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Students' Prior Knowledge, Ability, Motivation, Test Anxiety, and Course Engagement as Predictors of Learning in Communi...

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Title: Students' Prior Knowledge, Ability, Motivation, Test Anxiety, and Course Engagement as Predictors of Learning in Community College Psychology Courses
Physical Description: 1 online resource (90 p.)
Language: english
Creator: Barber, Michael
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: achievement, anxiety, college, efficacy, elaboration, engagement, gpa, interest, motivation, rehearsal
Human Development and Organizational Studies in Education -- Dissertations, Academic -- UF
Genre: Educational Psychology thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Many researchers and educators are interested in students' cognitive, motivational, test anxiety, and behavioral characteristics that relate to learning. The purpose of this study was to test a social cognitive model of student learning in psychology courses. Specifically, the model was designed to determine whether students' prior knowledge, ability (reading comprehension and prior grade point average), motivation (entity beliefs, achievement goal orientation, interest, and self-efficacy beliefs), test anxiety, and course engagement (learning strategies, homework, class participation, and quizzes) predict their achievement on exams in community college psychology courses. Participants were 210 undergraduate students enrolled in psychology courses at a southeastern community college. The results of the study showed that prior knowledge, reading ability, elaborative study strategies, and quizzes had direct positive effects on exam performance, whereas test anxiety had a direct negative effect on exam performance. In addition, prior grade point average, perceived self-efficacy, attendance, homework, and class participation had indirect positive effects on exam performance, whereas performance-avoidance goals had an indirect negative effect on exam performance. In addition, interest had a direct positive effect on mastery goals and elaborative cognitive processing. Consistent with prior research, self-efficacy beliefs predicted achievement goal orientations and cognitive strategies. Although prior grade point average did not directly predict exam performance, prior grade point average had a direct positive effect on attendance, homework, and class participation. Attendance had a direct positive effect on class participation, homework, and quizzes. Performance-avoidance goals had a direct positive effect on test anxiety and surface processing strategies. Last, class participation predicted homework scores, and homework scores predicted quiz scores. The findings from this study provide groundwork for future experimental research and implications for educational practice.
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.
Statement of Responsibility: by Michael Barber.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Ashton, Patricia T.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042543:00001

Permanent Link: http://ufdc.ufl.edu/UFE0042543/00001

Material Information

Title: Students' Prior Knowledge, Ability, Motivation, Test Anxiety, and Course Engagement as Predictors of Learning in Community College Psychology Courses
Physical Description: 1 online resource (90 p.)
Language: english
Creator: Barber, Michael
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: achievement, anxiety, college, efficacy, elaboration, engagement, gpa, interest, motivation, rehearsal
Human Development and Organizational Studies in Education -- Dissertations, Academic -- UF
Genre: Educational Psychology thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Many researchers and educators are interested in students' cognitive, motivational, test anxiety, and behavioral characteristics that relate to learning. The purpose of this study was to test a social cognitive model of student learning in psychology courses. Specifically, the model was designed to determine whether students' prior knowledge, ability (reading comprehension and prior grade point average), motivation (entity beliefs, achievement goal orientation, interest, and self-efficacy beliefs), test anxiety, and course engagement (learning strategies, homework, class participation, and quizzes) predict their achievement on exams in community college psychology courses. Participants were 210 undergraduate students enrolled in psychology courses at a southeastern community college. The results of the study showed that prior knowledge, reading ability, elaborative study strategies, and quizzes had direct positive effects on exam performance, whereas test anxiety had a direct negative effect on exam performance. In addition, prior grade point average, perceived self-efficacy, attendance, homework, and class participation had indirect positive effects on exam performance, whereas performance-avoidance goals had an indirect negative effect on exam performance. In addition, interest had a direct positive effect on mastery goals and elaborative cognitive processing. Consistent with prior research, self-efficacy beliefs predicted achievement goal orientations and cognitive strategies. Although prior grade point average did not directly predict exam performance, prior grade point average had a direct positive effect on attendance, homework, and class participation. Attendance had a direct positive effect on class participation, homework, and quizzes. Performance-avoidance goals had a direct positive effect on test anxiety and surface processing strategies. Last, class participation predicted homework scores, and homework scores predicted quiz scores. The findings from this study provide groundwork for future experimental research and implications for educational practice.
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.
Statement of Responsibility: by Michael Barber.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Ashton, Patricia T.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042543:00001


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1 STUDENTS PRIOR KNOWLEDGE ABILITY MOTIVATION TEST ANXIETY AND COURSE ENGAGEMENT AS PREDICTORS OF LEARNING IN COMMUNITY COLLEGE PSYCHOLOGY COURSES By MICHAEL E. BARBER 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

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2 20 10 Michael E. Barber

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3 To Tammy

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4 ACKNOWLEDGMENTS Although I grew up in an individualistic culture, I recognize that an accomplishment like completing a dissertation is a collective effort. I recognize my dependence on my family, my committee, and my colleagues. I acknowledge the sacrifices of my family a s I have pursued my degree. My wife and childr en have supported me unconditionally Their encouragement and love has been the primary motivating factor for me to complete my program. I thank my parents for teaching me the values of hard work determination and being teachable Without these values, I would not have been able to endure the r igor required to persist to the end. I deeply appreciate the contributions of my committee members. Dr. Patricia Ashton has helped me extend myself beyond my expectation s. She has taught me many things explicitly in the classroom and through her thoughtful feedback. In addition, I have implicitly learned that a good mentor cares deeply and gives space for her apprentice to take responsibility for his educational goals. Dr James Algina was always patient, kind, and helpful when I had questions about data analysis. I thank Dr. Tracy Linderholm for her help as I transitioned into the graduate program at the University of Florida and for her insightful feedback on my proposal Last, I thank Dr. Honeyman for his approachability and willingness to serve on my committee. Each committee member has made a very intimidating and seemingly insurmountable process easier for me. I thank my colleagues at Santa Fe College for their suppor t. I appreciate each student who completed the surveys and provided the data without compensation or complaint. I thank each person who offered encouragement and continued to ask about my progress. Dr. Dave Yonutas was instrumental in helping me obtain the data and constantly offered his encouragement. I thank Dr. Paul Hutchins for seeing the potential in me to pursue a Ph.D. when I was content with a

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5 Doug Diekow, alo ng with my fellow psychologists, Dr. Marisa McCloud, Dr. Jai Levengood, Dr. Angi Semegon, and Dr. Jacqueline Whitmore. I am grateful that I work at a college where my personal and intellectual growth is valued.

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6 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ ............... 4 LIST OF TABLES ................................ ................................ ................................ ........................... 8 LIST OF FIGURES ................................ ................................ ................................ ......................... 9 ABSTRACT ................................ ................................ ................................ ................................ ... 10 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .................. 12 Individual Differences and Learning ................................ ................................ ...................... 12 Theoretical Rationale of the Study ................................ ................................ ......................... 14 2 LITERATURE REV IEW ................................ ................................ ................................ ....... 18 Prior Knowledge and Ability ................................ ................................ ................................ .. 19 Prior Knowledge ................................ ................................ ................................ .............. 19 College Grade Po int Average ................................ ................................ .......................... 21 Reading Comprehension ................................ ................................ ................................ 22 Motivation ................................ ................................ ................................ ............................... 23 Implicit Theories of Intelligence ................................ ................................ ..................... 23 Achievement Goal Orientation ................................ ................................ ........................ 25 Interest ................................ ................................ ................................ ............................. 27 Self Efficacy Beliefs ................................ ................................ ................................ ....... 29 Test Anxiety ................................ ................................ ................................ ............................ 31 Course Engagement ................................ ................................ ................................ ................ 32 Learning Strategies ................................ ................................ ................................ .......... 32 Attendance ................................ ................................ ................................ ....................... 34 Homework ................................ ................................ ................................ ....................... 35 Course Participation ................................ ................................ ................................ ........ 35 Quizzes ................................ ................................ ................................ ............................ 36 Research Question ................................ ................................ ................................ .................. 38 Hypotheses ................................ ................................ ................................ .............................. 38 3 METHOD ................................ ................................ ................................ ............................... 42 Participants ................................ ................................ ................................ ............................. 42 Measures ................................ ................................ ................................ ................................ 42 Psychology Knowledge Pretest ................................ ................................ ....................... 42 College GPA ................................ ................................ ................................ .................... 42 Reading Ability ................................ ................................ ................................ ............... 43 Implicit Theories of Intelligence ................................ ................................ ..................... 43

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7 Achievement Goal Orientation ................................ ................................ ........................ 43 Inte rest and Learning Strategies ................................ ................................ ...................... 44 Self Efficacy Beliefs ................................ ................................ ................................ ....... 44 Test Anxiety ................................ ................................ ................................ .................... 44 Class Attendance ................................ ................................ ................................ ............. 45 Homework Assignm ents ................................ ................................ ................................ 45 Course Participation ................................ ................................ ................................ ........ 45 Exams ................................ ................................ ................................ .............................. 46 Procedures ................................ ................................ ................................ ............................... 46 4 ANALYSIS OF DATA ................................ ................................ ................................ .......... 47 Descriptive Statistics ................................ ................................ ................................ .............. 47 Analysis of the Proposed Model ................................ ................................ ............................. 47 Research Hypotheses ................................ ................................ ................................ .............. 49 5 DISCUSSION ................................ ................................ ................................ ......................... 62 Prior Knowledge and Ability ................................ ................................ ................................ .. 62 Prior Knowledge ................................ ................................ ................................ .............. 63 College Grade Point Average ................................ ................................ .......................... 64 Reading Comprehension ................................ ................................ ................................ 65 Motivation ................................ ................................ ................................ ............................... 66 Implicit Theories of Intelligence ................................ ................................ ..................... 66 Achievement Goal Orientation ................................ ................................ ........................ 67 Inte rest ................................ ................................ ................................ ............................. 69 Self Efficacy Beliefs ................................ ................................ ................................ ....... 71 Test Anxiety ................................ ................................ ................................ ............................ 72 Course Engagement ................................ ................................ ................................ ................ 72 Learning Strat egies ................................ ................................ ................................ .......... 73 Attendance ................................ ................................ ................................ ....................... 73 Homework ................................ ................................ ................................ ....................... 74 Course Participation ................................ ................................ ................................ ........ 75 Quizzes ................................ ................................ ................................ ............................ 75 Limitations of the Study ................................ ................................ ................................ ......... 76 Implications for Theory and Practice ................................ ................................ ..................... 77 Conclusions ................................ ................................ ................................ ............................. 78 REFERENCES ................................ ................................ ................................ .............................. 80 BIOGRAPHICAL SKETCH ................................ ................................ ................................ ......... 90

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8 LIST OF TABLES Table page 4 1 Means and standard deviations ................................ ................................ .......................... 53 4 2 Correlatio n matrix ................................ ................................ ................................ .............. 54 4 3 Total, direct, and indirect effects in revised model ................................ ............................ 58 4 4 Effects proposed in original model and effects in revised model ................................ ...... 60

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9 LIST OF FIGURES Figure page 2 1 Final structural equation model from Fenollar et al. (2007) ................................ .............. 40 2 2 The oretical model of relationships of characteristics to exam performance ...... 41 4 1 Revised mo del of relationships ch aracte r istics to exam performance ........... 61

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10 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 S TUDENTS PRIOR KNOWLEDGE ABILITY MOTIVATION TEST ANXIETY AND COURSE ENGAGEMENT AS PREDICTORS OF LEARNING IN COMMUNITY COLLEGE PSYCHOLOGY COURSES By Michael E. Barber December 2010 Chair: Patricia Ashton Major: Educational Psychology Many researchers and educators are interested in student cognitive, motivational, test anxiety and behavioral characteristics that relate to learning. T he purpose of this study wa s to test a social cognitive model of student learning in psychology cours es. Specifically, the model was designed to determine whether (reading comprehension and prior grade point average) motivation (entity beliefs, achievement goal orientation, intere st, and self efficacy beliefs), test anx iety and c ourse engagement (learning strategies, homework, class participation, and quizzes) predict their achievement on exams in community college psychology courses. Participants were 210 undergraduate students enrolled in psychology courses at a sout heastern community college. The results of the study showed that prior knowledge, reading ability, elaborative study strategies, and quizzes had direct positive effects on exam performance whereas test anxiety had a direct negative effect on exam performa nce In addition, prior grade point average, perceived self efficacy, attendance homework, and class participation had in direct positive effects on exam performance whereas performance avoidance goals had an indirect negative effect on exam performance In addition, interest had a direct positive effect on

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11 mastery goals and elaborative cognitive processing. Consistent with prior research, self efficacy beliefs predicted achievement goal orientations and cognitive strategies. Although prior grade point ave rage did not directly predict exam performance, prior grade point average had a direct positive effect on attendance, ho mework, and class participation. Attendance had a direct positive effect on class participation, homework, and quizzes. Performance avoi dance goals had a direct positive effect on test anxiety and surface processing strategies. Last, class participation predicted homework scores, and homework scores predicted quiz scores. The findings from this study provide groundwork f or future experimen tal research and implications for educational practice.

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12 CHAPTER 1 INTRODUCTION Individual Differences and Learning Significant economic, technological, and social changes have affected higher education (Dennis, 2004). With more people gaining access to higher education than ever before, researchers have begun to examine the factors that engage adult students and influe nce learning (Dennis, 2004). Researchers have examined teacher, student, content, and contextual variables that relate to learning and have found factors that relate to student learning in higher education (for a review, see Menges & Austin, 2001). Student individual difference variables that relate to learning include level of interest, perceived self efficacy, motivation, cognitive strategies, test anxiety and engagement. Researchers have used many different statistical methods to examine the relationsh ips between and learning in educational settings. R esearchers utilizing structural equation modeling have been able to elaborate on and refine correlational regression models ( Fenollar, Roman, & Cuestas, 2007; Hulleman, Durik, Sch weigert, & Harackiewicz, 2008; the researchers examining these relationships have focused on learners in primary and secondary school settings (e.g., Simons, Dewitte, & Lens, 2004). Researchers have shown that characteristics that relate to learning in children differ from those that relate to learning in adults (Valle et al ., 2003; Verm e tten, Vermut, & Lodewijks, 1999). For instance, adults use deeper levels o f processing and different motivational strategies while learning when compared to children (Verm e tten et al. 1999). Additional studies are needed that examine the relationships between and learning in adult populations.

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13 Even wh en researchers have examined the factors that relate to learning in adult populations they have disagreed over whether factors that relate to learning in one subject relate similarly to learning in other academic disciplines. Some researchers have examined how student characteristics relate to learning across disciplines in higher education (see McKenzie, Gow, & Schweitzer, 2004), and other researchers have suggested that factors related to learning may be discipline specific (see Donald, 1995). Factors that relate to student success in psychology courses, for example, may differ from factors that relate to succe ss in organic chemistry classes. Research with path modeling would be useful in identifying individual differences that relate to learning within discipline specific contexts because the importance of student characteristics most likely differs by discipli ne (Zeegers, 2004). Once research in many specific disciplines is conducted, researchers will be able to make comparisons between the findings to determine if characteristics that relate to learning differ by disciplines. Only a few researchers have used s tructural equation modeling to study the complex relationships between student that influence learning in higher education settings (e.g., Fenollar et al., 2007; Hoffman & Van den Berg, 2000; Lietz, 1996; Murray Harvey, 1993), and even fe wer such studies have examine d these relationships in college psychology courses. Busato, Prins, Elshout, and Hamaker (2000) use d structural equation modeling to examine how individual differences relate to learning in college psychology classes but cited problems in correlational patterns that prevented analysis of the data. To answer the important question of whether student characteristics relate to learning across disciplines or are discipline specific in higher education researchers need to study thes e relationships within specific disciplines. Much of the research on student characteristics related to learning in psychology courses in higher education has been cond ucted at 4 year institutions Factors related to learning in

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14 foundational community coll ege courses may differ from success factors for upper level university courses For instance, community college professo rs may focus more on content in foundational courses, and professors teaching upper division courses may focus more on critical analysis of theories or the creation of new knowledge. Therefore, student characteristics that relate to learning in community college psychology courses may not relate to learning in higher level university psychology courses. In addition, students who choose to attend community colleges tend to differ from students who enroll in universities in terms of academic preparedness, educational goals, age, and socioeconomic status (Grimes & David, 1999). Therefore, characteristics that relate to learning for community c ollege populations may not relate to learning in university populations. Research examining the relationship between student characteristics a nd learning in community college populations would allow researchers to compare the findings with the results of s tudies conducted in university settings. In addition to extending existing theory, identifying that relate to learning within discipline specific contexts should help educators improve educational practice. For instance, identifyi ng student characteristics that relate to learning in psychology courses at community colleges as opposed to those offered at universities may help instructors of these courses predict which students might be at risk of failing. The results of such researc h may reveal that some characteristics have a greater impact on student learning than others. Identifying ways to positively affect learning should be a main goal of educators. The purpose of this study test anxiety and class engagement predict achievement on exams in community college psychology courses. Theoretical Rationale of the Study Learning involve s changes in behavioral, cognitive, and emotional patterns as a result of experience (see Atkinson & Shiffrin, 1968 ; Broadbent, 1958; Mowrer & Klein, 2001; Piaget,

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15 1963; Pintrich, Marx, & Boyle, 1993 ) Many educational psychologists are interested in the c haracteristcs that influence learning in educational settings and utilize a variety of theoretical frameworks to design research. Social cognitive researchers, for instance, have identified a variety of motivational constructs with implications for educati onal practice For example, perceptions of self efficacy and achievement goal orientation are o ften related to cognitive processing (see Elliot, McGregor, & Gable, 1999). Cognitive processes like self regulation and learning strategies then, in turn, are r elated to behavioral performance on assessments (Alexander, Schulze, & Kulikowich, 1994; Elliot & McGregor, 2001 ; Elliot et al., 1999 ; Pintrich & De Groot, 1990). Using a social cognitive framework, I examine d how characteristics relate to learni ng in community college psychology courses. Social cognitive theorists provide a broader framework for understanding learning than behavioral or cognitive theories alone. Behaviorists defined learning as relatively permanent changes in behavior as a result of experience (Mowrer & Klein, 2001). Restricting the study of learning to behaviors limited the range of experiences behaviorists were able to explain. For example, behaviorists could explain behavioral changes that resulted from classical or operant con ditioning; however, they failed to acknowledge cognitive changes that may not be expressed through behavioral performance. Cognitive theorists expanded the study of learning to include changes in cognitive structures and processes that result from experien ce. Cognitive theorists began to explain learning in terms of schematic adaptation (Piaget, 1963) and changes in memory systems ( Atkinson & Shiffrin, 1968 ; Broadbent, 1958 ) Although cognitive models of memory extended the study of learning to include cogn itive processes and abilities, many researchers found that affective and motivational constructs that relate to learning were neglected by both behavioral and cognitive theories (e.g., Pintrich et al. 1993) Social cognitive theorists

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16 integrated affective cognitive, behavioral, and environmental characteristics into theories that can more holistically represent human functioning, including learning (e.g., Zimmerman, 1989). Social cognitive theorists provide a framework for understanding learning in the c ontext of personal characteristics (cognition, affect, and biological factors), behavior, and environmental influences (Bandura, 1986). Essentially, Bandura presented the idea that each component (person, behavior, and environment) is both an influential f orce on and a function of the other two forces. Bandura called the dynamic interplay between the person, behavior, and the environment reciprocal determinism Using the social cognitive framework, educational psychologists interested in learning that occurs in school settings may more completely represent the learning process as a dynamic interaction between the person ( e.g. his or her beliefs, e.g. test performance, class participation), and the environment ( e.g. classroom structure, teacher characteristics). Instead of seeing people as primarily reactive or passive, social cognitive theorists present a view of humans as using uniquely human capabilities to actively evaluate and direct their learning experiences. Researchers measure learning in educational settings in a variety of ways. For instance, teachers may assess student learning using exams ( e.g. recognition or recall based measurements), portfolios, presentations, or group projects Once students understand how learning will be assessed in a course (an environmental factor), student goals and self efficacy beliefs will play a role in the selection of cognitive strategies they use to regulate their learning in preparation for the a ssessment. In this study, I use d a social cognitive framework to examine the relationships among prior knowledge, ability, motivation, test anxiety course

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17 e ngagement and academic achievement In the following chapter, I explain how I investigat ed these relationships in community college psychology courses.

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18 CHAPTER 2 LITERATURE REVIEW Individual differences in learning, according to social cognitive theory, relate to a variety of personal, behavioral, and environmental influences Individual di fferences among students exist in interest and background knowledge in the subject, reading ability, motivation, and for learning which, in turn, affect acad emic achievement (see Elliot et al., 1999) Fenollar et al. (2007) tested a conceptual model involving perceived self efficacy, achievement goals, cognitive strategy, and effort as predictors of academic performance (see Figure 2 1 ). Essentially, Fenollar and associates found support for their model in which self efficacy beliefs indirectly influenced academic performance through their direct effects on achievement goals, study strategies, and effort. Also, achievement goals indirectly affected academic ac hievement through the direct effects on study strategies and effort. Last, deep processing study strategies and effort had direct positive effects on academic performance. Although the Fenollar et al (2007) model provided insight into variables that may predict academic achievement, additional individual difference s that predict academic performance in college populations may have been left out of their model The addition of other student characteristics such as prior knowledge, ability, test anxiety an d performance on course work to the Fenollar et al (2007) model may provide additional insight into the strength of the reported relationships when other variables are included. In this chapter, I review prior research with the purpose of construct ing a s tructural equation model of individual difference variables influencing college student learning.

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19 Prior Knowledge and Ability Individual differences in background knowledge and ability predict academic achievement. Students differ in their prior exposure to course material, their overall college grade point averages (GPA), and their ability to read and comprehend text. Researchers have found that those students with prior knowledge of a subject tend to perform better than those with no previous exposure t o the subject (see Alexander et al., 1994; Hudson & Rottmann, 1981). Researchers have also reported that overall college GPA predicts exam performance in introductory psychology college courses (Hardy, Zamboanga, Thompson, & Reay, 2003). Last, several stud ies have shown that the ability to read and comprehend text relates to achievement in college psychology courses (see Fields & Cosgrove, 2000; Gerow & Murphy, 1980; Jackson, 2005; Kessler & Pezzetti, 1990; Robert s, Suderman, Suderman, & Semb, 1990). In th e next section s research in the areas of prior knowledge, GPA, and reading comprehension is reviewed as it relates to this study Prior K nowledge Baddeley and Hitch (1974) and Cowan (1998) constructed information processing models that account for how background knowledge can affec t learning. According to these models, the central executive searches previously stored knowledge and activates relevant information when one learns new information. Prior knowledge in a specific domain may either facilitate or interfere with learning. If learners have mastered knowledge in a n area, then that knowledge is likely to facilitate additional learning. Learners w ith accurate background knowledge should process new information more efficiently and integrate new knowledge more effectively than students with inaccurate or no background knowledge. Accurate prior learning provides a scaffold for interpreting new inform ation and allows students to incorporate new information into their cognitive framework s R esearch findings lend support to this theory For example,

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20 Moos and Acevedo (2008) report ed that students with accurate prior knowledge tend to regulate their learni ng by planning, monitoring, and strategizing better than those with little or no prior knowledge Researchers have shown that students with prior knowledge of the subject matter of the course in a variety of academic disciplines (see Alexander et al., 1994 ; Greene, Costa, Robertson, Pan, & Deekens, 2010; Hailikari, Nevgi, & Komulainen, 2008; Hudson & Rottmann, 1981) including psychology (Thompson & Zamboanga, 2003, 2004), score higher on achievement tests in the discipline than students without such prior knowledge. Using regression analysis in a study of 209 college students, Alexander et al. (1994) found that prior knowledge of physics predicted achievement on a recall assessment. Greene et al. (2010) reported that participants with prior knowledge about the human circulatory system scored higher on an assessment after instruction than those with less prior knowledge. Hailikari et al. (2008) reported that prior knowledge in mathematics was the strongest predictor of achievement in math classes when control ling for academic self beliefs and prior academic success. These studies typify a robust body of research that prior knowledge facilitates learning In accordance with findings in other academic disciplines, researchers have also reported that accurate kn owledge of psychology predicts achievement in psychology courses. Using regression analysis in a study of 353 undergraduate students, Thomson and Zamboanga (2004) found that prior knowledge of psychology as measured on a pretest at the be ginning of the sem ester predicted exam scores in introductory psychology with ability, as measured by ACT scores and participation in course activities controlled. Thus, prior knowledge of the subject matter should aid in encoding, storage, and later retrieval of information when completing course assignments and taking exams. On the basis of these findings, I hypothesized that prior

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21 knowledge would be positively related to performance on homework, course acti vities, quizzes, and exams. College G rade P oint A verage In addition to prior knowledge, performance in previous college courses has predict ed future course performance in numerous studies. In particular, r esearchers have found that cumulative college GPA predict s success in future college courses (DeBerard, Spielmans, & Julka, 2004; Pursell, 2007 ; Zeegers, 2004) Zeegers reported that prior GPA was the strongest predictor of annual GPA in a group of 113 third year college students in a structural model tha t included entrance exam scores, study strategies, self regulation, and perceived self efficacy Pursell (2007) found that cumulative college GPA was a stronger predictor of success in organic chemistry classes than GPA in prerequisite chemistry courses al one. Thus, research support s the claim that prior academic performance predicts future performance on both global and specific measures of academic achievement. Cumulative college GPA has also been shown to predict performance on exams in college psycholo gy courses specifically (Hardy et al. 2003). Hardy et al. examined the relationship of background variables (prior GPA, aptitude test scores, and prior psychology coursework) as predictors of exam performance in an introductory psychology course. The auth ors predicted that the effect of background variables on achievement would be mediated by course involvement (attendance and participation). Only the background variables significantly predicted exam performance, with prior GPA being the strongest predicto r of exam performance. The course involvement variables did not predict exam performance. In accordance with these findings, I expect that students with higher cumulative college GPAs will perform better on homework, course participation quizzes, and exam s than those with lower college GPAs.

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22 Reading C omprehension Most people view literacy as an important component of learning in higher education. Students vary in their abilities to decode and construct meaning from text. Certainly, students must rely on re ading comprehension skills in some courses more than others. For instance, reading comprehension relates more strongly to achievement in courses that require more self study (i.e., online courses) than in more traditional lecture based courses (Roberts, et al. 1990). Several studies have shown that the ability to read and comprehend text relates to achievement in college psychology courses (see Fields & Cosgrove, 2000; Gerow & Murphy, 1980; Jackson, 2005; Kessler & Pezzetti, 1990; Roberts et al., 1990). Kessler and Pezzetti found that students with higher reading ability persisted through the end of the course and scored between 7% and 12% higher on exams than those with lower ability as measured on the Nelson Denny Reading Test (Brown, Fishco, & Hanna, 1 993). In addition to the Nelson Denny Reading Test, researchers have used student reading scores from college entrance exams as measures of reading ability (Fields & Cosgrove, 2000). Fields and Cosgrove (2000) found that when initial reading placement test scores were used to estimate reading ability, those students who scored at or above college level in reading received significantly higher grades in an introductory psychology course than students who scored below college level in reading. Overall, resea rchers have reported mixed findings regarding the relationship between reading comprehension and achievement in psychology courses Although some researchers have reported that reading comprehension is a strong predictor of achievement in psychology course s ( e.g., Roberts et al., 1990), other researchers have reported only minimal effects of reading ability on achievement (Jackson, 2005). These differences in findings are most likely the result of differing measures of reading comprehension and different op erational definitions of achievement. In addition, reading comprehension may predict achievement in some courses

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23 more than others due to the amount of reading required. In this study I expect ed reading ability as measured on initial college placement exams predict s their scores on homework, course participation quizzes, and exams. Motivation In addition to prior knowledge and ability factors, researchers have shown that motivation relates to academic achievement (see Busato et al., 2000; E lliot, 1999). Motivation is the process by which activities are started, directed, and maintained toward physical and psychological goals (Petri, 1996). In educational settings, many goals exist that may motivate students to learn. For instance, some stude nts may be motivated to learn material to demonstrate their mastery of the material w hereas others may be motivated to learn material to demonstrate their competence related to others (Elliot, 1997). Much of the research on motivation in educational setti ngs involves implicit theories of intelligence (Dweck, 1986), achievement goal orientation (Elliot, 1997, 1999), the level of interest a student has in specific academic disciplines (Middleton & Midgley, 1997; Skaalvik, 1997), and measures of perceived sel f efficacy (Malka & Covington, 2005; Zimmerman & Kitsantas, 2005). Research on these motivational variables is relevant to th is study and reviewed in the following sections. Im plicit Theories of Intelligence Dweck (1986) proposed that individuals differ i n the extent to which they believe intelligence is fixed or malleable. She referred to the belief that intelligence is fixed as an entity theory of intelligence and the belief that it is malleable as an incremental theory of intelligence. She theorized tha t implicit theories of intelligence predict the achievement goals students adopt. In her research, Dweck (2000) has shown that people with incremental theories of intelligence are more likely to adopt achievement goals that focus on mastery, persist in the face of challenging material, and view performance as reflective of effort. In contrast, those with entity

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24 theories of intelligence are more likely to adopt achievement goals that focus on performance compared to others, demonstrate learned helplessness w hen facing challenging material, and view performance as reflective of innate ability. Findings from several studies support the hypothesis that intelligence beliefs are predictive of achievement (see Dwec k, 1996; Kasimatas, Miller, & Marcussen, 1996 for a review). However, inconsistencies exist in the research findings regarding intelligence beliefs and achievement goals. In a regression analysis involving data collected from 180 undergraduate psychology students, Elliot and McGregor (2001) reported that mastery avoidance goals were positively related to entity beliefs and negatively associated with incremental beliefs. In contrast, however, Cury, Elliot, Da Fonseca, and Moller (2006) found that incremental beliefs correlated positively with mastery goal orientations and entity beliefs correlated positively with performance approach and performance avoidance goal orientation s. While some researchers relationships between intelligence beliefs and achievement goals ( e.g., Dupeyrat & Marin, 2005). Dupeyrat and Marin reported that int elligence beliefs did not predict performance goals and found that entity beliefs had a negative effect on mastery goal orientation. Dupeyrat and Marin reported that their findings may have differed from previous findings due to the uniqueness of their sa mple of adult students enrolled in a high school equivalency program. In and previous findings (Cury, et al., 2006; Kasimatas, et al., 1996) I hypothesized ievement goals. Specifically, entity beliefs about intelligence are positively related to performance approach and performance avoidance goals, and incremental beliefs about intelligence are positively related to mastery goals.

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25 Achievement G oal O rientation Dweck (1986) proposed a social cognitive model of achievement goal orientation that educational settings. Elliot (1997) more recently extended achievement goal theory to include a trichotomous framework: mastery, performance approach, and performance avoidance goal orientations. Essentially the theory purports that students who adopt different achievement goals approach learning differently. Those who adopt mast ery goals tend to be concerned with developing task mastery and competence. Those who adopt performance approach goals tend to be concerned with demonstrating competence relative to others, and those who adopt performance avoidance goals focus on avoid ing the appearance of incompetence relative to others. Elliot et al. (1999) found that achievement goal orientation was linked to cognitive strategy use, with those adopting mastery goals more likely to use deeper processing strategies (e.g., elaboration) tha n those who adopted performance oriented goals. More surface approaches to learning were preferred by those adopting performance avoidance goals. The authors reported that performance approach goals were positively related to exam performance whereas perf ormance avoidance goals were negatively related to exam performance. The authors found that mastery orientation was unrelated to exam performance. Some research has shown a link between achievement motivation and exam performance in college p sychology cou rses ( Busato et al., 2000 ; Darnon, Butera, Mugny, Quiamzade, & Hulleman, 2009; Jagacinski, Kumar, Boe, Lam, & Miller, 2010 ). In a study of 409 first year psychology students, Busato et al. reported that ach ievement motivation was positively related to perf ormance on the first psychology exam ( r = .14). Jagacinski et al. (2010) reported that in their study of 162 introductory psychology studen ts that mastery goals successfully predicted

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26 achievement on the final exam score at the beginning ( r = .24) and end ( r = .25) of the semester. In the same study, performance approach goals measured at the beginning of the semester did not predict final exam scores but predicted final exam scores when measured at the end of the term ( r = .26). Performance avoidance goals did not predict final exam scores at either point in the semester. More recently, Fenollar et al. (2007) found that achievement goals did not directly affect academic performance but rather mediate d the effect on performance through choice of cognitive st rategies and effort expended on course assignments Fenollar et al. also found that mastery goals had an indirect and positive effect on academic performance through deep processing strategies and self reported effort spent on course assignments. Performan ce approach goals had a direct effect on surface processing, but an indirect effect on academic performance through effort. Performance avoidance goals had no direct effect on deep or surface processin g strategies but did have indirect effects on academic performance through effort. In light of these findings, I expect ed and positive effect on exam performance through deep processing and performance on course assignments. I also expect ed to find that pe rformance approach goals are positively related to superficial processing strategies and have an indirect positive effect on exam performance through performance on course assignments. I expect ed to find that performance avoidance goals have a n in direct ne gat ive effect on exam performance through performance on course assignments. In addition to the relationship s between achievement goal theory and cognitive strategies, some researchers have reported mixed results concerning the link between achievement go al orientations and test anxiety (Middleton & Midgley, 1997; Pekrun, Elliot, and Maier, 2006;

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27 Putwain, Woods, & Symes, 2010; Skaalvik, 1997 ; Sideridis, 2005; Tanaka, Takehara, & Yamauchi, 2006 ). Middleton and Midgley reported that mastery goals were unrela ted to test anxiety in a study of sixth grade students whereas Skaalvik found that mastery goals were modestly negatively related to test anxiety in two samples of sixth and eighth grade students ( r = .23 and .16). Middleton and Midgley found that perf ormance approach goals were moderately positively associated with test anxiety ( r = .32), whereas Skaalvik found performance approach goals to be slightly negatively related to test anxiety in one sample (r .15) but unrelated in the second sample. More re cently, researchers have reported weak or non significant relationships between performance approach goals and test anxiety ( Putwain et al., (2010); Sideridis, 2005). Performance avoidance goals have been consistently positively related to test anxiety (Middleton & Midgley, 1997; Skaalvik, 1997) Middleton and Midgley reported a correlation of .41, and Skaalvik reported correlations of .25 and .35. Although the differences are small, t he stronger relationships reported by Middleton and Midgley might be due to their use of domain specific measures of achievement goals in contrast to the general measures used by Skaalvik. In th is study, I use d domain specific measures of achievement goals, and consistent with the results of Middleton and Midgley, I hypothe sized positive associations between the performance goals and test anxiety. Interest Researchers focusing on interest and academic achievement have provided insight into the role interest plays in student motivation for learning. In describing his person o bject theory of feeling related (e.g., positive affect) a nd value related (e.g., personal significance) valences. related construct, other researchers have

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28 described interest as task value (see Pintrich, Smith, Garcia, & McKeachie 1991). High levels of interes t in a discipline should relate to the formation of achievement goal orientations. For instance, those who report an interest in a subject and see the knowledge as personally relevant and useful should be more likely to develop mastery goal orientations th an those who do not express an interest in the subject. In fact, Hulleman et al. (2008) found that interest was positively related to mastery goal orientation ( r = .62). Researchers have reported the predictive value of student interest on exam performance ( Hidi & Renniger 2006; Hulleman et al. 2008; 2009). In a regression analysis of data from 202 sixth graders, Shen et al. (2007) reported that interest predicted physical education skill gain and scores on phys ical education exams. In addition to a direct effect of interest on achievement, Hidi (2006) suggested that the effect of interest on exam performance may be mediated by self regulatory processes such as perceived self efficacy and achievement goals. and a direct positive effect on exam performance and an indirect effect on exam performance through learning strategies. interest predicted elaborative ( r = .22) and rehearsal ( r = .15) learning strategies. They found that the relationship between interest and exam performance was not significant when they controlled for learning strategies suggesting that learning strategies mediated the relationship between interest and achievement. Interest also predicts achievement in psychology courses. Hulleman et al. (2008) conducted a study using 663 introductory psychology students. The researchers used regression analysis to examine interest in psychology at the beginning of the course as a predictor of achievement goals, future interest levels, and final course grades. Initial interest predicted

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29 subsequent interest levels measur did not predict final course grades dir ectly, but did indirectly predict final course grade through utility value (how personally relevant students perceived the m aterial to be ). Additional st udies examining the relationship of interest to achievement are needed. I n addition to predicting achievement goals and achievement i nterest also relates to class attendance in academic settings. In a survey of 220 college students, students indicated tha t the main motivator for attending class was that they considered the instructor, or the material, or both interesting (Gump, 2004). Therefore, in this study interest in psychology should have a direct positive effect on mastery goals and student attendanc e and a direct negative effect on performance approach and performance avoidance goals. Self Efficacy B eliefs Researchers have shown that student perceptions of self efficacy have consistently predicted achievement goal orientations (Green e & Miller, 199 6; Greene Miller, Crowson, Duke, & Akey 2004) and academic performance (Malka & Covington, 2005; Zimmerman & Kitsantas, 2005). Bandura (1986) defined self judgment s of their capabilities to organize and execute courses of action req uired to attain designated types of perceived self efficacy relates to their motivation to start, maintain, and direct behaviors toward academic outcomes, including learning. Students who believe they are competent and will do well on academic tasks are more likely to expend more effort and persist longer than those who believe they are less competent and able (Pintrich & Schunk, 2002). Researchers have reported links between self efficacy beliefs and achievement goal orientations (Bong, 2001; Fenollar et al. 2007; Greene et al. 2004; Vrugt, Langere i s, & Hoogstraten, 1997; Vrugt, Oort, & Zeeberg, 2002). Elliot (1999) has argued that students with

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30 high perceived self efficacy tend to adopt both mastery and performance approach goals and show high achievement wh ereas those with low perceived self efficacy tend to exert less effort and perform poorly. Vrugt et al (1997) and Vrugt et al. (2002) found support for El In addition, Fenollar et al (2007) found a direct positive effect of self efficacy beliefs on mastery goal orientation and a direct negative effect of self efficacy beliefs on performance avoidance goal orientation. The researchers found no direct relationship between perceived self efficacy and performance approach goals. Additional research is needed to examine these important relationships between perceived self efficacy and achievement goal orientations. On the basis of the findings abov e, I hypothesized that perceived self efficacy has a direct positive effect on mastery and performance approach goal orientations and a direct negative effect on performance avoidance goal orientation. Researchers have shown that self efficacy beliefs rela choice ( Fenollar et al. 2007; Greene & Miller, 1996; Miller Greene, Montalvo, Ravindran, & Nichols 1996). Students who feel confident in their abilities to succeed in academic settings are more likely to engage themsel ves in thinking and learning than those who are less confident (Pintrich, 1999; Pintrich & Schrauben, 1992). Several researchers have shown that perceived self efficacy is positively related to deep processing strategies in educational settings (e.g., Gree ne & Miller, 1996; Miller et al ., (1996); Salomon, 1984). Felonar et al. (2007) found that perceived self efficacy had a direct positive effect on deep processing cognitive strategies and a direct negative effect on surface processing strategies. These 198 6 ) claim that those high in perceived self efficacy will choose behavioral strategies that help them attain desired outcomes. According to these findings, I expect ed that self efficacy beliefs have a

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31 direct positive effect on deep processing cognitive strategies and a direct negative effect on surface processing strategies. Test Anxiety Although many believe ability and motivation are the primary predictors of academic performance, some researchers have re ported evidence that test anxiety is also related to phenomenological, physiological, and behavioral responses that accompany concern about possible negative consequences or failur 1998, p. 17). Theorists have disagreed as to the best way to operationalize and measure test anxiety. Instead of viewing test anxiety as one global dimension, some researchers have found it helpful to conceptualize test anxiety in four dimensions, namely, worry, tension, bodily symptoms, and test irrelevant thoughts (Benson & El Zahhar, 1994). Some researchers, however, have demonstrated that the more cognitive measures of anxiety (worry and test irrel evant thoughts) were most predictive of academic performance (McIlroy & Bunting, 2002). Although researchers have failed to agree on the cognitive processes that account for the relationship between test anxiety and achievement, the finding that test anxi ety affects academic performance is robust ( Seipp, 1991; Stowell & Bennett, 2010; Zeidner, 1998). Seipp (1991) conducted a meta analysis of 126 studies and found a negative correlation of r = .21 between test anxiety and academic performance. On a practic al level, Seipp found that students with low test anxiety outscored those with high test anxiety by almost a half of a standard deviation on academic assessments. In addition, McIlroy and Bunting reported negative relationships between test anxiety variabl es and test performance ( r = .34 for test irrelevant thoughts and test performance; r = .35 for worry and test performance), confirming previous research findings (e.g., Zeidner, 1998). Stowell and Bennett (2010) studied 68 students in a psychology cours e and

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32 found that test anxiety predicted exam performance in classroom ( r = .57) and online ( r = .28) settings. On the basis of these findings I predict ed that cognitive measures of test anxiety are negatively related to performance on exams in this stud y Course E ngagement achievement. Researchers have reported that students who use more elaborative learning strategies (i.e., connecting new information to previously learned information) perform better academically than those who use more surface strategies (i.e., rehearsal) ( Albaili, 1998; Elliot & McGregor, 2001; Elliot et al., 1999 ; Fenollar et al., 2007). Researchers measure student engagement in a variety of ways including attendance and participation in course activities. Those students who attend more classes tend to perform better academically than those who do not attend (Gunn, 1993; Snell, Mekies, & Tesar, 1995). Students who perform better on homework and cou rse activities also perform better than those who perform worse ( Cooper, 1989; Cooper, Robinson, & Patall, 2006; Paschal, Weinstein, & Wahlberg, 1984; Shernoff, Csikszentmihalyi, Schneider, & Shernoff, 2003; Trautwein, 2007 ). In th ese section s I review th e literature regarding learning strategies, attendance, homework, and course activities as it relates to th is study. Learning Strategies understanding. Individual differences i n the type of learning strategies students use relate to achievement. Although many students depend on rehearsal strategies, such as repeating terms over and over to learn them, others use more elaborative techniques that connect new information to existin g knowledge. Researchers have reported that learning strategies mediate the relationship between achievement goals and exam performance (see Fenollar et al., 2007;

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33 Vrugt & Oort, 2008). Undergraduates who adopt mastery goals tend to use more elaborative, de ep processing strategies than students who are more concerned with performance (Albaili, 1998; Elliot & McGregor, 2001; Elliot et al., 1999). Researchers have reported mixed results regarding the relationships between performance approach and performance a voidance goals with learning strategies. Many researchers failed to find relationships between performance goals and learning strategies, however, other researchers noted that many failed to make the distinction between performance approach and performance avoidance goals (Elliot et al., 1999; Wolters, 2004). When making the distinction between performance approach and performance avoidance goals, s ome researchers have found that performance approach goals relate positively to the use of rehearsal strategie s (see Dupeyrat & Martin, 2005; Elliot et al., 1999) whereas other researchers have found that performance approach goals relate positively to elaborative techniques (Wolters, 2004). Researchers have reported that learning strategies mediate the relations hip between achievement goals and a cademic performance Albaili (1998) found that performance goal orientation had a direct negative relationship to GPA, whereas learning goal orientation had an indirect relationship to GPA mediated by elaborative learning strategies and organization. As mentioned previously, Fenollar et al. (2007) reported that learning strategies mediated the relationship between achievement goals and academic performance. Specifically, Fenollar et al. reported that elaborative learning s trategies mediated the relationship between mastery goals and achievement. Fenollar et al. also found that performance approach goals predicted surface processing (rehearsal strategies), but rehearsal str ategies did not predict achievement. Performance avo idance goals did not predict learning strategies. These findings support earlier research that demonstrated that the relationship of learning goal orientation to achievement was

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34 mediated by elaborative learning strategies (Greene & Miller, 1996; Nolen, 1988). Although the research on this topic is correlational and prior knowledge and ability are not typically controlled, these findings suggest that the uses of more elaborative learning strategies should be p ositively related to scores on exams. Attenda nce It seems likely that students who attend more classes perform better on assessments than those who attend less, and research has shown that class attendance is related to academic achievement (Gunn, 1993; Snell, et al. 1995 ; Shimoff & Catania, 2001 ). Snell et al. found that students who attended 95% of the lectures in social science courses were more likely to earn grades of A or B than those who attended less, even when students dropping out of the course was controlled. Gunn reported a correlation o f r = .66 between attendance and achievement in introductory psychology courses. Shimoff and Catania (2001) found that students who were required to sign in at each class session attended introductory psychology courses more frequently and answered lecture based and text based questions on weekly quizzes more accurately than students not required to sign in However, not all research has found a significant relationship between attendance and achievement (Hardy et al., 2003). Hardy et al. acknowledged that their use of self report data for attendance may have affected the results and suggested that other researchers make an effort to collect behavioral data regarding class attendance to get more accurate estimates of the relationship between attendance and a chievement. Most of the research linking attendance to achievement has relied on correlational data. The relationship between attendance and achievement might be explained through their association with other variables, including interest in course materia l (Gump, 2004) and mandatory attendance polic ies (Gump, 2004) In a pilot study, I found that attendance did not directly predict exam performance, but had an indirect

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35 effect on exam performance through course assignments (homework, course activities, and quizzes). In this study I record ed attendance for each class period rather than us ing self report data. With a behavioral measure of attendance, I expect ed attendance is positively related to performance on homework, course activities, and quizz es. Homework initiated method for directing students to study more with positive academic outcomes (Cooper, 1989; Cooper, et al. 2006; Paschal, et al. 1984; Trautwein, 2007). It seems plausible that homework assignments would be related to achievement. Homework should increase exposure to course material, focus students on the most important aspects of content, and allow students to practice self initiated learning. Cooper et al. (2006) reported a synthesis of homework research between 1987 and 2003. Cooper et al. reported beta weights between .05 and .28 that linked homework and achievement for studies involving high school studen ts. However, most of the research reviewed by Cooper et al. involved self reported time spent on homework rather than actual homework data. The researchers suggest ed that future researchers use actual homework performance to predict achievement. In light o f these suggestions, I plan ed to investigate the relationship between homework and exam performance within the context of a model that includes prior knowledge (psychology pretest) and ability (GPA and reading ability), test anxiety and motivation (implicit beliefs about intelligence, perceived self efficacy, achievement goals, and interest) variables. I expect ed to find that homework performance relate s positively to performance on exams. Course Participation aries according to the extent of their involvemen t in course activities in class. Shernoff et al. (2003) reported that highly challenging course activities (e.g., solving a

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36 problem in a group) promoted higher engagement than low challenge activities (e.g., listening to lecture), and students reported more engagement when perceived control and task relevance were high. In addition, Marks (2000) reported that authentic work (work that students perceived as relevant to their goals ) increased engagement by enco uraging higher order thinking, depth of knowledge, and in class discussions of material. Although some instructors may provide challenging class activities and increase task relevance, background variables like ability, motivational, and personality influe nce whether students become fully engaged. Many measures of student engagement in class exist, including self report measures (see Ha n delsman Briggs, Sullivan, & Towler, 2005) and more behavioral measures, like the quality of written responses to reflecti ve questions, note taking, and summaries of group activities. By examining such measures of engagement in class activities researchers may avoid the social desirability bias of self report measures. As stated previously, Hardy et al. (2003) found that cour se involvement variables (attendance and participation) did not predict exam performance. Other researchers, however, have reported a link between course involvement and exam performance (see Hill, 1990). Hardy et al. acknowledged that their use of self re port data on course involvement may have affected the results and suggested that other researchers make an effort to collect objective data regarding lecture note taking to get more accurate estimates of class involvement. In my study, I measure d course pa rticipation accuracy of information. Utilizing these behavioral measures of student involvement, I hypothesized that students who complete course activities more accurately perform better on exams than those who are less accurate. Quizzes Researchers have shown that quizzes relate to exam performance when quiz content is

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37 whereas others use announced quizzes as mot ivation for students to study more frequently and gain vital feedback regarding mastery of the course material. Frequent quizzing tends to reduce the fixed interval effect that is, the tendency of students to study with greater frequency right before an exam then cease studying until the next exam in courses where instructors use only exam scores to calculate final course grades (Passer & Smith, 2001). Thus, quizzes may serve two functions: providing necessary feedback and encouraging more regular studyi ng. Researchers have presented mixed results from studies examining the relationship of announced quizzes on exam performance. Some researchers have reported that announced quizzes improved performance on exams (Geiger & Bostow, 1976; Johnson, Joyce, & Sen 2002; Lass, Morzuch, & Rogers, 2007; Noll, 1939), whereas others have reported no effect (Azorlosa & Renner, 2006; Lumsden, 1976 ; Wilder, Flood, & Stromsnes, 2001 ). Johnson et al. (2002) reported that students who spent more time repeatedly taking online quizzes performed better on course exams than those who spent less time taking online quizzes. Similarly, Lass, Morzuch, and Rogers (2007) reported that feedback from online quizzes was associated with small increases in course exam scores. In contrast, A zorlosa and Renner (2006) reported that students reported studying more and feeling more prepared for exams in sections that included announced quizzes. However, the researchers reported that there were no differences in exam performance between quiz and n o quiz sections. A serious flaw in the study, however, was the inconsistency between quiz format (multiple choice) and exam format (essay) and the practice of providing the exam questions several weeks prior to the exam. It seems that students in the quiz and no quiz sections would be able to successfully prepare for the exam questions regardless of which condition they were in.

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38 In this study I examine d the relationship between quizzes and exams where quiz and exam formats were similar, and where quiz fee dback provide d students with i nformation about their mastery of course objectives. I hypothesized that students who perform well on quizzes in the course also perform well on exams. Research Question The research question investigated in this study was do e s prior knowledge, ability (GPA, reading ability), motivation (implicit theories of intelligence, achievement goal orientation, interest), test anxiety and course engagement (learning strategies, attendance, homework, course participation quizzes) predic t performance on course examinations in community college psychology courses, with ethnicity, gender, number of college credits earned, and age controlled. Figure 2 2 presents a theoretical model of the relationships that we re tested in this study. Hypotheses The following hypotheses were examined in the study. Hypothesis 1. Students who enter community college psychology courses with greater prior knowledge of the subject matter perform better on c ourse assignments (homework, course participation a nd quizzes ) and exams than those with less knowledge. Hypothesis 2 Prior GPA has a direct positive effect on exam performance and an indirect effect on exam performance through course assignments (homework course participation and quizzes ) Hypothesis 3 Reading ability as measured by college entrance exam reading scores relate s positively to achievement on c ourse assignments (homework, course participation and quizzes ) and exam performance. Hypothesis 4 Implicit theories of intelligence of students re late to their achievement goal orientation. Specifically, those with entity theories of intelligence are more likely to adopt performance goal orientations, whereas those with incremental theories of intelligence are more likely to adopt mastery goals. Hyp othesis 5 a. Students with performance goal orientations are more likely to use shallow processing learning strategies (rehearsal), whereas those with mastery goals are more likely to use deeper processing learning strategies (elaboration).

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39 Hypothesis 5b. Achievement goals predict performance on course assignments (homework, class participation, and quizzes). approach goals have a direct positive effect on course assignments. P erformance avoidance goals have a direct negat ive effect on c ourse assignments. Hypothesis 5 c Students who adopt mastery goal orientations report lower levels of test anxiety than those who adopt performance goals. Hypothesis 6 a. Interest has a direct positive effect on mastery goal orientation and h as a direct negative effect on performance approach and performance avoidance goal orientations. Hypothesis 6 b. Interest predict s class attendance. Students with higher interest in the course material are more likely to attend class than those with less i nterest. Hypothesis 6c. Interest predict s cognitive learning strategies. Students with higher interest levels are more likely to use elaborative learning strategies than those with less interest. Those students with lower interest levels are more likely to use rehearsal strategies than those with higher interest. Hypothesis 7a. Perceived s elf efficacy has a direct positive effect on mastery and performance approach goal orientations and a direct negative effect on performance avoidance goal orientation. Hypothesis 7b. Perceived s elf efficacy has a direct positive effect on deep processing cognitive strategies and a direct negative effect on surface processing strategies. Hypothesis 8 Students who report higher levels of test anxiety perform worse on exa ms than those who report lower levels of test anxiety. Hypothesis 9 Students who utilize more elaborative learning strategies perform better on exams than those who use rehearsal strategies. Hypothesis 10 Students who attend more classes perform better o n homework, course participation, and quizzes Hypothesis 1 1 Students who perform better on homework and class participation assignments perform better on exams. Hypothesis 1 2 Students who perform better o n quizzes perform better on exams.

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40 Figure 2 1. Final structural equation model of Fellonar et al. (2007). [Adapted from Fenollar et al. (2007). The final structural equation model. (Page 883, Figure 2). The British Psychological Society: Leicester United Kingdom.]

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41 Figure 2 2. Theoretical test anxiety, and course engagement characteristics to performance on exams

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42 CHAPTER 3 METHOD Participants A convenience sample of a pproximately 270 undergraduates enrolled in my psychology courses (General Psychology, Developmental Psychology, The Psychology of Social Behavior) at a community college during the s pring 2008 (January through May) semester were asked to participate in the study. The students range d in age from tradit ional aged college students to mature students of non traditional ages. Although some high school students were enrolled in these classes, their scores on the measures were not included in the study because of the difficulty of seeking parental consent for their participation in the study and the likelihood that some parents would refuse participation possibly creating a systematic bias in the results of the study. Measures Psychology K nowledge P retest An examination consisting of multiple choice questions from the unit exams in the course was administered to students during the first week of classes. To obtain a more accurate measure course content knowledge, they were asked not to guess if they did not know the was included for each question for students to choose if they were not reasonably sure they knew the answer to the question. College GPA College GPA was system.

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43 Rea ding Ability Reading ability was assessed using college placement test scores on the reading section of the ACT or The Accuplacer College Placement Test (CPT). The CPT was developed by the College Board to provide academic readiness information. This compu terized placement test skill level and automatically determines which ques tions are asked based upon answers to pr evious questions. The CPT is not a timed test. There are four tests available: Reading Comprehension, Sentence Skills, Arithmetic and Elementary Algebra. A concordance table was used to translate CPT reading scores to ACT equivalents (Aims Community College, 1999). Im plicit Theories of Intelligence A 3 item scale published in Dweck, Chiu, and Hong (1995) was used to measure implicit theories of intelligence. Participants indicate d their agreement with the three statements that measure entity theories of intelligence on a 6 point Likert type scale rangin g from (1) strongly agree to (6) strongly disagree scores of 393 undergraduates Achievement Goal Orie ntation The achievement goals questionnaire by Elliot and Church (1997) was used to assess achievement goals for the course. This questionnaire consists of six questions for each of the three achievement goals in the goal orientation framework. For master y goals, for instance, participants indicate d their agreement with statements concerning their desire to understand the performance approach goals, items ref er to the extent to which students are focused on

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44 demonstrating their competence It is important to me to do well compared to others in this class. Interest and Learning Strategies Three subscales of the Motiv ated Strategies for Learning Questionnaire (MSLQ ) ( Pintrich et al. 1991 ) comprising 16 questions representing task v alue (interest) and c ognitive strategy use (rehearsal and elaboration ) were used in the study ( Pintrich et al., 1991 ). Internal consistency estimates reported by Pintrich et al. of the factor scores to be used in this study reported in the MSLQ test manual are as follows: Task Value, Rehearsal, Elaboration, Self Efficacy B eliefs The Self Efficacy for Learning and Performance subscale of the MSLQ was used in this study (Pintrich et al., 1991). Nine questions comprise the scale upon which participants indicate d their agreement with statements on a 7 poi nt scale from 0 ( not at all true of me) to 7 ( very true of me ). Sample items include d I expec I'm certain I can understand Cronbach alpha of .93 for a sample of 380 university and community college students. Test Anxiety The Revised Test Anxiety Scale (Benson & El Zahhar, 1994) is a four factor 20 item scale. The four factors include Worry (6 items), Tension (5 items), Bodily Sympto ms (5 items), and Test Irrelevant Thoughts (4 items). Participants respond to the items on a 4 point Likert type scale ranging from (1) almost never to (4) almost always with higher scores indicating higher levels of test anxiety. Only the Worry uring the test I think about how I should have Test Irrelevant Thinking were measured in this study because

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45 prior researc h has demonstrated that these cognitive measures of anxiety are most predictive of academic performance (McIlroy & Bunting, 2002) Benson and El Zahhar reported reliability estimates of for the Worry scores and for the Test Irrelevant Thinking scores of 202 American undergraduate students. Because both of these scales measure cognitive distractions while taking a test the Worry and Test Irrelevant T hought item scores were combined and used as a global indicator of test anxiety Class A ttendance Class attendance was measured by collecting student signatures on a sign in sheet each class period throughout the semester. Attendance scores were calculated for each participant by multiplying the number of days attende d by a variable that standardizes the attendance scores for students who attended 3 days a week for 50 minute periods with students who attended 2 days a week for 75 minute periods. Attendance scores were summed creating an overall attendance total for eac h student. Homework A ssignments Each week, students answer ed five open ended conceptual questions that require d them to apply their understanding of concepts in the reading to concrete scenarios. Homework assignments were collected weekly and assigned a score that reflect s the accuracy of the All homework scores were added together to create an overall homework total for each student Course Participation I prepared and distributed a course packet for s tudents that include d detaile d unit learning objectives, an outline for course lectures, in class group activities, in class reflective writing activities, and out of class learning activities. Students were required to complete the exercises in the course packet for participation poi nts. At the end of each unit, the instructor collected and

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46 reviewed the course packets and assigned a score based on the accuracy and thoroughness of the responses. A course participation score w as calculated by summing the total of all course participation points earned by each student. Exams Exam scores serve d as the outcome variable in this study. Four assessments were administered during the course in the form of unit exams. Each unit exa m consist ed of information covered only in the specified unit. Cumulative exams were not administered. The exams consisted of multiple choice questions and essay questions. Each participant had one overall exam score comprised of the sum of all four course exam totals. Procedures At the beginning of the semester students were asked to sign a consent form if they agree d to give their permission for me to use their data in my dissertation study. Students were required to complete all the measures to be used in this study as part of the course whether they sign ed the consent form or not. Students were asked to complete the Psychology Knowledge Pretest and the measures of interest, goal orientation, test anxiety, and implicit theories of intelligence during the first week of class using WebCT, electronic software that allows students to take surveys, exams and quizzes online. As the instructor of the courses, I did not have access to the results of the measures listed above or knowledge of which students consen t ed to participate in the study for the duration of the semester to reduce experimenter biases. Data on attendance, homework, participation, quizzes, and exams were collected during the semester. Pre semester GPAs and reading admission test scores were obtained on each

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47 CHAPTER 4 ANALYSIS OF DATA The purpose of this study was to test a model of student achievement to see whether prior knowledge and ab ility (GPA, reading ability), motivation (implicit theories of intelligence, achievement goal orientation, interest), test anxiety and course engagement (learning strategies, attendance, homework, course participation quizzes) predict performance on cour se examinations in community college psychology courses, with ethnicity, gender, number of earned college credits, and age controlled. In this chapter I report the descriptive statistics, tests of the hypothesized model, revisions to the model, and outcom es of the tests of the research hypotheses. Descriptive Statistics The sa mple consisted of 210 undergraduate students enrolled in psychology course s at a community college in the southeastern United States. More female students (75%) participated in the st udy than male s (25%). Most of the students ident ified their ethnicity as White (73%). The remaining participants identified themselves as Hispanic ( 12% ) Black (8%), A merican Indian (5%), Asian (0.5%), and o ther (1.4%) Age of the participants ranged from 18 to 61, with a mean of 21. The mean number of college credit hours earned by the participants was 35. Participants were enrolled in one of three psychology courses at the college: General Psychology (50%), Developmenta l Psychology (35%), or The Psychology of Social Behavior (15%). Means and standard deviations for all of the variables are presented in Table 4 1. The correlation s among the variables are presented in Table 4 2. Analysis of the Proposed Model I estimated the proposed model using Mplus. I controlled for gender, age, ethnicity, and number of college credit hours earned by including these variables as predictors for all of the

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48 endogenous variables in the model. To control for possible effects due to the differences in courses (General Psychology, Developmental Psychology, The Psychology of Social Behavior), course was used as a predictor of course assignments (homework, class p articipation, quizzes) and exam performance in the model The initial go odness of fit test and indices indicated poor model fit (91 ) = 204.26 p < 0 1 comparative fit index (CFI) = .89 Tucker Lewis index (TLI) = .72 root mean square err or of approximation (RMSEA) = .08 I evaluated modification indices in an effort to improve the fit of the model. The largest modification index was 23.2 0 indicating that a direct path between GPA and attendance would improve the model fit. I added GPA as a predictor of attendance and re es timated the model. The revised model still indicated poor fit (9 0 ) = 179 .0 4 p < 0 1 CFI = .91 TLI = .78 RMSEA = .07 The l argest modification index (21.77 ) indicated that allowing the errors to correlate between perceived self efficacy and interest would improve the model. However, the test statistics showed the model did not fit the data ( 89 ) = 155.46 p < 0 1 CFI = .94 TLI = 83 RMSEA = .06 After examining the revised model, I decided to allow the errors of elaboration and rehearsal to correlate based on the large modification index (16. 48 ). I allowed these errors to correlate, ran the analysis again, and improved the model fit. The model fit was still poor despite improvements in the fit indices ( 88 ) = 138.07 p < 0 1 CFI = .9 5 TL I = 87 RMSEA = .0 5 The modification index between homew ork and participation was 13.99 I added a path between homework and class participation, then re estimated the model. The goodness of fit test and indices indicated a good fit (87 ) = 122 71 p < 0 1 CFI = .97 TLI = .91 RMSEA = .04. Table 4 3 includes a list of the direct, indirect, and total effects of the relationships tested in the model. Table 3.4 presents the significant and nonsignificant effects for the proposed and revised models.

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49 Rese arch Hypotheses The proposed research hypotheses were tested with gender, age, ethnicity, and number of earned college credits controlled This section includes the results of the hypotheses tests. Please see Table 3 .4 for a list of direct and indirect effects tested in the model. Specific indirect effects are not presented on Table 3.4 but are presented in the description of the results of the hypotheses tests where these effects were found. Hypothesis 1 was s tudents who enter community college psycholo gy courses with greater prior knowledge of the subject matter perform better on course assignments (homework, course participation quizzes) and exams than those with less prior knowledge. Hypothesis 1 was partially supported. S tudents with greater prior k nowled ge of psychology perform ed be tter on quizzes ( = .09 ; p = 05 ) and exams ( = .15 ; p < 0 1 ) than those with less prior knowledge. However, prior knowledge did not predict performance on homework or participation Hypothesis 2 was p rior GPA predict s exam performance and ha s an indirect effect on exam performance through course assignments (homework, course participation and quizzes). Prior GPA did n ot show a d irect effect on exam or quiz performance However, prior GPA had a direct effect on homewor k ( = .17 ; p < 0 1 ) and class participation ( =.25 ; p < 0 1 ) GPA had an indirect relationship with exam t = .03; p < 0 1 ) Other significant indirect effects between GPA and exam were mediated through attendance and = .04; p < 0 1 ), attendance, hom ework, and quiz ( .03; p < 0 1 ), and class participation, hom ework, and quiz ( .01; p = 01) I added a path between GPA and attendance after reviewing modification indices and found a direct effect of prior GPA on atten dance ( .34; p < 0 1 ). Hypothesis 3 was r eading ability as measured by college entrance exam reading scores relate s positively to achievement on course assignments (homework, course participation and quizzes) and exam performance. Reading ability relat e d positively to achievement on homework

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50 ( .1 7 ; p < 0 1 ) and exam performance ( .41 ; p < 0 1 ) However, r eading ability did not predict class participation and qu iz performance Hypothesis 4 was i mplicit theories of intelligence of students enrolled in community college psychology courses relate s to their achievement goal orientation. Specifically, those with entity theories of intelligence were expected to be more likely to adopt performance goal orientations, whereas those with incremental theories of intelligence were expected to be more likely to adopt mastery goals. I mplicit theories of intelligence did not predict whether students were more likely to adopt mastery goals, performance approach, or performance a voidance goals Hypothesis 5a was s tudents with performance goal orientations are more likely to use shallow processing learning strategies (rehearsal), whereas those with mastery goals are more likely to use deeper processing learning str ategies (elaboration). Students with pe rformance avoidance goal orientations were more likely to report use of rehearsal strategies ( .23 ; p < 0 1 ). Those with maste ry goals were not more likely to report using elaborative learning strategies. Performance approach goal orientations did not predict whether students were more likely to use rehears al strategies Hypothesis 5b was s approach goals have a direct positive effect on performance on course assignments, wh erformance avoidance goals have a direct negat ive effect on performance on c ourse assignments. Achievement goals did not predict performance on course assignments in this study. Hypothesis 5 c was s tu dents who adopt mastery goal orientation s report lower levels of test anxiety than those who adopt performance goals. Mastery and performance approach goal orientations did not predict self reported anxiety in this study. However, performance avoidance

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51 goal o rientations ha d a direct positive relationship to test anxiety ( .44 ; p < .0 1 ). In addition, performance avoidance goals had an indirect effect on exam performance through anxiety ( = .06 ; p = 0 4 ). Hypothesis 6a was i nterest has a direct positive effect on mastery goal orientation and a direct negative effect on performance approach and performance avoidance goal orientations. I nterest had a direct positive effect on mastery goal orientation ( .54 ; p < 0 1 ). The results also showed that interest had a direct negative effect on perform ance avoidance goal orientation ( .18 ; p = 01 ) However, interest did not predict performance approach goa l orientation Hypothesis 6b was i nterest predict s class attendance. Specifically, I expected that students with higher interest in the course material would be more likely to attend class than those with less interest. In th is study, in terest did not predict class attendance Hypothesis 6 c was p articipants who indicate high er levels of interest in the course are more likely to use elaborative learning strategies than those with lower interest. As predicted, interest had a direct positive effect on elaboration ( .20; p < 0 1 ). I also hypothesized a direct negative effect of interest on rehearsal. Interest did n ot predict rehearsal in this study. Hypothesis 7a was p erceived self efficacy has a direct positive effect on mastery and performance approach goal orientations and a negative effect of perceived self efficacy on performance avoidance goal orientation. Se lf efficacy beliefs had a direct positive effect on mastery ( .17; p < 0 1 ) and performance approach ( .29 ; p < 0 1 ) goals, and a direct negative effec t on performance avoid ance ( .24; p < 0 1 ) goals. Hypothesis 7b was p erceived self efficacy has a direct positive effect of on elaboration strategies and a direct negative effect of perceived self efficacy on rehearsal strategies. As hypothesized, perceived self effica cy predicted elaboration ( .25 ; p < 0 1 ). Although

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52 perceived self effi cacy pred icted rehearsal ( .26 ; p < 0 1 ), the relationship was a direct positive relationship rather than the predicted negative relationship. Hypothesis 8 was s tudents who report hi gher levels of test anxiety perform less well on exams than those who report lower levels of test anxiety. Consistent with the prediction, anxiety had a direct negative eff ect on exam performance ( .13 ; p = 03 ). Hypothesis 9 was s tudents who use more elaborative learning strat egies perform better on exams tha n those who use rehearsal strategies. In this study, rehearsal did not predict exam performance. However, participants who reported using more elaborative strategies performed better on exams ( .12 ; p = 04 ). Hypothesis 1 0 was s tude nts who attend more c lasses perform better o n homework, course participation and quizzes As expected, attendance predicted accuracy on homework ( .45 ; p < 0 1 ), class participation ( .64; p < 0 1 ) and quiz performance ( .27 ; p < 0 1 ). Attendance had a significant indirect effect on exam performance thro ugh quiz ( .11 ; p < 0 1 ), homework and quiz ( .08 ; p < 0 1 ), and class participation, homework, and quiz ( .03; p < 0 1 ). Hypothesis 1 1 was s tudents who perform bet ter on homework assignments and course participation perform better on exams. H omework and course participation did not have direct effect s on exam performance. However, I found a significant indirect effect of homework on exam through quiz performance ( .17 ; p < 0 1 ). I also found a signifi cant indirect effect of course participation on exam through homework and quiz performance ( .05; p < 0 1 ) and the path I added on the basis of modification indices between course participation and homework was significant ( .28 ; p < 0 1 ). Hypothesis 1 2 was s tudents who per for m better on quizzes perform better on exams. Quiz performance had a positive direct effect on exam scores ( .41 ; p < 0 1 ).

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53 T able 4 1 Means and standard deviations Variable N M SD Min Max Age 210 21.05 5.51 18.00 61.00 College c redit h ours e arned 209 34.65 21.38 0.00 112.00 Reading 178 21. 54 4. 51 13.00 32.00 GPA 206 3.0 2 0.72 0.00 4.00 Attendance 210 60. 01 10.9 1 13.92 69.60 Participation 186 368.61 47.42 58.00 400.00 Homework 209 73.3 2 24.6 3 0.00 112.00 Quiz 210 292.2 7 8 8.83 16.00 400.00 Intelligence beliefs 201 12.55 3.49 3.00 18.00 Anxiety 200 20.4 3 5.98 10.00 37.00 Mastery g oals 201 34.6 2 4.51 15.00 42 .00 Performance a pproach g oals 202 24.93 8.1 1 7 .00 4 2 .00 Performance a voidance g oals 202 25.9 4 6.3 7 6.00 40.00 Elaboration 202 32.21 4.6 4 19.00 42.00 Rehearsal 203 20.02 4.29 7.00 28.00 Interest 201 35.9 5 4.1 0 21.00 42.00 Perceived s elf e fficacy 203 47.2 4 5.7 0 30.00 60.00 Prior k nowledge 194 10.78 4.0 0 0.00 20.00 Exam 184 410.03 52.65 261.60 505.60

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54 Table 4 2. Correlation m atrix ATT ANX MAS PAP PAV ELA INT REH PRT HWK QUZ ATT 1.00 ANX 0 5 1.00 MAS 09 1 0 1.00 PAP 0 1 04 1 8 1.00 PAV 0 2 *** 46 ** 19 0 8 1.00 ELA 02 01 *** 34 1 2 04 1.00 INT 01 01 *** 60 05 ** 22 *** 37 1.00 REH 11 11 08 01 17 *** 36 15 1.00 PRT *** 75 08 09 03 02 07 04 01 1.00 HWK *** 74 16 1 1 04 0 4 13 07 03 *** 7 4 1.00 QUZ *** 70 ** 20 1 4 0 1 0 6 0 7 08 0 2 *** 6 8 *** 70 1.00 EXM ** 31 *** 33 ** 2 3 07 1 5 ** 20 16 0 4 *** 43 *** 47 *** 57 SEF 04 16 *** 3 5 *** 29 *** 30 *** 36 *** 3 2 ** 18 0 1 03 02 RE A 06 17 1 9 0 2 0 7 1 3 11 0 5 09 ** 2 1 1 6 GPA *** 34 12 09 04 03 09 02 04 *** 48 *** 48 ** 4 1 IQB 00 04 06 11 00 07 03 07 06 0 1 04 PRE 05 18 16 06 0 7 ** 22 10 01 02 0 1 0 4 CRE .05 .10 .02 .01 .05 .11 .04 .04 .12 .03 .03 AGE .03 .06 .01 .01 .09 .09 .05 .02 .01 .06 .03 Note ATT = attendance ANX = anxiety MAS = mastery goals PAP = performance approach goals PAV = performance avoid goals ELA = elaborative strategies INT = interest REH = rehearsal strategies PRT = class participation HWK = homework QUZ = quiz EXM = exam SEF = self efficacy REA = reading GPA = prior grade point average IQB = intelligence beliefs PRE = pretest CRE = credit hours earned, AGE = age *p < .05. ** p < .01. *** p < .001.

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55 Table 4 2. Continued ATT ANX MAS PAP PAV ELA INT REH PRT HWK QUZ CR1 *. 14 01 .09 .08 03 05 1 0 0 1 *. 16 ***. 28 04 CR2 03 01 .04 .08 0 5 09 .04 01 .02 14 **. 20 ASI 0 3 11 ** 18 1 5 01 03 15 13 01 00 01 AMI 08 13 1 1 0 3 00 08 03 04 09 09 04 BLA 04 09 0 3 0 3 05 11 03 05 02 01 12 HIS 08 04 0 2 12 09 01 03 04 07 08 02 SEX 09 16 0 4 09 18 12 14 14 18 16 04 Note ATT = attendance ANX = anxiety MAS = mastery goals PAP = performance approach goals PAV = performance avoid goals ELA = elaborative strategies INT = interest REH = rehearsal strategies PRT = class participation HWK = homework QUZ = quiz CR1 = course 1 CR2 = course 2 ASI = Asian AMI = American Indian BLA = Black HIS = Hispanic SEX = sex. *p < .05. ** p < .01. *** p < .001.

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56 Table 4 2. Continued EXM SEF REA GPA IQB PRE CRE CR1 CR2 AGE ASI EXM 1.00 SEF *.15 1.00 RE A ***. 54 06 1.00 GPA ***.38 .12 19 1.00 IQB .11 .03 .00 .02 1.00 PRE ***.31 **.19 *. 18 .10 ** .19 1.00 CRE .04 .09 18 .08 .05 .10 1.00 CR1 .10 .09 05 .10 .06 .10 .12 1.00 CR2 .10 .15 .0 4 .06 .05 .14 ** .21 *** .73 1.00 AGE .02 .03 18 .01 .05 .10 ***.37 .07 .10 1.00 ASI .05 .09 .04 .03 .11 .05 .09 .05 .07 .13 1.00 AMI .04 .05 ** 26 .02 .11 .03 **.18 .08 .10 .01 .02 BLA .13 .01 20 .05 .00 .06 .03 .05 .00 .05 .02 HIS .00 .01 .0 4 .02 .02 .03 .06 .04 .10 .05 .03 SEX .11 .04 .11 .03 .06 .00 .08 .11 .06 .03 .04 Note EXM = exam SEF = self efficacy REA = reading GPA = prior grade point average IQB = intelligence beliefs PRE = pretest, CRE = credit hours earned CR1 = course 1, CR2 = course 2, AGE = age, ASI = Asian, AMI = American Indian, BLA = Black, HIS = Hispanic, SEX = sex. *p < .05. ** p < .01. *** p < .001

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57 Table 4 2. Continued AMI BLA HIS SEX AMI 1.00 BLA .07 1.00 HIS .09 .11 1.00 SEX .04 .08 .09 1.00 Note AMI = American Indian, BLA = Black, HIS = Hispanic, SEX = sex. *p < .05. ** p < .01. *** p < .001

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58 T able 4 3. Total, direct, and indirect effects in revised model Variable Effect INT ATT ANX MAS PAP PAV ELA REH PRT HWK QUZ EXM INT Total --.0 1 --***. 54 .06 .1 8 **.2 0 .1 2 --------Direct --.0 1 --***. 54 .06 .1 8 **.2 0 .1 2 --------Indirect ------------------------SEF Total ------**. 1 7 ***. 29 ** .2 4 ** .2 5 **.2 6 ------*.05 Direct ------**. 1 7 ***. 29 ** .2 4 ** .2 5 ** .2 6 --------Indirect ----------------------*.05 REA Total ----------------.0 3 ***.1 7 .0 5 ***. 4 7 Direct ----------------.0 3 ***.1 7 .0 5 ***. 41 Indirect ----------------------* .0 5 GPA Total --***.3 4 ------------***.2 5 ** .1 7 .0 3 ***. 2 5 Direct --***.3 4 ------------***.2 5 ** .1 7 .0 3 1 0 Indirect ----------------------***. 1 6 IQB Total ------.0 4 10 .0 0 ------------Direct ------.0 4 10 .0 0 ------------Indirect ------------------------PRE Total ----------------.0 2 .0 4 09 **. 19 Direct ----------------.0 2 .0 4 09 .1 5 Indirect ----------------------.04 ATT Total ----------------***.6 4 ***.4 5 ***.2 7 ***.2 6 Direct ----------------***.6 4 ***.4 5 ***.2 7 --Indirect ----------------------***.2 6 ANX Total ----------------------* .1 3 Direct ----------------------* .1 3 Indirect ------------------------Note INT = interest, ATT = attendance, ANX = anxiety, MAS = mastery goals, PAP = performance approach goals, PAV = performance avoid goals, ELA = elaborative strategies, REH = rehearsal strategies, PRT = class participation, HWK = homework, QUZ = quiz, EXM = exam, SEF = self efficacy, REA = reading, GPA = prior grade point average, IQB = intelligence beliefs, PRE = pretest. *p < .05. ** p < .01. *** p < .001.

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59 T able 4 3. Continued Variable Effect INT ATT ANX MAS PAP PAV ELA REH PRT HWK QUZ EXM MAS Total ----.0 2 ------.1 4 --.01 .0 2 .0 1 --Direct ----.0 2 ------.1 4 --.01 .0 2 .0 1 --Indirect ------------------------PAP Total ----.0 4 --------.0 6 .0 6 .0 4 .0 1 --Direct ----.0 4 --------.0 6 .0 6 .0 4 .0 1 --Indirect ------------------------PAV Total ----***.4 4 --------**.2 3 .0 3 .0 7 .0 4 ** 1 0 Direct ----***.4 4 --------**.2 3 .0 3 .0 7 .0 4 --Indirect ----------------------** 1 0 ELA Total ----------------------* .1 2 Direct ----------------------* .1 2 Indirect ------------------------REH Total ----------------------.0 4 Direct ----------------------.0 4 Indirect ------------------------PRT Total ------------------***.2 8 .1 5 *. 1 1 Direct ------------------***.2 8 .1 5 --Indirect ----------------------**.1 1 HWK Total --------------------***. 4 1 ***. 17 Direct --------------------***. 4 1 --Indirect ----------------------***. 17 QUZ Total ----------------------***.4 1 Direct ----------------------***.4 1 Indirect ------------------------Note INT = interest ATT = attendance, ANX = anxiety, MAS = mastery goals, PAP = performance approach goals, PAV = performance avoid goals, ELA = elaborative strategies, REH = rehearsal strategies, PRT = class participation, HWK = h omework, QUZ = quiz, EXM = exam *p < .05. ** p < .01. *** p < .001.

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60 T able 4 4. Effects proposed in original model and effects in revised model Variable INT ATT ANX MAS PAP PAV ELA REH PRT HWK QUZ EXM INT SEF * GPA IQB REA PRE ATT ANX MAS PAP PAV ELA REH PRT HWK QUZ Note. INT = interest ATT = attendance ANX = anxiety MAS = mastery goals PAP = performance approach goals PAV = performance avoid goals ELA = elaborative strategies REH = rehearsal strategies PRT = class participation HWK = homework QUZ = quiz EXM = exam SEF = self efficacy GPA = prior grade point average IQB = intelligence beliefs REA = reading PRE = pretest. = hypothesized relationship that was significant in the revised model = hypothesized relationship t hat was not significant in the revised model = significant relationship not proposed in original model

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61 Figure 4 1. test anxiety, and course engagement characteristics to performance on exams

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62 CHAPTER 5 DISCUSSION The purpose of this study is to test a model motivation, test anxiety and academic engagement individual differences on achievement on exams in un dergraduate psychology courses at a community college Specifically, I hypothesized prior knowledge, ability (GPA, reading ability), motivation (implicit theories of intelligence, achievement goal orientation, interest), test anxiety and co urse engagement (learning strategies, attendance, homework, course participation quizzes) predict performance on course examinations in community college psychology courses, with ethnicity, gender, number of college credits earned, and age controlled. In addition to replicating prior research findings in the area of student achievement, I expanded a model proposed by Fenollar et al (2007) by adding variables and including student performance on course assignments in lieu of a self reported measure of student effort. In this chapter, I discuss the results of this study as they relate to prior research and discuss implications for theory and practice In addition, I suggest direction s for future research. Prior Knowledge and Ability The findings from thi s study provide evidence that as expected, variation in background knowledge and ability predict academic achievement. Of all the variables included in the model, and quiz performance have the largest direct effect s on their perf ormance on exams. This finding is consistent with prior research ( see Fields & Cosgrove, 2000; Gerow & Murphy, 1980; Jackson, 2005; Kessler & Pezzetti, 1990; Roberts et al., 1990). I also found support for prior studies that have shown that students with p rior knowledge of a subject tend to perform better on quizzes and exams than those with less prior knowledge (see Alexander et al., 1994; Hudson & Rottmann, 1981). S ome researchers using correlational

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63 analysis have reported that overall college GPA predict s exam performance in college psychology courses (Hardy, et al. 2003) in my study GPA only has a direct effect on course assignments (homework and clas s participation) and ha s an indirect effect on exam performance through course assignments. In this sec tion, I discuss the results i n the areas of prior knowledge, GPA, and r eading comprehensi on as they relate to prior research and make recommendations for future research. Prior K nowledge Consistent with prior research ( Alexander et al., 1994; Hudson & Rottmann, 1981 ; Thompson & Zamboanga, 2003, 2004), the results of this study support the hypothesis that students with greater prior knowledge of psychology perform better on course assessments than those with less knowledge. Specifically, pretest scores p redict scores on qu izzes and exams This finding lends support to t he information processing theories proposed by Baddeley and Hitch (197 4) and Cowan (1998) that describ e how prior knowledge facilitates learning Contrary to theory, the analysis does not show that prior knowledge predicts accuracy on homework or class participation. If prior knowledge predict s learning and performance on course activities, one would expect s ignificant relationships among pretest with homework and class participation scores Alternative theories may account for the relationship between prior knowledge and performance on quizzes and exams. The relationship between th e pretest, quizzes, and exam scores may be affecte d by test taking skills in addition to accurate prior knowled ge I n this study, the pretest, quizzes, and exams were comprised primarily of multiple choice questions (exams included one written response question in addition to the multiple choice questions). Homework and class participation assignments were open ended questions that required written responses. Some students have better test taking skills for multiple choice tests than others (e.g. variability in the ability to narrow down choices to facilitat e better

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64 guessing) which may have contributed to the congruence in scores between th e pretest, quizzes, and exams (see Samson 1985) Due to the c onflicting findings in th is study additional research is needed to control for t est taking skills when estima ting the relationship between prior knowledge and learning. College G rade P oint A verage In contrast to findings reported by Hardy et al. (2003), p rior college GPA d oes not have a direct effect on exam or quiz performance in th is study. However the findin gs of th is study support the unconfirmed hypothesis tested by Hardy et al. that the relationship between prior GPA and exam performance is mediated by course attendance and performance on course assignments Prior GPA predict s performance on homework and class participation In addition, GPA ha s a significant indirect effect on exam. The effect of GPA on exam i s mediated through the following paths : homework and quiz ; atte ndance and quiz ; attendance, hom ework, and quiz ; class p articipation, hom ework, and quiz ; and attendance, class particip ation, and quiz Differences in the findings of this study and the research of Hardy et al. (2003) may be due to differences in measures of course engagement Hardy et al. used a self report measure of attendance and lecture involvement along with instructor reported homework scores to estimate student involvement in t he course. In addition, Hard y et al. asked students to report their own GPAs and college placement test scores In this study, I obtained actual attendance records, student generated class notes, homework scores and quiz grades to measure student engagement in the course and obtained college GPAs from student records rathe r than having participants estimate them. In sum, I relied less on self report data as measures of ability and class engagement that may be subject to biases and errors (e.g., inaccurate memories, social desirability). Hardy et al. had a smaller sample size ( N = 108) than the sample size in this study ( N = 210). In sum, future studies should further examine the relationship between GPA and exam

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65 performance in light of the conflicting findings between this study and other studies showing effects of GPA on achievement Reading C omprehension On the b as is o f pr ior research linking reading comprehension to exam performance ( Fields & Cosgrove, 2000; Gerow & Murphy, 1980; Jackson, 2005; Kessler & Pezzetti, 1990; Roberts et al., 1990 ), I predicted that ial college placeme nt exams predict s their scores on homework, class participation, quizzes, and exams. Of all the variables included in the model, reading ability performance. In addition, I found reading ability predicts achievement on homework However, reading ability does not predict class participation and quiz performance. Several possibilities might account for these mixed findings. First, homework assignments in th is study were completed prior to lecture s and students had to read the textbook to answer the questions accurat ely. Reading comprehension most likely plays a larger role in assignments where students must rely on text to generate their responses. Class participation assignments we re less text dependent. Participants worked together in groups and relied more on lecture presentation s than text for class participation activities Second, t he main difference between quizzes and exams involves the time allotted for each assessment. Quizzes are administered online through a learning man agement system and are timed whereas exams a re administered in class and a re not timed. Researchers have reported mixed results regarding how timed tests affect reading comprehensio n and achievement. S ome researchers have reported that time d tests reduce reading comprehension and performance of students with learning disabilities and normally achieving students (e.g., Halla, 1998) However, other researchers have reported that students with learning disabilities make larger gains than normal ly achieving students when assessments are not timed versus time d (e.g., Lesaux, Pearson,

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66 & Siegel, 2006). Students with learning disabilities tend to suffer greater deficits in performance than normally achieving students when tests are timed. Fu ture stud ies should examine how reading skill affects different types of as signments in addition to continuing to examine how time constraints may interfere with reading comprehension in student s with learning disabilities and normally achieving students. Motivatio n Motivation variables did not predict performance on exams, but some motivation variables had indirect effects on exam performance through other variables in the model Results do not support 2000 ) concept of implicit theories of intelligence. Ho wever, interest and perceived self efficacy predict achievement goal orientation and cognitive strategy use. Concerning achievement goals, this study add s support to previous findings regarding links between performance avoidance goals and the use of rehearsal strategies. However, p erformance approach goals fail to predict cognitive strategies in this study contrary to findings of other researchers (see Ell iot, 1997, 1999; Fenollar et al., 2007). Last, performance avoid goals predict self reported test anxiety However, mastery goals and performance approach goals d o not predict anxiety. In th e following section, I discuss the effects of the motivation varia bles in the model and make recommendations for future studies. Im plicit Theories of Intelligence (2000) research, I hypothesized that i mplicit theor y of intelligence predict their achievement goal orientation s Dweck repo rted that students with an entity theor y of intelligence were more likely to adopt performance goal orientations, whereas those with an incre mental theor y of intelligence were more l ikely to a dopt mastery goals. I n th is study, i mplicit theor y of intelligence does not predict whether they are more likely to adopt mastery goals, performance approach, or performance avoidance goals.

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67 ies of intelligence and achievement goals in this study may be due at least in part to the lack of variance in the scores on the measure of ies of intelligence. Other researchers have found weak or nonsignificant relationships between theories of intelligence and achievement goals (e.g., Dupeyra t & Marin 2005; Spinath & Stiensmeier Pelster, 200 1 ). Dupeyrat and Marin reported that entity and incremental theories of intelligence did not predict performance goals in their study of adults in a pr ogram earning the equivalency of a high school diploma Contrary to the model proposed by Dweck (2000), Dupeyrat and Marin found that students with entity beliefs about intelligence were less likely to adopt mastery goals. Dupeyrat and Marin suggested th at researchers explore other predictors of achievement goal orientation entity and incremental theories of intelligence as one continuous and unidimensional construct It is possible that people view some aspects of intelligence as fixed and some as malleable. In th is study, other motivation variables were included as predictors of achievement goals, namely, interest, and perceived self efficacy Both interest and perceived self efficacy predict achievement goals. Interest ha s a direct positive effect on mastery goal orientation and a direct negative effect on perform ance avo idance goal orientation Perceived s elf efficacy predicts mastery performance approach an d performance avoidance goals. In light of the findi ngs of this study, further research in this area should include student, instructor, and course structu re variables that might predict achievement goal orientation. Achievement G oal O rientation In support of the achievement goal theory of Elliot et al. (19 99) I found that pe rformance avoidance goal orientations predict the use of rehearsal strategies The findings support the theory that students who adopt performance avoidance goals are more likely to make use of more shallow processing strategies. In con trast to goal theory, in this study mastery goals do not

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68 predict elaborative learning strategies although the effect approached significance with a probability of .08 The relationship between mastery goals and elaboration reported by Fenollar et al. (2007) may have been modified by the inclusion of interest as a predictor of elaboration in the present study. I found that interest and perceived self efficacy are stronger predictors of elaboration than mastery goals. I nconsistent results have be en reported in the literature regarding the link between performance approach goals and cognitive strategies (see Meece, Blumenfeld, & Hoyle, 1988; Harackiewicz, Barron, Carter, Letho, & Elliot 1997). On the basis of the Fenollar et al. (2007) model, I hy pothesized that performance approach goals hav e a positive effect on rehearsal strategies and an indirect and positive effect on exam performance In contrast to the findings of Fenollar et al. (2007), in this study performance approach goal orientations d o not predict whether students are more likely to use rehearsal strategies. Lack of a significant relationship between performance approach goals and learning strategies has been reported by other researchers (Middleton & Midgley, 1997; Wolters 2004). on doing better than others may have less to do with their choice of study strategies and more to do with other outcomes such as self concept, self consciousness, and test anxiety. Additional research is needed to clar ify the inconsistencies in the findings of these studies Contrary to my expectations, in this study achievement goals do not predict performance on class assignments. Fenollar et al. (2007) found that achievement goals did not directly affect academic per formance but rather mediated the effect on performance through choice of cognitive strategies and effort expended on course assignments Fellonar et al. used a self report measure of effort on course assignments, whereas I used behavioral measures of stu de nt engagement in this study. In this study, m astery, performance approach, and performance avoidance goals do

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69 not predict homework, class participation, or quiz performance in this study. Student perceptions of effort most likely differ from behavioral mea sures of engagement. When indicating effort on self report measure s, students may be more susceptible to self presentation biases or more likely to overestimate their actual effort In the future, researchers should further examine how achievement goals re late to performance on course assignments. Researchers have reported mixed results concerning the link between achievement goal orientations and test anxiety (Middleton & Midgley, 1997; Skaalvik, 1997). Middleton and Midgley reported that mastery goals we re unrelated to test anxiety whereas performance goals were positively associated with test anxiety. In accordance with their findings, I predicted that s tudents who adop t mastery goal orientations report less test anxiety than students who adopt performan ce goals. The results of this study, however, were similar to mastery goal orientation does not predict test anxiety. In addition, performance approach goal orientation also does not predict self reported anxiety in this study. Middleton and Midgley (1997) and Skaalvik (1997) reported that p erformance avoidance goals were positively related to test anxiety. Consistent with th ese studies performance avoidance goal orientations ha ve a direct positive relationship on test anxiety i n this study In addition, performance avoidance ha s a small indirect effect on exam performance through anxiety These findings support a growing consensus in the research that students who seek to avoid being labeled as incompetent in relation to others tend to also report greater anxiety in testing situations than students who are less likely to hold performance avoidance goals Interest As predicted, interest ha s a direct positive effect on mastery goal orientation Students who view the informat ion in the course as useful, personally meaningful, and interesting are more likely to report a desire to gain a deeper and more thorough understanding of the material The

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70 results also show that interest has a direct negative effect on perform ance avoidance goal orientation Th at is, students with less interest in psychology a re more likely to worry about their performance in the class I nterest does not predict performance approach goal orientation in this study In addition to the relationships between interest and achievement goals, interest predicts elaboration in this study T hat is participants who indicat e high interest in psychology a re more likely to report making connections between course concepts and us e deeper processing strategies. This finding corresponds to interest had an indirect effect on exam performance through learning strategies. Although the correlation between interest and rehearsal strateg ies was significant ( r = .15; p = .03) interest does not predict rehearsal strategies in the model. R esearchers need to further explore the links between interest and cognitive strategies. In a survey of undergraduate perceptions of variables th at motivate course attendance, Gump (2004) indicated that students were more likely to attend class if they found the instructor, the material, or both interesting. in this study with higher inte rest in the course m aterial a re not more likely to attend class than those with less interest. Although intention to attend class on the basis of interest I measure d and actual attendance behaviors However, i n this study, the possibility of finding an effect of interest on attendance was limit ed ost participants report high levels of interest in the course material ( M = 35.95 SD = 4. 10) where 42.00 i s the highest possible score on the interest scale. Also, students a re given points for attendance as part of their final grade in the course, although the weight of attendance points on the final grade i s small

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71 (5%). Mean attendance point s for the study indicate that atten dance i s high overall ( M = 60.01, SD = 10.91) with 69 .6 points indicat ing perfect attendance in the course. In the future, researchers should examine the relationship between interest and attendance in academic areas of varying interest to students. In addition, researchers might examine how interest predict s attendance in the absence of an incentive for attendance. It seems plausible that when attendance is not rewarded by course policies that students with more intrinsi c motivation (i.e., personal interest in the subject material) w ill be more likely to attend. Self Efficacy B eliefs In accord with previous findings ( Fenollar et al., 2007; Vrugt et al ., 1997; Vrugt et al., 2002) and the achievement goal theory of Elliot et al. (1999), self efficacy beliefs ha ve a direct positive effect on mastery and performance approach goals, and a direct negative effec t on performance avoid goals. That is students high in perceived self efficacy are likely to adopt mastery and perform ance approach motivation goals than students low in perceived self efficacy. In contrast, those low in perceived self efficacy a re more likely to adopt performance avoid goals that is, to seek ways to avoid revealing their low performance to self and othe rs, than students high in perceived self efficacy. These findings add support to a well established trend in the literature regarding self efficacy beliefs and achievement goals. I expected that perceived self efficacy has a direct positive effect on elab oration strategies and a direct negative effect on rehearsal strategies. As hypothesized, self effica cy beliefs predict elaboration Th at is students who a re more likely to express higher confidence in their perceived ability to do well in the course report using elaborative strateg ies more often than students with lower perceived self efficacy. Although perceived self effi cacy predicts rehearsal t he relati onship is a direct positive relationship rather than the predicted negative relationship reported by Fenollar et al. (2007). Other researchers ha ve reported positive relationships between

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72 perceived self efficacy and rehearsal strategies consistent with the findings in this study (see Bartels, Magun Jackson, & Kemp, 2009). Bartels et al., (2009) reported that perceived self efficacy significantly predicted both rehearsal ( .63) and elaboration ( .31) in their regress ion analysis The findings in this s tudy suggest that students high in perceived self e fficacy are likely to use both elaborative and rehearsal strategies. In the f uture research ers should explore the inconsistencies in the literature regarding perceived self efficacy and rehearsal cognitiv e strategies. Test Anxiety In addition to the effects of ability and m oti vation, I found that test anxiety play s a role in achievement. C onsistent with prior research test anxiety ha s a direct negative effect on exam performance ( see Seipp, 1991; Ze idner, 1998) Students who report high levels of test anxiety tend to perform less well on exams compared to students with less anxiety. This finding lends support to a robust body of research relating test anxiety with exam performance ( for a review, see Seipp, 1991; Zeidner, 1998 ) Effectively measuring test anxiety continues to be an issue in the research and I used only the cognitive components of test anxiety as predictors. Benson and El Zahhar (1994) suggested perceptions of biological effects of anxiety in addition to cognitive indicators. In the f uture researchers should examine the paths between motivational variables, biological indicators of anxiety, and exam performance to assess the impact of biological a s well as cognitive components of anxiety Course E ngagement In this study, s cognitive strategi e s and achievement on course assignments predict exam performance in the present study I extended the model of Fenollar et al. (2007) by including more behavioral measures of student effort than the self report measures Fenollar et al.

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73 used. I included behavioral measures of attendance, homework, class participation, and performance on quizzes In accordance with previous research (Gunn, 1993; Snell, et al. 1995) I found that attendance ha s a direct positive effect on course assignments and participation, and an in direct positive effect on exam performance Homework predict s quiz scores and ha s an indirect effect on exam performance through quizzes Course participation predict s homework, but does not predict quizzes or exams Last, q uizzes predic t exam performance. In the following sections I discuss the findings of this study regarding learning strategies, attendance, h o mework, and course participation in light of previous research and make suggestions for further study Learning Strategies As predicted in this study, s tudents who use more learning strat egies r equiring elaboration of concepts perform better on exams than students who use rehearsal strategies. Students who report using more elaborative strategies perform better on exams than those who scored lower on elaboration These findings support earlier research that demonstrated elaborative learning strat egies predict achievement ( Fenollar et al., 2007; Green e & Miller, 1996; Nolen, 1988). In an extension of prior research, I found support for the link between elaborative learning strategie s and achievement when including previously excluded predic tors of exam performance in the model of Fenollar et al. This study adds additional vali dation to prior research showing that found deep processing strategies predict exam performance. In this study, rehearsal does not predict exam performance. Attendance I found that attendance does not directly predict exam performance but ha s an indirect effect on exam performance through course assignments (homework, course participation and quizzes). In th is study, a ttendance predict s homework accurac y class participation, and quiz performance. Attendance ha s an indirect effect on exam performance through the following

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74 paths: quiz ; homework and quiz ; class participation, homework, and quiz. These findings support previous findings that attendance predicts achi evement (Gunn, 1993; Snell, et al. 1995). Students who attend class tend to do better on course assignments than those who attend less. There were strengths and limitations regarding the measurement of attendance in th is study. Using attendance data gath ered by the instructor eliminated the limitations encountered by Hardy et al. (2003) when they used self reported attendance data. However, attendance i s undoubtedly influenced by the course structure in th is study As noted previously, students receiv e cr edit for attending classes toward their overall course grade resulting in a high average class attendance ( M = 60.01, SD = 10.91, where 69.60 points indicate perfect attendance). In the f uture research ers should examine the relationship between attendance and achievement in courses that do not have attendance policies that may counteract natural attendance patterns Homework On the basis of previous research ( Cooper, 1989; Cooper, et al., 2006; Paschal, et al. 1984; Trautwein, 2007 ), I predicted that students who d o well on homework assignments also perform well on exam s Although homework does not directly predict exam performance, it d oes have a significant indirect effect on exam through quiz performance. Homework ha s a direct po sitive effect on quiz performance In this study, homework i s co mprised of open ended questions that students respond to in a written paragraph. The structure of the courses also influenc es the relationships between assignments in this study. Clearly, dif ferent types of homework assignments may influence t he relationship between homework and exam performance For instance, reading assignments assigned as homework may relate to achievement differently than graded, written assignments.

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75 In the f uture researc h ers should explore how different types of homework predict exam performance. Course Participation Similar to the findings regarding homework and exam performance, I found that course participation does not directly predict exam performance but ha s a significant, indirect effect on exam performance through homework and quizzes Unlike previous researchers who relied on self report measures of course participation (see Hardy et al., 2003; Handelsman et al., 2005) I used more behavioral measures of stu dent involvement. Specifically, the se results are consistent with the findings of Hardy et al. (2003) Other researchers, however, have reported a direct link between course participation and exam performance (see Hill, 1990). The inconsistencies in the fi ndings may be due to the various ways that researchers operationalize student participation. Further research is needed to examine the inconsistencies in the findings regarding course participation and achievement. Quizzes Consistent with previous researc h that scores on announced quizzes predict exam performance (Geiger & Bostow, 1976; Noll, 1939) I found that q uiz performance ha s a direct positive effect on exam performance. Significant predictors of quiz performance includ e homework, prior knowledge, a nd attendance. Azorlosa and Renner (2006) reported that announced quizzes had no effect on exam performance in their study. However, one of the limitations in their research was a mismatch between quiz type (multiple choice) and exam type (essay). In th is study quizzes and exams consist of multiple choice responses and the results suggest that when quiz and exam types are consistent, quizzes more accurately predict exam performance than when they are not

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76 Limitations of the Study The results of th is study have the potential to provide information that could be useful in improving educational practice in community college psychology courses. It is important to note, however, that limitations of the study may have reduced the significance of the study First the results of the study may have limited generalizability. R esearchers have found evidence that variables related to learning in higher education may be discipline specific (see Donald, 1995). Variables that may predict student success in psycho logy courses, for example, may differ from those that predict success in organic chemistry classes. Also, variable s related to success in community college courses that focus on acquisition of basic knowledge may differ from those that predict success in u pper level university courses where students are required to critically analyze theories or create new knowledge in a discipline Therefore, variables that predict learning in community college psychology courses may not predict learning in higher level un iversity psychology courses. Last, students who choose to attend community colleges tend to differ from students who enroll in universities in terms of academic preparedness, educational goals, age, and socioeconomic status (Grimes & David, 1999). Therefor e, variables that predict learning for community college student s may not relate to learning in university s t udent s The use of self report measures of intelligence beliefs, interest, achievement goal orientations, self efficacy, test anxiety, and learning strategies may have also limited the validity of the results of the study (see Hersen, 2004). Participants may have be en biased when responding to self report measures including experimenter expectations social desirability and self presentati on biases. In addition, leaving out a measure of general academic ability (e.g., Verbal GRE or ACT scores) and personality characteristics such as c onscientiousness locus of control and work avoidance may have affected the results. These characteristics should be included in future studies. Also, I was the only instructor in the study which may have introduced

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77 experimenter biases and effects due to having only one i nstructor. Last, t he results of the study are based on correlational data and interventions are needed to validate cause and effect relationships suggested in the results of the test of the model Implications for Theory and Practice Using a social cogniti ve framework, I found several cognitive, motivational and class engagement variables predict academic achievement. First, the results of this study support perceived self efficacy. Specifically, perceived self efficacy predict s achievement goals and cognitive strategies in the study. Although self efficacy beliefs d o not predict exam performance directly, they predic t choices students ma k e regarding achievement goals and approaches to learning In addition findings regarding an xiety and exam performance a re replicated in this study ; participants with higher levels of anxiety tend to perform worse on exams. Current practices that require remediation for students with reading comprehension difficulties seem justified by the findin gs that show college entrance reading ability predict exam performance and reading dependent homework assignments. I also found support for prior claims that elaborative cognitive strategies predict exam performance. Last, I found some support for theories regarding the impact of course assignments on exam performance. Homework, attendance, and class participation ha ve in direct positive effect s on exam performance, and quizzes ha ve a direct positive effect on exam performance. In contrast, I f i nd no conception of the relationship between implicit theories of intelligence theory their achievement goals, learning strategies, and academic achievement The findings of th is study d o relationship between mastery and performance approac h goals with cognitive strategies. However, performance avoid goals predict the use of rehearsal strateg ies

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78 Practically, those interested in making the link between theory and practice may be tempted to make suggestions for application However, suggesting causal links between the variables would be premature. The results of structural equation models are based on correlational data. Experimental research would be necessary to verify cause and effect rel ationships. Conclusion s The social cognitive model of student learning tested in this study provides information regarding student characteristics that may influence student learning in community college psychology courses. Those interested in understandin g these characteristics and increasing the likel ihood that students learn should examine the relationships among ability, test anxiety motivation, engagement and their academic performance. This study offers groundwork for researchers to conduc t experimental studies to further verify causal links among the variables. For instance, the findings of this study reveal the need to explore strategies for increasing studen t interest in course material to determine if students are more likely to adopt mastery goals and use more elaborative strategies Also, attendance is a s ignificant predictor of performance on course assignments. S tudies that contrast performance of students in courses with attendance policies against those in courses wi th no such policies would provide needed information regarding the causal links between attendance and achievement. Similarly, experimental treatments to reduce anxiety and increase reading ability as they relate to exam performa nce would help instructors and administrators make informed decisions regarding policies aimed at improving student learning. In addition to encouraging new lines of experimental research, replication studies are needed to determine if the variables that p redict student achievement in this study also predict achievement in other courses and other educational institutions For instance, researchers should

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79 conduct similar research in other academic disciplines such as math ematics natural sciences, and commun ications to investigate the role of ability, motivation, test anxiety and e ngagement in achievement. Similarly researchers should examine how individual difference s influence achievement in upper division courses where students are often less c oncerned with gaining foundational knowledge and more concerned with construction of knowledge. Furthermore, replication of this study in different cultures would help determine if the reported relationships are characteristic of college students around th e world I am optimistic that with additional research a clearer understanding of the modifiable influences on student learning will emerge that will i mprove educational practice in higher education.

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90 BIOGRAPHICAL SKETCH Michael E. Barber was born in Sarasota, Florida and raised in northeast Florida. He the University of North Texas. Returning to While working toward a doctoral degree in educational leadership at the University of North Florida, Michael took an edu cational psychology course and subsequently decided to pursue a Ph.D. in educational psychology at the University of Florida. Michael holds a position as an Associate Professor of Psychology at Santa Fe College in Gainesville