effectiveness of online/distance education presents the evidence for the conduct of increasing research in online/distanc e education. It also provides the rationale for the investigation of online success factors in this study. The review of Algebra/mathematics education can grant the support for the selection of specific courses in which the present study is conducted and demonstrates the relationships between a traditional teaching format and online education. The review of online success factors grounds the present work in the related studies and provides the support for the selection of factors in the present study. Effectiveness of O nline/ D istance E ducation Well designed distance education courses/programs can provide effective learning with innovative pedagogy, rich experience, and deep understanding of knowledge (Cavanaugh, 2001). Many studies have been done t o examine the effectiveness of distance education. Research on distance education effectiveness has mainly focused on several aspects: student learning outcomes, student instructor interaction during the PAGE 28 28 learning process, and student and faculty attitude and satisfaction with the learning experience (Gallagher & McCormick, 1999). Cavanaugh (2001) conducted one metaanalysis study to examine the effectiveness of interactive distance education in the K 12 learning environment. She reviewed 19 experimental and quasi experimental studies selected with strict criteria including the focus of study, publication date, research design, and calculated effect sizes to assess the effects of some technologies including videoconferencing and online telecommunications on student achievement and to investigate the success factors for effective distance education. All these studies covered a wide range of subject areas and grade levels. The overall effect size for the 19 studies, 0.147, indicated the small positive effect o f distance education over traditional education. No significant differences were found in grade levels, ability levels, content areas, technology use, and achievement measure. The author concluded distance education can be at least as effective as traditional education to help students achieve academic goals and that offering distance courses at the secondary level could enrich the course curriculum and students knowledge structure. Sherry, Jess, and Billig (2002) conducted one action research stu dy to evaluate the effectiveness of online learning in improving student media literacy and multimedia techniques. They collected data quantitatively using surveys and qualitatively using interview and focus groups from students and instructor. The results showed technologies that are integrated into online learning can help students acquire a variety of skills such as creating multimedia projects, editing digital artifacts, designing web pages, and promoting student learning motivation. They concluded that the technology - PAGE 29 29 enriched learning environment in general and online education in specific has a positive influence on student achievement. Allen et al. (2002) reviewed 25 empirical studies comparing student satisfaction between distance education and tradi tional classrooms. The criteri a used to select the studies included the presence of comparison group and sufficient statistical information that effect sizes can be calculated. The results showed overall there was no significant difference in satisfaction level though students showed a slight preference toward traditional education over distance education. The researchers also examined the effects of communication and interaction on student satisfaction level and found that there was virtually no effect on communication methods (video, audio, written). They supported the implementation of distance education by providing the evidence that distance education will not reduce student satisfaction with the learning experience. Aragon, Johnson, and Shaik (2002) ex amined the impact of learning style preference on student academic success between online and traditional learning environments to investigate the effectiveness of online learning. Thirty eight students taking a graduatelevel instructional design course, with nineteen in a traditional classroom and nineteen in an online course, taught by the same instructor in a Midwestern university participated in this study. The two groups of students were equivalent with respect to their demographic information such as age, undergraduate GPA, and year of baccalaureate graduation. The researchers found that there were differences in learning style between these two groups, though these differences were not significant when student success was controlled. It indicated onl ine education can be as effective as traditional f2f education in helping students succeed academically PAGE 30 30 even though they have different learning style preferences. The researchers advocated for the development of online education based on the results. Swan (2003) looked beyond the no significant differences phenomena and reviewed the literature on the effectiveness of online learning focusing on the three types of interaction: student instructor, student student, and student content. She believed online education is effective as compared to traditional education, and some unique characteristics of the online technology can be further utilized to improve the online learning effectiveness. Based on the literature review, she gave some suggestions for the improvement of online learning environments such as providing timely and constructive feedback to students, integrating activities to establish online community, encouraging students to share experiences and thoughts during their learning process, and ensuring the clarity and consistency of the course materials. Zhao et al. (2004) employed a metaanalytical approach to investigate the effectiveness of distance education. The researchers found many individual studies reporting significant differences between distance education and traditional education; some found distance education more effective while others found traditional more effective. They selected 51 out of thousands of articles for review with some criterion such as that they needed to be journal articles, they must possess empirical data, and they needed enough statistical information to calculate effect size. The researchers analyzed several variables in this metaanalysis including study related variables such as design of study, measurement employed, instruction related variables such as instructor (status, involvement level), learner (status, background), curriculum (content area, degree), and milieu (interaction, media, setting). Effect size was calculated for the PAGE 31 31 estimation of differenc e between distance education and traditional education. The results showed overall there is no significant difference between distance education and traditional education. However, the wide range of effect size ( 1.43, 1.48) indicated distance education was much more effective in some studies while was much less effective in other studies. Interestingly, the researchers found publication time was a factor for the effectiveness: studies published before 1998 are more likely to find no significant difference while studies published after 1998 are more likely to find significant effectiveness favoring distance education. The researchers believed that distance education is getting better with the advance of technologies and design principles. The instructor involvement was found to influence the effectiveness: traditional education was more effective when involvement was low and distance education was more effective when it was high. This confirmed the importance of instructor involvement in the form of teacher f eedback, and student teacher discussion for successful distance learning. The researchers also found the content area can predict the difference between distance education and traditional education: distance education was more effective in fields such as B usiness, Computer Science, and Medical Science; no significant difference was detected in Social Science and Science fields; distance education was slightly effective over traditional education in Military, Mathematics and Specific Skills. Though the researchers did not examine the learner variables such as gender, or learning styles, they believed groups with certain characteristics are more likely to succeed in distance learning. This study also found a blended learning environment mixing distance educati on and a certain amount of f2f meeting was most effective and called for more comprehensive studies in the distance education field. PAGE 32 32 Cavanaugh et al. (2004) reviewed 14 studies to examine the effectiveness of K 12 distance educat ion. The studies selected were related to distance education published between 1999 and 2004 under the following criteri a : type of publication, K 12 focus, quantitative experimental or quasi experimental studies, and enough statistical information for the calculation of effect size. They specifically looked at the effects of distance education on student academic achievement and the effects of different features of distance education including content area, duration and frequency of distance education, student grade level, school type, interaction, and instructor role on academic achievement. The overall effect size, zero, showed distance education is as effective as traditional education. The wide range of effect size ( 1.158, 0.597) indicated some distance education courses/programs were much better than traditional education while others were much worse. Publication and methodological variables such as year and type of publication, measurement employed in the study and statistical power, and distance education experience variables such as duration and frequency of distance education, instructor role, and type of interactions had no significant influence on effect sizes. However, instructional and program variables such as student grade level, school type, a nd content area did influence effect sizes significantly. The researchers concluded with the promotion of implementation of K 12 distance education with close collaboration among different stakeholders including teachers, researchers, policymakers, develop ers, and parents, and more rigorous research in this field to guide the practice and implementation of K 12 distance education. Stewart et al. (2005) evaluated the effectiveness of one online case based continuing education program for family physicians i n improving their knowledge and PAGE 33 33 skills. They randomly assigned the participants into experimental groups with the implementation of intervention: online learning and control group without the intervention. They analyzed the knowledge and skill growth measured by two knowledge questionnaires and charts quantitatively and the posts and emails qualitatively. The results showed the intervention had positive effects on knowledge growth and the quality of practice for these physicians. The researchers confirmed t he promis e of the broad implementation of online education in general. Williams (2006) reviewed 25 comparative studies from 1990 to 2003 on distance education in allied health science education to examine the learning effectiveness on student achievement and the instructional design (ID) components contributing to the effectiveness. The overall effect size, 0.15, with confidence interval from 0.07 to 0.23, showed distance education was slightly more effective than traditional education with respect to impr oved student achievement. The results also showed the integration of ID components in distance courses had a positive effect on achievement. The researcher suggested the effective distance education courses should incorporate various ID components. The study was concluded with the promotion of distance education courses/programs and a call for more research on the effect of different aspects such as educational level on the effectiveness of distance education. In 2009 US Department of Education ( DOE ) releas ed a report about a metaanalysis of empirical studies from 1996 to 2008 to evaluate the effectiveness of online learning practice. The studies included in this metaanalysis were selected based on the criteri a : rigorous research design including random as signment or controlled quasi experimental design to contrast online to traditional education, objective learning PAGE 34 34 outcome was measured and enough information to calculate effect size. The researchers found overall online students outperformed traditional st udents with respect to their learning outcomes with effect size of 0.24 favoring online students. Hybrid learning students had a larger gain over traditional students relative to purely online students over traditional students with effect size of 0.35 fav oring hybrid learning students when comparing hybrid learning and traditional learning and effect size of 0.14 favoring online students when comparing purely online learning and traditional learning. Some methodological variables including sample size, content knowledge, research design, and equivalence of instructional approach were evaluated whether they accounted for the results and equivalence of instructional approach was found to be a significant moderator variable. This report confirmed that the combination of a variety of elements in online learning such as instructional strategies, integrated technologies, and students effort rather than the delivery medium per se is what results in better learning outcomes. The researchers also found that very few rigorous research studies of K 12 online learning effectiveness have been published. Only 7 out of 99 studies included in this meta analysis focused on K 12 level, and the effect size comparing online learning and traditional learning at K 12 levels was not significant. Therefore, the researchers caution the application of the results to K 12 virtual learning environments and more rigorous research is needed to guide the practice and implementation of K 12 online education. The proved effectiveness of onl ine/distance education in the literature provides the support for the present study. M any effectiveness studies have focused on the student outcome This lends the relevance to the selection of student academic achievement as PAGE 35 35 the dependent variable in the present study. Another focus in many effective studies, student teacher interaction, presents the rationale for the investigation of teacher comments which could be the indicator of student teacher interaction in the present study Algebra / M athematics E d ucation C onsiderable educational and psychological research has been conducted to identify the success factors in mathematics fields (Grouws, 1992). In the literature on mathematics education, many researchers have focused on a variety of factors that asso ciated with mathematics learning including student attitude and background, family backgroundsocioeconomic, peer environment, instructor factors, and curriculum and instruction (Beaton & Dwyer, 2002; Kifer, 2002; Wilkins, Zembylas, & Travers, 2002). These factors have been categorized into three major topics by Schiefele and Csikszentmihalyi (1995): learner characteristics such as learning styles, learning strategies, and locus of control, home environment such as socioeconomic status (SES) and family size, and school environment such as instructor experience, instruction quality, technique support and resources. However, in other studies, these factors have been grouped into two categories: learner characteristics and learning environment characteristics w hich include home learning environment and school learning environment (Catsambis, 1995; Ercikan, McCreith, & Lapointe, 2005; Ho et al., 2000). To be consistent with Roblyer et al., (2008)s study of K 12 distance education we used two groups of factors i n the present study. Learner characteristics variables Research has shown learner personal characteristics variables such as prior knowledge and background, motivation or self concept are strongly associated with PAGE 36 36 student mathematics achievement (Cats ambis, 1995; Ercikan, McCreith, & Lapointe, 2005; Ho et al., 2000). Some affective variables such as self concepts, attitudes toward mathematics, self confidence mathematics learning ability, motivation, locus of control, and perceptions of the usefulness of mathematics have been found to relate to student academic achievement in mathematics (Bassarear, 1991; Duranczyk, 1997; House, 1995; House, 1993; Marsh & Yeung, 1997; Reyes, 1984). Increased study time and the taking of advanced coursework also can posi tively affect students' mathematics achievement (Secada, 1992). Student English language proficiency is another factor for student mathematics achievement in U.S. (Jacobson, 2000; Secada, 1992). Bilingual students whose native language is not English will be likely to achieve higher performance in mathematics if they receive the instruction in their native language (Secada, 1992). Ercikan et al. (2005) conducted one exploratory research study examining factors that might affect students achievement in mathematics and their participation in advanced mathematics courses in three countries: Canada, Norway, and the US. They found students personal and home environment variables strongly affect their achievement in mathematics and participation in advanced math ematics courses in these three countries. These researchers specifically confirmed the relationship between attitude toward mathematics and participation in advanced mathematics courses in these countries and the relationship between SES related variables and achievement in mathematics in the US. Higbee and Tomas (1999) conducted one research study to examine the relationship between noncognitive variables including math anxiety, perceived PAGE 37 37 usefulness of mathematics, self control ability, and self confidence in ones ability to learn mathematics and academic achievement in mathematics. The participants were 23 college freshmen and student score in their math courses was collected as the indicator of academic achievement. The researchers found student attitu des toward mathematics, motivation, self management skills and self confidence are related to academic achievement. Jacobson (2000) examined success factors in high school mathematics using a variety of statistical techniques: multiple regressions, ANOVA, and path analysis with a sample size of 1205 high school students. She found student beliefs/confidence in their mathematics learning ability and family background/ SES have strong effect on academic achievement in mathematics courses. Interestingly, she al so found student's primary language and writing ability are significant success factors in mathematics learning. Based on the review of literature on mathematics education, Reyes (1984) asserted the affective variables including students confidence in their mathematics learning ability, attitude toward mathematics, and mathematics anxiety are related to mathematics learning. Reyes believed students confidence in mathematics learning or self concept about mathematics learning has a positive relationship wi th mathematics achievement. Mathematics anxiety can negatively affect student mathematics achievement. Reyes believed students attitude toward mathematics and their perceived usefulness of mathematics can affect their decision about the mathematics course they will take. Students who valued mathematics more tended to take more mathematics courses, which, in turn, could contribute to their higher achievement. PAGE 38 38 Edge and Friedberg (1984) conducted one study to evaluate the effect of student ACT scores, high s chool prior knowledge in calculus, gender, family size, and high school size on student academic achievement in the first college calculus course. They found the long term perseverance/self control ability and student pre experience/knowledge in Algebra ca n significantly affect student achievement in the first semester of calculus for freshman. Schiefele and Csikzentmihalyi (1995) conducted one research study using 108 high school freshmen and sophomores to examine the relationships between interest, learni ng motivation, prior mathematics knowledge/mathematic ability, student mathematics learning experience, and academic achievement in mathematics. The researchers found mathematics ability is a significant predictor of academic achievement, and the predictability of interest for achievement is different for students at different grade levels. At 9th or 10th grade level, interest is a good predictor of achievement. Belcheir ( 2002) reported a research study on the exploration of variables that can predict suc cess in math courses. The sample of participants wa s 734 college students enrolled in one intermediate Math course. This study include d learner variables such as student math learning attitude and dispositions, study skills, and student commitments. This s tudy did not find time on task as a good predictor of course success as expected. However, the researcher further explained that some valuable information could be missing in this study because the researcher did not collect information about how students spent time studying and whether students felt the amount of time they could use on the course was sufficient. Student motivation and commitment were found to be the most significant predictors of success for the Algebra courses. The researcher also PAGE 39 39 emphasi zed that the instructors should let students know early on how they are performing in order for them to succeed in the courses. Learning environment variables Research has shown learning environment characteristics variables including family background and support, school or classroom resources, teacher/classroom are strongly associated with student mathematics achievement (Catsambis, 1995; Ercikan, McCreith, & Lapointe, 2005; Ho et al., 2000). High parent expectations (Cohen, 1987; Marjoribanks, 1988; Scot t Jones, 1984; Seginer, 1986; Thompson, Alexander, & Entwisle, 1988) can positively affect students' mathematics achievement. Instructional strategies including the implementation of computer Algebra software (Elington, 2003; Lawson, 1995; Mayes, 1995; Stephens & Konvalina, 2001), the use of other technologies such as calculators in general and graphing calculators in specific (Elington, 2003), and collaborative problem solving strategy and visual technique support (Higbee & Thomas, 1999) can positively aff ect student mathematics achievement. Teacher variables such as teaching behaviors are another type of factor that related to student academic achievement (Schoen et al., 2003). House and Telese (2008) investigated the relationships between instructional strategies and self confidence in mathematics learning ability and Algebra academic achievement in the US and Japan using 2003 TIMSS assessment data. The sample includes 4244 students from Japan and 7862 students from the US. They found the teaching and learning strategies and students' self confidence/beliefs in mathematics learning ability are significantly associated with student Algebra academic achievement in these two countries. Interestingly, they found the instructional strategy of cooperative lea rning activities (work ing in small groups) negatively affect ed student achievement PAGE 40 40 and students who worked on problems on their own (active learning strategy) more often tended to achieve higher performance. Students who can associate their mathematics knowledge with their daily lives tend ed to achieve lower test score. More research is needed on the effect of learning strategies (group work or independent study) and the triangulation of a variety of academic measurements during the study of Algebra lea r nin g factors. Elington (2003) investigated the effects of calculators including basic, scientific, and graphing on students' achievement and attitude levels through the examination of 54 studies, 26 of which targeted high school students. She found the use of calculators in the testing system and instruction can increase students strategic skills, computational and conceptual skills, and problem solving skills and promote students positive attitude toward learning mathematics. Based on the empiric al studies, Hollar and Norwood (1999) and Shoaf Grubbs (1993) found the use of graphing calculators can increase students overall mathematics ability including the understanding of function, the ability for modeling, interpreting, and translating. In addi tional to students increased ability in mathematics problem solving, the integration of technology during the mathematics learning process can also promote collaboration among students during group interactions and class discussions (Goos et al., 2003). Wheland et al. (2003) examined two types of factors that affect student academic performance in an intermediate Algebra course: instructor characteristics English speaking status (nonnative English speaker), teaching assistant or adjunct faculty, and st udent characteristics: attendance. The effect size was also calculated in the study. The researchers found the instructor characteristics variables: English speaking status, PAGE 41 41 teaching assistant or adjunct faculties do not have significant effect on student performance while student attendance can significantly affect academic performance. Schoen et al. (2003) also analyzed teacher variables related to student achievement within one reform based project, the Core Plus Mathematics Project involving 40 teachers and their 1466 students in 2 schools. They found teaching behaviors such as following the guidance and recommendations of standards and aligning the instruction with the high mathematics expectations are related to higher academic achievement. Algebra teaching and learning A significant amount of research has been conducted on Algebra teaching and learning considering its importance as the momentum to push society to move forward. The focus of Algebra teaching and learning research has been shifted from students understanding of Algebra activities to the way students construct meaning of Algebra procedures and objects (Kieran, 2007). Based on these studies recommendations and suggestions have been provided to help improve Algebra teaching and learning quality. Smith, diSessa, and Roschelle (1993) believe school Algebra instruction should build upon the strengths and the resources within the perceptions students have based on their own experience in relation to Algebra concepts. Students should grasp the ability to solve illdefined tasks that are more closely connected with the questions they may have in the real life rather than the well defined ones within the school settings (Resnick, 1987). Based on the review of literature on Algebra t eaching and learning for students with different backgrounds, Secada (1992) recommended some instructional strategies including increased school time, more mathematics course taking, use of students' native language for instruction, direct instruction for structured curriculum and PAGE 42 42 basic mathematics skills, and divide of whole class activities into group and individual work. Considerable research has been conducted on the approaches to Algebra learning. Bednarz, Kieran, and Lee (1996) reported the four approaches that have been focused on at an international colloquium on Algebra in early 1990, including generalization of numerical and geometric patterns and laws regarding Algebraic relationships, functional situations, modeling of mathematical phenomena, and problem solving. Drijvers (2003) described the similar approaches for Algebra learning in more detail: Problem solving approach: view Algebra as a way to solve problems that can be expressed in equations. Functional approach: view Algebra as a way to investigate the functions and relations among different variables. Generalization approach: mainly focus on the examination of patterns or models and configurations, and focus on the generalization of relations among different variables. Language approach: view Algebra as a way to convey mathematics ideas in which Algebra is merely a representation structure composed of symbols without specific context attached meaning. Dri j vers also identified some aspects that make learning Algebra difficult. These inclu de: 1 The difficulty for students to relate the formal algorithmic procedures with informal while meaningful methods 2 The abstract characters of Algebra problem solving approaches that students cant connect them with the concrete situations 3 The Algebraic lan guage includes particular symbols and rules that are difficult for students to grasp PAGE 43 43 Drijvers pointed out the importance of reification of expressions and formulas during the Algebra learning process. Students need to possess the ability to comprehend the structure and meaning of formulas and expressions, which as he called symbol sense (p.49). Furthermore, Drijvers explained some key elements of one theory for mathematics education: Realistic Mathematics Education (RME) and their meaning and relations t o Algebra learning: Guided reinvention and progressive mathematization: with the guidance from the teacher, students are given the opportunities to develop formalized mathematics knowledge by employing the informal strategies and apply the knowledge in the concrete life situations. Didactical phenomenology: design activities that encourage students to develop their own mathematics learning strategies Horizontal and vertical mathematization: Horizontal: by employing empirical methods for example observation and experimentation, students can structure and solve the problem with mathematics formulas or conventions. Vertical: based on these problem solving experiences and beyond, students can develop mathematical framework in regarding with the relations among symbol. The review of Algebra/mathematics education demonstrates the relationships between f2f learning environments and online education with respect to the factors of academic achievement It lends the support for the selection of success factors in th e present study. For example, many Algebra/mathematics education studies have focused on a variety of factors that associated with mathematics learning outcome. These factors include learner characteristics such as learning styles and learning strategies, and learning environment characteristics such as family background and school learning resources. This supports the categorization of factors in the present study. PAGE 44 44 Success F actors in Online Learning Given the fact of extraordinary development of online e ducation in the last two decades, little research has been conducted to examine success factors in the online learning environment as compared to the traditional learning environment. With the high early dropout and failure rates in the online learning env ironment (Carr, 2000a; Roblyer & Elbaum, 2000), there is an urgent need for more research on success factors to prevent students from dropping out of virtual or physical school and ensure their success in this learning environment (Barbour & Reeves, 2009; Bernard et al., 2004a; Butz, 2004; Dickson, 2005; McLeod et al., 2005). At the K 12 level, there is great concern about the readiness for students to take online courses and succeed academically because they are not socially and emotionally mature as compared to students in higher education (Picciano & Seaman, 2007). The review of studies examining success factors in online learning environments as well as traditional learning environments can better guide the practice of K 12 online education. Schrum and Hong (2001) administered a survey with 70 institutions and found several factors can influence student success in elearning environment: learning styles, prior technology experience, personal disposition, study habits, and tools accessibility. Brown and Liedholm (2002) conducted a comparative study between online education and traditional education and found students personal effort on learning tasks could make a difference in academic performance. Swan (2002) investigated the correlation between 22 cours e design factors and student academic achievement and satisfaction with learning experience. She found three factors: transparent interface/clarity and consistency in course design, instructor feedback/instructor student interaction, and dynamic online dis cussion are associated with the success of online learning. These PAGE 45 45 three factors could be three necessary steps for the establishment of an online learning community (Swan et al., 2000), which can affect students learning outcomes in online education. Ro blyer and Marshall (2003) evaluated one instrument, the Educational Success Prediction Instrument (ESPRI) which was created to predict success in Virtual High School ( VHS ) courses. The constructs measured by this instrument related to success in VHS course s included time management, achievement, motivation, self responsibility, prior technology skills, self regulation, and self confidence. They evaluated ESPRI with 135 students in 13 virtual high schools and found ESPRI is a 0.92) to predict student success in the online learning environment, and certain personality characteristics and attitude were associated with online learning success. Roblyer et al. (2008) created the instrument: ESPRI V2 based on ESPRI and reevaluated it with a bigger sample size: 4110 students from VHS. They found the four factors measured by ESPRI V2: technology use/self efficacy, achievement beliefs, instructional risk taking, and organization strategies can predict student success in VHS courses though its harder to predict failure, and the Cronbach V2 is a reliable instrument. The researchers concluded the combination of prior knowledge, cognitive characteristics such as self efficacy and achievement beliefs, and environment variables such as Internet accessibility and technical support can predict student online success. According to Roblyer et al. (2008), two lines of research emerged to address success factors in online learning: studies focusing on learner characteristics and studies focusing on learning environment characteristics. Learner characteristics include PAGE 46 46 student cognitive factors such as locus of control and learning styles; prior technology skills and attitudes; and experience and prior knowledge about course content while learning environment characteristics include technology support, course content area, and accessibility to the Internet. At present, no clear set of characteristics have been identified to predict the success of the virtual learning environment and no conclusive model has been created to apply in online learning practice (Roblyer & Davis, 2008; Tallent Runnels et al., 2006). Other learner characteristics including personal effort/participation in academic activities, whether has individual educ ational plan, race/ethnicity, and family background/participation in free/reduced lunch programs, and learning environment characteristics including teacher comment/feedback/instructor student interaction and school type (private or public school) also hav e been proved in some studies to correlate to student academic achievement. However, these factors influences have not been investigated systematically. The review of these factors in light of the relationship with student academic achievement in other st udies can provide deeper understanding of success in online learning in general and the K 12 virtual school environment in specific and shed light on the establishment of a model to predict online learning success in general and online Algebra/mathematics learning in specific Teacher comments/ t eacher student interaction Teacher comments and s tudent teacher interaction is a critical component in academic learning (Boaz, 1999; Laurillard, 1997; Parker, 1999; Schaffer & Hannafin, 1993; Summer, 1991; S wan, 2003; Williams, 2006). It can affect learning in traditional f2f learning environments (Christophel, 1990; Kelly & Gorham, 1988; Rodriguez, Plax & Kearney, 1996) and online learning environments (Blomeyer, 2002; Jiang & Ting, 2000; Johnson et al., 2000; Swan, et. al., 2000; Swan, 2002; Swan, 2003; Tallent Runnels et PAGE 47 47 al., 2006). Interaction, well described by Cavanaugh, is the "core of teaching" (2001, p. 3) and at the heart of online learning (2007, p. 6). T he presence of interactivity is vital for t he quality education in distance learning (Blomeyer, 2002; Flottemesch, 2000; NACOL, 2006; Parker, 1999; Zhao et al., 2004). It can help students evaluate their learning progress and adjust the instructional strategies if necessary to improve the learning outcome which will lead to a deeper understanding of knowledge (Hiebert & Grouws, 2007; Parker, 1999; SchoenfeldTacher, McConnell, & Graham, 2001). Student instructor interaction can provide the social support for students during the learning process, whi ch is conducive to higher academic achievement and the development of social skills (Parker, 1999). It also has a positive relationship with students satisfaction with their learning experience (Liaw & Huang, 2000; Swan, 2002; Usun, 2004). The educators active facilitation in the form of teacher comment and feedback in online learning is an important factor that influences students academic performance ( Cavanaugh et al., 2005; Dickson, 2005; Ferdig, Papanastasiou, & DiPietro, 2005; Hughes et al., 2005; Karp & Woods, 2003; Lin 2001; Peters 1999; Phipps & Merisotis, 2000; Smouse, 2005; Zucker, 2005). Jiang and Ting (2000) conducted one study to examine instructor activity in online learning and found the students perceived learning is correlated with the number of feedback comments per student that the instructor made. This relationship also has been confirmed by Swan et al. (2000) and Swan (2002). Anderson and Kuskis (2007) argued many of the pedagogical benefits brought by instructor feedback/student tea cher interaction such as those related to motivation are relevant to distance education as well as the conventional classroom education. PAGE 48 48 Hughes et al. (2005) believed that teachers individual feedback can increase communication opportunities for students who are shy and may not participate in academic activities, and these opportunities are helpful to develop closer relationships between the instructor and students. Constructive and timely feedback from instructor is one of the success factors for the prac tice of an effective virtual learning course/program (Cavanaugh, 2004). Frequent and open communication between students and instructor is identified as an important component to build a virtual community during online learning ( Lin 2001; Murphy, Mahoney & Harvell, 2000) The development of a learning community in an online K 12 course is considered an important factor for students better academic performance ( Lin 2001; Oren, et al., 2002; Ronsisvalle & Watkins 2005; Wang & Newlin, 2000) Wang and Newlin ( 2000) argued the social support provided by the learning community could improve students' academic achievement as well as their involvement and interest in online academic activities. Oren, et al. ( 2002) believed t eachers should act as a moderator to faci litate and scaffold students learning and encourage various interactions especially peer to peer interaction to let students learn from each other. They emphasized teachers' supportive feedback to encourage student to student social interactions for the formation of the social groups during the leaning process and beyond. ODwyer et al. (2007) conducted a quasi experimental study to examine the impact of one Algebra I online initiative on students learning outcomes and found that online students themselves also highly value the student instructor interaction during the learning process. PAGE 49 49 Participation in online academic activities T he number of times students logged into the LMS and how long they stayed in the LMS could be considered as the indicators of participation in online academic activities. The time spent in academic activities has been identified as a very important factor that has a strong effect on success in online education (Cavanaugh, 2007), faceto face instruction (Rocha, 2007), and blended programs (Cavanaugh, 2009). The activities students engaged in during online study is a predictor of final scores, with students who participate in academic activities at high level performing better than those who do not in online learning (Wang & Newlin, 2000). Dietz (2002) believed one of the most significant predictors of success is attendance which could be reflected by the number of times students log into an LMS. These findings were confirmed by Dickson (2005) that participation in online academic act ivities, which is measured by clicks in the LMS, is a strong predictor of final scores in online learning. Race/ethnicity Considerable studies have been conducted on the relationship between race/ethnicity and academic achievement in traditional learning environment. Racial gaps in student test score are undeniable facts (Bali & Alvarez, 2004; Hall et al., 2000). The student body in online K 12 schools often represents the community that is served by the traditional school system ( Ronsisvalle & Watkins, 2005) The findings in the literature of the relationship between race/ethnicity and student academic achievement in traditional learning environments could shed light on success factors studied in K 12 online learning environments. Through a metaanalysis of 16 studies of race differences in mathematics performance from grades 4 to 8, Lockhead et al. (1985) found Asian Americans usually PAGE 50 50 perform at the highest level in math, followed by Caucasian students, and then Hispanic. All the three groups perform bett er than African American students. Hall et al. (2000) also found significant differences in student math achievement among different ethnicities in a study on gender and racial differences in mathematics performance among 5th and 8th grade students in the United States. These differences continue at the higher level. The math skills of most African American in 12th grade, as Barth (2001) described, are only equivalent to the skills of Caucasian students in the eighth grade. U.S. DOE released a report in 2004 about the gaps in academic achievement in different content areas such as reading, math, and science among different racial and ethnic groups based on the data collected since the mid1980s. At 4th grade level, 41% of whites and 38% of Asians were profic ient readers while the number for African Americans, Hispanics, and Native Americans was 13%, 15%, and 16%, respectively. In mathematics, 48% of Asians and 43% of Whites achieved at proficient level while only 10% of African Americans, 16% of Hispanics, and 17% of Native Americans achieved at this level. Participation in school free lunch/family SES Participation in school free or reduced programs has a correlation with student academic achievement, and the magnitude of correlation is weaker as grade level rises (McLoyd, 1998). Klein et al. (2000) conducted a research study on the relationship between students participation in free or reduced lunch programs and school test score using the data about 2000 Texas 5th graders in reading and math. They found the percentage of students participating in the free or reduced lunch program in a school can affect the schools mean test score. The researchers believed participation in these programs could be considered a sign of the level of poverty which has a strong PAGE 51 51 relationship with student academic performance at the school level. Participation in a school lunch program was also frequently used as the measurement of students family Social Economics Status (SES) in the literature on student academic achievement (Sirin, 2005). The level of the family support including the resources provided for students and education values can influence student academic achievement (Hiebert & Grouws, 2007). Higher SES families provide students more resources at home and social capital, both of which can improve chances for their academic success (Coleman, 1988). A considerable body of research has been done on the relationship between SES and student academic performance. The magnitude of this relationship was found to be strong in tw o metaanalytic studies conducted more than twenty years apart from each other: 0.343 in Whites (1982) metaanalysis and 0.299 in Sirins (2005) metaanalysis. In K 12 online learning environment participation in school reduced lunch program/family SES c ould also be associated with student academic success. Learning ability/ p resence of i ndividual educational plan Student learning ability is a factor that can influence student academic success during the learning process (Keeler & Horney, 2007). The vir tual school student body is a diverse population including students with different learning disabilities (Dickson, 2005; Ferdig, Papanastasiou, & DiPietro, 2005). Virtual school offers individual education plans for these students during the learning process. Therefore, whether or not a student has an individual education plan could be a sign of the level of learning abilities. The review of studies on the relationship between learning ability and academic achievement could shed light on the decision making process to provide more opportunities for students with special needs to succeed in the K 12 online learning environment. PAGE 52 52 According to Keeler et al. (2007), students may bring some characteristics that could physically or psychologically inhibit their acc ess to the information or tools on the Internet, preventing their success in the online learning environment. The virtual school learning environment has the potential to bridge gaps between disabled students and other students without learning disabilities with respect to the success opportunities in online learning. For example, for students with different levels of learning disabilities, technologies such as computer, internet, audio, video, animation, gaming and simulation could help reduce their disadv antages as compared to students without disabilities (Coombs & Banks, 2000; Richardson et al., 2004). Some instructional design strategies have been recommended to ensure online courses meet the special needs of students with disabilities (Keeler et al., 2007; Rose & Blomeyer, 2007). These include assurance of accessibility to the information for students with disabilities and the support in course materials and learning activities for these students. Virtual courses need to be designed with accommodations specifically for students with disabilities to access course materials and should benefit all learners under the framework of Universal Design for Learning principals (Rose & Blomeyer, 2007). Since the emergence of universal design technology and the requi rement for the development of Learning Management System ( LMS ) to integrate components to meet the special needs of disabled students to align with the American with Disabilities Act (ADA) (Watson, 2007), the opportunities to achieve higher performance for disabled online students has been greatly increased. The proposition of early adoption of technology infused education for disabled online students (OConnor, 2000) also will benefit their PAGE 53 53 online learning. Over a decade virtual school programs have succes sfully provided quality education to students with special needs (Rose & Blomeyer, 2007). However, even with the bridging gaps with regard to online success opportunities for disabled online students, they are still underrepresented in online education (K inash & Crichton, 2007). For example, even though different learning management systems such as WebCT and Blackboard are generally accessible to disabled students, there are still inherent problems limiting them from fully utilizing the functions in their courses therefore limiting their chances of success in online learning (Asuncion et al., 2006). Many online courses still have barriers preventing students with disabilities from fully accessing materials as other students do (Edmonds, 2004; Keeler & Horney, 2007), which will affect their success in online learning. Keeler and Horney (2007) conducted one study to evaluate the elements of online course design that address students special needs. They found some problems still existed in the five categories of design elements: accessibility, website design, technologies used, instructional methodologies, and support systems, which can prevent students special needs being fully addressed. According to National Center for Education Statistics (NCES, 2000), t hough computer and communication technologies may be especially beneficial for disabled students (Johnson, 1986), providing them the access to these technologies could be more expensive than regular students because they may need special equipment to use t hese technologies. The lack of accommodations for students with special needs may exclude them from fully participating in online learning (Keeler & Horney, 2007). Disabled students lack the opportunities to use these communication technologies for a variety of reasons PAGE 54 54 including insufficient ly trained special education teachers and inadequate support services for them to use these technologies (NCES, 2000). This could lead to the academic gap between students with disabilities and students without these dis abilities in online learning. Students learning ability could affect other academic performance in addition to achievement such as academic engagement. Kersting (1997) interviewed 10 deaf students to examine their learning experience and found these students had lower academic engagement in learning activities unless they got sufficient support during their learning process. Richardson, Long, and Foster (2004) compared deaf students and their peers without hearing loss regarding academic engagement in dis tance learning. There were 267 students with a hearing loss and 178 students without this disability in an open university who participated in this study. The results showed students with hearing loss could not perform well on communication and some other tasks in comparison to students without this disability, which could affect the academic achievement negatively for these disabled students. School type The gap in student achievement between private and public schools has been documented in many studies (Chubb & Moe, 1990; Coleman & Hoffer, 1987; Coleman, Hoffer, & Kilgore, 1982). In 2006, the U S Department of Educations National Center for Education Statistics (NCES) released a report about the academic achievement differences in reading and math at grade levels 4 and 8 between private and public schools (Braun, Jenkins, & Grigg, 2006). This report showed on average the private school mean score was higher than the public schools. Students in private schools achieved at higher levels academically than t hose in public schools. However, many PAGE 55 55 studies did not control other important variables such as student SES, or grade level when examining the difference between public and private schools and many of them have been done more than two decades ago, so the data could be already outdated. Therefore, more studies with new data and new methodology are encouraged to be conducted. A study (Lubienski & Lubienski, 2005) supported by the National Assessment of Educational Progress (NAEP) compared student achievement in mathematics between public and private schools using a student sample of 4th and 8th graders. There were over 13000 4th grade students from 607 schools, 385 of which were public schools and 222 were private schools, and over 15000 8th grade students fro m 740 schools, 383 of which were public schools and 357 were private schools who participated in this study. The results showed overall students in private schools outperformed their counterparts in public schools; however, after controlling for students SES, public schools outperformed private schools. The larger proportion of high SES students in private schools accounted for their overall outperformance. The researchers called for more research on the examination of effectiveness of public and private schools. The review of success factors grounds the present work in the related literature. It helps the establishment of the model in the present study with respect to the selection of independent variables For example, the family SES has been proved to relate to student academic performance in many studies, which provides the support for the inclusion of the participation in school reduced or free lunch programs which could be the indicator of family SES in the model. Many studies in this literature are quantitative PAGE 56 56 studies. This can shed lights on the quantitative research method utilized in the present study. Conclusion The review of literature on effectiveness of online/distance education demonstrates well designed online/distance course can be as effective as its traditional counterpart with respect to helping improve student academic achievement. This presents the evidence for the increasing research in online/distance education and provides the rationale for the present study. The review of literature on Algebra/mathematics education illustrates that several issues such as learner prior knowledge and learning ability, study time, and instructional strategies need to be addressed during the process of Algebra/mathematics teaching and learning. It also indicates a variety of approaches such as problem solving, generalization of geometric patterns and Algebraic relationships, and functional situations should be utilized to improve learning efficiency. This section builds the connections between tradi tional education and online education. Both of these two education formats share success factors though the effect could be different in these two environments. The review of success factors confirmed the relationship between student demographic information, participation level in academic activities, and teacher comments and student academic achievement. These are also the variables of interest in the present study. Even after more than 10 years of extraordinary growth in K 12 online learning, litt le research has been done as compared to post secondary education (Cavanaugh, 2007; Cooze & Barbour, 2005; Means et al., 2009; Picciano & Seaman, 2007; Picciano & Seaman, 2009; Ronsisvalle & Watkins, 2005). The amount of evidencebased research or empiric al study applicable to guide educators instruction and policy makers decision PAGE 57 57 relevancies is slight ( ODwyer, Carey, & Kleiman, 2007) Many states have no data on the current practice of K 12 online education such as the number of students taking courses online, the number of online programs existing, and how these programs are managed (Watson, 2007). After review of 99 comparative studies regarding online education versus traditional education published between 1996 and 2008, the U.S. Department of Education found that only 7 of them involved K 12 learners ( Means et al., 2009). The development of K 12 online education is advancing different ly from the development of postsecondary online education (Picciano & Seaman, 2009). Therefore, the practice of onli ne education in higher education may not be applied to the K 12 environment. The dearth of studies on academic achievement in K 12 virtual learning environment in comparison with that in traditional learning environments form the rationale for more quantit ative research in this field to guide the implementation and practice of online learning at this level ( Cooze & Barbour, 2005; Means et al., 2009; Picciano & Seaman, 2007; Picciano & Seaman, 2009; Smith, Clark, & Blomeyer 2005; Watson, 2007). Quantitative data collection is the required research methodology to support the understanding of the efficacy in K 12 virtual school (Smith, Clark, & Blomeyer 2005). Currently the lack of new data regarding K 12 online learning is attributed to a variety of reasons including lack of requirement in many states for data collection on online students and the significant growth of K 12 online education practice causing the difficulty of data collection (Picciano & Seaman, 2007). Researc h conducted in virtual schools is rare because of its comparatively new practice, and currently available data cant provide enough information for accurate estimation of its practice (Glass, 2009). PAGE 58 58 More research has been called for that focuses on students academic performance, particularly on the factors influencing the success of students in K 12 virtual learning environments (Smith, Clark, & Blomeyer 2005). The question of whether the factors that affect students achievement in the traditional school learning environment play the sam e role in the virtual school learning environment remain to be answered. A cademic performance is considered as the single greatest indicator of school completion ( Battin Pearson, Newcomb, & Abbott 2000), and lowering the school dropout rate is a national priority. The investigation of the factors that influence student academic performance in virtual schools is of critical interest to educators, researchers, virtual school program administrators, and policy makers. PAGE 59 59 CHAPTER 3 METHODOLOGY Introduction Ther e are five sections in the methodology chapter: research design, population and sample, instrumentation, data collection and analysis, and limitations. What type of design employed in this study is explained in research design section. Then the population that this study is targeting, sample that has been selected and sampling techniques employed are described. Instruments utilized in this study are detailed in the section of instrumentation. The process of data collection and analysis are then explained. A nd limitations if any are pointed out at the end. The purpose of this study is to examine the factors including LMS utilization, teacher comment /feedback and s tudent demographic information that can influence the success of Algebra courses in K 12 virtual learning environments. The research questions in this study are: Does the level of LMS utilization influence Algebra/mathematics performance in online education? Does teacher comment or feedback influence Algebra/mathematics performance in online educati on? Do student demographic information such as race/ethnicity, grade level, status in virtual school, whether have individual educational plan (IEP), and participation in free/reduced lunch programs influence Algebra/mathematics performance in online educ ation? Research Design The present study is descriptive in nature. The researcher described some factors predictability of Algebra learning outcome with out intervening. The researcher collected the data at the end of 200809 academic year. These data incl ude student demographic PAGE 60 60 information, their participation level in online academic activities, teacher comments, student EOC test score, and the score on one state standardized mathematics test The variables of interest in this study include: teacher com ments (TEACHERCOM), student participation level of online academic activities the number of times students log ged into the LMS (TOTALLOG), the time they stayed in the LMS (TOTALMIN), and student demographic information whether students have IEP students grade level s in their physical schools (GRADE), rac e/ethnicity (RACE), students status in the virtual school ( full time or part time students PT/FT), and the participation in free or reduced lunch (FRL) programs. They are independent variables in the study. Student EOC test score and the score on one state standardized mathematics test are dependent variables in this study. Participants and Data C ollection The data were collected during the 200809 academic year from one state virtual school in the Midwestern US region. This virtual school was implemented in 2007. A similar pilot project was conducted in spring, 2009, based on its first year (200708) data This dissertation builds on the results of the pilot project However, the present study is di fferent from the pilot project in many respects. For example, the present study investigated the success factors not only for Algebra EOC test but also for one state standardized mathematics test which was missed in the pilot project. The present study was conducted with different sample from the pilot project. Students statewide from bricksandmortar public and private schools as well as home school students were eligible to enroll in this virtual school. Enrolled students resided in most of the states school districts. The school hired content area teachers who met state certification and PAGE 61 61 other requirements. A single LMS was utilized by this virtual school to manage course content and deliver instruction at the secondary level. Th e students needed to take the EOC test at the end of each semester during 200809 academic year after they completed the course. Some students also took one state standardized mathematics test at the end of academic year. Students who completed the four Algebra courses: Algebra I (1st half), Algebra I (2nd half), Algebra II (1st half), and Algebra II (2nd half) and took the EOC tests and the students who took the state standardized mathematics test grade 6, 7, or 8 participated in this study (Due to lack of information about stud ents who took the standardized test grade 3, 4, or 5, they were dropped from the study). The number of students who took the four Algebra EOC tests was : 1 01 75, 26, and 36 respectively. D ue to the insufficient power for data analysis caused by the small s ample size of Algebra II (1st half) and Algebra II (2nd half), 26 and 36, these two groups were dropped from the study. Within the two Algebra I groups, 64 out of 101 students in Algebra I (1st half) ( 63.4 %) and 59 out of 75 students in Algebra I (2nd hal f) ( 78.7%) were second year students in this virtual school. Students who took the two Algebra EOC tests were from grades 8 to 12. Students who took the standardized mathematics test grade 6, 7, or 8 were from grade 6 to 10 and the number was: 74 (grade 6) 73 (grade 7), and 107 (grade 8). All of these participants were first year students in this virtual school. Instrument Success in an online course can be measured by academic achievement including the grades students earn and their performance on advanc ed placement exams ( Ronsisvalle & Watkins, 2005; Tallent Runnels et al, 2006) Dickson (2005) used student final score as the measure of student performance in online courses during the PAGE 62 62 data analysis in a study conducted to investigate the variability of s tudent performance in online courses. Full time online schools assessed student achievement in the same way as all public schools (Watson, Gemin & Ryan, 2008). Student achievement in many supplemental online programs is also assessed by course grade or EOC test score (Watson, Gemin, & Ryan, 2008). Indeed, some virtual schools and their teachers are paid on the basis of successful students, defined as those passing their courses. Algebra EOC T est Students who took the Algebra I EOC test in the virtual school participated in this study. The Algebra EOC tests were tests administered at the end of each semester in this virtual school. According to the virtual school administration, they have high correspondence to the states Algebra and mathematics content stan dards. The purpose of the EOC test, as described by the states department of education ( 2009), is to : Measuring student achievement and progress toward postsecondary readiness Identifying students strengths and weaknesses Communicating expectations for all students Meeting state and national accountability requirements Evaluating programs The Algebra I EOC test includes one session of multiple choice items and one session of performance events ( Missouri Department of Elementary and Secondary Educat ion (MDESE), 2009a). The items in the multiple choice session are developed specifically for students in this state (see Appendix A for some released samples). The items in the performance events session are longer, and focusing on more challenging PAGE 63 63 tasks t hat require students to work through different problems, arguments, or require extended writing (see Appendix B for some released samples). These EOC tests are intended to measure students skills in number and operations, Algebra ic relationships, and data and probability. This state has its standards for Algebra (see Appendix C for the state Algebra standards). The Appendices A E provide the evidence of alignment between the Algebra I EOC test and state Algebra standards. State Standard ized Test Students who took one state standardized mathematics test grade 6, 7, or 8 after they finished one year of study in this virtual school during 200809 academic year participated in this study This standardized mathematics test is aligned with the state Show Me Standards which are the educational standards of this state For m athematics, the Show Me standards require students in state public schools to obtain knowledge of (MDESE, 2008) 1 A ddition, subtraction, multiplication and division; other number sense, includi ng numeration and estimation; and the application of these operations and concepts in the workplace and other situations 2 G eometric and spatial sense involving measurement (including length, area, volume), trigonometry, and similarity and transformations of shapes 3 D ata analysis, probability and statistics 4 P atterns and relationships within and among functions and Algebraic geometric and trigonometric concepts 5 M athematical systems (including real numbers, whole numbers, integers, fractions), geometry, and num ber theory (including primes, factors, multiples) 6 D iscrete mathematics (such as graph theory, counting techniques, matrices) Th is grade level state standardized mathematics test is a standards based test designed to measure the skills for each grade of st udents in the state where this virtual PAGE 64 64 school is located (MDESE, c). It also has a national norm referenced test that can be used to compare students in this state with students across the country This component helps align the state standardized test with the National Council of Teachers of Mathematics (NCTM) standards See Appendix D for NCTM mathematics standards for grades 68. There are three types of questions in this grade level standardized mathematics test: 1. multiple choice items that are developed specifically for students in this state or the questions in the national norm referenced survey; 2. constructed response items that require students to provide the response rather than selecting the options among different choices; 3. performance events items as described above in EOC test that are longer, and focusing on more challenging tasks that require students to work through different problems, arguments, or require extended writing (MDESE, c ). See Appendix E for released items of this standardized test (spring 2006) at grade level 6. Th is state has standards for mathematics at different grade levels See Appendix F for the state standards for mathematics at grade level 6 (due to the space limit, the author did not attach released items of this standardized test (spring 2006) at grade 7 and 8 and state standards for mathematics at grade level 7 and 8). The Appendices D F provide the evidence of alignment between this state standardized mathematics test and NCTM mathematics standards and state mat hematics standards. Data A nalysis Due to the very small sample size of minority groups including Asian American, Hispanic American, Indian American, and African American in this study, these four groups were combined as one category during data analysis in this study: Minority. There are only two categories in the categorical variable: Racial/Ethnicity, Caucasian PAGE 65 65 American Students and M inority Students. Other categorical variables were coded accordingly during data analysis. Table 3 1 shows the coding inf ormation. Students who took the two Algebra EOC tests were from grades 8 to 12. Students who took the standardized mathematics test grade 6, 7, or 8 were from grade 6 to 10. These two sets of groups were overlap to some degree. Therefore, the analysis was conducted for these two sets of groups separately. Some of the participants in state standardized test groups will take Algebra course in this virtual school. The analysis of these groups can add to the knowledge of success factors in Algebra. This virtu al school student body included students s tatewide from bricks andmortar public and private schools as well as home school students. The physical schools as well as the home schools that students attend could affect student academic performance through sc hool culture, technical support, and resources available for students. Student test score s within the same physical school are not independent of one another. Therefore, any evaluation of the influence of student level factors such as grade level, r ace, and teacher comment on these score s must account for the influence of school characteristics. T o investigate the Algebra/ mathematics success factors in the K 12 online learning environment, H ierarchical L inear M odeling ( HLM ) technique was used to account for the clustering of student score within one school caused by school characteristics. HLM was carried out by the software program HLM 6.06 for data analysis in this study. The fully unconditio nal or Random ANOVA (RA) model was estimated at the beginning in order to partition the variance into withinschool ( Sigma Square) and betweenschool (Tau) components. After that, all independent variables PAGE 66 66 were added into the model. Generalized estimating equation was applied for the estimation of correlation coefficien ts. Limitations Limitations of this study include: 1 The small sample size could affect the power for statistic claims. 2 The coding strategy for race/ethnicity variable could mask influential information. 3 Many home school students and the very small number of students from many different physical schools (some only had one student) may cause data analysis difficulty. PAGE 67 67 Table 31. Coding of the independent variables Variable Categories Student status (part time or full time) 0: part time 1: full time IEP (individual educational plan) 0: without individual educational plan 1: with individual educational plan FRL (free or reduced lunch) 0: not in free or reduced lunch programs 1: in free or reduced lunch program RACE 0: Caucasi an American student 1: minority student PAGE 68 68 CHAPTER 4 RESULTS Introduction As stated in Chapter 1, the study reported here examined the success factors including student demographic information, teacher comments, and student participation level in online ac ademic activities in K 12 virtual learning environments. This chapter is organized based on the three research questions posed in Chapter 1. It first describes the sample of this study. It then reports the effects of LMS utilization teacher comment, and s tudent demographic information such as race/ethnicity and whether have IEP on Algebra/ mathematic achievement in virtual learning environments Sample The data were collected during the 200809 academic year by a consulting company that works with one stateled virtual school in the Midwestern US region. This consulting company collected student demographic information and their performance on two types of test s : EOC test and state standardized test. The researcher obtained the data from this consulti ng company. The criteri a for the participation in this study were : (1). students who completed Algebra online courses during the 200809 year and took the EOC test at the end of each semester; or (2)students in this virtual school who took one state standardized mathematics test at the end of the 2008 09 academic year. EOC T ests T aker This virtual school offer ed four Algebra courses: Algebra I (1st half), Algebra I (2nd half), Algebra II (1st half), and Algebra II (2nd half) during the 200809 academic year. The number of students who completed these four Algebra courses and took the EOC tests at the end of each semester during that academic year was : 1 01 75, 26, and 36 PAGE 69 69 respectively. These four groups participated in this study. However, due to the insu fficient power for data analysis caused by the small sample size of Algebra II (1st half) and Algebra II (2nd half), 26 and 36, these two groups were dropped from the study. Within the two Algebra I groups, 64 out of 101 students in Algebra I (1st half) ( 63.4.2%) and 59 out of 75 students in Algebra I (2nd half) ( 78.7%) were second year students in this virtual school. See table 41 for student demographic information The sample can be described as primarily white, not participating in school free or reduced lunch programs, and part time virtual school students without individual educational plans. State S tandardized T est T aker There were 487 students in this virtual school during 200809 academic year who took a state standardized mathematics test at the end of academic year. This standardized mathematics test has different grade levels from 3 to 8. Due to lack of information about students who took the state standardized mathematics test grade level 3, 4 and 5, only students who took the state standar dized mathematics test grade level 6, 7, and 8 participated in this study. The number of students in these three groups is 74, 73, and 107 respectively. All of these participants were first year students in this virtual school. See table 42 for student demographic information. The sample can be described as primarily white, participating in school free or reduced lunch programs, and part time virtual school students with individual educational plans. As stated in Chapter 3, the physical schools students attend ed could affect student academic performance via the resources the school provided for students, technical support, and school culture. Students test scores within one school are not independent of one another. Any evaluation of the variables at student level such as teacher PAGE 70 70 comments, grade level, and race on student test score must account for the influence of school characteristics on this dependent variable. The Hierarchical linear modeling ( HLM) technique was carried out by the software program H LM 6.06 for data analysis to account for the clustering of students scores within one school caused by the school characteristics. The fully unconditional or Random ANOVA (RA) model was utilized to partition the total variance of student test score into w ithin school (Sigma Square) and betweenschool (Tau) components at the beginning during the analysis. After that, all the independent variables were added into the model. Generalized estimating equation was then applied for the estimation of coefficients o f the different variables. RA M odel The RA model was estimated for each dataset to partition the variance of student test score into within school (Sigma Square) and betweenschool (Tau) components. Level 1 Model Y = B0 + R Level 2 Model B0 = G00 + U0 The Intra Class Correlation (ICC) was calculated according to the formula: Tau/ (Tau+ Sigma Square) for each dataset. Results for the RA model are presented in Table 4 1 Table 41 demonstrates the ICC for all the five datasets is equal to or above 0.7. Th is finding shows the betweenschool variance was large in comparison with the within school variance for the five groups of students. This tells us the students within the same school are similar with respect to their academic achievement in mathematics and in Algebra particularly in comparison with the students from different schools. PAGE 71 71 Coefficients for the V ariables After estimating the RA model, all the independent variables were added into the model at the level 1 (student level). With the exception o f the data on the standardized test in grade 6, t h e generalized estimating equation (GEE) procedure was then used to estimat e the coefficients of these variables. For the data on the standardized test in grade 6, ordinary least square was used. The results are presented in Tables 42 to 44. Summarization of the results is presented following Table 44. Level 1 Model Y = Y = B0 + B1*(GRADE) + B2*(RACE) + B3*(FRL) + B4*(IEP) + B5*(PT/FT) + B6*(TEACHERCOM) + B7*(TOTALLOG) + B8*(TOTALMIN) + R Level 2 Model B0 = G00 + U0 B1 = G10 B2 = G20 B3 = G30 B4 = G40 B5 = G50 B6 = G60 B7 = G70 B8 = G80 The results of the GEE are presented in Tables. PAGE 72 72 EOC T est Table 42 shows the estimate of the variable effect coefficients for the two Algebra EOC tests: Algebra I (1) and Algebra I (2). The variables that have significant effects on student final score on these two tests are highlighted in grey State S tandardized M athematics T est Table 43 shows the estimate of the variable effect coefficients for the tw o state standardized mathematics tests grade 7 and grade 8. The variables that have significant effects on student final score on these two tests are highlighted in grey Ordinary Least Square (OLS) was applied for the dataset: mathematics standardize test grade 6 for the estimate of effect coefficient. There are 74 students in this group. Five of them are from the same school and all other 69 students are from different schools. There is almost no clustering for student scores due to the small sample siz e at student level (74) and comparatively large sample size at school level (70). Thus, least squares estimates with robust standard errors cant be applied to correct the errors associated with the clustering of student scores within one same school. Inst ead, OLS was applied for the estimates of coefficients of the independent variables. Table 46 shows the results. Descriptive S tatistics Standardized Coefficient and Reduction of Variance Descriptive statistics analysis was conducted for each group to de monstrate the mean and variance of the factors (independent variables) and student score (dependent variable) See table 47 and 48 for this information for these two sets of participants To compare among different factors with respect to the importance in determining student score, standardized coefficient ( ) was calculated according to the formula: k =bk Sxk Sy (bk is the unstandardized coefficient, Sxk is the standard deviation of the PAGE 73 73 corresponding independent variable, and Sy is the standard deviation of the dependent variable). See table 49 and 410 for standardized coefficients for these two sets of participants. Standardized coefficient demonstrates that how increases in the independent variables affect relative position within the group. Fo r example, the standardized coefficient of TEACHERCOM was 0.56 for Algebra I (2). It means with 1 standard deviation increase in TEACHERCOM, student test score increased 0.56 standard deviation. The adjusted R square (Rc 2) wa s calculated according to t he formula: Rc 2= 1 VAR e/VAR t ( VAR e is the least squares estimates of the model with all the predicators, VAR t is the least squares estimates of the RA model) to show the reduction of test score variance from the RA model with the addition of the fact ors. See table 4 11 for adjusted R square for the five groups. The adjusted R square also shows the variance that is accounted for by these factors. For example, Rc 2 is 0.15 for Algebra I (2). It means the eight factors accounted for 15 percent of test score variance. Research Q uestion 1 Does the level of student participation in academic activities predict Algebra/mathematics performance in online education? The participation in online academic activities can be reflected through the number of times stud ents logged into the Learning Management System (LMS) and how long they stayed in the LMS. This study used these two variables as the indicators of the level of student participation. Other indicators of participation not collected by the virtual school's data system, such as the time students spent online on academic tasks and the time they spent on non academic tasks, will not be part of this study. Time on task has been identified as a critical factor in relation to the improvement of understanding level PAGE 74 74 of subject matter (Bransford et al., 1999). As Bransford et al. mentioned, students need to take time to make meaning of the concepts in the subject areas and build the connections to their preexisting knowledge. Based on one study on the effect of one Al gebra I online learning model on students academic outcome, ODwyer et al. (2007) concluded online students spent more time on interacting with one another on academic topics than their counterparts in traditional classroom. Peer to peer interaction could help improve online students learning outcome. The time spent in academic activities has a strong effect on success in online education (Cavanaugh, 2007), faceto face instruction (Rocha, 2007), and blended programs (Cavanaugh, 2009). It can predict stu dent final grade in online learning (Wang & Newlin, 2000). To investigate the effects of student participation in online academic activities on student achievement in mathematics and Algebra in particular in online learning environments, the number of time s students logged into the LMS and how long they stayed in the LMS were analyzed using HLM along with other factors in one single equation. Other student time on task outside the LMS was not measured in the school data system. EOC T est Table 42 shows TO TALLOG (number of times student log into the LMS) had a non significant effect ( 0.03, p=0.230) on student final score for Algebra I (1) and the direction shows students who logged into the LMS less tending to perform better than those students who logged in to the LMS more. The effect of TOTALLOG is significant for Algebra I (2) ( 0.02, p=0.006), with students who logged into the LMS less achieved higher scores. There is a weak and non significant effect of TOTALMIN (total minutes students stay in LMS) on final score for Algebra I (1) (0.0005, p=0.304), with students who stay ed in the LMS longer tending to achieve higher scores. The effect of PAGE 75 75 TOTALMIN is significant for Algebra I (2) (0.0004, p=0.008). The direction of the effect tells us students who stay e d in the LMS longer perform ed better. State S tandardized T est Table 43 shows TOTALLOG has no significant effect ( 0.02, p=0.725) on student score in the grade 7 mathematics standardized test. The direction shows students who logged into the LMS more tending to perform better than those students who logged into the LMS less. The effect of TOTALLOG is also not significant ( 0.07, p=0.117) for the grade 8 mathematics standardized test, with students who logged into the LMS less achieving higher scores. Table 44 shows TOTALLOG also has no significant effect ( 0.04, p=0.414) for the grade 6 mathematics standardized test, with the same direction as it in grade 8. Table 43 shows there is a weak and non significant effect of TOTALMIN (0.0004, p=0.680) on student s core in the grade 7 mathematics standardized test, with students who stay ed in the LMS longer tending to achieve higher scores. The effect of TOTALMIN is nearly significant (0.001, p=0.057) for grade 8. The direction of the effect tells us students who stay ed in the LMS longer tend ed to perform better. Table 44 shows there is a weak and non significant effect (0.0005, p=0.544) of TOTALMIN for the grade 6 mathematics standardized test, with students who spent more time in the LMS tending to achieve higher s core s. Research Q uestion 2 Does teacher comment or feedback predict Algebra/mathematics performance in online education? Bransford et al. (1999) emphasized the importance of frequent feedback from the instructors for students to monitor their learning pr ocess and evaluate their understanding levels and the learning strategies during the learning process. Based on PAGE 76 76 the feedback, students could revise their thinking and enrich their knowledge structure as they move along. Teacher feedback or teacher comment on student assignments, papers, and projects has been identified as a critical factor that can influence student academic performance in online education ( Cavanaugh et al., 2005; Dickson, 2005; Ferdig, Papanastasiou, & DiPietro, 2005; Hughes et al., 2005; Peters, 1999; Zucker, 2005). Phipps and Merisotis (2000) believed that student teacher interaction and the timely and constructive teachers feedback to students assignments and questions are critical characteristics of the teaching/learning benchmarks for the quality of online learning. Watson and Ryan (2006) showed there are big difference s in students experiences between virtual classrooms with minimal teacher involvement and those with greater student teacher interactions via different means such as email, online message, online discussion forum, phone, etc. Based on a quasi experimental study on the impact of one state wide Algebra I online initiative on students learning outcomes, ODwyer et al. (2007) found that online students highly value the st udent instructor interaction during the learning process. The critical value of teacher feedback and teacher comment for success in online learning is also applicable to students with special needs. Based on a study of students with learning disabilities (SLD) and students with attention deficit hyperactivity disorder (ADHD), Smouse (2005) found communication with and feedback from instructors was the most valuable aspect of online courses. To investigate the effect of teacher comment or teacher feedback on student achievement in mathematics and Algebra in particular in online learning environments, the number of teacher comments was analyzed using HLM along with other factors in one single equation. PAGE 77 77 EOC T est Table 42 demonstrates t he non significant effect of TEACHERCOM (teacher comments) for Algebra I (1) (0.04, p=0.450). Interestingly, the direction shows students with fewer teacher comments tended to achieve higher scores than those with more teacher comments. This could be due to the lower need for corrective feedback from teachers for students with better performance during the learning process. The effect of TEACHERCOM for Algebra I (2) is also not significant (0.01, p= 0.912). Its direction is different from the one in Algebra I (1). In Algebra I (2), students who received more teacher comments tended to perform better than those students who received less teacher comments. State S tandardized T est Table 43 demonstrates the significant effect of TEACHERCOM ( 0.46, p=0.041) for the grade 7 mathemat ics standardized test. The direction shows students with more teacher comments perform ed better in this test. The effect of TEACHERCOM for grade 8 is not significant (0.002, p=0.990), with the same direction as it is in the grade 7 mathematics standardized test. Table 44 shows there is no significant effect of TEACHERCOM ( 0.02, p=0.949) on student score in the grade 6 mathematics standardized test. Interestingly, the direction shows students with more teacher comments tended to achieve lower scores in t his test. Research Q uestion 3 Do student demographic information, such as race/ethnicity, grade level, status in virtual school, whether have IEP and participation in free/reduced lunch programs, predict Algebra /mathematics performance in online education? PAGE 78 78 The v irtual school student body is a diverse population of learners that includes students with different learning disabilities (Dickson, 2005; Ferdig, Papanastasiou, & DiPietro, 2005). In the present study, the v irtual school follows individual educati on plans for students with special needs during the learning process. Therefore, whether or not a student has an individual education plan could be considered as a sign of student learning ability which can affect student academic achievement during the le arning process (Keeler & Horney, 2007). Currently, many popular LMS, such as WebCT and Blackboard, still have different problems that can prevent students with disabilities from fully utilizing their functions even though they are generally accessible to t hese disabled online students (Asunci on et al., 2006). Students learning ability may be related to their learning outcome through some other factors such as academic engagement (Richardson et al., 2003). Based on a study comparing online students with a h earing loss and those without this disability with respect to the relationship between students academic engagement and their perceptions of the academic quality of the courses, Richardson et al. (2003) found the correlation between hearing status and students academic engagement and their perceived academic quality of the courses. Students with hearing disability cant perform well on communication and some other tasks during online learning process es as compared to students without this disability. This in turn, may negatively impact these disabled students academic achievement. Research shows that eligibility for school free or reduced lunch programs correlates with academic achievements, with students not participating in these programs achiev ing better performance (McLoyd, 1998). Participation in these programs could be considered as the indicator of the family poverty level, which has a PAGE 79 79 strong relationship with student academic achievement at school level (Klein et al. 2000). C onsiderable research has also found student academic achievement difference among different racial groups, with Hispanic and African American students lagging behind Caucasian and Asian American students (Bali, 2004; Barth, 2001; Hall et al 2000; Lockhead et al., 1985). Tho ugh students race/ethnicity and their participation in school free or reduced lunch programs have been proved to correlate with student academic achievement in traditional faceto face education, their effects have not been examined systematically for vir tual learning environments. In this study, student demographic information including race/ethnicity, participation in free/reduced lunch programs, learning ability, grade level, and status in virtual school were investigated with other factors in one singl e equation. EOC T est Table 42 shows the participation in free or reduced lunch programs has no significant effect ( 0.04, p=0.992) on student EOC test score in Algebra I (1), with students who did not participate in these programs tending to achieve hig her scores. The non significant effect ( 4.36, p=0.172) of the participation in these programs w as also observed for Algebra I (2), with the same direction as it for Algebra I (1). There is no significant difference (0.93, p=0.826) in student EOC test scor e between Caucasian American students and the minority students for Algebra I (1) (see Table 42). The direction shows minority students tend to perform better than Caucasian American students. No significant difference ( 2.53, p=0.455) w as also found for Algebra I (2). However, the direction is different from Algebra I (1), with Caucasian American students tending to perform better. PAGE 80 80 Student grade level has a significant effect ( 3.89, p=0.030) on student EOC test score for Algebra I (1), with students in lower grade levels achiev ing better scores than their counterparts in higher grade levels. The significant effect ( 3.36, p=0.005) of grade level has also been found for Algebra I (2), with the same direction as for Algebra I (1). Table 42 demonstrates t he non significant effect ( 1.37, p=0.825) of student learning ability on EOC test score for Algebra I (1). The direction shows the students who do not have individual educational plans tended to perform better. Similarly, the non significant effect ( 2.26 p=0.557) of student learning ability is observed for Algebra I (2), with the same direction as for Algebra I (1). Table 42 shows student status in the virtual school (full time or part time) has no significant effect (1.78, p=0.733) on the EOC test scor e for Algebra I (1), with full time students tending to achieve better performance than part time students. The non significant effect of student status (6.49, p=0.096) has also been found for Algebra I (2), with the same direction as for Algebra I (1). State S tandardized T est Table 43 shows the participation in free or reduced lunch programs has no significant effect ( 0.89, p=0.936) on student score in the grade 7 mathematics standardized test, with students who participated in these programs tendi ng to perform better. However, the strong and significant effect of participation in these programs ( 61.40, p=0.000) has been found for the grade 8 standardized test, with the same direction as it is for the grade 7 standardized test. A significant differ ence ( 21.28, p=0.046) in student score in the grade 7 mathematics standardized test between Caucasian American students and minority students is observed (see Table 43). The direction shows the Caucasian American students achieved higher scores than the minority students in this test. Table 43 demonstrates there is no significant difference ( - PAGE 81 81 9.31, p=0.254) between these two ethnicity groups for the grade 8 standardized test, with the same direction as it for the grade 7 standardized test. Students g rade level in his/her physical school has no significant effect ( 0.17, p= 0.083) on score in the grade 7 mathematics standardized test, with students in lower grade level s tending to achieve higher scores (see Table43). The non significant effect of stud ent grade level (1.71, p=0.623) has also been found for the grade 8 mathematics standardized test (see Table 43). However, the direction is different for the grade 7 standardized test. Table 43 shows there is a strong and significant effect of student le arning ability ( 41.90, p=0.001) on student test score in the grade 7 standardized test, with students without individual educational plans perform ing better than those students with educational plans in the virtual school. The significant effect of student learning ability (21.92, p=0.022) was also observed for the grade 8 mathematics standardized test. Interestingly, the direction tells us students with individual educational plans achieved better scores Table 43 demonstrates the non significant effect of student status in virtual school on student score for the grade 7 standardized test (4.97, p=0.614), grade 8 standardized test ( 11.98, p=0.146). However, the two directions are different, with the direction for the grade 7 test showing full time studen ts tending to perform better than part time students and the direction for grade 8 test showing part time students tending to perform better. Shown in t able 44 the participation in free or reduced lunch programs has no significant effect ( 26.54, p=0.496) on student score in the grade 6 mathematics standardized test, with s tudents who did not participate in these programs tending to achieve better scores than their counterparts who participate d in these programs. There PAGE 82 82 is a nearly significant difference ( 21.48, p=0.068) in student score between Caucasian American students and the minority students for the grade 6 standardized test. The direction shows Caucasian American students tended to perform better than the minority students. Student grade level has no significant effect ( 8.13, p=0.106) on student score in the grade 6 standardized test, with students who are in the lower grade levels tending to achieve better performance. A n on significant while strong effect was observed for student learning abili ty ( 40.10, p=0.348) in the grade 6 mathematics standardized test (see Table 44). The direction tells us students without individual educational plans tend ed to perform better than their counterparts who had educational plans. Table 44 demonstrates the non significant effect (11.62, p=0.344) of student status in the virtual school for the grade 6 standardized test, with full time students tending to achieve better scores than part time students. Summary of Findings The purpose of this study is to examine the factors including LMS utilization, teacher comment /feedback and s tudent demographic information that can influence the success of Algebra courses in K 12 virtual learning environments. The three research questions formulated sought to (1) discover the influence of student participation in online academic activities on student mathematics achievement in virtual learning environments; (2) explore whether teacher comment or feedback can predict student academic achievement in online mathematics courses; and (3) investigate the differences in online mathematics achievement among students with different demographic information. The results of question one showed the influence of participation in online academic activities on achievement could be different bas ed on mathematics levels. PAGE 83 83 The indicators of student participation in online academic achievements in this study include the number of times student logged into the LMS (TOTALLOG) and how long they stayed in the LMS ( TOTALMIN ). TOTALLOG has a significant influence on student performance in Algebra I (2) EOC test ( 0.002, p=0.006) while not in Algebra I (1) EOC test. The direction of the significant influence showed students who logged into the LMS less performed better. Similarly, TOTALMIN also has a signifi cant influence for Algebra I (2) EOC test ( 0.0004, p=0.008) while not for Algebra I (1) EOC test. The direction of the significant influence indicated students who spent more time in the LMS achieved better performance. For the state standardized mathemati cs test, TOTALLOG has no significant influence on student performance at all the three levels: grade 6 to 8. Similarly, TOTALMIN also has no significant influence at the three levels. The results of question two provided the evidence that teacher comment c an affect student mathematics performance at different levels depending on the type of mathematics tests. In this study, teacher comment has no significant effect on student achievement in the two Algebra courses: Algebra I (1) and Algebra I (2). For the s tate standardized mathematics test, teacher comment has a significant effect on student achievement at the grade 7 level (0.46, p=0.041) while not at grade 6 and grade 8 levels. The significant effect at the grade 7 level showed students with more teacher comments perform ed better in th e test. The results of question three showed some demographic information was predictive of student online mathematics achievement while others may not be and the predictability also depended on the type and the level of t he mathematics test. The participation in free or reduced lunch programs, race/ethnicity (Caucasian American or PAGE 84 84 minority), student learning ability, and student status in the virtual school (full time or part time) were not predictive of student performanc e in Algebra I (1) and Algebra I (2) EOC tests. However, student grade level is predictive of student performance in Algebra I (1) ( 3.89, p=0.030) and Algebra I (2) ( 3.36, p=0.005) EOC tests, with students in lower grade levels achieved higher scores. Fo r the state standardized mathematics test, the participation in free or reduced lunch programs, student grade level and status in the virtual school were not predictive of student performance at all three levels: grade 6 to 8. The participation in free or reduced lunch programs was a significant predictor only at the grade 8 level ( 61.40, p=0.000) The direction showed students not participating in these programs performed better. Race/ethnicity was a significant predictor only for the grade 7 level test ( 21.28, p=0.046) with Caucasian American students performing better than the minority students Student learning ability was a significant predictor for the grade 7 level ( 41.90, p=0.001) and the grade 8 level (21.92, p=0.022) tests. They have different directions. For the grade 7 level test, students without individual educational plans performed better than those with individual educational plans while for the grade 8 level test, students with individual educational plans performed better. PAGE 85 85 Ta ble 41: EOC test takers demographics Algebra I (1st half) GRADE 8: 4(4.0%), 9: 35(34.7%), 10: 37(36.6%), 11: 15(14.9%), 12: 10(9.9%) RACE W hite: 82(81.2%), Other Minority: 19(18.8%) FRL 0: 68(67.3%), 1: 33(32.7%) IEP 0: 94(93.1%), 1: 7(6.9%) PT/FT 0: 87(86.1%), 1: 14(13.9%) Algebra I (2nd half) GRADE 7 : 2(2.7%), 8: 13(17.3%), 9: 24(32.0%), 10: 24(32.0%), 11: 9(12.0%), 12: 3(4.0%) RACE W hite: 62(82.7%), Other Minority: 13(17.3%) FRL 0: 53(70.7%), 1: 22(29.3%) IEP 0: 70(93.3%), 1: 5(6.7%) PT/ FT 0: 62(82.7%), 1: 13(17.3%) PAGE 86 86 Table 42: Standardized test takers demographics Standardized test Grade 6 GRADE 6 : 35(47.3%), 7 : 28(37.8%), 8 : 6(8.1%), 9 : 4(5.4%), 1 0 : 1(1.4%) RACE W hite: 60(81.1%), Other Minority: 14(18.9%) FRL 0: 6 (8.1%), 1: 68(91.9%) IEP 0: 5(6.8%), 1: 69(93.2%) PT/FT 0: 61(82.4%), 1: 13(17.6%) Standardized test Grade 7 GRADE 7 : 29(39.7%), 8: 32(43.8%), 9: 9(12.3%), 10: 2(2.7%), missing: 1(1.4%) RACE W hite: 59(80.8%), Other Minority: 14(19.2%) FRL 0: 16(21. 9%), 1: 57(78.1%) IEP 0: 19(26.0%), 1: 54(74.0%) PT/FT 0: 68(93.2%), 1: 5(6.8%) Standardized test Grade 8 GRADE 8: 63(58.9%), 9: 39(36.4%), 10: 4(3.7%), missing: 1(0.9%) RACE W hite: 83(77.6%), Other Minority: 24(22.4%) FRL 0: 20(18.7%), 1: 87(81.3 %) IEP 0: 20(18.7%), 1: 87(81.3%) PT/FT 0: 89(83.2%), 1: 18(16.8%) PAGE 87 87 Table 43 : Overview of RA model for different datasets Test Variables df Sigma Square Tau ICC Algebra I (1) None 79 41.79 225.26 0.84 Algebra II (2) None 56 63.25 146.04 0.70 Standardized test Grade 6 None 69 43.33 1797.90 0.98 Standardized test Grade 7 None 63 468.66 1189.96 0.72 Standardized test Grade 8 None 93 401.44 1052.68 0.72 PAGE 88 88 Table 44 : Least squares estimates of fixed effects (with robust standard errors) Test Fixed Effect Coefficient Standard Error T ratio d.f. P value Algebra I (1) GRADE 3.89 1.76 2.21 92 0.030 RACE 0.93 4.22 0.22 92 0.826 FRL 0.04 3.93 0.01 92 0.992 IEP 1.37 6.19 0.22 92 0.825 PT/FT 1.78 5.20 0.34 92 0.733 TEACHERCOM 0.04 0.06 0.76 92 0.450 TOTALLOG 0.03 0.03 1.21 92 0.230 TOTALMIN 0.0005 0.00 1.04 92 0.304 Algebra I (2) GRADE 3.36 1.14 2.96 66 0.005 RACE 2.53 3.37 0.75 66 0.455 FRL 4.36 3.15 1.38 66 0.172 IEP 2.26 3.83 0.59 66 0. 557 PTFT 6.49 3.85 1.69 66 0.096 TEACHERCOM 0.01 0.09 0.11 66 0.912 TOTALLOG 0.02 0.01 2.90 66 0.006 TOTALMIN 0.0004 0.00 2.74 66 0.008 PAGE 89 89 Table 45 : Least squares estimates of fixed effects (with robust standard errors) Test Fixed Effect Coefficient Standard Err or T ratio d.f. P value Standardized test Grade 7 GRADE 0.17 0.10 1.76 64 0.083 RACE 21.28 10.50 2.03 64 0.046 FRL 0.89 11.03 0.08 64 0.936 IEP 41.90 11.93 3.51 64 0.001 PT/FT 4.97 9.81 0.51 64 0.614 TEACHERCOM 0.46 0.22 2.09 64 0.041 TOTALLOG 0.02 0.06 0.35 64 0.725 TOTALMIN 0.0004 0.00 0.41 64 0.680 Standardized test Grade 8 GRADE 1.71 3.47 0.49 98 0.623 RACE 9.31 8.11 1.15 98 0.254 FRL 61.40 9.57 6.42 98 0.000 IEP 21.92 9.39 2.33 98 0.022 PT/FT 11.98 8.17 1.47 98 0.146 TEACHERCOM 0.002 0.16 0.01 98 0.990 TOTALLOG 0.07 0.04 1.58 98 0.117 TOTALMIN 0.001 0.00 1.92 98 0.057 PAGE 90 90 Table 46 Ordinary Least squares estimates of fixed effects Test Fixed Effect Coefficient Standard Error T ratio d.f. P value Standardized test Grade 6 GRADE 8.13 4.97 1.64 65 0.106 RACE 21.48 11.59 1.85 65 0.068 FRL 26.54 38.75 0.69 65 0.496 IEP 40.10 42.42 0.95 65 0.348 PT/FT 11.62 12.18 0.95 65 0.344 TEACHERCOM 0.02 0.26 0.06 65 0.949 TOTALLOG 0.04 0.05 0.82 65 0.414 TOTALMIN 0.0005 0.00 0.61 65 0.544 PAGE 91 91 Table 47 Descriptive statistics for EO C test takers Test Variables N Mean Std. Deviation FINAL GRADE 101 70.82 15.598 GRADE 101 9.92 1.026 RACE 101 .19 .393 Algebra I (1) FRL 101 .33 .471 IEP 101 .07 .255 PT/FT 101 .14 .347 TEACHERCOM 101 20.78 21.390 TOTALLOG 101 215.36 125.47 6 TOTALMIN 101 10783.93 6418.637 FINAL GRADE 75 79.00 13.650 Algebra I (2) GRADE 75 9.45 1.119 RACE 75 .17 .381 FRL 75 .29 .458 IEP 75 .07 .251 PT/FT 75 .17 .381 TEACHERCOM 75 15.31 21.526 TOTALLOG 75 461.04 288.174 TOTALMIN 75 27425.76 17097.182 PAGE 92 92 Table 48 Descriptive statistics for standardized test takers Test Variables N Mean Std. Deviation Standardized test Grade 6 MAP_SCALE_SCORE 74 664.07 41.827 GRADE 74 6.76 .919 RACE 74 .19 .394 MAP_FRL 74 .92 .275 IEP_STU DENT 74 .93 .253 PTFT 74 .18 .383 TEACHERCOM 74 11.32 18.144 TOTALLOG 74 354.65 181.272 TOTALMIN 74 23006.26 13370.676 Standardized test Grade 7 MAP_SCALE_SCORE 73 676.18 41.913 GRADE 73 9.03 10.704 RACE 73 .19 .396 MAP_FRL 73 .78 .417 IEP _STUDENT 73 .74 .442 PTFT 73 .07 .254 TEACHERCOM 73 11.15 19.715 TOTALLOG 73 253.25 141.193 TOTALMIN 73 16720.51 9324.034 Standardized test Grade 8 MAP_SCALE_SCORE 107 696.23 38.020 GRADE 107 8.36 .994 RACE 107 .22 .419 MAP_FRL 107 .81 .392 IEP_STUDENT 107 .81 .392 PTFT 107 .17 .376 TEACHERCOM 107 11.28 18.569 TOTALLOG 107 293.93 202.395 TOTALMIN 107 18067.10 14548.311 PAGE 93 93 Table 49 Standardized coefficients for EOC test takers Tests Factors Standardized Coefficients Alge bra I (1) GRADE 0.2 6 RACE 0.3 6 FRL 0.0 5 IEP 0.74 PT/FT 2.42 TEACHERCOM 2.4 7 TOTALLOG 0.1 8 TOTALMIN 0.0 3 Algebra I (2) GRADE 0.2 8 RACE 0.86 FRL 5.24 IEP 1.2 4 PT/FT 9.85 TEACHERCOM 0.56 TOTALLOG 0.2 7 TOTALMIN 0.0 2 PAGE 94 94 Table 410 Standardized coefficients for standardized test takers Factors Standardized Coefficients Standardized test Grade 6 GRADE 0.1 8 RACE 9.2 1 MAP_FRL 18.52 IEP_STUDENT 36.89 PTFT 17.59 TEACHERCOM 0.9 5 TOTALLOG 0. 40 TOTALMIN 0.0 4 Standardized test Grade 7 GRADE 0.04 RACE 0.7 9 MAP_FRL 0.93 IEP_STUDENT 44.41 PTFT 2.8 6 TEACHERCOM 35.70 TOTALLOG 0.143 TOTALMIN 0.0 3 Standardized test Grade 8 GRADE 0.04 RACE 3.92 MAP_FRL 57.44 I EP_STUDENT 21.92 PTFT 11.49 TEACHERCOM 0. 10 TOTALLOG 0.76 TOTALMIN 0.07 PAGE 95 95 Table 411 Adjusted R squares Tests VAR e VAR t R c 2 Algebra I (1) 236.69 243.29 0.0 3 Algebra I (2) 159.04 186.32 0.1 5 Standardized test Grade 6 1412.24 1749.46 0 .19 Standardized test Grade 7 1440.78 1756.73 0.1 8 Standardized test Grade 8 1146.03 1445.5 0.2 1 PAGE 96 96 CHAPTER 5 DISCUSSION AND IMPLI CATIONS Introduction This chapter summarizes the findings of the present study and presents the important conclusions drawn from the data shown in Chapter 4. It also addresses the implications for teaching, research, and policy making process es in the discussion of the findings. This chapter presents the implications based on the three research questions in the present study. Summary of Study To explore different success factors for online mathematics in general and online Algebra in specific, the present study investigated the effect of a variety of variables on student achievement on Algebra EOC test s and state standard mathematics tests. The present study used the secondary data collected from a state led virtual school in the Midwestern US region. The variables include learner characteristic variables such as student demographic information and participation level in online academic activities and learning environment characteristics variables such as teacher comment in the present study. Overview of the P roblem The U.S has experienced an astonishing growth in online education at K 12 level during the past decade. The enroll ment of K 12 virtual school students has increased from 40,000 in 200001 academic year to 1 million in 200708 academic year (Clark 2001; Glass, 2009; Newman, Stein & Trask, 2003; Peak Group, 2002; Picciano & Seaman, 2009; Picciano & Seaman, 2007; Setzer & Lewis, 2005; Tucker, 2007; Zandberg, Lewis, & Greene, 2008). With the large population of online learners, its PAGE 97 97 possible to evaluate the effectiveness of online courses. However, currently, there is no one single model being created to predict online suc cess and no clear set of characteristics that have been identified in this regard (Roblyer & Davis, 2008; Tallent Runnels et al., 2006). Math has been considered a very important force to push a society forward. Many countries emphasize the improvement o f math knowledge and they develop policies to attract more people into this field. Having good academic performance in math subjects at the K 12 level is important for students to pursue advanced degrees in this field. It will help prepare more students to have careers in Science, Technology, Engineering, and Mathematics ( STEM ) and increase workforce for U.S. in these fields which could provide strong momentum for this country to move forward in many aspects The quality of Algebra courses is essential in building the number of U.S. students who are ready for advanced degrees in STEM and career success in these fields. Purpose S tatement and R esearch Q uestions The purpose of this study is to examine the factors including LMS utilization, teacher comment /feed back and s tudent demographic information that can influence the success of Algebra courses in K 12 virtual learning environments. The research questions in this study are Does the level of LMS utilization influence Algebra/mathematics performance in onlin e education? Does teacher comment or feedback influence Algebra/mathematics performance in online education? Do student demographic information such as race/ethnicity, grade level, status in virtual school, whether have individual educational plan (IEP) and participation in free/reduced lunch programs influence Algebra/mathematics performance in online education? PAGE 98 98 Review of the M ethodology The present study is descriptive in nature. The researcher described the relationship between some factors and student learning outcome without intervening. The researcher received the secondary data from one state led virtual school in the Midwestern US region that collected student demographic information, participation in online academic activities, teacher comments, and academic achievement on Algebra EOC tests and state standardized mathematics test s in the 200809 academic year. The present study builds on the results of Liu and Cavanaugh (2010) s study. This virtual school was launched in 2007. Liu and Cavanaughs study used the first year (200708) data collected by this virtual school and the present study used the second year data. The present study investigated success factors for both Algebra EOC tests and one state standardized mathematics test while Liu and C avanaughs study only investigated these factors for Algebra EOC tests. The data regarding student demographic information, participation in online academic activities and teacher comments were collected by the LMS utilized by this virtual school for cours e content delivery. Student academic achievement on (1) the Algebra I, II EOC tests administered at the end of semester designed based on the state Algebra standards and (2) one state standard mathematics test, designed based on the state mathematics standards was collected. HLM was carried out by the software program HLM 6.06 for data analysis in this study to account for the clustering of academic score s of students recruited from the same physical school. The rest of chapter 5 will discuss the outcomes and review the implications associated with the three research questions designed to examine the impact of different success factors on student mathematics achievement. This chapter will close with the conclusions drawn from the findings shown in chapter 4. PAGE 99 99 Findings In the present study, RA model was analyzed at the beginning to partition the total variance of student score into withinschool and betweenschool components. The intraclass correlation coefficient was calculated for the five groups and it w as Algebra I (1) .84, Algebra II (2) .70, Standardized test Grade 6 .98, Standardized test Grade 7 .72, and Standardized test Grade 8 .72 respectively. This shows the betweenschool variance was big in comparison with the withinschool variance f or all these five groups especially for the Standardized test Grade 6 group. Partially, it could be attributed to the small number of students per school. The big ICC indicated s tudents from different schools are different from each other with respect to t heir academic achievement. This finding confirmed the gap between private and public schools in student academic achievement found in other studies (Braun, Jenkins, Grigg, Tirre, Spellings, Whitehurst, & Schneider, 2006; Demircioglu & Norman, 1999; Lubiens ki & Lubienski, 2005). It also could indicate that it will take time for the standardized testing criterion to be well implemented in the school system of this state. In the present study, standardized coefficient ( was calculated according to the formula: k =bk Sxk Sy (bk is the unstandardized coefficient, Sxk is the standard deviation of the corresponding independent variable, and Sy is the standard deviation of the dependent variable). It can be used to com pare among different factors with respect to the importance in determining test score. Table 49 shows that for Algebra I (1) = 2.42) and teacher comment = 2.47) were most important factors, and for Algebra I (2) group, partici pation in school free or reduced lunch = = 9.85) were most important factors. Table 410 shows that for standardized test grade 6, participation in school free or reduced PAGE 100 100 = 18.52), whether have IE = = = = 35.70) were most important factors while for standardized test grade 8, participation in school = = 11.49) were most important factors. These findings show that the same factors can influence student test score differently for different online Algebra cour ses. The adjusted R square (Rc 2) was also calculated according to the formula: Rc 2= 1 VAR e/VAR t to show the reduction of test score variance from the RA model with the addition of the factors. Table 410 shows that the same set of factors accounted for student score variance at different degree for different tests. All these findings demonstrated the complexity of the investigation of factors influencing success in online Algebra. The following Table 51 shows the summary of the significance and direction of the factor effect on student academic performance in the five tests The + sign indicates the positive direction of the factor effect and the sign indicates the negative direction of the factor effect. The X sign indicates the factor had a sig nificant effect on student academic performance in the corresponding test Research Question 1 Does the level of LMS utilization influence Algebra/mathematics performance in online education? The time spent in academic activities has been identified as a very important factor that has strong effect on success in online education (Cavanaugh, 2007), faceto face instruction (Rocha, 2007), and blended programs (Cavanaugh, 2009). Based on a study for the investigation of the cognitivemotivational and demographic characteristics of PAGE 101 101 online students and the predictors for their success, Wang and Newlin (2000) found out students who participate in online activities at a high level tend to perform well in the online course. They concluded the total online course activity is a predictor of students final grades. Compared to the students in traditional classrooms, online students spend more time in the virtual learning environments on interacting with one another on academic topics (ODwyer et al., 2007). The peer t o peer interaction, in turn, could help improve online students learning outcomes ( Cavanaugh, 2007) In the present study, the numbers of times students logged into the LMS and how long they stayed in the LMS were considered the indication of student part icipation level on online academic activities. The number of times students logged into the LMS also has been identified as a strong predictor of student academic performance in online learning (Dietz, 2002; Dickson, 2005). Compared to traditional classr oom instructors, online instructors lack of the regular set of cues about students' confusion or frustration during the learning process such as their facial expression and body positions. The measure of time students spent in the online academic activities can provide online instructors the information about students understanding of content materials. A lower level of involvement in online activities in the course at the beginning of the semester could be an early warning sign of failure later during the learning process. Therefore, online instructors should closely monitor these behaviors via LMS login data to prevent students who show warning signs at the beginning from failure. The influence of time students spent in the LMS was found to be positive f or the five groups and significant for Algebra I (2) These findings are aligned with the PAGE 102 102 statement Wang and Newlin (2000) made in their study mentioned above that students participating in online academic activities at a higher level achieve better perfor mance in online learning T hey echo the call for sustained time on task for cognitive learning (Gallagher, 2009) and provide support for the emphasis of expanded learning time, including with online courses, to improve academic achievement (Cavanaugh, 2009). These findings could be explained by the call for changes in instructional practices in mathematics education by many educational reformers such as the implementation of standards for mathematics instruction from the National Council of Teachers of Math ematics (NCTM, 1989, 1991) and the active involvement in academic activities is one of their arguments (Forman, 1996). This also confirmed the value of increased participation in learning activities in mathematics education emphasized in Formans article. However, its surprising to the researcher that this factor only had a significant effect for Algebra I (2) in this study considering many other studies already showed the importance of time on task for the improvement of student achievement. Many of the students taking Algebra I (2) course are from higher grade levels for credit recovery or to make up failing grades in their physical schools. The increased engaged time on task could be more effective with respect to the improvement of academic achievement than the other four groups. Nevertheless, the significant effect of time spent in the LMS for 1 out of 5 groups calls for m ore studies on activities that engage student s during their stay in the LMS as an explanation for the findings The effect of the n umber of times students logged into the LMS on student academic achievement is negative for the 4 out 5 groups and negative and significant for Algebra I (2) To some degree, t his is contradict ory to the belief that the number of PAGE 103 103 times students login to the LMS is a strong and positive predictor of success in online learning ( Dietz, 2002; Dickson, 2005) It is possible that if students are logging into the course environment more often, they are staying and working for shorter time periods, negatively impac ting their concentration on their studies. This also call s for more research on the investigation of LMS utilization with bigger sample size and diversified mathematics tests. Implications Related to Research Question 1 Several implications for research, policy and practice can be drawn from the outcomes associated with research question one even though the results found in this study are mixed. These implications provide guidance for future studies to investigat e the effect of time on task and the form of activities students engag ed in when they stay in the LMS on academic achievement in virtual learning environments The positive and significant effect of the time spent in LMS for Algebra I (2) shows students who spent more time in the LMS perform ed better than students spending less time in the LMS I t is plausible that each log in session of highperforming students was longer than the session length for lower performing students, showing that highperforming students may benefit from sustained time on t ask rather than more frequent but shorter time on task. This explanation would support flexible online courses that allow students to stay in the course for extended periods of time while working on complex and abstract content. The positive influence of t ime spent in the LMS provides the support for the improvement of many LMSs to make them more user friendly with attractive interfaces that motivate students to spend more time in the system engaging in academic activities delivered during the learning proc ess, as well as teaching practices that foster PAGE 104 104 connectedness among teachers and students and time management strategies for students who do not have high self regulation abilities As mentioned above, the significant effect of time spent in the LMS on onl y 1 out of 5 groups and the contradictory directions of the effect of number of times student logged into the LMS call s for more research in this field. Future researchers should investigate the activities students engaged in each time they logged into the LMS and the distribution of the logged in times throughout the semester for deeper understanding of the effect of time on task on academic achievement in virtual learning environments. This information could be used to help teachers have better knowledge about the activities in which students are more interested and their engagement level in academic activities during the learning process. Online instructors and course designers could design and d evelop better online activities, specifically activities that are more individualized, diverse, and authentic to increase engaged learning time. Research Question 2 Does teacher comment or feedback influence Algebra/mathematics performance in online education? Educators active facilitation of online learning and teachers feedback are important factors that influence students academic performance during the learning process (Dickson, 2005; Cavanaugh, Gillan, Bosnick, Hess, & Scott, 2005; Hughes, McLeod, Brown, Maeda, & Choi, 2005; Ferdig, Papanastasiou, & DiPietro, 2005; Zucker, 2005). Many of the pedagogical benefits brought by the student teacher interaction such as those related to motivation and feedback are relevant to distance education as well as the conventional classroom education (Anderson & Kuskis, 20 07). Teacher individual feedback and comments are especially helpful for students who are PAGE 105 105 shy and may not participate in academic activities to increase their communication opportunities (Hughes et al 2005), which could help improve learning outcomes. Based on the results of a study of students with learning disabilities (SLD) and students with attention deficit hyperactivity disorder (ADHD), Smouse (2005) found communication with and feedback from instructors was the most valuable aspect of online courses. Timely and constructive teachers feedback to students assignments and questions have been identified as the critical characteristics of the teaching/learning benchmarks for the quality of online learning (Phipps & Merisotis, 2000). The influence of teacher comments on student academic achievement is positive and significant for standard test grade 7. This provides the evidence of the importance of teacher comments and teacher student interaction for the improvement of learning outcomes in online learni ng environments (Cavanaugh et al., 2005; Peters, 1999; Williams, 2006; Zucker, 2005). It confirmed the relationship between social interaction and mathematics learning (Cobb, Yackel & Wood, 1992; Vogit, 1996). This finding also align with the belief that i n mathematics education, the interactions between adults (instructor) and children (students) have impacts on the quality of learning outcome via the psychological benefits brought to students such as critical thinking and self reflection (Oers, 1996) and on students cognitive development (Voigt, 1994). In the process of interaction or negotiation during mathematics learning, students can build the connections between materials and mathematics terms (Voigt, 1994), which can be proved, to some degree, by these this finding. However, its surprising to the researcher that the influence of this factor is found to be positive and significant for 1 out of 5 groups considering many studies have shown the positive effect of this factor on PAGE 106 106 academic achievement. Var iables other than teacher comments especially the number of teacher comments investigated in the present study such as teaching styles, quality of teacher comments, etc., could have more influence on student achievement. D ue to the small sample size for each group in the present study, readers should be cautious about the interpretation of the findings Implications Related to Research Question 2 The positive and significant effect of teacher comment or feedback for standard test grade 7 provides the support for the statement about the importance of this factor in other studies. This finding could shed light on the development of online courses that integrate teacher feedback and teacher student interaction as critical components during the course design. It also indicates the importance of timely and constructive feedback from online instructors for the success in online learning. However, the significant effect of this factor for only 1 out 5 groups in the present study showed more research is needed wit h a bigger sample size and on the form and content of teacher feedback for insightful explanation. It also supports the call for more study about the most effective interaction type, tools, and frequency for the participants in online learning ( Cavanaugh, 2007) for online instructors to better facilitate the learners to achieve success. The integration of qualitative dimensions of interaction in future study also will assist in the triangulation of quantitative dimensions as shown in the present study to better understand the importance of teacher student interaction in online learning (Weiner, 2003). PAGE 107 107 Research Question 3 Do student demographic information such as race/ethnicity, grade level, status in virtual school, whether have IEP and participation in free/reduced lunch programs influence Algebra/mathematics performance in online education? Students demographic information such as participation in free or reduced lunch programs, race/ethnicity and whether have IEP has been proved to relate to students academic achievement in other studies. McLoyd (1998)s study show ed there is a correlation between student participation in school free or reduced lunch program s and student academic achievement : the magnitude is weaker as grade level rises. Student parti cipation in these programs can be considered as the indicator of his/her family Social Economics Status (SES) (Sirin, 2005), which has already been proved to affect student academic performance in many studies (Coleman, 1988; Sirin, 2005; White, 1982). In the present study, the influence of participation in these programs on achievement is negative for all the five groups and significant for standard test grade 8. For standard test grade 8 group, s tudents who did not participated in these lunch programs achieve d higher scores than students who participate in these programs. This provides support for the findings regarding the correlation between this factor and achievement in other studies mentioned above. The negative influence of this factor found in the present study can add to the body of knowledge about the correlation between SES and academic achievement. Other studies about the relationship between participation in free/reduced lunch programs or SES and academic achievement are all conducted in traditi onal learning environments. The result in the present study demonstrates the possibility for the generalization of study findings between traditional learning environment s and virtual learning environment s. On the other hand, the PAGE 108 108 influence of participation in free or reduced lunch programs is only significant for 1 out of 5 groups in this study is surprising to the researcher considering the body of research demonstrating the correlation between this factor and student academic achievement. The virtual school student body is a diverse population including students with different learning disabilities (Dickson, 2005; Ferdig, Papanastasiou & DiPietro, 2005). Virtual schools offer or support individual educational plans (IEP) for these students during the lear ning process. Therefore, whether a student has an individual educational plan could be a sign of the level of learning abilities. Many technologies utilized in virtual school learning environments could help bridge gaps between students with disabilities a nd students without these disabilities with respect to the success opportunities in online learning (Coombs & Banks, 2000) However, students with disabilities are still underrepresented in online education (Kinash & Crichton, 2007). The present study prov ides some evidence for this claim. For example, the influence of IEP on student achievement is negative and significant for standardized test grade 7 with s tudents who d id not have an IEP (usually students without learning disabilities) perform ed better th an students who ha d an IEP (usually students with learning disabilities). However, for standard test grade 8, the influence of IEP is positive and significant favoring students with learning disabilities. This finding could indicate that the virtual school may be able to help improve academic achievement for students at risk for failure in their physical schools. It also could be a sign of bridging gaps between students with learning disabilities and others without these disabilities with respec t to their academic performance possibly due to the academic support provided through the IEP. PAGE 109 109 Racial gaps in student test score have been proved in many other studies conducted in traditional learning environments (Bali & Alvarez, 2004; Barth, 2001; Hall et al., 2000; Lockhead et al ., 1985). The student body in online K 12 schools represents the community that is served by traditional school system ( Ronsisvalle & Watkins, 2005) The findings about the racial gaps in student achievement in other studies could apply to the present study as well. The significant racial difference for standardized test grade 7 and nearly significant difference for standardized test grade 6 provides the evidence for the findings in other studies. The directions of the difference in the two groups show white American students perform better than other minority groups as a whole. However, t he finding that the significant racial difference was only found for 1 out 5 groups could be due to the coding system that combined different minority groups into one category potentially masking important information regarding the differences in student academic achievement among different racial groups. Future study could be conducted to investigate these differences with bigger sample size. The effect of student grade level in physical school was found to be negative and significant for two Algebra I groups, with students from lower grade levels perform ing better than those from higher grade levels. Students taking the standardized tests are from lower lev els (grade 68) compared with the students who took the Algebra I courses and Algebra I EOC tests (most of them from grade 9 12). Algebra I is a required course for high school graduation. Many students in higher grades such as grade 11, 12 take Algebra I courses in this virtual school as credit recovery or remediation to make up failing grades in their physical schools to meet the graduation requirement. It could be the explanation for the negative and significant effect of this factor for the two Algebra I groups. The PAGE 110 110 effect of s tudent status in the virtual school (part time or full time online students) was not significant for all the five groups Implications Related to Research Question 3 In the present study, the influence of participation in free or reduced lunch programs is negative for all the five groups and significant for standard test grade 8. For standard test grade 8 group, s tudents who participated in these lunch programs achieved lower scores than students who did not participate in these pr ograms. This echoes the belief that family SES could affect student academic achievement via its influence on parental involvement in virtual learning environments (Black, 2009). This finding can guide the decision making process in the virtual schools by encouraging them to be sensitive to the needs of students with low family SES background and to take measures to bridge the gap in access to resources that could influence student academic achievement. The effect of IEP on student achievement is negative f or 4 out of 5 groups and significant for standard test grade 7 in the present study. For standard test grade 7 group, students with IEP ( usually students with learning disabilities ) achieved lower performance than others without IEP This could provide support for the integration of instructional strategies such as hiring academic coaches or tutors or advanced technologies during the online learning process to help students with disabilities succeed. The negative and significant effect of grade level for t he two Algebra I groups could be explained by the situation that many students in higher grade levels take Algebra I courses in this virtual school to make up the credits lost in their physical schools to meet the graduation requirement This has the impli cation for the virtual PAGE 111 111 school during online course design for the implementation of certain strategies such as peer support and online tutoring, or flexible timelines and multiple paths to help higher grade students in Algebra I courses to achieve better p erformance. Online teachers also should provide individual assistance based on the needs of different student s. Broad I mplications for O nline C ourse D esign and O nline T eaching In September 2007, International Association for K 12 Online Learning (iNACOL) endorsed the National Standards of Quality for Online Courses based on the Southern Regional Education Board (SREB) Standards for Quality Online Courses In February 2008, iNACOL released National Standards for Quality Online Teaching based on SREBs Stan dards for Quality Online Teaching and Online Teaching Evaluation for State Virtual Schools The SREBs two sets of standards have been widely used by the 16 states in the southern United States. iNACOL National Standards of Quality for Online Courses stan dards were designed to provide states, districts, online programs, and other organizations with a set of quality guidelines for online course content, instructional design, technology, student assessment, and course management. (iNACOL, 2006, p.1). There are 6 categories in iNACOL standards: 1 Content 2 Instructional Design 3 Student Assessment 4 Technology 5 Course Evaluation and Management 6 21st Century Skills. Under each category there are a set of standards. National Standards for Quality Online Teaching is designed to provide states, districts, online programs, and other organizations with a set of quality guidelines for online teaching and instructional design. (iNACOL, 2008, p.1). There are 13 categories in these standards: PAGE 112 112 A The teacher meets the profess ional teaching standards established by a statelicensing agency or the teacher has academic credentials in the field in which he or she is teaching. B The teacher has the prerequisite technology skills to teach online. C The teacher plans, designs and incorporates strategies to encourage active learning, interaction, participation and collaboration in the online environment. D The teacher provides online leadership in a manner that promotes student success through regular feedback, prompt response and clear expe ctations. E The teacher models, guides and encourages legal, ethnical, safe and healthy behavior related to technology use. F The teacher has experienced online learning from the perspective of a student. G The teacher understands and is responsive to students with special needs in the online classroom. H The teacher demonstrates competencies in creating and implementing assessments in online learning environments in ways that assure validity and reliability of instruments and procedures. I The teacher develops and delivers assessments, projects, and assignments that meet standards based learning goals and assesses learning progress by measuring student achievement of learning goals. J. The teacher demonstrates competencies in using data and findings from assessments a nd other data sources to modify instructional methods and content and to guide student learning. K The teacher demonstrates frequent and effective strategies that enable both teacher and students to complete self and preassessments. L The teacher collabor ates with colleagues. M The teacher arranges media and content to help students and teachers transfer knowledge most effectively in the online environment. (Instructional Design) Under each category there are a set of standards. Many of the findings, derivat ive outcomes, or implications in the present study align with the two sets of standards. The following two tables show these alignments. PAGE 113 113 Conclusions This dissertation examined the impact of some variables including students demographic information, teacher comments, and student utilization of the LMS on academic performance in Algebra EOC tests and state standard mathematics tests using a sample of students from a state led virtual school in the Midwestern U.S region. The results show different variables a ffect student Algebra/mathematics achievement in different ways. No single factor investigated in the present study has been found to be significant for all five groups. It could be due to the limitations mentioned in Chapter 3: Methodology. It also indicated that some other factors such as instructional strategies utilized, teacher experience and student prior subject knowledge could have been missed in the present study They should be investigated in the future studies on success factors in the virtual s chooling. Outcomes of this study have some specific implications for researchers, practitioners, and policy makers. The results show the time student spent in the LMS has positive influence on student academic achievement. This provides the support for th e online instructional designers or LMS developers to utilize more advanced technologies such as some educational games and refine the course delivery system to motivate students learn the content and spend more time engaging the academic activities. It al so can lend relevance to online instructors for the implementation of instructional strategies to encourage students to focus on the learning tasks during their stay in the course delivery systems. The results of data analysis in this study show the influences of many factors are mixed. Some are positive, and others are negative. Even for the same factor, the influence could be in different directions for different tests. This indicates that the investigation of success factors of online learning is a compl ex process in which PAGE 114 114 quantitative methodology independently may not be able to effectively measure the influence of the factors on academic achievement. Therefore, future research seeking to investigate the success factors in online learning should utilize mixed methodology incorporating quantitative and qualitative methods. This dissertation has implications for policy making process es at state and national level regarding quality virtual schooling and research support. At state level s in which the virtual school is implement ed effective and well designed LMS should be utilized for course delivery and management. The LMS interface should be user friendly that can attract students attentions in longer periods during the learning process. Components such as online forum, incorporation of social networking software, online synchronous audio/video conferencing should be integrated in the LMS to encourage more and diversified teacher student and student student interact ion To increase success opportunities for all students, virtual schools should take some measures to increase access of students from lower SES households the learning resources such as additional lab time, one on one computer/laptop, or extra instructi onal time. Virtual school should provide in dividualized assistance based on students different needs such a s for students with learning disabilities it could be individual education plans for students taking the online courses for credit recovery it could be peer to peer support or group project s At national level, more support should be provided to help build better designed state led virtual schools to increase access to more effective learning resource for all students. More national standards regarding quality virtual schooling should be created to guide the practice and implementation of state level virtual school s. Both at state and PAGE 115 115 national level, policy makers should grant more resources to support more empirical study collecting quantitative and qualitative data to provide evidence for policies making process. More research is needed on student academic achievement, online success model, and longitudinal study on virtual school retention. One data system regarding virtual school practice should be built both at state and national level fr om which the researchers can draw the information they need to conduct the secondary research similar to the present study. These secondary research studies can supplement the first hand studies though they may have l imitations such as missing information like the present study that lacks of qualitative data for some factors Since the establishment of the first virtual school at the end of 20th century, it has experienced an extraordinary development during the last one decade. However, with its short h istory, K 12 virtual schools are still a relatively new concept for many researchers and educators. Compared to online education at post secondary level, little research has been done in K 12 virtual learning environments (Cavanaugh 2007; Cooze & Barbour, 2005; Means et al., 2009; Picciano & Seaman, 2007; Picciano & Seaman, 2009; Ronsisvalle & Watkins, 2005). The present study is the first research on success factors in K 12 virtual learning environments. At present, no clear set of characteristics have been identified to predict success in virtual learning environments, and no conclusive model has been created to apply in online learning practice (Roblyer & Davis, 2008; Tallent Runnels et al., 2006). However, to help improve the practice and implementation of virtual schooling, Smith et al. (2005) emphasized the empirical studies on student academic achievement. Given the dearth of research on success factors in K 12 online learning environments, t his dissertation should serve as the PAGE 116 116 starting point for mor e studies utilizing both qualitative and quantitative methods to help the development of one success model to improve student academic achievement in virtual schooling. PAGE 117 117 Table 51 : S ignificance and Direction of the E ffect of F a ctors Course\ Factor Gr ade Level Race Free or Reduced Lunch IEP Student Status Teacher Comment Number of Times Logged into the LMS Time Spent in the LMS Algebra I 1 st half X + + + 2 nd half X + + X + X MAP Grade 6 + + Grade 7 X X + + X + + Grade 8 + X + X + + TOTAL of 5 2 1 1 2 0 1 1 1 PAGE 118 118 Table 52: Alignment with National Standards in Quality Online Course Findings, derivative outcomes, or implications of the present study Aligned standards in iNACOL National Standards of Quality for Online Courses Tests used in the present study align with state or national standards. Course tasks and assessments align with the required local, state, and national assessments that are associated with the course. (A) This virtual schoo l hires the authorized course provider to implement the Learning Management System (LMS) and content area teachers who met state certification and other requirements as online instructors. The course provider is authorized to operate in the state in which the course is offered. (E) The teacher meets the professional teaching standard established by a state licensing agency or the teacher has academic credentials in the field in which he or she is teaching and has been trained to teach online and to use the course. (E) Flexible online courses that allow students to stay in the course for extended periods of time while working on complex and abstract content. The course instruction includes activities that engage students in active learning. (B) The course provides opportunities for students to engage in higher order thinking, critical reasoning activities and thinking in increasingly complex ways. (B) Improvement of many LMSs to integrate teaching practices that foster connectedness among teachers and students. Development of online courses that integrate teacher feedback and teacher student interaction as critical components during the course design. The course design provides opportunities for appropriate instructor student interaction, including timely and frequent feedback about student progress. (B) The course provides opportunities for appropriate instructor student and student student interaction to foster mastery and application of the material and a plan for monitoring that interaction. (B) Int egration of instructional strategies or advanced technologies during the online learning process to help students with disabilities to succeed. The course meets universal design principles, Section 508 standards and W3C guidelines to ensure access for all students. (D) PAGE 119 119 Table 53: Alignment with National Standards in Quality Online Teaching Findings, derivative outcomes, or implications of the present study Aligned standards in iNACOL National Standards for Quality Online Teaching The importance of timel y and constructive feedback from online instructors for the success in online learning. Encourages interaction and cooperation among students, encourages active learning, provides prompt feedback, communicates high expectations, and respects diverse talent s and learning styles. (D The teacher provides online leadership in a manner that promotes student success through regular feedback, prompt response and clear expectations.) Establishes and maintains ongoing and frequent teacher student interaction, student student interaction and teacher parent interaction. (D) Provides timely, constructive feedback to students about assignments and questions. (D) Personalizes feedback (support, growth and encouragement). (D) Creates a warm and inviting atmosphere that pr omotes the development of a sense of community among participants. (C The teacher plans, designs and incorporates strategies to encourage active learning, interaction, participation and collaboration in the online environment) The finding that students wh o participated in free or reduced lunch programs achieved lower scores than students who did not participate in these programs for state standardized test grade 8 group could guide the decision making process in the virtual schools by encouraging them to be sensitive to the needs of students with low family SES background and to take measures to bridge the gap in access to resources that could influence student academic achievement. Provides activities, modified as necessary, that are relevant to the needs of all students. (G The teacher understands and is responsive to students with special needs in the online classroom.) PAGE 120 120 Table 53. Continued Findings, derivative outcomes, or implications of the present study Aligned standards in iNACOL National Standards for Quality Online Teaching Online teachers should provide individual assistance based on the needs of different student s. Provides activities, modified as necessary, that are relevant to the needs of all students. (G) Personalizes feedback (support, growth and encouragement). ( D) Provides evidence of effective learning strategies that worked for the individual student and details specific changes in future instruction based upon assessment results and research study (datadriven and researchbased). (J The teacher demonstrates competencies in using data and findings from assessments and other data sources to modify instructional methods and content and to guide student learning.) PAGE 121 121 APPENDIX A ALGEBRA I MULTIPLE CHOICE RELEAS ED SAMPLES PAGE 122 122 PAGE 123 123 PAGE 124 124 PAGE 125 125 PAGE 126 126 PAGE 127 127 PAGE 128 128 PAGE 129 129 PAGE 130 130 PAGE 131 131 PAGE 132 132 PAGE 133 133 PAGE 134 134 PAGE 135 135 PAGE 136 136 PAGE 137 137 PAGE 138 138 PAGE 139 139 PAGE 140 140 PAGE 141 141 PAGE 142 142 APPENDIX B ALGEBRA I PERFORMANCE EVENT RELEASED SAMPLES PAGE 143 143 PAGE 144 144 PAGE 145 145 APPENDIX C STATE ALGEBRA STANDARDS Number and Operations N.1.A.AI compare and order rational and irrational numbers, including finding their approximate locations on a number line N.1.B.AI use real numbers and various models, drawing, etc. to solve problems N.1.C.AI use a variety of representations to demonstrate an understanding of very large and very small numbers N.2.B.AI describe the effects operations such as multiplication, division, and computing powers and roots on the magnitude of quantities N.2.D.AI apply operations to real numbers, using mental computation or paper andpencil calculations for simple cases and technology for more complicated cas es N.3.D.AI judge the reasonableness of numerical computations and their results N.3.E.AI solve problems involving proportions Algebraic Relationships A.1.B.AI generalize patterns using explicitly or recursively defined functions A.1.C.AI compare and contrast various forms of representations of patterns A.1.D.AI understand and compare the properties of linear and nonlinear functions A.1.E.AI describe the effects of parameter changes on linear, exponential growth/decay and quadratic functions including intercepts A.2.A.AI use symbolic Algebra to represent and solve problems that involve linear and quadratic relationships including equations and inequalities A.2.B.AI describe and use Algebraic manipulations, including factoring and rules of PAGE 146 146 integer exponents and apply properties of exponents (including order of operations) to simplify expressions A.2.C.AI use and solve equivalent forms of equations (linear, absolute value, and quadratic) A.2.D.AI use and solve systems of linear equations or inequaliti es with 2 variables A.3.A.AI identify quantitative relationships and determine the type(s) of functions that might model the situation to solve the problem A.4.A.AI analyze linear and quadratic functions by investigating rates of change, intercepts and z eros Data and Probability D.1.A.AI formulate questions and collect data about a characteristic which include sample spaces and distributions D.1.C.AI select and use appropriate graphical representation of data and given onevariable quantitative data, di splay the distribution and describe its shape D.2.A.AI apply statistical measures of center to solve problems D.2.C.AI given a scatter plot, determine an equation for a line of best fit D.3.A.AI make conjectures about possible relationships between 2 characteristics of a sample on the basis of scatter plots of the data PAGE 147 147 APPENDIX D NATIONAL COUNCIL OF TEACHERS OF MATHEMATICS MATHEMATICS STANDARDS FOR GRADES 6 8 Table A 1. Number and Operations Standard for Grades 6 8 Expectations Instructional programs from prekindergarten through grade 12 should enable all students to In grades 6 8 all students should Understand numbers, ways of representing numbers, relationships among numbers, and number systems work flexibly with fractions, decimals, and percent s to solve problems; compare and order fractions, decimals, and percents efficiently and find their approximate locations on a number line; develop meaning for percents greater than 100 and less than 1; understand and use ratios and proportions to represent quantitative relationships; develop an understanding of large numbers and recognize and appropriately use exponential, scientific, and calculator notation; use factors, multiples, prime factorization, and relatively prime numbers to s olve problems; develop meaning for integers and represent and compare quantities with them. Understand meanings of operations and how they relate to one another understand the meaning and effects of arithmetic operations with fractions, decimals, and integers; use the associative and commutative properties of addition and multiplication and the distributive property of multiplication over addition to simplify computations with integers, fractions, and decimals; understand and use the inver se relationships of addition and subtraction, multiplication and division, and squaring and finding square roots to simplify computations and solve problems. PAGE 148 148 Table A 1. Continued Instructional programs from prekindergarten through grade 12 should enab le all students to In grades 6 8 all students should Compute fluently and make reasonable estimates select appropriate methods and tools for computing with fractions and decimals from among mental computation, estimation, calculators or computers, and paper and pencil, depending on the situation, and apply the selected methods; develop and analyze algorithms for computing with fractions, decimals, and integers and develop fluency in their use; develop and use strategies to estimate the results of rational number computations and judge the reasonableness of the results; develop, analyze, and explain methods for solving problems involving proportions, such as scaling and finding equivalent ratios. PAGE 149 149 Table A 2. Geometry Standard for Grades 6 8 Expectations Instructional programs from prekindergarten through grade 12 should enable all students to In grades 6 8 all students should Analyze characteristics and properties of two and threedimensional geometric shapes and develop mathematical arguments about geometric relationships precisely describe, classify, and understand relationships among types of two and threedimensional objects using their defining properties; understand relationships among the angles, side lengths, perimeters, areas, and volumes of similar objects; create and critique inductive and deductive arguments concerning geometric ideas and relationships, such as congruence, similarity, and the Pythagorean relationship. Specify locations and describe spatial rel ationships using coordinate geometry and other representational systems use coordinate geometry to represent and examine the properties of geometric shapes; use coordinate geometry to examine special geometric shapes, such as regular polygons or thos e with pairs of parallel or perpendicular sides. Apply transformations and use symmetry to analyze mathematical situations describe sizes, positions, and orientations of shapes under informal transformations such as flips, turns, slides, and scaling; examine the congruence, similarity, and line or rotational symmetry of objects using transformations. Use visualization, spatial reasoning, and geometric modeling to solve problems draw geometric objects with specified properties, such as side l engths or angle measures; use two dimensional representations of threedimensional objects to visualize and solve problems such as those involving surface area and volume; use visual tools such as networks to represent and solve problems; use g eometric models to represent and explain numerical and Algebraic relationships; recognize and apply geometric ideas and relationships in areas outside the mathematics classroom, such as art, science, and everyday life. PAGE 150 150 Table A 3. Measurement Standard for Grades 6 8 Expectations Instructional programs from prekindergarten through grade 12 should enable all students to In grades 6 8 all students should Understand measurable attributes of objects and the units, systems, and processes of measurement understand both metric and customary systems of measurement; understand relationships among units and convert from one unit to another within the same system; understand, select, and use units of appropriate size and type to measure angles, perim eter, area, surface area, and volume. Apply appropriate techniques, tools, and formulas to determine measurements use common benchmarks to select appropriate methods for estimating measurements; select and apply techniques and tools to accurately find length, area, volume, and angle measures to appropriate levels of precision; develop and use formulas to determine the circumference of circles and the area of triangles, parallelograms, trapezoids, and circles and develop strategies to find the area of morecomplex shapes; develop strategies to determine the surface area and volume of selected prisms, pyramids, and cylinders; solve problems involving scale factors, using ratio and proportion; solve simple problems involving rates and derived measurements for such attributes as velocity and density. PAGE 151 151 Table A 4. Data Analysis and Probability Standard for Grades 6 8 Expectations Instructional programs from prekindergarten through grade 12 should enable all students to In grades 6 8 all students should Formulate questions that can be addressed with data and collect, organize, and display relevant data to answer them formulate questions, design studies, and collect data about a characteristic shared by two populations or different characteristics within one population; select, create, and use appropriate graphical representations of data, including histograms, box plots, and scatterplots. Select and use appropriate statistical methods to analyze data find, use, and interpr et measures of center and spread, including mean and interquartile range; discuss and understand the correspondence between data sets and their graphical representations, especially histograms, stem and leaf plots, box plots, and scatterplots. Deve lop and evaluate inferences and predictions that are based on data use observations about differences between two or more samples to make conjectures about the populations from which the samples were taken; make conjectures about possible relationshi ps between two characteristics of a sample on the basis of scatterplots of the data and approximate lines of fit; use conjectures to formulate new questions and plan new studies to answer them. Understand and apply basic concepts of probability u nderstand and use appropriate terminology to describe complementary and mutually exclusive events; use proportionality and a basic understanding of probability to make and test conjectures about the results of experiments and simulations; compute p robabilities for simple compound events, using such methods as organized lists, tree diagrams, and area models. PAGE 152 152 Table A 5. Problem Solving Standard for Grades 6 8 Instructional programs from prekindergarten through grade 12 should enable all students to build new mathematical knowledge through problem solving; solve problems that arise in mathematics and in other contexts; apply and adapt a variety of appropriate strategies to solve problems; monitor and reflect on the process of mathematical pr oblem solving. Table A 6. Reasoning and Proof Standard for Grades 6 8 Instructional programs from prekindergarten through grade 12 should enable all students to recognize reasoning and proof as fundamental aspects of mathematics; make and investigate mathematical conjectures; develop and evaluate mathematical arguments and proofs; select and use various types of reasoning and methods of proof. Table A 7. Communication Standard for Grades 6 8 Instructional programs from prekindergarten through grade 12 should enable all students to organize and consolidate their mathematical thinking through communication; communicate their mathematical thinking coherently and clearly to peers, teachers, and others; analyze and evaluate the mathematical thinking and strategies of others; use the language of mathematics to express mathematical ideas precisely. Table A 8. Connections Standard for Grades 6 8 Instructional programs from prekindergarten through grade 12 should enable all students to recognize and use connections among mathematical ideas; understand how mathematical ideas interconnect and build on one another to produce a coherent whole; recognize and apply mathematics in contexts outside of mathematics. Table A 9. Representation Standard for G rades 6 8 Instructional programs from prekindergarten through grade 12 should enable all students to create and use representations to organize, record, and communicate mathematical ideas; select, apply, and translate among mathematical representations to solve problems; use representations to model and interpret physical, social, and mathematical phenomena. PAGE 153 153 APPENDIX E MAP GRADE 6 RELEASED ITEMS SPRING 06 PAGE 154 154 PAGE 155 155 PAGE 156 156 PAGE 157 157 PAGE 158 158 PAGE 159 159 PAGE 160 160 PAGE 161 161 PAGE 162 162 PAGE 163 163 PAGE 164 164 PAGE 165 165 PAGE 166 166 PAGE 167 167 APPENDIX F STATE S TANDARDS FOR M ATHEMATICS AT GRADE LEVEL 6 Number and Oper ations N.1.A.06 compare and order integers, positive rationals and percents, including finding their approximate location on a number line N.1.B.06 recognize and generate equivalent forms of fractions, decimals and percents N.1.C.06 recognize equivalent representations for the same number and generate them by decomposing and composing numbers, including expanded notation N.1.D.06 use factors and multiples to describe relationships between and among numbers, including whole number common factors and co mmon multiples N.2.B.06 describe the effects of addition and subtraction on fractions and decimals N.3.C.06 add and subtract positive rational numbers N.3.D.06 estimate and justify the results of addition and subtraction of positive rational numbers N .3.E.06 solve problems using equivalent ratios Algebraic Relationships A.1.B.06 represent and describe patterns with tables, graphs, pictures, symbolic rules or words A.1.C.06 compare various forms of representations to identify a pattern A.1.D.06 ide ntify functions as linear or nonlinear from a table or graph A.2.A.06 use variables to represent unknown quantities in expressions A.2.B.06 recognize equivalent forms for simple Algebraic expressions including associative and distributive properties PAGE 168 168 A. 3.A.06 model and solve problems, using multiple representations such as graphs, tables, expressions and equations A.4.A.06 compare situations with constant or varying rates of change Geometric and Spatial Relationships G.1.A.06 identify the properties o f one, two and threedimensional shapes using the appropriate geometric vocabulary G.1.B.06 describe relationships between the corresponding angles and the length of corresponding sides of similar triangles (whole number scale factors) G.2.A.06 use c oordinate geometry to construct geometric shapes G.3.A.06 describe the transformation from a given preimage to its image using the terms reflection/ flip, rotation/ turn and translation/ slide G.3.C.06 create polygons and designs with rotational symmetr y G.4.A.06 use spatial visualization to identify isometric representations of mat plans G.4.B.06 draw or use visual models to represent and solve problems Measurement M.1.A.06 identify and justify an angle as acute, obtuse, straight or right M.1.C.06 solve problems involving elapsed time (hours and minutes) M.2.A.06 estimate a measurement using either standard or nonstandard unit of measurement M.2.B.06 select and use benchmarks to estimate measurements of 0 45 90 180 360degree angles PAGE 169 169 M. 2.C.06 describe how to solve problems involving the area or perimeter of polygons M.2.E.06 convert from one unit to another within a system of measurement (mass and weight) Data and Probability D.1.A.06 formulate questions, design studies and collect data about a characteristic D.1.C.06 interpret circle graphs; create and interpret stem andleaf plots D.2.A.06 find the range and measures of center, including median, mode and mean D.2.B.06 compare different representations of the same data and evaluate how well each representation shows important aspects of the data D.3.A.06 use observations about differences between 2 samples to make conjectures about the populations from which the samples were taken D.4.A.06 use a model (diagrams, list, sample space, or area model) to illustrate the possible outcomes of an event PAGE 170 170 LIST OF REFERENCES Adelman, C. (1995). The New College Course Map and Transcript Files: Changes in CourseTaking and Achievement, 1972 1993 Washington, DC: U.S. Department of Education, N ational Center for Education Statistics. Aivazidis, C., Lazaridou, M., & Hellden, G. F. (2006). A Comparison Between a Traditional and Online Environmental Educational Program. The Journal of Environmental Education, 4554. Allen, E., & Seaman, J. (2007) Online nation: Five years of growth in online learning. Needham, MA: Sloan Consortium. Allen, E., & Seaman, J. (2006). Making the grade: Online learning in the United States, 2006. Needham, MA: The Sloan Consortium. Allen, M., Bourihis, J., Burrell, N. & Mabry, E. (2002). Comparing student satisfaction with distance education to traditional classrooms in higher education: A metaanalysis. The American Journal of Distance Education, 16(2), 8397. Allen, M., Burrell, N., Timmerman, E., Bourhis, J., & Ma bry, E. (2007). Literature of Satisfaction. In Handbook of Distance Education, ed. M. G. Moore, 149156. Mahwah, NJ: Lawrence Erlbaum Associates. Allen, M., Mabry, E., Mattrey, M., Bourhis, J., Titsworth, S., & Burrell, N. (2004). Evaluating the effectiv eness of distance learning: A comparison using metaanalysis. Journal of Communication, 54, 402420. Anderson, T., & Kuskis, A. (2007). Interaction. In Handbook of Distance Education. Edited by Moore, M. G. TFLEA. Anglin, G., & Morrison, G. (2000). An a nalysis of distance education research: Implications for the instructional technologist. Quarterly Review of Distance Education, 1 (3), 180194. Aragon, S. R., Johnson, S. D., & Shaik, N. (2002). The influence of learning style preferences on student succes s in online vs. faceto face environments. American Journal of Distance Education, 16(4): 27 243. Asuncion, J., Fichten, C., Wolforth, J., Hewlett, M., Klomp, R., & Barile, M. (2006). Accessibility of eLearning in postsecondary education: Student and faculty perspectives. Retrieved from California State University at Northridge, Center on Disabilities, Technology and Persons with Disabilities Conference Web site: http://www.csun.edu/cod/ conf/2006/proceedings/3061.htm PAGE 171 171 Bali, V. A., & Alvarez, R. M. (2004). The race gap in student achievement scores: Longitudinal evidence from a racially diverse school district. Policy Studies Journal, 32, 393 415. Barbour, M. K., & Reeves, T. C. (2009). The reality of virtual schools: A review of the literature. Computers & Education, 52, 402 416. Barker, K., & Wendel. T. (2001). E Learning: Studying Canadas virtual secondary schools. In Society for the Advancement of Excellence in Education, Research S eries #8. Kelowna, British Columbia: Society for the Advancement of Excellence in Education. Barth, P. (Ed.) (2001). Youth at the crossroads: Facing high school and beyond. Thinking K 16, 5(1): 1 24. Bartlett, T. (1997). The hottest campus on the Internet. Business Week, 3549, 7780. Bartley, S. J. & Golek, H. J. (2004). Evaluating the Cost Effectiveness of Online and Face to Face Instruction. Educational Technology and Society 7(4). 167175. Bassarear, T. (1991). An examination of the relationship bet ween attitudes and beliefs on achievement in a college developmental mathematics course. Research and Teaching in Developmental Education, 7(2), 4356. Bata Jones, B., & Avery. M. D. (2004) Teaching pharmacology to graduate nursing students: Evaluation a nd comparison of webbased and faceto face methods. Journal of Nursing Education. 43 : 185 9. Battin Pearson, S., Newcomb. M., & Abbott. R. (2000). Predictors of early high school dropout: A test of five theories. Journal of Educational Psychology 92(3): 568582. Beaton, A. E., & O'Dwyer, L., M. (2002). Separating school, classroom and student variances and their relationship to socioeconomic status. In D. F. Robitaille & A. E. Beaton (Eds.), Secondary analysis of the TIMSS data (pp.2 11231). Boston, MA: Kluwer Academic Publishers. Bednarz, N., Kieran, C., & Lee, L. (1996). Approaches to Algebra: Perspectives for Research and Teaching. Dordrecht, Holland: Kluwer Academic. Belcheir, J. M. (2002). What Predicts Success in Intermediate Algebra ? A research report for Institutional Assessment at Boise State University. Available at: http://www.boisestate.edu/iassess/reports/2002/RR200206.pdf Berenson, S., Carter, G., & Norwood, K. (1992). The at risk student in college developmental Algebra School Science and Mathematics, 92(2), 5558. PAGE 172 172 Bernard, R.M., Abrami, P.C., Lou, Y., & Borokhovski, E. (2004a). A methodological morass? How we can improve the quality of quantitative research in distance education. Distance Education, 25 (2), 176198. Bernard, R. M., Lou, Y., Abrami, P. C., Wozney, L., Borokhovski, E., Wallet, P., Wade, A., & Fiset, M. (2004b). How does distance education compare with classroom instruction? A metaanalysis of the empirical literature. Review of Educational Research 74(3): 379439. Black, E. (2009). An evaluation of familial involvements' influence on student achievement in K --12 virtual schooling. Ph.D. dissertation, University of Florida, United States -Flo rida. Retrieved May 25, 2010, from Dissertations & Theses @ University of Florida FCLA. (Publication No. AAT 3367406). Blaylock, T. H., & Newman, W. J. (2005). The impact of computer based secondary education. Education 125 (3): 373384. Blomeyer, R. ( 2002). Online learning for K 12 students: What do we know now? North Central Regional Educational Laboratory. Available at: http://www.ncrel.org/tech/elearn/synthesis.pdf Boaz, M. (1999). Eff ective methods of communication and student collaboration. In Teaching at a distance: A handbook for instructors 4148. Mission Viejo, CA: Archipelago Productions and League for Innovation in the Community College. Bogden, J. (2003). Cyber charter school s: A new breed in the education corral. Available at: http://www.ed.gov/about/offices/list/os/technology/plan/2004/site/bb/files/post092 _1.pdf Bothun, G. D. (1998). Distance education: Effective learning or content free credits? Cause/Effect 21(2), 2831, 3637. Bransford, D. J., Brown, L. A., & Cocking, R. R. (1999). How people learn: Brain, Mind, Experience, and School. National Academy Press. Was hing, D. C. Braun, H., Jenkins, F., & Grigg, W. (2006). Comparing private schools and public schools using hierarchical linear modeling (No. 2006461). Washington, DC: National Center for Education Statistics. PAGE 173 173 Brophy, J. E. (1990). Effective schooling f or disadvantaged students. In M. S. Knapp & P. M. Shields (Eds.), Better schooling for the children of poverty: Alternatives to conventional wisdom (Vol. 2: Commissioned papers and literature review. Chapter LX. Study of Academic Instruction for Disadvantaged Students, U.S. Department of Education contract no. LC88054001). Washington, DC: Government Printing Office. Brown, B. W., & Liedholm, C. E. (2002). Can Web courses replace the classroom in principles of microeconomics? American Economic Review, 92(2), 112. Brown, S. W. & Kulikowich, J. M. (2004). Teaching statistics from a distance: What have we learned? International Journal of Instructional Media, 31: 117. Butz, C. (2004). Parent and student satisfaction with online education at the elementary a nd secondary levels. Unpublished dissertation. Las Vegas, NV: University of Nevada at Las Vegas. Campbell, M., Floyd, J., & Sheridan, J. B. (2002). Assessment of student performance and attitudes for courses taught online versus onsite. The Journal of Applied Business Research, 18(2), 4551. Carr, S. (2000a). As distance education comes of age, the challenge is keeping the students. Chronicle of Higher Education [Online serial]. Available: http://chronicle.com. Carr, S. (2000b). Psych students learn more through distance ed, but are less satisfied. The Chronicle of Higher Education. Carswell, L., Thomas, P., Petre, M., Price, B., & Richards, M. (2000) Distance education via the Internet: the student experience. British Journal of Education Technology 2946 Catsambis, S. (1995). The path to math: Gender and racial ethnic differences in mathematics participation from middle school to high school. Sociology of Education, 67, 199215. Cavanaugh, C. (2009). Getting students more learning time online: Dis tance education in support of expanded learning time in k 12 schools. Washington, DC: Center for American Progress. Cavanaugh, C. (2007). Students Achievement in Elementary and High school. In Handbook of Distance Education, ed. M. G. Moore, 157168. Mahw ah, NJ: Lawrence Erlbaum Associates. PAGE 174 174 Cavanaugh, C. (2004). Distance Learning Success Factors in the RPR Cycle and Virtual School Accreditation Standards. In C., Cavanaugh (Eds.), Development and Management of Virtual Schools: Issues and Trends (pp. 6983) Information Science Publishing, Idea Group Inc. Cavanaugh, C. (2001). The effectiveness of interactive distance education technologies in K 12 learning: A metaanalysis. International Journal of Educational Telecommunications 7 (1): 7388. Cavanaugh, C. Gillan, K. J., Bosnick, J., Hess, M., & Scott, H. (2005). Succeeding at the gateway: Secondary algebra learning in the virtual school. Jacksonville, FL: University of North Florida. Cavanaugh, C., Gillan. K. J., Kromrey, J., Hess, M., & Blomeyer, R. (20 04). The Effects of Distance Education on K 12 Student Outcomes: A MetaAnalysis. Naperville, IL: Learning Point Associates. Available online at http://www.ncrel.org/tech/distance/k12distanc e.pdf Caywood, K. & Duckett, J. (2003) Online vs. oncampus learning in teacher education. Teacher Education and Special Education 26 : 98105. Chazan, D. & Yerushalmy, M. (2003). On appreciating the cognitive complexity of school algebra: Research on algebra learning and directions of curricular change. In Kilpatrick, J., Schifter, D. & G. Martin, A Research Companion to the Principles and Standards for School Mathematics (pp. 123135). Reston: NCTM. Christophel, D. (1990). The relationship among teacher immediacy behaviors, student motivation, and learning. Communication Education, 39 (4), 323240. Chubb, J., Moe, T. (1990). Politics, Markets, and Americas Schools Washington, D.C.: Brookings Institution Clark, T. (2003). Virtual and distance education in American schools Handbook of Distance Education ed. M. G. Moore and W. G. Anderson, 673699. Mahwah, NJ: Lawrence Erlbaum Associates. Clark, T. (2001). Virtual schools: Trends and issues A study of virtual schools in the United States. San Fra ncisco, CA: Western Regional Educational Laboratories. Available at: http://www.wested.org/online_pubs/virtualschools.pdf Clark, T. (2000). Virtual high schools: State of the states A study of virtual high school planning and preparation in the United States. Center for the Application of Information Technologies, Western Illinois University. Available at: http://www.imsa.edu/programs/ivhs/pdfs/stateofstates.pdf PAGE 175 175 Clark, T., & Berge, Z. (2005). Perspectives on Virtual Schools. In Z. L. Berge & T. Clark (Eds.), Virtual schools: Planning for success (pp. 919). New York, NY: Teachers College Press. Cobb, P., Yackel, E., & Wood, T. (1992). Interaction and learning in mathematics classroom situations. Educational Studies in Mathematics, 23, 99122. Cohen, J. (1987). Parents as educational models and definers. Journal of Marriage and the Family, 49, 339349. Coleman, S. J. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94 S95S120. Coleman, S. J., Hoffer, T. (1987). Public and Private High Schools: The Impact of Communities. New York: Basic Books. Coleman, S. J., Hoffer, T, & Kilgore, S. (1982). High School Achievement: Public, Catholic, and Private Schools Compared. New York: Basic Books. Coma Del Corral, M. J., Guevara, J. G., Luquin, P. A., Pena, H. J., & Otero, J. J. M. (2006). Usefulness o fan Internet based thematic l earning network: Comparison of effectiveness with traditional teaching. Medical Informatics and the Internet in Medicine, 31(1), 5966. Coombs, N., & Banks, R. (2000). Distance learning and students with disabilities: Easy tips for teachers. Retrieved from California State University at Northridge, Center on Disabilities. Technology and Persons with Disabilities Conference Web site http://www.csun.edu/cod/conf/2000/proceedings/0119Coombs.htm Cooze, M., & Barbour, M. (2005). Learning Styles: A Focus on E Learning Practices and Pedagogy and their Implications for Designing E Learning for Secondary School Students in Newfoundland and Labrador. Malaysian Online Journal of Instructiona l Technology. 2(1). ISSN: 18231144. Cornford, J., & Pollock, N. (2003). Putting the university online: Information, technology and organizational change, Philadelphia: Open University Press. Cox, R. D. (2005). Online education as institutional myth: R ituals and realities at community colleges. Teachers College Record. 107(8),17541787. Crotty, M. (1998). The foundations of social research: meaning and perspective in the research process. London: Sage Publications. DAmico, E. (2008). Searching for th e Right Formula. Chemical Week. 170(28). P: 3136. PAGE 176 176 Davis, D. J. (1984). Evaluation and comparison of teleconference training with faceto face training and the effects on attitude and learning. Dissertation Abstracts International, 46, (No. AAC850579). Dede, C. (1990). The evolution of distance learning: Technology mediated interactive learning. Journal of Research on Computing in Education, 22, 247265. Dees, C. S. (1994). An investigation of distance education versus traditional course delivery using comparisons of student achievement scores. Northern Illinois University. Unpublished dissertation. Demircioglu, H., & Norman, N. (1999). Effects of some variables on chemistry achievements and chemistry related attitudes of high school students. Ha cettepe 16 17: 40 44 [1999] Detwiler, E. J. (2008). Comparing Student Performance in Online and Blended Sections of a GIS Programming Class. Transactions in GIS, 12(1): 131144. Dickson, W. P. (2005). Toward a deeper understanding of student performance in virtual high school courses: Using quantitative analyses and data visualization to inform decision making. Naperville, IL: Learning Point Associates. Dietz, T. L. (2002). Predictors of success in large enrollment introductory courses: An examination of the impact of learning communities and virtual learning resources on student success in an introductory level sociology course. Teaching Sociology 30, 80 88. Doing the Math and Making an Impact. Retrieved from: http://www.ima.umn.edu/newsltrs/updates/summer03/ Dominguez, P. S., & Ridley, D. R. (2001). Assessing distance education courses and discipline differences in their effectiveness. Journal of I nstructional Psychology, 28(1), 1519. Donlevy, J. (2003). Teachers, technology, and training: Online learning in virtual high school. International Journal of Instructional Media, 30(2): 117121. Drijvers P.H.M. (2003). Learning algebra in a computer al gebra environment. Design research on the understanding of the concept of parameter Dissertation. Utrecht: CDb press. Duranczyk, I. M. (1997). Unanticipated outcomes of developmental math educationA qualitative study. Selected Conference Papers, Nation al Association for Developmental Education, 3, 1921. PAGE 177 177 Dutton, J., Dutton, M., & Perry, J. (2002). How do online students differ from lecture students? Journal of Asynchronous Learning Networks, 6(1), 120. Edmonds, C. D. (2004). Providing access to students with disabilities in online distance education: Legal and technical concerns for higher education. The American Journal of Distance Education 18 (1): 5162. Ellington, A. J. (2003). A in etaanalysis of the effects of calculators on students' achievement and attitude levels in precollege mathematics classes. Journal for Research in Mathematics Education, 34, 433463. Ercikam, K., McCreith, T., & Lapointe, V. (2005). Factors associated with mathematics achievement and participation in advanced mathema tics courses: An examination of gender differences from an international perspective. School Science and Mathematics, 105, 5 14. Ernst, V. J. (2008). A Comparison of Traditional and Hybrid Online Instructional Presentation in Communication Technology. Jou rnal of Technology Education. 19(2): 4049. Evans, J. R., & Haase, I. M. (2001). Online business in the twenty first century: An analysis of potential target markets. Internet Research: Electronic Networking Applications Policy, 11 (3), 246260. Everston, C., Anderson, C., Anderson, L., & Brophy, J. (1980). Relationships between classroom behaviors and student outcomes in junior high mathematics and English classes. American Educational Research Journal, 17, 4360. Ferdig, R. E., Papanastasiou, E., & DiP ietro, M. (2005). Teaching and learning in collaborative virtual high schools. Report submitted to the North Central Regional Educational Laboratory as part of the K12 Online Learning Initiative. Flottemesch, K. (2000). Building effective interaction in distance education: A review of the literature. Educational Technology, 40(3), 4651. Forman, A. E. (1996). Learning Mathematics as Participation in Classroom Practice: Implications of Sociocultural Theory for Educational Reform. In L. P. Steffe, P. Nesher, P, Cobb, G. A. Goldin, & B. Greer (Eds) Theories of Mathematical Learning. Lawrence Erlbaum Associates, Inc. NJ, 115130. Fortune, M. F., Shifflett, B, & Sibley, R. E. (2006). A Comparison of Online (High Tech) and Traditional (High Touch) Learning in Business Communication Courses in Silicon Valley. Journal for Education for Business, 81(4), pp. 210214. PAGE 178 178 Freedman, G., Darrow, R., Watson, J., & Lorenzo, G. (2002). California virtual school report: A national survey of virtual educati on practice and policy with recommendations for the State of California. Clarence Center, NY: Lorenzo Associates Inc. Available at: http://web.archive.org/ web/20061207213241/http://www.edpath.com/images/VH SReport.pdf Gallagher, P., & McCormick, K. (1999). Student satisfaction with two way interactive distance education for delivery of early childhood special education coursework. Journal of Special Educati on Technology 14(1) 32 47. Gallagher, W. (2009). Rapt: Attention and the focused life. New York: Penguin. Garrison, D. R. (2003). Cognitive presence for effective asynchronous online learning: the role of reflective inquiry, self direction and metacognition. In J. Bourne & J. C. Moore (eds.), Elements of Quality Online Education: Practice and Direction, 4758. Needham, MA: SloanC. Glass, V. G. (2009). The realities of K 12 virtual education. Boulder and Tempe: Education and the Public Interest Center & Education Policy Research Unit. Available at: http://epicpolicy.org/publication/realities K 12virtual education Glenn, A. S. (2001). A comparison of distance learning a nd traditional learning environments. (ERIC Document Reproduction Service No. ED 457 778. Glenn, L. M., Jones, C. G., & Hoyt, J. E. (2003). The effect of interaction levels on student performance: A comparative analysis of webmediated versus traditional delivery. Journal of Interactive Learning Research, 14, 285299. Goodwin, B. N. Miklich, B. A., and Overall, J. U. (1993). Perceptions and attitudes of faculty and students in two distance learning modes of delivery: online computer and telecourse. ( ERIC Document Reproduction Services No. ED371 708). Goos, M., Galbraith, P., Renshaw, P., & Geiger, V. (2003). Perspectives on technology mediated learning in secondary school mathematics classrooms. Journal of Mathematical Behavior, 22, 7389. Greenway, R. & Vanourek, G. (2006). The Virtual Revolution: Understanding Online Schools. Education Next. P 3541. Greer, B. (1996). Theories of Mathematics Education: The Role of Cognitive Analyses. In L. P. Steffe, P. Nesher, P, Cobb, G. A. Goldin, & B. Greer (E ds) Theories of Mathematical Learning. Lawrence Erlbaum Associates, Inc. NJ, 179196. Grouws, D. (Ed.). (1992). Handbook of research on mathematics teaching and learning. New York: Macmillan PAGE 179 179 Gubernick, L., & Ebeling, A. (1997). I got my degree through e mail. Forbes 159(12), 8492. Gunawardena, C. N. & McIsaac, M. S. (2004). Distance Education. In D. H. Jonassen (Ed.), Handbook of research for educational communications and technology (2nd edition). (pp. 355396). Mahwah, NJ: LEA. Hall, C.W., Davis N.B., Bolen, L.M. and Chia, R. (2000) Gender and racial differences in mathematics performance. Journal of Social Psychology, 139(6), 677689. Hassell, B. C., & Terrell, M. G. (2004). How can virtual schools be a vibrant part of meeting the choice provi sions of the No Child Left Behind Act? Virtual School Report Available at: http://www.doe.virginia.gov/VDOE/Instruction/title1/Hassel Terrell VirtualSchools.pdf Heines, R. A., & Hulse, D. B. (1996). Twoway interactive television: An emerging technology for university level business school instruction. Journal of Education for Business 71(2), 7476. Helphinstine, L. (1995). Technology and distance education. Vocational Education Journal, 70(2), 4748. Hernandez, F. J. (2005). Equity and access: The promise of virtual schools. In Z. L. Berge & T. Clark (Eds.), Virtual schools: Planning for success (pp. 2034). New York, NY: Teachers College Press. Hie bert, J., Grouws, A. D. (2007). The effects of classroom mathematics teaching on students learning. In Second Handbook of Research on Mathematics Teaching and Learning, edited by Frank K. Lester, Jr., pp. 371404. Charlotte, N.C.: Information Age Publishi ng; Reston, Va. National Council of Teachers of Mathematics, 2007. Hill, J. R. (1997). Distance learning environments via World Wide Web. In B.H. Khan (Ed.). Web based instruction (pp. 7580). Englewood Cliffs, NJ: Educational Technology Publications. Ho llar,J. C., & Norwood, K. (1999). The effects of a graphing approach intermediate algebra curriculum on students' understanding of function. Journal for Research in Mathematics Education, 30, 220226. Ho, H.Z., Senturk, D., Lam, A., Zimmer, J. M., Hong, S., Okamoto, Y., Chiu, S. Y., Nakazawa, Y., & Wang, C. P. (2000). The affective and cognitive dimensions of math anxiety: A cross national study. Journal for Research in Mathematics Education, 31, 541 557. PAGE 180 180 Hong, K. S. (2002). Relationships between student s' and instructional variables with satisfaction and learning from a webbased course. Internet and Higher Education 5: 267281. House, J. D. (1995). Noncognitive predictors of achievement in introductory college mathematics. Journal of College Student De velopment, 36, 171181. House, J.D. (1993). Achievement related expectancies, academic self concept, and mathematics achievement of academically under prepared adolescent students. Journal of Genetic Psychology, 154, 6171. Hughes, J. E., McLeod. S., B rown. R., Maeda. Y., & Choi, J. (2007). Academic Achievement and Perceptions of the Learning Environment in Virtual and Traditional Secondary Mathematics Classrooms. American Journal of Distance Education 21(4): 199214. Hughes, J. E., McLeod, S., Brown, R., Maeda, Y., & Choi, J. (2005). Staff development and student perceptions of the learning environment in virtual and traditional secondary schools. Naperville, IL: North Central Regional Educational Laboratory, Learning Point Associates. International North American Council for Online Learning (iNACOL). (2009). TOP TEN MYTHS ABOUT VIRTUAL SCHOOLS. Available at: http://www.inacol.org/resources/docs/Ten%20Myt hs%20About%20Virtual%20Sc hools.pdf International North American Council for Online Learning (iNACOL). (2008). National Standards for Quality Online Teaching. Available at: http://www.inacol.org/research/nationalstandards/NACOL%20Standards%20Qua lity%20Online%20Teaching.pdf International North American Council for Online Learning (iNACOL). (2007 ). National Standards of Quality for Online C ourses Available at: http://www.inacol.org/research/nationalstandards/NACOL%20Standards%20Qua lity%20Online%20Courses%202007.pdf I nternational North American Council for Online Learning & the Partnership for 21st Century Skills. (2006). Virtual Schools and 21st Century Skills. Available at: http://www.21stcenturyskills.org/documents/VSand21stCenturySkillsFINALPaper. pdf Jahng, N., Krug, D., & Zhang, Z. (2007). Student achievement in online distance education compared to faceto face education. The European Journal of Open and Distance Lear ning Available at: http://www.eurodl.org/materials/contrib/2007/Jahng_Krug_Zhang.htm PAGE 181 181 Jiang, M. & Ting, E. (2000). A study of factors influencing students perceived learning in a webbased course environment. International Journal of Educational Telecommunications, 6 (4), 317338. Johnson, C. D. (1986). Practical Answers to Concerns about Teaching the Handicapped. Technology Teacher 45 (8) (May June): 1113. Johnson, S. D., Aragon, S. R., Shaik, N., & Palma Rivas, N. (2000). Comparative analysis of learner satisfaction and learning outcomes in online and faceto face learning environments. Journal of Interactive Learning Research, 11 (1), 2949. Jones, T. (1992). Students evaluation questionnaire for the fall semester of 1991. A summary and report. (ERIC Document Reproduction Services No. ED 345 716). Joy, E.H. & Garcia, F.E. (2000). Measuring Learning Effectiveness: A New Look at NoSignificant Difference Findings. Journal of Asynchronous Learning Networks, 4 (1); 3339, Available at: http://www.sloan c.org/publications/jaln/v4n1/pdf/v4n1_joygarcia.pdf Karp, G. G., Woods, L. M. (2003). Wellness NutriFit Online Learning in Physical Education for High School Students. The Journal of Interactive Online Learning, 2 (2). ISSN: 15414914. Karr, C. L., Weck, B., Sunal, D. W., & Cook, T. M. (2003). Analysis of the effectiveness of online learning in a graduate engineering math course. Journal of Interactive Online Learning, 1(3). Keegan, D. (1996). Foundations of distance education (3rd ed.). London: Routledge. Keeler, C. G., & Horney, M. (2007). Online course designs: Are special needs being met? American Journal of Distance Education, 21(2), 6175. Keeler, C. G., Richter, J., Anderson Inman, L., Horney, A. M., & Ditson, M. (2007). Exceptional Learners: Differentiated Instructional Online. In C. Cavanaugh & R. Blomeyer (Eds.), Wh at Works in K 12 Online Learning (pp. 125141). International Society for Technology in Education (ISTE). Kelley, D. & Gorham, J. (1988) Effects of immediacy on recall of information. Communication Education, 37 (2), 198207. Kellogg, L., & Politoski, K. (2002). Virtual schools across America: Trends in K 12 online education. Los Angeles, CA: Peak Group Research Corporation. Kerka, S. (1996). Distance learning, the internet, and the worldwide web. Columbus, OH: ERIC Clearinghouse. PAGE 182 182 Kersting, S. A. (1997) Balancing between deaf and hearing worlds: Reflections of mainstreamed college students on relationships and social interaction. Journal of Deaf Studies and Deaf Education, 2, 252263. Key, P. J. (1997). Experimental Research and Design, retrieved from http://www.okstate.edu/ag/agedcm4h/academic/aged5980a/5980/newpage2.htm Kieran, C. (2007). Learning and Teaching Algebra at the Middle School through College Levels: B uilding Meaning for Symbols and Their Manipulation. In Second Handbook of Research on Mathematics Teaching and Learning, edited by Frank K. Lester, Jr., pp. 707 762. Charlotte, N.C.: Information Age Publishing; Reston, Va. National Council of Teachers of M athematics, 2007. Kifer, E. W. (2002). Students' attitudes and perceptions. In D. F. Robitaille & A. E. Beaton (Eds.), Secondary analysis of the TIMSS data. Boston: Kluwer Academic Publishers. Kinash, S., & Crichton, S. (2007). Supporting the Disabled St udent. In Handbook of Distance Education. Edited by Moore, M. G. TF LEA. Klein, S. P., Hamilton, L. S., McCaffrey, D. F., & Stecher, B. M. (2000). What do test scores in Texas tell us? Retrieved Mar 23, 2009, from the Rand online database (#IP 202) on the World Wide Web: http://www.rand.org/publications/IP/IP202 Kleinman, J., & Entin, E. B. (2002). Comparison of inclass and distance learning students' performance and attitudes in an introductory computer science course. The Journal of Computing in Small Colleges, 17(6). Kozma, R., Zucker, A., Espinoza, C., McGhee, R., Yarnall, L., Zalles, D., & Lewis, A. (2000). The online course experience: Evaluation of the virtual high school's third year of implementation, 19992000. Available at: http://ctl.sri.com/publications/downloads/VHS_Online_Experience.pdf LaRose, R., Gregg, J., & Eastin, M. (1998). Audiographic telecourses for the Web: An experiment. Journal of Computer Mediated Communication, 4(2). Laurillard, D. (1997). Learning formal representations through multimedia. In N. Entwistle, D. L. K. Hounsell & F. Marton, (Eds.) The experience of learning, (2nd ed.) Edinburgh, Scotland: Scottish Academic Press. Lawson, D. A. (1995). The effectiveness of a computer assisted learning program in engineering mathematics. International Journal of Mathematical Education in Science and Technology 26 (4), 567574. PAGE 183 183 Ledman, E. R. (2008). COMPARING STUDENT LEARNING IN ONLINE AND CLASSROOM FORMATS OF THE SAME COURSE. Developments in Business Simulation and Experiential Learning, 35: 351 352. Available at: http://sbaweb.wayne.edu/~absel/bkl/vol35/35bq.pdf Liao, Y. C. (1998). Effects of hypermedia versus traditional instruction on students achievement: A metaanalysis. Journal of Research on Computing in Education, 30(4), 341359. Liaw, S., & Huang, H. (2000). E nhancing interactivity in webbased instruction: A review of the literature. Educational Technology, 40(3), 4145. Lim, J., Kim, M., Chen, S. S., & Ryder, E. C. (2008). An Empirical Investigation of Student Achievement and Satisfaction in Different Learni ng Environments. Journal of Instructional Psychology, 35(2), 113119. Lin, C.S. (2001). Implementation of the Virtual School: Best Cyber Academy, in Advances in Multimedia Information Processing PCM 2001, (Eds.) Shum, Heung Fu, Liao, Mark, & Chang, ShihFu, IEEE Lecture Notes in Computer Science, p.p. 316323. Springer Verlag, Berlin, Germany. Lipman A. J., Sade R. M., Glotzbach A. L., Lancaster C. J., Marshall M. F. (2001). The incremental value of Internet based instruction as an adjunct to classroom instruction: a prospective randomized study. Academy of Medicine. 76:1060 1064. Liu, F., & Cavanaugh, C. (2010). High Enrollment Course Success Factors in Virtual School: Factors influencing student academic achievement. International Journal on E Learning Lockee, B. B., Burton, J. K., & Cross, L. H. (1999). No Comparison: Distance Education Finds a New Use for "No Significant Difference". Educational Technology Research and Development, 7(3), 3342. Lockhead, M., Thorpe, M., Brooks Gunn, J., Casserl y, P., & McAloon, A. (1985). Sex and ethnic differences in middle school mathematics, science, and computer science: What do we know? Princeton, NJ: Educational Testing Service. Lubienski, S. T., & Lubienski, C. (2005). A new look at public and private sc hools: Student background and mathematics achievement. Phi Delta Kappan, 86, 696699. Maki R. H., Maki W. S., Patterson M, Whittaker P. D. (2000). Evaluation of a Webbased introductory psychology course: I. Learning and satisfaction in online versus lec ture courses. Behavior Research Methods, Instruments, and Computers. 32:230 239. PAGE 184 184 Marjoribanks, K. (1988): Perceptions of family environments, educational and occupational outcomes: Social status differences. Perceptual and Motor Skills, 66, 39. Marsh, H.W., & Yeung, A.S. (1997). Causal effects of academic self concept on academic achievement: Structural equation models of longitudinal data. Journal of Educational Psychology, 89, 4154. Mayes, R. L. (1995). The application of a computer algebra system as a tool in college algebra. School Science and Mathematics 95 (2), 6167. Mayzer, R., & Dejong, C. (2003). Student satisfaction with distance education in a criminal justice graduate course. Journal of Criminal Justice Education 14(1): 3752. McClear y, I. D., & Egan, M. W. (1989). Program design and evaluation: twoway interactive television. The American Journal of Distance Education, 3(1), 5060. McCollum, H. (1990). A review of research on effective instructional strategies and classroom manageme nt approaches. In M. S. Knapp& P. M. Shields (Eds.), Better schooling for the children of poverty: Alternatives to conventional wisdom (Vol. 2: Commissioned papers and literature review Chapter 12. Study of Academic Instruction for Disadvantaged Students, U.S. Department of Education contract no. LC88054001). Washington, DC: Government Printing Office. McFarland, D. & Hamilton, D. (2006). Factors affecting student performance and satisfaction: Online versus traditional course delivery. Journal of Computer Information Systems, 46(2), 2532. McLeod, S., Hughes, J. E., Brown, R., Choi, J., & Maeda, Y. (2005). Algebra achievement in virtual and traditional schools. Naperville, IL: Learning Point Associates. McLoyd, V. (1998). Socioeconomic disadvantage and child development. American Psychologist, 53 185204. Means, B., Toyama, Y., Murphy, R., Bakia, M., Jones, K. (2009). Evaluation of EvidenceBased Practices in Online Learning: A MetaAnalysis and Review of Online Learning Studies. Washington, D.C. Availab le at: http://www.ed.gov/rschstat/eval/tech/evidence basedpractices/finalreport.pdf Missouri Department of Elementary and Secondary Education (MDESE) ( 2009a). End of Course Assessments. Available at: http://dese.mo.gov/divimprove/assess/eoc.html PAGE 185 185 Missouri Department of Elementary and Secondary Education (MDESE) ( 2009b). Endof Course Exams Frequently Asked Questions. Available at: http://www2.semo.edu/map/documents/map/FAQupdateJanuary09.pdf Missouri Department of Elementary and Secondary Education (MDESE) ( 2009c). GradeL evel Assessment. Available at: http://dese.mo.gov/divimprove/assess/grade_level.html Missouri Department of Elementary and Secondary Education (MDESE) ( 2008). Show Me Standards Mathemat ics Available at: http://dese.mo.gov/standards/math.html Moore, M. G., & Kearsley, G. (1996). Distance education: A systems view. Belmont, CA: Wadsworth Publishing Company. Moses, R. (1994). Remark s on the struggle for citizenship and math/science literacy. Journal of Mathematical Behavior, 13, 107112. Moses, R., Kamii, M., Swap, S., & Howard, J. (1989). The Algebra Project: Organizing in the spirit of Ella. Harvard Educational Review, 59, 423443 Murphy, K., Mahoney, S., & Harvell, T. (2000). Role of contracts in enhancing community building in web courses. Educational Technology & Society, 3 (3). Myers, D. E., & Milne, A. M. (1988). Effects of home language and primary language on mathematics achievement. In R. R. Cocking & J. P. Mestre (Eds.), Linguistic and cultural influences on learning mathematics (pp. 259293). Hillsdale, NJ: Lawrence Erlbaum. Naber, D., & LeBlanc, G. (1994). Providing a human biology laboratory for distant learners. T he American Journal of Distance Education, 8(2), 5870. National Center for Education Statistics (NCES). (2000). What are the Barriers to the Use of Advanced Telecommunications for Students with Disabilities in Public Schools? Available at: http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2000042 National Council of Teachers of Mathematics. (1989). Curriculum and evaluation standards for school mathematics. Reston, VA: National Counci l of Teachers of Mathematics. National Council of Teachers of Mathematics. (1991). Professional standards for teaching mathematics. Reston, VA: National Council of Teachers of Mathematics. Navarro, P., & Shoemaker, J. (2000). Economics in the cyberclass room Journal of Economic Perspectives, 14(2), 119132. PAGE 186 186 Newman, A., Stein, M., & Trask, E. (2003). What can virtual learning do for your school? Boston, MA: Eduventures. Available at: http://www.webct.com/service/ViewContent?contentID=17983410 Neuhauser, C. (2002). Learning style and effectiveness of online and faceto face instruction. American Journal of Distance Education 16(2): 99113. O'Callaghan, B. R. (1998). Computer int ensive algebra and students' conceptual knowledge of function. Journal for Research in Mathematics Education, 29, 2140. OConnor, B. (2000). E learning and students with disabilities: From outer edge to leading edge. Keynote address for NETWorking 2000 Conference, Flexible Learning, Australia. Retrieved from http://www.flexiblelearning.net.au/nw2000/main/key04.htm ODwyer, L., Carey, R. & Kleiman, G. (2007). A Study of the effecti veness of the Louisiana Algebra I online course. Journal of Research on Technology in Education, 39(3), 289306 Oers, V. B. (1996). Learning Mathematics as a Meaningful Activity. In L. P. Steffe, P. Nesher, P, Cobb, G. A. Goldin, & B. Greer (Eds) Theories of Mathematical Learning. Lawrence Erlbaum Associates, Inc. NJ, 91113. Oren, A., Mioduser, D., & Nachmias, R. (2002). The development of social climate in virtual learning discussion group. The International Review of Research in Open and Distance Le arning, 3(1), 119. Parker, A. (1999). Interaction in distance education: The critical conversation. Educational Technology Review ,12, 1317. Parsad, B., & Lewis, L. (2008). Distance Education at DegreeGranting Postsecondary Institutions: 200607. Natio nal Center for Education Statistics, U.S. Department of Education. Patrick, S. (2004). Are schools ready for todays students? A sneak preview of the national education technology plan (NETP) Presented at the National Educational Computing Conference, Seattle, WA. Paulsen, K., Higgins, K., Miller, S., Strawser, S., & Boone, R. (1998). Delivering instruction via interactive television and videotape: Student achievement and satisfaction. Journal of Special Education Technology, 13, 5977. Peak Group. (2002). Virtual schools across America. Los Altos, CA: Author. PAGE 187 187 Perraton, H. (2000). Rethinking the research agenda. International Review of Research in Open and Distance Learning, 1 (1). Available at: http://www.pucmm.edu.do/RSTA/Academico/TE/Documents/ed/rra.pdf Peters, Otto. (1999). Teaching and Learning in Distance Education: Analysis and Interpretations from an International Perspective. London: Kogan Page Limited. Phipps, R., & Merisotis, J. (2000). Quality on the line: benchmarks for success in internet based distance education. Washington, DC: Institute for Higher Education Policy [Online]. Available at: http: //www2.nea.org/he/abouthe/images/Quality.pdf Phipps, R., & Merisotis, J. (1999). The Institute for Higher Education Policy. What's the difference? A review of contemporary research on the effectiveness of distance learning in higher education. Washington DC. Available online at http://www.ihep.org/Publications/publications detail.cfm?id=88 Picciano A. G., & Seaman, J. (2009). K 12 Online Learning: A 2008 Follow up of the Sur vey of U.S. School District Administrators. Needham, MA: Sloan Consortium. Available at: http://www.sloanconsortium.org/publications/survey/pdf/k 12_online _learning_2008.pdf Picciano A. G., & Seaman, J. (2007). K 12 Online Learning: A Survey of U.S. School District Administrators Needham, MA: Sloan Consortium. Available at: http://www.sloan c.org/publications/jaln/v11n3/pdf/v11n3_3piccianoseaman.pdf Pirrong, G., & Lathen, W. (1990). The use of interactive television in business education. Educational Technology 4954. Prater, E., & Rhee, H. (2003). The impac t of coordination methods on the enhancement of business writing. Decision Sciences Journal of Innovative Education, 1(1), 5770. Ragan, R. E., & Kleoppel, J. W. (2004) Comparison of outcomes on like exams administered to inresidence and asynchronous di stance based Pharm. D. students. Journal of Asynchronous Learning Networks. 8(4): 1524. Resnick, L. B. (1987). Learning in school and out. Educational Researcher, 16(9), 1320. Rice, K. L. (2006). A comprehensive look at distance education in the K 12 c ontext. Journal of Research on Technology in Education, 38(4), 425448. Richardson, J. T. E., Long, G. L., & Foster, S. B. (2004). Academic engagement in students with a hearing loss in distance education. Journal of Deaf Studies and Deaf Education, 9, 6885. PAGE 188 188 Ritchie, H., & Newby, T. J. (1989). Classroom lecture/discussion vs. Live televised instruction. A comparison of effects on student performance, attitude, and interaction. The American Journal of Distance Education 3 (3). 3645. Rivera, J. C., Rice, M. L. (2002). A comparison of student outcomes & satisfaction between traditional & web based course offerings. Online Journal of Distance Learning Administration, 5(3). Robinson, W. B. (1970). The effects of two semester of secondary school calcul us on students first and second quarter calculus grades at the University of Utah. Journal of Research in Mathematics Education, 1, 5760. Roblyer, M. D., & Davis, L. (2008). Predicting success for virtual school students: Putting researchbased models i nto practice. Online Journal of Distance Learning Administration, XI (IV). Roblyer, M. D., Davis, L., Mills, S., Marshall, J., & Pape, L. (2008). Toward practical procedures for predicting and promoting success in virtual school students. The American Journal of Distance Education, 22(2), 90109. Roblyer, M. D., & Elbaum, B. (2000). Virtual learning? Research on virtual high schools. Learning & Leading with Technology, 27(4), 5861. Roblyer, M.D., Freeman, J., Stabler, M., & Schneidmiller, J. (2007). External evaluation of the Alabama ACCESS Initiative: Phase 3 report. Eugene, OR: International Society for Technology in Education. Roblyer, M. D., & Marshall, J. C. (2003). Predicting the success of virtual high school students: Preliminary results f rom an educational success prediction instrument. Journal of Research on Technology in Education 35(2): 241256. Rocha, E. (2007). Choosing more time for students: The what, why, and how of expanded learning. Washington: Center for American Progress. Rod riguez, J. L., Plax, T. G. & Kearney, P. (1996). Clarifying the relationship between teacher nonverbal immediacy and student cognitive learning: affective learning as the central causal mediator. Communication Education, 45, 293305. Ronsisvalle, T., & Wa tkins, R. (2005). Student success in online K 12 education. Quarterly Review of Distance Education, 6(2), 117124. Rose, R.M., & Blomeyer, R.L. (2007). Access and Equity in Online Classes and Virtual Schools. North American Council for Online Learning. Retrieved Aug 31, 2009, from http://www.nacol.org/docs/NACOL_EquityAccess.pdf PAGE 189 189 Rovai, A. P. (2002). A preliminary look at the structural differences of higher education classroom communities i n traditional and ALN courses. Journal of Asynchronous Learning Network, 6(1). Russell, G. (2004). Virtual schools: A critical view. In C. Cavanaugh (Ed.), Development and management of virtual schools: Issues and trends (pp. 125). Hershey, PA: Idea G roup. Russell, T. L. (1999).The no significant difference phenomenon. NC: Office of Instructional Telecommunications, North Carolina State University. Ryan, W. F. (1996). The effectiveness of traditional vs. audiographics delivery in senior high advanced mathematics courses. American Journal of Distance Education, 11(2), 4555. Saba, F. (2000). Research in distance education: A status report. International Review of Research in Open and Distance Education 1 (1), 19. Available at: http://www.pucmm.edu.do/RSTA/Academico/TE/Documents/ed/rdesr.pdf Saint Paul Public Schools (2007). Why is math important? Retrieved from: http://governors.spps.org/Why_is_math_important.html Sawyer, T. (1988). The effects of computerized and conventional study guides on achievement in college students. Journal of Computer Based Instruction, 15, 8082. Schaffer, L., & Hannafin, D. (1993). The effects of progressive interactivity on learning from interactive video. Educational Communication and Technology Journal, 34(2): 8996. Schoen, H. I,., Cebnlla, K. J., Finn, K. F., & Fi, C. (2003). Teacher variables that relate to student ac hievement when using a standards based curriculum. Journal for Research in Mathematics Education, 34, 228259. Schlosser, L., & Simonson, M. (2002). Distance education: Definition and glossary of terms Bloomington, IN: Association for Educational Communi cations and Technology. SchoenfeldTacher, R., McConnell, S., & Graham, M. (2001). Do no harm A comparison of the effects of online vs. traditional delivery media on a science course. Journal of Science Education and Technology, 10(3), 257265. Scholl ie Research and Consulting. (2001). Student achievement and performance levels in online education research study. Alberta Online Consortium, Edmonton, Alberta. PAGE 190 190 Schrum, L., & Hong, S. (2001). The potential for electronic educational environments: Ensuring student success Paper presented at the annual meeting of the American Educational Research Association, Seattle, WA. Schuman, A., & Sims, R. (1999). Learning in an online format versus an inclass format: An experimental study. T.H.E. Journal, 26 (11), 5 456. Schutte, J. G. (1996). Virtual teaching in higher education: The new intellectual superhighway or just another traffic jam? Available at: http://www.csun.edu/sociology/virexp.htm Scott Jone s, D. (1984). Family influences on cognitive development and school achievement. Review of Research in Education, 11, 259304. Secada, W. G. (1992). Race, ethnicity, social class, language, and achievement in mathematics. In D. A. Grouws (Ed.), Handbook o f research on mathematics teaching and learning (pp. 623660). New York: Macmillan. Seginer, R. (1986). Mothers' behavior and sons' performance: An initial test of an academic achievement path model. Merrill Palmer Quarterly, 32(2), 153166. Setzer, C. J ., & Lewis, L. (2005). Distance education courses for public elementary and secondary school students: 2002 2003 (No. NCES 2005010). Washington, DC: National Center for Education Statistics. Shachar, M., & Neumann, Y. (2003). Differences between traditional and distance education academic performances: A metaanalytic approach. The International Review of Research in Open and Distance Learning, 4(2). Available online at http://www.irrodl.org/index.php/irrodl/article/viewArticle/153 Sherry, L., Jesse, D. & Billig, S. (2002). Creating a WEB of Evidence of Student Performance in A Technology Rich Learning Environment International Journal on E Learning. 1 (1), pp. 3342. Shoaf Grubbs, M.M. (1993). The effects of the graphs: calculator on female students' cognitive levels and visit thinking. (Doctoral dissertation, Columbia University 1992). Dissertation Abstracts International, 54(01), 119. Sirin, S. R. (2005). Socioeconomic s tatus and student achievement: A metaanalytic review of research. Review of Educational Research, 75(3), 417453. Smeaton, A. & Keogh, G. (1999). An analysis of the use of virtual delivery of undergraduate lectures. Computers & Education, 32, 8394. PAGE 191 191 Smi th, J., diSessa, A., & Roschelle, J. (1993). Misconceptions reconceived: A constructivist analysis of knowledge in transition. Journal of the Learning Sciences, 3(2), 115163. Smith, R., Clark. T., & Blomeyer, R. (2005). A synthesis of new research on K 1 2 online learning. Naperville, IL: Learning Point Associates. Smouse, T. (2005). Students with either specific learning disabilities or attention deficit hyperactivity disorder: Perceptions of self as learning in online courses at Florida Virtual School and in the traditional learning environment. Unpublished dissertation, University of Central Florida. Souder, W. E. (1993). The effectiveness of traditional vs. satellite delivery in three management of technology masters degree programs. The Ameri can Journal of Distance Education, 7(1), 37 53. Spooner, F., Jordan, L., Algozzine, B., & Spooner, M. (1999). Student ratings of instruction in distance learning and oncampus classes. Journal of Educational Research, 92(3), 13240. Stanic, G., & Har t, L. (1995). Attitudes, persistence, and mathematics achievement: Qualifying race and sex difference. In W. Secada, E. Fennema, & L. B. Adajian. (Eds), New Directions for Equity in Mathematics Education Cambridge University Press. Stewart M, Marshall JN Ostbye T, Feightner JW, Brown JB, Harris S, Galajda J. (2005). Effectiveness of casebased online learning of evidencebased practice guidelines. Fam Med, 37: 13138. Summer, J. (1991). Effect of interactivity upon student achievement completion interv als and affective perceptions. Journal of Educational Technology System, 19(1): 5357. Summer, J., Waigandt. A., & Whittaker. T. (2005). A comparison of student achievement and satisfaction in an online versus a traditional faceto face statistics class. Innovative Higher Education 29 (3): 233250. Swan, K. (2003). Learning effectiveness: what the research tells us. In J. Bourne & J. C. Moore (Eds) Elements of Quality Online Education, Practice and Direction. Needham, MA: Sloan Center for Online Education, 1345. Swan, K. (2002). Building communities in online courses: the importance of interaction. Education, Communication and Information, 2 (1), 2349. PAGE 192 192 Swan, K., Shea, P., Fredericksen, E., Pickett, A, Pelz, W. & Maher, G. (2000). Building knowledge building communities: consistency, contact and communication in the virtual classroom. Journal of Educational Computing Research, 23 (4), 389413. Tallent Runnels, M. K., Thomas, J. A., Lan, W. Y., Cooper, S., Ahern, T. C., Shaw, S. M., & Liu, X. (2006). Te aching courses online: A review of the research. Review of Educational Research, 76(1), 93135. Thompson, M.S., Alexander, K.L., & Entwisle, D.R. (1988). Household composition, parental expectations, and school achievement. Social Forces, 67, 424451. Tu cker, B. (2007). Laboratories of reform: Virtual high schools and innovation in public education. Education Sector Reports. Retrieved March 10, 2009 from: http://www.educationsector .org/usr_doc/Virtual_Schools.pdf Tucker, S. (2001). Distance education: Better, worse, or as good as traditional education? Journal of Distance Learning Administration, 4 (4). Available at: http://www.westga.edu/~distance/ojdla/winter44/tucker44.html Ungerleider, C, & Burns, T. (2003). A systematic review of the effectiveness and efficiency of networked ICT in education: A state of the art report to the Council of Ministers Ca nada and Industry Canada. Ottawa: Industry Canada. U.S. Department of Education, Office of Educational Technology. (2004) Toward a New Golden Age in American Education: How the Internet, the Law and Todays Students Are Revolutionizing Expectations. Washi ngton, D. C. U.S. Department of Education (DOE), National Center for Education Statistics (NCES) (2008). Digest of Education Statistics, Chapter 6 (NCES 2008022). Usun, S. (2004). Interaction in Turkish distance education system. AACE Journal, 12(2), 123140. Valenta, A., Therriault, D., Dieter, M. & Mrtek, R. (2001). Identifying student attitudes and learning styles in distance education. Journal of Asynchronous Learning Networks, 5 (2), 111127. Voigt, J. (1994). Negotiation of mathematical meaning an d learning mathematics. Educational Studies in Mathematics, 26, 275298. Voigt, J. (1996). Negotiation of Mathematical Meaning in Classroom Process: Social Interaction and Learning Mathematics. In L. P. Steffe, P. Nesher, P, Cobb, G. A. Goldin, & B. Greer (Eds) Theories of Mathematical Learning. Lawrence Erlbaum Associates, Inc. NJ, 2150. PAGE 193 193 Wang, A. Y., & Newlin, M. H. (2000). Characteristics of students who enroll and succeed in psychology webbased classes. Journal of Educational Psychology, 92(1) 137143. Watson, J. (2007). A national primer on K 12 online learning Arlington, VA: North American Council for Online Learning. Available at: http://www.inacol.org/resources/docs/nati onal_report.pdf Watson, J., Gemin, B., & Ryan, J. (2008). Keeping pace with K 12 online learning: A review of statelevel policy and practice. Evergreen, CO: Evergreen Consulting Associates. Available at: http://www.kpk12.com/downloads/KeepingPace_2008.pdf Watson, J., & Kalmon, S. (2005). Keeping pace with K 12 online learning: A review of state level policy and practice. Naperville, IL: Learning Point Associates. Watson, J., & Ryan, J. (2007). Keeping pace with k 12 online learning: A review of state level policy and practice. Evergreen, CO: Evergreen Consulting Associates. Watson, J., & Ryan, J. (2006). Keeping pace with K 12 online learning: A review of state level policy and pract ice. Evergreen, CO: Evergreen Consulting Associates. Available at: http://www.inacol.org/resources/docs/Keeping%20Pace%20with%20K 12%20Online% 20Learning%202006.pdf Webster, J., & Hackley, P. (1997). Teaching effectiveness in technology mediated distance learning. The Academy of Management Journal, 40(6), 12821309. Weems, G. H. (2002). Comparison of Beginning Algebra Taught Onsite versus Onli ne. Journal of Developmental Education, 26(1): 1018. Wegner, S., Holloway, K., & Garton, E. (1999). The effects of internet based instruction on student learning. Journal of Asynchronous Learning Networks, 3(2), 19. Weiner, C. (2003). Key ingredients to online learning: Adolescent students study in cyberspace. International Journal on E Learning, July September, 4450. Welsh, J., & Null, C. (1991). The effects of computer based instruction on college students comprehension of classic research. Behav ior Research Methods, Instruments and Computers, 23, 301305. White, C. (2000). Learn online: Students and faculty respond to online distance courses at Grant MacEwan community college. T.H.E. Journal, 27(9), 6670. PAGE 194 194 Wick, R. W. (1997). An Analysis of the Effectiveness of Distance Learning at Remote Sites Versus On site Locations in High School Foreign Language Programs. University of Minnesota: Unpublished dissertation. Wilkins, J. L. M., Zembylas, M., & Travers, K. J. (2002). Investigating correlates of mathematics and science literacy in the final year of secondary school. In D. F. Robitaille & A. E. Beaton (Eds.), S econdary analysis of the TIMSS data (pp. 291316). Boston, MA: Kluwer Academic Publishers. Williams, S. L. (2006). The Effectiveness of Distance Education in Allied Health Science Programs: A MetaAnalysis of Outcomes. American Journal of Distance Education, 20(3), 127141. Zandberg, I., Lewis, L., & Greene, B. (2008). Technology Based Distance Education Courses for Public Elementary and Secondary Schools Students: 200203 and 200405. National Center for Education Statistics, U.S. Department of Education. Available at: http://nces.ed.gov/pubs2008/2008008.pdf Zhang D. (2005). Interactive multimediabased eleaning: A study of effectiveness, American Journal of Distance Education, 19(3), 149162. Zhang, P. (1998). A case study on technology use in distance learning. Journal of Research on Computing in Education, 30(4), 398419. Zhao, Y., Lei, J., Yan, B., & Tan, S. (2004).What makes the difference? A practical analysis of research on the effectiveness of distance education. Available online at http://ott.educ.msu.edu/liter ature/report.pdf Zucker, A. (2005). A study of student interaction and collaboration in the Virtual High School. Naperville, IL: Learning Point Associates. PAGE 195 195 BIOGRAPHICAL SKETCH Feng Liu was born in 1973 in Liaocheng City, Shandong Province, China. As th e second child of one threechild family, he attended No.1 High School, Zhongyuan Oil Field in Puyang City, Henan Province, China. Feng Liu graduated from Nanjing Normal Universitys Computer Science Department with a B achelor of S cience in computer scienc e e ducation in 1995. He has taught computer science courses at postsecondary level including Nanjing Material Polytechnic School and Nanjing University of Finance & Economics for ei ght and half years. Feng Liu came to United States at January 2004 to further his education at Georgia College & State University where he earned a Master of E ducation in e ducational t echnology in May 2006. In August of 2006, Feng Liu enrolled as a doctoral fellow in the Educational Technology program in School of Teaching and Learning at the University of Florida (UF). During his study at UF, Feng Liu has focused on research in the learning technologies. His research interests include the investigation of online learning success and the effectiveness of virtual schooling, the employment of advanced research methods and statistical approaches in educational research, and the use of egame/simulation for knowledge gain, attitude change and motivation in areas such as science and second language acquisition. He has several publicat ions in these areas. |