Citation
Examining the Self-Regulated Learning Bahaviors, Career Self-Efficacy, and Academic Achievement of Online Career and Technical Education High School Students

Material Information

Title:
Examining the Self-Regulated Learning Bahaviors, Career Self-Efficacy, and Academic Achievement of Online Career and Technical Education High School Students
Creator:
D'Angelo, Tyler Lee
Place of Publication:
[Gainesville, Fla.]
Florida
Publisher:
University of Florida
Publication Date:
Language:
english
Physical Description:
1 online resource (124 p.)

Thesis/Dissertation Information

Degree:
Master's ( M.S.)
Degree Grantor:
University of Florida
Degree Disciplines:
Agricultural Education and Communication
Committee Chair:
Bunch,James Charles
Committee Co-Chair:
Myers,Brian E
Graduation Date:
5/3/2019

Subjects

Subjects / Keywords:
academic -- achievement -- career -- cte -- decision-making -- education -- flvs -- learning -- online -- regulatory -- self-efficacy -- self-regulated -- self-regulation
Agricultural Education and Communication -- Dissertations, Academic -- UF
Genre:
bibliography ( marcgt )
theses ( marcgt )
government publication (state, provincial, terriorial, dependent) ( marcgt )
born-digital ( sobekcm )
Electronic Thesis or Dissertation
Agricultural Education and Communication thesis, M.S.

Notes

Abstract:
Instructors must prepare students to meet the learning objectives in a course. When instructors use online-learning platforms as the main modality for instruction, learners may find challenges in mastering learning objectives. In particular, career and technical education coursework offered through an online learning platform aims to prepare students with the knowledge, skills, and training needed to be successful in a career. The purpose of this study was to describe the influence of perceived self-regulated learning strategies and career self-efficacy on academic achievement of learners enrolled in career and technical education online-learning courses. A quantitative research design revealed that students enrolled in online career and technical education courses use a variety of learning strategies to excel in coursework, thus leading to a high career self-efficacy. However, data revealed mixed results on how career self-efficacy and self-regulatory learning behaviors influenced academic achievement outcomes. It is recommended that instructors should consider assessing career self-efficacy by examining student use of self-regulatory learning behaviors based on observations on their engagement in an online career and technical education course. Additionally, instructors in online career and technical education fields should consider how academic achievement is evaluated through their assessment of instructional tasks. ( en )
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis:
Thesis (M.S.)--University of Florida, 2019.
Local:
Adviser: Bunch,James Charles.
Local:
Co-adviser: Myers,Brian E.
Statement of Responsibility:
by Tyler Lee D'Angelo.

Record Information

Source Institution:
UFRGP
Rights Management:
Applicable rights reserved.
Classification:
LD1780 2019 ( lcc )

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EXAMINING THE SELF REGULATED LEARNING BAHAVIORS, CAREER SELF EFFICACY, AND ACADEMIC ACHIEVEMENT OF ONLINE CAREER AND TECHNICAL EDUCATION HIGH SCHOOL STUDENTS By A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR TH E DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2019

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To those wh o believe education is a moral right for all

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4 ACKNOWLEDGMENTS I would like to thank both of my parents for encouraging me to pursue this degree. If it were not for both of them, I would not be where I a m today. My mom, Melissa Anderson, always taught me the value of working hard to get what you want in life. It is because of the life lessons you taught me growing up that helped me with stand this process. My father showed me the value of You taught me the life skills to overcome any challenge that life gives me. I hope to be half the man and father that he has been to me. To my siblings, Jeremy, Cole, and Tessa, I am thankful to have each of you in my life. To my oldest brother, Jeremy, you taught me to dream big. I am forever inspired by your passion and drive that you carry throughout your life . If it was not for you to set the example, I may have never pu rsued this degree. To my younger brother, Cole, yo u showed me to not take life too seriously. I am amazed by how much you have grown up over the years. Thank you for serving our country as a Mari ne. Your service to our country is second to none. To my youngest sister, Tessa, I am thankful for your love an d support throughout this process. To my committee chair and advisor, Dr. J.C. Bunch, I cannot say thank you enough. You believed in me, disciplined me, and guided me throughout t his entire process. This thesis has been arduous and frustrating at times, but you showed m e to muster resil i ency through your guidance and patience. Thank you for believing in my abilit ies to be successful in this program. I am forever grateful for your mentorship as a future researcher throughout this process. To my mentor and undergraduate advisor, Dr. Brian Myers, thank you for being an inspiration and a prime example of academic succ ess. You have showed me

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5 patience, wisdom, and guidance throughout my time at the University of Florida. You have been a champion for my success well before I started in the graduate program. I cannot describe how much I admire and respect you. I am forever grateful that you agreed to invest in my success through the program. I will never be able to thank you enough. To some of my closest friends throughout my time in the graduate program George Grant, Isabella Damiani, and Brianna Shanholtzer. I would like to thank you for being my confidants. No matter where life takes each of us after our studies, I am forever thankful for each of you for making my graduate program some of the best years I have had. Thank you for supporting and encouraging me throughout t he process. Each of you pushed me to be a better graduate student, friend, and well rounded person. To all of my Rolfs 408 colleagues, thank you for providin g me with the strength to perse vere and work diligently. I cannot thank each of you enough for bei ng the best support network that one could ask for. In particular, thank you to Peyton Beattie , Jessica Harsh , Colby Silvert, and Teresa Suits . To Peyton and Jessica, words cannot express how thankful I am for showing me the ropes when I first started the program. Both of you gave me a strong footing by leading as examples . Thank you for your support, wisdom, and encouragement. To my mentee s , Colby Silvert and Teresa Suits , thank you for being the best mentees that a mentor could ask for. I am eager to watc h both of you grow academically, professionally, and personally throughout your studies . To April Fleetwood and Catherine Clark, you made this research study possible.

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6 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 9 LIST OF FIGURES ................................ ................................ ................................ ........ 10 LIST OF ABBREVIATIONS ................................ ................................ ........................... 11 ABSTRACT ................................ ................................ ................................ ................... 12 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 14 Research Problem ................................ ................................ ................................ .. 18 Purpose & Objectives ................................ ................................ ............................. 22 Significance of St udy ................................ ................................ .............................. 23 Definition of Terms ................................ ................................ ................................ .. 24 Threats to Validity ................................ ................................ ................................ ... 25 Assumptions ................................ ................................ ................................ ........... 25 Chapter Summary ................................ ................................ ................................ ... 26 2 REVIEW OF LITERATURE ................................ ................................ .................... 28 Theoretical Framework ................................ ................................ ........................... 28 Self Regulated Learning Theory ................................ ................................ ....... 28 Social Cognitive Career Theory ................................ ................................ ........ 34 Previous Research ................................ ................................ ................................ . 37 Self Efficacy ................................ ................................ ................................ ..... 37 Self Regulated Learning Behaviors ................................ ................................ .. 42 Conceptual Model ................................ ................................ ................................ ... 47 Chapter Summary ................................ ................................ ................................ ... 50 3 METHODOLOGY ................................ ................................ ................................ ... 51 Research Design ................................ ................................ ................................ .... 51 Population & Sample ................................ ................................ .............................. 52 Procedures ................................ ................................ ................................ ............. 53 Instrumentation ................................ ................................ ................................ . 54 Self regulated online learning questionnaire ................................ .............. 54 Career decision making self efficacy short form ................................ ........ 55 Demographic information questionnaire ................................ .................... 56 Data Analysis ................................ ................................ ................................ ... 56 Limitations ................................ ................................ ................................ ............... 57

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7 Chapter Summary ................................ ................................ ................................ ... 57 4 RESULTS ................................ ................................ ................................ ............... 59 Objective One: Describe the Demographic Characteristics of Online Learners Enrolled in Career and Technical Education Courses. ................................ ........ 59 Objective Two: Identify the Self Regulated Learning Strategies Used by Online Learners Enrolled in High School Career and Technical Education Courses. ..... 60 Objective Three: Determine the Self Perceived Career Decision Making Self Efficacy of Online Learners Enrolled in High School Career and Technic al Education Courses. ................................ ................................ ............................. 60 Objective Four: Examine the Relationship Between Perceived Self Regulated Learning Strategies and Academic Achievement of Online Learners Enrolled in High School Career And Technical Education Courses. ................................ .. 61 Objective Five: Decision Making Self Efficacy and Academic Achievement of Online Learners Enrolled in High School Career and Technical Education Courses. .................... 61 Objective Six: Examine the Relationship Between Perceived Career Decision Making Self Efficacy and Perceived Self Regulated Learning Strategies of Online Learners Enrolled in High School Career And Technical Education Courses. ................................ ................................ ................................ .............. 61 Objective Seven: Determine if Demographic Variables of Students Enrolled in High School Career and Technical Education Courses Predict Self Regulated Learning Behaviors. ................................ ................................ ............................. 61 Objective Eight: Determine if Demographic Variables of Students Enrolled in High School Career and Technical Education Courses Predict Career Decision Making Self Efficacy. ................................ ................................ ............ 62 Chapter Summary ................................ ................................ ................................ ... 62 5 SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS ................................ .. 72 Objectives ................................ ................................ ................................ ............... 72 Summary of Findings ................................ ................................ .............................. 73 Objective One ................................ ................................ ................................ ... 73 Objective Two ................................ ................................ ................................ ... 74 Objective Three ................................ ................................ ................................ 74 Objective Four ................................ ................................ ................................ .. 74 Objective Five ................................ ................................ ................................ ... 75 Objective Six ................................ ................................ ................................ .... 75 Objective Seven ................................ ................................ ............................... 75 Objective Eight ................................ ................................ ................................ . 75 Discussion and Implications ................................ ................................ .................... 75 Conclusions ................................ ................................ ................................ ............ 82 Recommendations for Practi tioners ................................ ................................ ........ 83 Recommendations for Future Research ................................ ................................ . 84 Summary ................................ ................................ ................................ ................ 85

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8 APPENDIX A IRB APPROVAL LETTER ................................ ................................ ....................... 86 B APPROVAL FOR SOL Q ................................ ................................ ........................ 87 C APPROVAL FOR CDSE SF ................................ ................................ ................... 89 D FLVS RESEARCH PROPOSAL FORM ................................ ................................ .. 91 E FLVS APPROVAL CORRESPONDENCE ................................ ............................ 101 F NON DISCLOSURE AGREEMENT FORM ................................ .......................... 102 G STUDENT ASSENT LETTER ................................ ................................ ............... 105 H INFORMED CONSENT FORM ................................ ................................ ............. 1 06 I INFORMED CON SENT FORM (18 AND OLDER) ................................ ............... 107 J STUDENT REMINDER EMAILS ................................ ................................ ........... 108 K SELF REGULATED ONLINE LEARNING QUESTIONAIRE (SOL Q) .................. 110 L CAREER DECISION MAKING SELF EFFICACY SHORT FORM (CDSE SF) .... 112 M DEMOGRAPHIC INFORMATION SURVEY QUESTIONAIRE (DIQ) ................... 113 LIST OF REFERENCES ................................ ................................ ............................. 116 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 123

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9 LIST OF TABLES Table page 4 1 Selected Characteristics of FLVS Online CTE Course Takers ........................... 64 4 2 Selected Age of FLVS Online CTE Course Takers ................................ ............ 65 4 3 Regulatory Behaviors ................................ ........................ 65 4 4 Making Self Efficacy ................................ ...... 67 4 5 Regulated Learning Strategies and Academic Achievement of Online Instruction ................................ .............. 68 4 6 Making Self Efficacy and Academic Achievement of Online Instruction ................................ . 68 4 7 Making Self Efficacy and perceived Self Regulated Learning Strategies of online instruction ................................ ................................ ................................ ........... 68 4 8 Self Regulated Learning Strategies depending on their selected p ersonal demographic characteristics ................................ ................................ ............... 69 4 9 Career Decision Making Self Efficacy depending on their selected personal demographic characteristics ................................ ................................ ............... 70

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10 LIST OF FIGURES Figure page 2 1 Phases and processes of self regulated learning theory (Zimmerman & Moylan, 2009). ................................ ................................ ................................ .... 29 2 2 ................................ ....... 35 2 3 The Social Cognitive Career Theory provides a social cognitive view of the academic career choice process (Lent et al. 1994). ................................ .......... 35 2 4 Conceptual Model: Self regulatory processes for career decision making in online coursework. ................................ ................................ .............................. 48

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11 LIST OF ABBREVIATIONS CD SE Career Decision Making Self Efficacy CD SE SF Career Decision Making Self Efficacy Short Form CTE Career and Technical Education DIQ Demographic Information Question n aire FLVS Florida Virtual School SOL Q Self Regulated Online Learning Question n aire

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12 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for th e Degree of Master of Science EXAMINING THE SELF REGULATED LEARNING BAHAVIORS, CAREER SELF EFFICACY, AND ACADEMIC ACHIEVEMENT O F ONLINE CAREER AND TECHNICAL EDUCATION HIGH SCHOOL STUDENTS By May 2019 Chair: James Charles Bunch Major: Agricultural Education and Communication Instructors must prepare students to meet the learning o bjecti ves in a course . When instructors use online learning platforms as the main modality for instruction, learners may find challenges in masteringlearning objectives. In particular, c areer and technical education coursework offered through an online learning platform aims to prepare students with the knowledge, skills, and training needed to be successful in a career . The purpose of this study was to describe the influence of perceived self regulated learning strategies and career self efficacy on academic ach ievement of learners enrolled in career and technical education online learning courses. A quanti tative research design revealed that students enrolled in online career and technical education courses use a variety of learning strategies to excel in coursework, efficacy. However, data revealed mixed results on how career self e fficacy and self regulatory learning behaviors influenced academic achievement outcomes. It is recommended that instructors should efficacy by examining student use of self regulatory learning behaviors based on obs ervations on their engagement in an online

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13 career and technical education course. Additionally, instructors in online career and technical education fields should consider how academic achievement is evaluated through their assessment of instructional task s.

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14 CHAPTER 1 INTRODUCTION Traditionally, students have attend ed classes on campus to earn course cr edit. However, many students have explored alternative means to receiving an education. Today, many students have turned to distance education courses to provide quality education in the comfort of their own home s . Distance education is when one or more tech nologies are used to deliver instruction (Seaman, Allen, & Seaman, 2018). While distance education has tra ditionally been used in both informal and formal education, advancements in technology have led to growing concern for creating more formal education courses that are both effective and stimulating for distance learners. As technology advanced, more course s have been designed to meet the needs of distance learners through online platforms (Davis, Chen, Hauff, & Houben, 2018). Over the last 20 years, distance learning programs have continued to change as new technologies have emerged to deliver instruction a nd curriculum (Davis et al., 2018) . Research regarding online learning began in the late 1990 s, with a considerable amount of research examining various learning outcomes between traditional , on campus classroom instruction versus online instruction. Initial research in the field has found that distance learning is just as effective as on campus instruction (Russell, 1999). In the early 2000 s, a shift in online learning research occurred to better un derstand student motivation in distance education cou rse s . Research has shown that students should be centered as the locus for controlling their own learning in distance education c ourse s (Dutton, Dutton, & Perry, 2002) . According to Dutton et al. (2002), s tudents who controlled their l earning had greater o utcomes regarding student achievement and course success. Since research has suggest ed that instructional

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15 practices in distance education should be student centered, this required learners to take more respon sibility for their learning, as opposed to stude nts in face to face courses (Dutton, Dutton, & Perry, 2002) . pressure to integrate technology into instruction in order to enhance the learning outcomes for students, while promoting proficiency in technolo gy use (Davis et a l., 2018) . As online learning has continued t o advance, more institutions have continued t o offer more courses online, drawing significant student enrollment s . Distance education enrollment has increased every year since 2004, with post secondary instituti ons being the leaders in encouraging online learning (Seaman et al., 2018) . By the fall of 2016, approximately 32% of all students enrolled in a post secondary insti tution in the United States had taken at least one distance educatio n course, which equals 6.3 million students nationwide (Digest of Educational Statistics, 2017; Seaman et al., 2018). As of 2 018, online enrollment rates exceed ed traditional enrollment rates at the post secondary level in most subject areas (Rich & Dereshiwsky, 2011 ; Seaman et al., 2018 ). While online learning has been increasing at the post secondary level, considerable interest has developed to explore online learning at the secondary level ( Digest of Educational Statistics, 2017 ) . Online learning has been mainly influence d by policy at the secondary level, since policy has driven spending decisions in education (Horn, 2013) . Online learning has created many challenges, especially in states where students have been required to have a certain number of days that they attend school. Various states have passed legislation either in favor or against the use of online learning in secondary education . Several states, such as Illinois, Maine, New Jersey, and Tennessee, have limited the

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16 use of online learning (Horn, 2013). Many of these states have found funding issues with education, since many students in these states have been able to take online courses outside of their school district (Horn, 2013) . Other states such as California, Florida, Louisiana, Michigan, Texas, Utah, and West Virginia have passed legislation that supports the use of online learning, either as a blended learning format or the use of full web based courses (Horn, 2013). Many states have seen this as an opportunity to provide means of innovative experiences for learners using competency based learning (Horn, 2013) . Overall, the use of online learning has slowly gained attention at the secondary level, largely driven by state policies. At the secondary level, state legislation has attempted to shape online le either by increasing or decreasing its use (Horn, 2013) . In 2012, Florida Governor Rick Scott signed House Bill 7063, formally known as the Digital Learning Bill (Florida Department of Education, 2012). This bill ame nded section 1003.428(2)(c) in s tate regulations, mandating that every student who chooses to complete the 24 credit option for a high school diploma complete one online course with a minimum 2.0 grade point average on a 4.0 scale (Florida Department of Education, 2017a). Students with a n individual educational plan (IEP) or students who started high school during the 2012 school year were exempt from this bill. The Digital Learning Bill was created as an initiative to better prepare secondary students for the growing expansion of online learning at the post secondary level (Florida Department of Education, 2017a) . T herefore, t he Digital Learning Bill has led to many students in secondary schools taking online coursework in Florida (Florida Department of Education, 2017a) .

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17 With the expansi on of online learning, new courses have been created for learners at the secondary level. In particular, Career and Technical Education (CTE) courses have gained attention over the last several years (Florida Department of Education, 2017b) . CTE provides s tudents with the academic knowledge, technical skills, and training necessary to enter the workforce (ACTE, 2018a). CTE has taken place for several hundred years in some form or another, with vocational education beginning in non formal settings. Career an d technical education began to expand at the secondary l evel after passage of the Smith Hughes National Vocational Education Act, also known as the Vo cational Act of 1917. The Smith Hughes National Vocational Educational Act provided federal funding for CT E programs in agriculture, trade, and industrial development (Smith Hughes National Vocational Education Act of 1917, 1917). Since the passage of the Smith Hughes National Vocational Education Act, other important legislative acts have helped shaped CTE in to what it is today (Impertore, 2017). Today, CTE is offered in 16 different career cluster s and over 80 career pathways that a learner can specialize in (ACTE, 2018a). The career clusters range from agriculture, finance, health science, law, marketing, and STEM courses related to specific job fields (ACTE, 2018a). Instructors in many of these career clusters have partnered with industry leaders to train learners for technical or job specific skills (ACTE, 2018a). At the secondary level, student enrollmen ts in CTE courses have continued to grow in recent years. During the 2018 2019 school year, an estimated 94% of all secondary students will enroll in a career and technical education course (ACTE, 2018a). Many of these students have taken these courses to help them prepare for

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18 employment upon completion of the program. In 2018, nearly 8.4 million individuals annually sought certificates or associa te degrees in a CTE field after completing high school (ACTE, 2018a). CTE has been continuing to grow as more course takers have become interested in gaining skills that are pertinent to their chosen field of interest. Research Problem Alternative instruction using online learni ng platforms should foster student success through meaningful p edagogy of learning objectives (Rashid & Asghar, 2016). Dewey (1938) environment has a major role in his or her ability to form impulses and habits, thus pro ducing the mo tivation to learn. Kolb (1984) went even further by stating that learner s acquire new knowledge through meaningful concrete experiences. In traditional face to face courses, the instructor is able to create these concrete experiences through direct interac tion. When the instructor is no longer able to provide direct instruction in a concrete sequential experience through direct instruction , the learner is left as the main locus for engage ment in the teaching and learning process (Rashid & Asghar, 2016) . Be cause there is no direct interaction between the learner and the instructor while the student is engaging in a learning task, instructors have difficu lty ensuring that the students are taking responsibility in their own learning (Rashid & Asghar, 2016) . In the context of this study, an online learning environment may influence the habits and impulses practiced by learners as they progress through assigned learning tasks. While every student processe s learning differently, research has suggested that an on line learning platform can be as effective or more effective than traditional face to face learning (Cavanaugh, 2007 ; McLaren, 2004 ; Melton, Graf, & Chopak Foss, 2009; Russell, 1999 ). Other empirical research has refuted this finding, suggesting that

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19 stude nt attributes and instructional modality greatly influence learning outcomes (Paden, 2006; Smith, Heindel, & Torres Ayala, 2008 ). Student attributes such as self motivation, self efficacy, cognitive processing skills, and ability to self regulate learning through various strategies may influence their level of success in an online course (Cavanaugh, 2007 ; Melton et al., 2009 ; Smith et al. , 2008 ). Self regulated learning is the processes that learners enact systematically to focus on their thoughts, feelings, and actions to attain a learning goal (Zimmerman, 2000). As student enact these critical thinking processes, they are more likely to meet the learning objective in an online course ( Cavanaugh, 2007 ; Melton et al., 2009; Rashid & Asghar, 2016) . I ntegrating technology , such as using a web based platform as a class room environment , can create high student motivation and an increase in self directed l earning ( Cavanaugh, 2007 ; Rashid & Asghar, 2016). However, no research has been found to suggest that o nline CTE courses can provide the ability to support students to enact se lf regulated learning behaviors efficacy is influenced . As a result of the Digital Learning Bill in Florida, more secondary students have enrolled in c ourses offered by Florida Virtual School (FLVS). FLVS is one of the oldest, most established online learning platforms in the U.S. for K 12 students, offering over 150 courses (Florida Department of Education, 2017b) . During the 2016 2017 school year, FLVS had nearly 87,462 students enrolled in one or more distance education a number that was expected to increase over the next several years (Florida Department of Education, 2017b). As more students have turned towards FLVS to fulfill their degree r equirement, FLVS has continued efforts in promoting distance learning for secondary students by expanding in other content areas (FLVS, 2018) . FLVS has

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20 worked to develop a plethora of courses that range from both core coursework to various electives (FLVS, 2018) . However, FLVS has been particularly interested in being the pioneer for online CTE coursework (FLVS, 2018) . With increased access for online CTE coursework in Florida, conflicting laws may still limit these students to participate in CTE s tudent organizations (Kararo & Knobloch, homeschool regulations regarding participation in CTE student organizations and (b) the ability for students to participate base d on their enrollment status (Kararo & Knobloch, 2018). In particular, Kararo & Knobloch (2018) examined membership eligibility for homeschooled students into the National FFA Organization based on state policy . The content analysis of these policies revealed a low potential for these students to participate in CTE student organizations for Florida students (Kararo & Knobloch, 2018). Nonetheless, CTE has provided many personal benefits to students. Students enrolled in a CTE course have been more apt to partic ipate in career and technical education student organizations, achieve better grades, have a higher career self efficacy, and possess more employability skills for their specific career path (NRC CTE, 2018). Secondly, CTE coursework has led to an increase in problem solving skills, communication skills, mathematics skills, time management, and critical thinking skills (Alfeld, Hansen, Aragon, & Stone, 2006 ; Parr, Edwards, & Leising, 2009; Young, Edwards, Leising, 2009). Furthermore, t he increase in student motivation through CTE engagement has led to a greater likelihood that these students will complete their degree on time, thus reducing the number of dropouts in the 11 th and 12 th grades (Gottfried & Plasman, 2018 ; NRC CTE, 2018 ). Additionally, Kriesman an d Stange

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21 (2017) found that students who participated in a CTE course earn a higher w age of approximately 3.2% more dollars than students who did not participate in a CTE course dur ing their time in high school CTE coursework helps train students, while con necting them with industry. CTE has helped fill employment gaps and shape American business by closing the skills gap, thus building a more competitive workforce (ACTE, 2018a). This has significant impacts for the American economy, both nationally and at t he local level. In Oklahoma, graduates from the CareerTech system generated a $3.5 billion economic impact annually to the state (ACTE, 2018a ; 2018b ). Other states have reported an even greater return on the investment in CTE coursework, including states s uch as Iowa and Wisconsin (ACTE, 2018a). Because of the many impacts that CTE can have on the economy, many parents have seen the value in CTE. Approximately 89% of parents think that students should receive more education about career choices while they a re in high school (ACTE, 2018b; 2018c). Parents have been a main influencer for their child to participate in a particular course, and support for CTE from parents has pushed their child towards internships and on the job training opportunities (ACTE, 2018 b; 2018c). As CTE continues to gain attention, there is a need to offer this type of education format to benefit the industry, the overall economy, desires for their child's learning outcomes, and student satisfaction for lifelong careers. While students continue to benefit from career and technical education coursework , a growing shift in government and research agenda policies are in support of better understanding career development through technology based online learning for various CTE areas (NRC CTE, 2018) . The National Research Center for Career and

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22 Technical Education (NRC CTE) has discussed research priorities for the next several years. The NRC CTE has suggested research related to ( 1) online instruction of CTE courses to advance effo rts in alternative instruction and (2) research related to career guidance and counseling for students enrolled in CTE courses (NRC CTE, 2018). Through examining the various influences of self regulated l earning and career decision making self education cou rses, this study aligns with the National Research Center for Career and Technical Education (NRC CTE) research priorities. In addition , this study aligns with Ame rican Association for Agricultur al Education ( AAAE) National Research P r iority Area # 4: Meaningful, Engaged Learni ng in All Environment reating and evaluating meaningful learning environments is essential to educa ting future generations. This task is complex and many assumptions about pedagogical practice should be investigated to determine appropriate processes to guide engagemen (Edgar, Retallick, & Jones, 2016, p. 39). Purpose & Objectives Th e purpose of this study was to describe the influence of perceived self regulated learning strategies and c a reer decision making self efficacy on academic achievement of learners in secondary career and technical education online learning course s . Objectiv es that guided this study were: 1. Describe the demographic characteristics of online learners enrolled in career and technical education courses. 2. Identify the self regulated learning strategies used by online learners enrolled in high school career and technical education courses.

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23 3. Determine the self perceived career decision making self efficacy of online learners enrolled in high school career and technical education courses. 4. Examine the relationship between perceived self regulated learning strategies and academic achievement of online learners enrolled in high school career and technical education courses. 5. making self efficacy and academic achievement of online learners enrolled in h igh school career and technical education courses. 6. Examine the relationship between perceived career decision making self efficacy and perceived self regulated learning strategies of online learners enrolled in high school career and technical education co urses. 7. Determine if demographic variables of students enrolled in high school career and technical education courses predict self regulated learning behaviors. 8. Determine if demographic variables of students enrolled in high school career and technical education courses predict career decision making self efficacy. Significance of Study While research has been conducted that describes self regulated learning strategies that students implement within courses, there has been no known research that examine s these strategies using an online learning platform for online learners enrolled in secondary career and technical education course s . Additionally, no known research has been found that explains the role of career readiness of learners enrolled in high sc hool career and technical educational courses. R esearch is needed to better understand how self regulated learning strate gies career decision making self efficacy may influence academic achievement . B y unders tanding the influence of secondary students preferred learnin g strategies and career decision making self efficacy on academic achievement in online career and technical education courses , this information will add to the gap in the literature.

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24 Practitio ners may benefit from this research. Information learned from this study will assist instructors in design ing meaningful curriculum and pedagogical practices for online career and technical education c ourses. As legislative policy shifts toward increasing distance learning, there has been a need to better understand how learners respond when an online learning platform is used as the main modality for instruction . This research study may help instructors with understanding i f these learning activities aide students in their ability to use self regulatory learning strategies , as well as how making self efficacy . Additionally, this study will help provide information for instructors to build enriched learning experiences that maximize student achievement in career and technical education courses. This research may lead to instructors re evaluating best practices used in order to better meet the needs of learners in career and technical education courses. Def inition of Terms Academic Achievement the summation of metacognitive , motivational, and beh a vioral outcomes of the learning process ( Zimmerman & Schunk, 1989) . In this study, academic achievement was measured by th e cou rse grades collected . Career Deci sion Making Self Efficacy the confidence in ones ability to effectively engage in decision making tasks or activites relating to his or her making self efficacy was measured using the Career Decision Making Self Efficacy S h ort F orm (CDSE SF). Distance education education that uses one or more technologies to deliver instruction to students who are separated from the instructor (Seaman et al., 2018). Students in this study were enrolled in various online career and technical education courses . Learning strategies cognitive strategies that are oriented toward successful task performances (Pressley, Harris, & Marks, 1992). In this study, learning strategies were operationally de fined as the strategies that students used to accomplish a learning activity.

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25 Metacognition the knowledge of cognition, as well as the regulation of cognition through the use of higher order thinking, reasoning, and learning skills during learning ( Schr a w, 1997; Zimmerman & Moylan, 2009 ). In this study, metacognition was operationalized as a construct for self regulated learning. Motivation the activation and persiste nce of behavior that is rooted in cognitive activities (Bandura, 1977) . In this stud y, motivation was the underlying construct for self regulatory learning. Online learning environment the use of the I nternet to deliver a course (Seaman et al., 2018). In this study, the online learning environment was student s actively participating in the online career and technical education course s selected . Self efficacy establish and implement actions necessary to perform behaviors at var ious levels (Bandura, 1977). In this study, self concerning learning ability levels . Self regulated learning processes that learners enact systematically to focus on their thoughts, feelings, and actions to attain a goal (Zimmerman, 2000). In this stu dy, self regulated learning was measured using the SOL Q. Threats to Validity The following threats to validity were addressed during this study: Maturation threat is the change in participant responses due to the progression of time (Ravid, 2015). Th is threat was reduced based on a short amount of time allotted for data collection. Instrumentation is the way measurements are completed and scored within the study (Ravid, 2015). This study used an electronic data collection service in the online cours e, thus eliminating errors as respondents responded to the questionnaire items . Selection is an internal validity threat in which students are selected based on learner characteristics (Ravid, 2015). This study used simple random sample to select student s, and then collected data on learner characteristics. Morta lity is the loss of respondents during the study (Ravid, 2015). This was eliminated due to a short collection period of data. Assumptions Several assumptions were made in conducting this study, including the following:

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26 Respondents of this study were assumed to be bona fide respondents of interest. Every participant employed a wide variety of learning strategies that could be assessed through the SOL Q instrument. The CDSE SF instrument accurately assessed respondents making self efficacy. Respondents of this study accurately and honestly completed the instrument items. Chapter Summary Opportunities for high school students to enroll in online coursework has cont inued to expanded , primarily due to shifting legislation in support of distance education (Davis et al., 2018 ; Florida Department of Education, 2017a; Horn, 2013; Seaman et al., 2018 ) . As alternative instructional methods using an online learning platform have been created, there has been a need to better understand the learning processes that students enact ( Edgar et al. , 2016 ; Rashid & Asghar, 2016 ; NRC CTE, 2018 ) . In particular, CTE courses have started to gain momentum because of the value and impact th at they can have during and after high school ( Gottfried & Plasman , 2018; Kriesman & Stange, 2017; NRC CTE, 2018; Parr et al., 2009; Young et al., 2009) . CTE courses offer students beneficial skills needed to be successful in various career pathways, as well as fill unemployment gaps by bridging skills and competencies in various industries ( ACTE, 2018a; 2018b; 2018c) . Various sources have been pressuring practitioners to re create these meaningful learning experience s for CTE in an on line platform ( Florida Department of Education, 2017b) . Therefore, t he purpose of this study was to describe the influence of perceived self regulated learning strategies and career decision making self efficacy on academic

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27 achievement of learners in secon dary career and technical education online learning courses.

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28 CHAPTER 2 REVIEW OF LITERATURE Chapter 1 provided the rationale for examining self regulated learning strategies and career decision making self efficacy of learners enrolled in online career an d technical education courses through FLVS . Chapter 1 discussed the purpose of this study, which was to describe the influence of perceived self regulated learning strategies and career decision making self efficacy on academic achievement of learners in s econdary career and technical education online learning courses. Chapter 2 will describe the theoretical and conceptual frameworks guiding this study. This chapter will also present empirical literature pertinent to the conceptual framework. Theoretical Fr amework Self Regulated Learning Theory This study was constructed on the foundation of s elf regulated learning theory and social cognitive career theory. S elf regulated learning theory , a lso known as self regulation, demonstrates the process in which learners focus on their thoughts, actions, and feelings to attain an academic goal ( Schunk, 2012; Zimmerman, 1981, 1983; Zimmerman & Martin ez Pons, 1986 ). S elf regulated learning theory was first introduced by Zimmerman (1981), who f ounded the theory on the concept that learners actively seek out knowledge and skills through physical and psyc hological means ( Karoly & Kafner, 1982; Zi mmerman & Martinez Pons, 1986). Self regulated learning has been studied in many forms since it was fir st introduced . Since self re gulated learning theory was first developed, Zimmerman (2009) expanded the theory to better understand the cognitive an d affective aspects in that a learner must utilize to r egulate behaviors (See Figure 2 1) .

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29 Figure 2 1. Pha ses and processes of self regulated learning theory (Zimmerman & Moylan, 2009).

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30 Zimmerman and Moylan (2009) defined self regulated learning theory using th ree phases for self regulation: (a) the forethought phase, (b) the performance phase, and (c) the self refle ction phase . The forethought phase is where students approach a task or course, analyze the task, and develop self motivational beliefs. Learners create a plan on how they will achieve the task. The task analysis is the first component of this phase because students must analyze how the task must be accomplish ed and establish the value of that task for them (Zimmerman & Moylan, 2009). In doing so, a learner must establish a performance level that they want to achieve and establish the standards by which th ey will be assessed (Winne & Ha dwin, 1998). Many students find problems when they are unaware of the expectations of assessment , thus , stude nts ha ve difficulty establishing goals. Additionally , constructs that a learner uses to approach a task ( Pintrich, Smith, Garcia, & McKeachie, 1993 ; Zimmerman & Schunk, 1989 ) . Task value is the pe rsonal aspect by which a learning activity helps a learner reach his or her personal goals (Zimmerman & Moylan, 2009). Students who are more interested in the task are more willing to learn from it using reflection , thus activating self regulated learning strategies (Zimmerman & Schunk, 1989). Interest is guided by emotion, which can be activated through personal meaning that the task has for a learner (Renninger, Hidi, & Krapp, 1992). After a learner has analyzed the task, the student must decide what se lf motivated strategies are most effective to accomplish a learning activity. Elements of self motivation include : (a) self efficacy, (b) task orientation, (c) outcome expectations , and (d) goal orientation. Self efficacy is a student personal belief in ability to

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31 perform the necessary behaviors to successfully accomplish a task (Bandura, 1977, 1997 ). During a learning activity, self efficacy is used to maintain an overall positive and emotional well being (Bandura, 1977, 1997 ) . Various factors can self efficacy. Outcome expectations are the beliefs about the success a learner will have once a task is met (Zimmerman, 201 1). Outcome expectations are found to be positively related to self efficacy (Bandura, 1997; Zimmerman, 2011) . Students who have lo w outcome expectations will not make an effort to succeed, in turn, having low self efficacy (Zimmerman, 2011). Outcome expectation differs from self efficacy , because of the expected outcome that a learner may have (Zimmerman, 2011) . The performance phase involves a wide variety of variables , and can be divided into two constructs (a) self control and (b) self observation. Self control involves using metacognitive and motivational strategies to meet the goals set in the forethought ph ase (Zimmerma n & Moylan, 2009). Metacognition is the knowledge of cognition, as well as the regulation of cognition through the use of higher order thinking, reasoning, and learning skills during instruction (Schraw, 1997; Zimmerman & Moylan, 2009). Knowle dge of cognition refers to how much learners use memory to learn new information (Zimmerman & Moylan, 2009) . As knowledge of cognition is acquired, it is then used to regulat e memory through learning (Schraw, 1997; Zimmerman & Moylan, 2009) . Research has f ound significant relations between metacognition relating to self regulated learning (Dinsmore, Alexander, & Loughlin, 2008; Kitsantas & Zimmerman, 2006; Schunk , 2012). In order for a learner to apply metacognitive learning behaviors, a learner may utili ze a wide variety of tactics to help meet the metacognitive aspects needed to reach

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32 self contro l ( Schunk & Zimmerman, 2008 ; Zimmerman & Moylan, 2009) . The metacognitive strategies that a learner must employ include (a) task strategies, (b) self instruction , (c) imagery, (d) time management, (e) environmental structuring, and (f) help seeking tactics (Zimmerman & Moylan, 2009). While each student may use a variety of these tactics through various tasks, the behaviors behind the task must come from a (Schunk & Zimmerman, 2008) . If a learner is willing to learn and not to avoid the instructional task, the learner will be able to r egulate metacognitive processes (Schunk & Zimmerman, 2008). As a learner is engagi ng in co ursework, students may use a variety of motivational strategies, which may include (a) interest incentives and (b) self consequences (Zimmerman & Moylan, 2009) . Interest incentives include using self directed messag es used to remind learners of the goals. L earner s use motivation to regulate their interest incentives in a particular task and use interest in centives in instances where they are faced with an exceptionally difficult task (Wolters, 2003 ; Zimmerman & Moylan, 2009 ) . Self consequence is use d when student s quit because the learner feel s that they have not made enough progress (Wolters, 2003) . Self consequence can be enhanced through both internal and external factors (Wolters, 2003 ; Zimmerman & Moylan, 2009 ). Self consequence can be enhanced through reward and praise from others , or it can be curved through the use of persistence from an internal locus (Wolters, 2003) . If a learner uses this strategy when one smaller goal is achieved, then the learner is more willing to work toward the task wi th a high interest, thus progressing on the task ( Wolters, 2003; Zimmerman & Moylan, 2009 ).

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33 The se lf reflection phase involves lea rners justifying their success or failure of a task through positive or negative emotions , which can otivation and self regulation for future tasks ( Wilson & Narayan, 2016; Zimmerman & Moylan, 2009). The self reflection ph ase involves both self judg me nt and self reaction ( Zimmerman & Moylan, 2009 ) . Self judg ment occurs when a learner self assesses the qua lity of hi s or her work. Within self judg ment, a learner uses both self evaluation and causal attribution to judge his or her work ( Zimmerman & Moylan, 2009 ) . Self evaluation is the process by which learners evaluate their work based on the criteria and go als in the forethought phase ( Zimmerman & Moylan, 2009 ) . Learners may assess their performa nce using a variety of criteria , thus i nfluencing the attributes that they make ( Wilson & Narayan, 2016; Zimmerman & Moylan, 2009). Self judg ment has an influence reaction , and is the response and explanations learner s make to determine if they have succeeded or failed ( Weiner, 1986 ; Zimmerman & Moylan, 2009 ) . During self reaction, a learner makes attributions to their performance from various so urces ( Zimmerman & Moylan, 2009 ) . Many learners will attribute their performance to internal or external factors, which may include : (a) ability level, (b) luck, or (c) support from others (Weiner, 1986 ; Zimmerman & Moylan, 2009 ). As students learn how to judge attributions for failure or success, they influence self efficacy and their outcome expectations for future tasks through adaptive or defensive decisions (Weiner, 1986 ; Zimmerman & Moylan, 2009 ) . As students take into consideration their performanc e from prior tasks, they are influenced affectively and cognitively towards future tasks ( Zimmerman & Moylan, 2009 ; Zimmerman, 2011). Zimmerman (1986) described this theory as being cyclical in nature

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34 because of the influence of the decisions made in the self reflection phase, a learner may have varied self efficacy, outcome expectations, task value, and goal orientation ( Zimmerman & Moylan, 2009 ) . Learners utilize adaptive strategies based on self reflection to correct mistakes for future tasks ( Zimmerman & Moylan, 2009 ) . As a learner adapts, he or she may maintain the learning goals and motivation for future tasks (Zimmerman & Moylan, 2009). Elements from social cognitive theory have played a role in the de velopment of self regulation theory, particularly in the development of the forethought phase and the self reflection phase (Bandura, 1977, 1986; Pintrich & Zusho, 2002; Zimmerman, 1981, 1983, 2000; Zimmerman & Moylan, 2009). Zimmerman ( 1981; 1983) further described self regulated learning from a social cognitive perspective because he found that or behavior (Zimmerman, 1981 , 2000). Using elements from social cognitive t heory, the model has been revised to incorporate more defining information (Zimmerman & Moylan, 2009). Social Cognitive Career Theory Social cognitive theory is known as the shared interaction between behaviors, environmental conditions, and personal learner cognition that help to determine motivation (Bandura 1985, 1986; See Figure 2 2). The social cognitiv e theory explains cognitive behavior (Bandura, 1986). Bandura (1986) described cognitive career theory, which draws on social c ognitive theory, suggests that personal

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35 Figure 2 2. al Model (Bandura, 1985). Figure 2 3. The Social Cognitive Career Theory provides a social cognitive view of the academic career choice process (Lent et al. 1994).

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36 influen ces and contextual influ ences such as self efficacy or outcome expectations help shape career interests, goals, and actions (Bandura, 1977, 1986; Lent, Brown, & Hackett, 1994; See Figure 2 3). Social cognitive career theory explains the relationship betwee n cognitive, self regulatory, and self (Bandura, 1986; Lent et al., 1994). Social cognitive career theory draws on constructs such as (a) goal selection, (b) outcome expectations, and (c) ca reer decision making self efficacy to explain possible barriers and supports to entering prospective careers (Lent et al. 1994). Demographic inputs such as : (a) age, (b) race, (c) ethnicity, (d) gender, and (e) health are some of the various contexts that influence how a person will discover a career (Lent et al.,1994). As social cognitive career theory has been adapted to formal educational settings, the experiences a learner is exposed to begins to shape their career interests (Betz, Klein, & Taylor, 1996 ; Lent et al., 1994). Lent et al. (1994) explained that learning experiences that help shape future careers are largely mediated through cognitive proce sses. A learner draws on their background experience to help determine their career choice prior to expo sure to educational experiences about careers (Betz et al. , 1996; Lent et al., 1994) . Exposure to various learning experiences, either through formal or informal reer decision making self efficacy and outcome expectations (Betz et al. , 1996; Lent et al., 1994). As a learner is exposed to a variety of career interests, they begin to rely on self regulatory and self reflective processes ( Lent et al., 1994 ) . Career decision making self

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37 interest ( Betz et al. , 1996 ; Lent et al., 1994). A learner relies on their interest in a career to develop goals and actions necessary to overcome possible barr iers to entering that career ( Betz et al. , 1996 ; Lent et al., 1994). If a person establishes career goals and actions that cannot be obtained, a person often feels a lower career self efficacy ( Betz et al. , 1996 ; Lent et al., 1994). A learner will draw on their career interest, shaped by prior experiences, in order to maintain career goals and actions to enter into a career ( Betz et al. , 1996 ; Lent et al., 1994) . Previous Research Self Efficacy As self regulated learning theory and social cognitive theory have been used , research has found a direct correlation wi th self efficacy, motivation , and use of independent self regulatory learning behaviors between both learning theories. Learners with high self efficacy will present high motivation using various le arning strategies that help to meet the performance or task (Bandura, 1985, 1997). Thus, self efficacy has been shown to influence self regulated learning and career decision making behaviors (Bandura 1985; Lent et al., 1994; Zimmerman, 1981, 1983, 2000). Various forms of self efficacy have been studied, some of which may include (a) academic self efficacy, (b) technology self efficacy, and (c) career decision making self efficacy (Bandura 1985; Lent et al., 1994; Zimmerman, 1981, 1983, 2000) . A study condu cted by Komarruju & Nadler ( 2013 ) examined the relationship b etween self efficacy and cognitive metacognitive ori entations to predict academic achievement of undergraduate students enrolled in a university course . Using a quantitative approach, r esearchers concluded that s tudents with a high self efficacy indirectly influences higher academic achievement (K omarruju & Nadler, 2013).

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38 efficacy directly influences regulatory learning behaviors, which had a direct influence on academic outcomes. This is likely because s tudents feel that they can better control the learning strategies they need to enact to study material, especially if they feel unmotiva ted or distracted (Komarruju & Nadler, 2013 ). A similar study conducted by Wang et al. (2013) examined (a) student characteristics, (b) technology self efficacy, (c) self regulated learning strategies, and (d) academic course outcomes of undergraduate and graduate students at a southeastern university in the United States. Wang et al. ( 2013 ) concluded that self effi c acy influenced student motivation through the use of self regulatory learning behaviors (Wang et al., 2013). Learne rs with a high self efficacy were able to acquire this knowledge through implementing one or more self regulatory behaviors. Similarly to Komarruju & Nadler ( 2013 ) , s tudents with higher self efficacy are likely to set goals that are challenging and require them to seek out new knowledge ( Wang et al., 2013 ). As learners set goals to accomplish tasks, students have an increased self efficacy and increased motivation a s each goal is accomplished (Komarruju & Nadler, 2013 ; Wang et al., 2013 ). Wang et al. (2013) stated that instructors should pay more attention to students who have not taken an online course in the past in order to e ncourage them to participate, persist , and enact self regulatory behaviors to promote a higher self efficacy. A study conducted by C ho and Shen (2013) examined the relationship of goal orientation and academic self effica cy on student achievement through examining (a) students ability to med itate effort regulation, (b) metacognition regulation, and (c)

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39 interaction regulation of undergraduate students . Researchers found that s t udents who perceived a higher ability to create more achievable goals had self reported a higher self efficacy were ab le to use effective metacognitive processes to regulate their learning (Cho & Shen, 2013). Learners that used intrinsic goal orientation towards classroom instruction had a direct effect on their ability to use metacognitive processes of self regulation (C ho & Shen, 2013). The use of intrinsic goal orientation, thus, indirectly affected student achievemen t (Cho & Shen, 2013). However, students' extrinsic goal orientation did not appear to have a direct relationship on any variables measured (Cho & Shen, 201 3) . Students who have intrinsic goal orientations were able to persist with learning through challenging tasks. Conversely, students wit h extrinsic goal orientation were unable to persist through coursework (Cho & Shen, 2013). A possible explanation for t his may be that students rely on intrinsic motivational factors when creating goals that satisfy their needs (Cho & Shen, 2013). This finding is allows them to engage in l earning , suggesting that student learning is posi tively affected when criteria are explicitly stated ( Fraile, Panadero, & Pardo, 2017; Pintrich et. al., 1993). Bradley, Browne, and Kelley (2017) conducted a study that examined the influence of perceived self efficacy and use of self regulatory learning strategies on academic achievement of undergraduate post secondary l earners taking an online course at the university level . Researchers found a strong correlation between students who reported taking previ ous online courses and their self efficacy (Bradley et al., 2017 , Wang et al., 2013 ) . Students who had taken an online course prior to the study were

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40 more likely to have a higher self efficacy than those who had not taken an online course (Bradley et al., 2017 ; Wang et al., 2013 ) . Additionally, l earners who ha ve an improved self efficacy were likely to use more effective self regulated learning strategies to accomplish learning tasks , compared to learners with a low self efficacy (Bradley et al., 2017 ). Bra dley et al. (2017) stated that learn ers with a low self efficacy were likely to have a low connect edness to school, and thus, were less likely to implement self regulated learning strategies. Bradley et al. (2017) recommended that instructors using online learning platforms for instruction engage learners and help students improve their se l f efficacy through various instructional methods. Instruction should help engage the learner in course content so that students feel more associated to school activities (Bradley et al., 2017). Additionally, Bradley et al., (2017) recommended that the instructor play a crucial role in helping to facilitate and build these strategies with students. Self efficacy can have both direct and indirect effects on student achieveme nt ( Ibrahim, Was, & Randall, 2010). A study conducted by Ibrahim et al. (2010) explored the relationship between self efficacy and student achievement using u ndergraduate students enro lled at a midwestern university (Ibrahim et al., 2010). S elf efficacy had a direct influence in determining the overall student success in the course (Ibrahim et al., 2010) . I brahim et al. (2010) suggested that this is because student s belief about their efficacy to manage academic rigor requires them to maintain their emotions through decrea sing their stress and anxiety. This is consistent with empi r ical literature, which suggest that s tudents who have a high self efficacy are able to maintain their emotional

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41 well being, and often experience less negative thoughts than students' who report a low self efficacy (Bandura, 1993; Bandura, 1997; Schunk & Zimmerman, 2008 ). Additionally, Ibrahim et al. ( 2010 ) noted a negative relationship between student self efficacy and goal orientation. While this study focused on acade mic goal orientation ra ther than career goal orientation, Ibrahim et al., (2010) noted that s tudents who reported a low self efficacy were more likely to adopt performance avoidance goals for their learning. A possible explanation is that students who have negative beliefs about their performance are less likely to set goals to accomplish those tasks (Ibrahim et al., 2010 ). Additionally, students that had a low self efficacy may not exhibit strong metacognitive skills because those skills are not directly t aught in the classroom (Ibrahim et al., 2010). Thus, learners may struggle due to low self efficacy. Additionally, Ibrahim et al. ( 2010 ) found that s tudents' use of academic go al orientation and selection may have an indirect effect on learning strategies used , and is directly affected by student s use of metacognitive self re gulation (Ibrahim et al., 2010). Using goal orientation and selection, a student can apply metacognitive self regulation skills to help with studying and strategies that help them to succeed (Ibrahim et al., 2010). Additionally, Ibrahim et al. (2010) noted that s tudent s who use at least one or more self regulated learning strategy well and reported a high goal orientation to ward their learning are able to implement organizational skills , cri tical thinking towards the lesson, and apply problem solving skills well (Ibrahim et al., 2010). W ilson & Narayan (2016) had also found that self efficacy was not statistically related to learning st rategies use during each learning task in a blend ed learning course. Respondents self reported a higher task self efficacy after having completing a

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42 high t ask performance, meaning that learners who completed a task with a high task self efficacy had performed better than expected (Wilson & Narayan, 2016) . In short, students who had a high task performance developed a high task self efficacy and used this dur ing succeeding tasks , rather than relying on self regulated learning strategies (Wilson & Narayan, 2016). The researchers concluded that students that have a high self eff icacy may not implement effective or may implement fewer learning strategies because they feel over confident in their abilities to perform the task (Wilson & Narayan, 2 016). Likewise, learners who report a low self efficacy may have used more learning strategies or strategies that w ere more effective to perform well on assigned learning t asks (Wilson & Narayan, 2016) . Self Regulated Learning Behaviors Higher self regulation claims to promote more successful career development experiences and, r eferring to the social cognitive career theory, mastery learning experiences are likely to lead to high career decision making self efficacy (Gaylor & Nicol, 2016; Lent & Brown; 2013; Lent et. al., 1994, 2000). Self regulatory behaviors can be observed through both physical and psychological behaviors in which students enact during learning (Zimmerma n, 2013). Zimmerman & Moylan (2009) defined several self regulatory strategies , some of whi ch include (a) metacognitive monitoring and processing, (b) time management, (c) environmental structuring, (d) help seeking, (e) self instruction, (f) interest incentives, and (g) self consequences. Researchers have since consolidated several of these behaviors into three concepts, which include (a) self instruction, (b) interest incentives, and (c) self consequences (Jansen, van Le euwen, Janssen, Kester, & Kalz , 2017). These constructs can be

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43 combined to measure the overall ability of a person to persist through a learning activity (Jansen et al., 2017) . According to Suh and Flores (2017), students that enact self regulatory learnin g behaviors are able to mediate the ir perceived career de cision making self efficacy . Their study concluded that s tudents who have completed classroom assignments that were rigorous and have enacted effective self regulatory learning strategies were able t o increase their overall career decision making self efficacy for that course (Suh & Flores, 2017 ) . S tudents who have used a high level of self regulatory proces ses were able to maintain a high level of career decision making self efficacy through mediatin g emotional and academic well being (Suh & Flores, 2017). Since the self regulated learning processes of the study were academically driven, this research highlighted the importance of using self regulatory process es to improve both academic performance an d career development ( Gaylor & Nicol, 2016 ; Suh & Flores, 2017 ) . Iwamoto, Hargis, Bordner, & Chandler (2017) assessed the level of self regulated learning strategies that undergraduates' students used in the northern Pacifi c region. R esearchers used a sur vey methodology using descripti ve statistics to analyze (Iwamoto et al., 2017) . Iwamoto et al. (2017) found that many students at the undergraduate institution sampled have high self c onfidence in completing their education, which reduced their perceived test anxiety. Since many undergraduate students sampled had a low test anxiety, many students did not rank their study skills as high. Since students showed a high perceived self effica cy, this lead to students having a high over self appraisal (Iwamoto et al., 2017). Although many students reported an over confidence in their

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44 ability and reported a high intrinsic motivation level , many students were rated low towards the use of cognitive and self regulated learning practices (Iwamoto et al., 2017). Metacognitive Processing : While various research has supported the relationship between self efficacy and self regulated learning , research has suggested that self efficacy does not have direct effects on metacognitive learning strate gies or academic achievement (Fadlelmula, Cakiroglu, & Sungur, 2015; Sen, 2016; Wilson & Narayan, 2016 ). Rather , self efficacy had an indirect effect i n meditating cognitive learning strategies (Fadlelmula et al. 2015; Sen, 2016). Schraw (1997) stated that both aspects of metacognition are significantly related, but only when learners have a high ability to monitor their learning. Schraw (1997) stated th at evidence suggests that the knowledge of cognition may be a prerequisite for regulation of cognition. Conversely, Swanson (1990) demonstrated that metacognition is unrelated to self regulated learning, and learners with low aptitude used self regulated l earning strategies to compensate. Additionally, researchers have found that metacognition and self regulated learning strategies were not highly correlated with aca demic achievement (Pintrich et al., 1993). Millis (2016) stated that metacognition is a crit ical factor in determining if a student will yield higher learning outcomes . Curriculum can easily be adapted so that learners are challenged to use metacognitive processes (Millis, 2016). Sen (2016) conducted a study that examined the relationship betwe en high school students' (a) perceived learning strategies, (b) self efficacy beliefs, and (c) their ability to use ef fort to regulate their learning. Sen (2016) found that self efficacy was related with students' deep learning strategies and effort regula tion . However, Sen

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45 (2016) stated that self efficacy varied because of students' individual motivational attitudes differ . Sen (2016) speculated that individual motivation might differ, according to their individual differences and the structure of the course that was sampled for this study. efficacy, metacognitive learning strategies, and academic achievement. This is similar to previous research findings that state i t is important for students to increase their self efficacy beliefs in order to have students use deep learning strategies and to incr ease the effort that they forgo in regulating their learning ( Komarraju & Nadler, 2013 ; Sen, 2016). There are various influences that shape a learn student's ability to find a conducive learning environment that is stimulating ( Fadlelmula et al. 2015; Ryan, Gheen & Midgley , 1998) . Previous research on classroom goal structure has indicated that a classroom environm ent that actively adopts academic goals may strengthen student motivation ( Fadlelmula et al. 2015; Ryan et al., 1998). Fadlelmula et al. (2015) conducted a study that explored the relationship between (a) motivational beliefs, (b) use of self reg ulated learning strategies, and (c) academic achievement. Researchers found that the learning environment can influence (a) adopt, (b) select, and (c) create academic and career oriented goals (Fadlelmula et al., 2015). Fadlelmula e t al., (2015) found that goal structure was positively related to students' adoption of mastery goals. This research made a clear distinction between the types of goals that students were implementing. Classrooms that focus on mastery of learning tasks, ra ther than mastery of performance oriented tasks , showed higher student achievement (Fadlelmula et al., 2015). In addition, goal orientations were found to be positively relate d to student

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46 organization, elaboration, and metacognitive self regulation strategies, but was not directly related to student achievement (Fadlelmula et al., 2015). Therefore, the ability to find a conducive classroom environment has an indirect relationship to student achievement through increased motivational behaviors (Fadlelmula et al., 2015) . Persistence relies on a student to draw from an internal locus to avoid academic consequence (Zimmerman & Martinez Pons, 1986). Students who are able to use self regulated l earning strategies such as persistence were able to work through difficult coursework compared to students who were unable to use one or more self regulated learning strategies (Komarruju and Nadler, 2013). In the literature, having high persistence may le ad to higher adaptive achievement related outcomes (Harackiewics, Barron, Pintrich, Elliot, & Thrash, 2002). Additionally research has found that learners wh o are able to persist through their coursework have lower test anxiety, will be less likely to make negative jud gments about their work, and will be less likely to avoid challenging work (Middleton & Midgley, 1997; Pintrich & Schunk, 2002). For students who are unable to persist internally, they may rely more heavily on seeking help from others such as other students or the instructor (Komarruju and Nadler, 2013). Feedback is essential to the development of self regulated learning. As students become aware of their poor performance, they are likely to have a decreased self efficacy and a decreased desire to implement self regulated learning strategies (Wilson & Narayan, 2016). Reciprocally, students who receive positive feedback are likely to have an increased self efficacy and implement more self regulated learning strategies (Wilson & Narayan, 2016). Re flection was found to have a significant relationship between past performance feedback and implementing future learning strategies

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47 (Wilson & Naray an, 2016). Wilson & Narayan (2016) have suggested that instructors should pick low performing learners and te ach them how to implement various learning strategies. Constructive feedback has been found to be a critical element in aiding students in improving their learning strategies, while identifying areas that students could improve upon. Conceptual Model The proposed f ramework for self regulatory processes for career decision making in onl ine coursework combines both self re gulatory learning theory and social cognitive career theory to guide the study. This f r amework draws on elements of the experiential , cog nitive, self regulatory, and self refle ctive processes that influence student success in an online career exploration course. All of the components of the conceptual model are built on the constructs addressed in the literature, suggesting a cohesive model that is both linear and cy clical in nature . The conceptual model suggests that the re is a relationship between selected demographic information of interest and a learner s p erceived career d ecision making self efficacy. Additionally, a relationship may ex use of self regulatory learning behaviors efficacy and use of self regulatory learning behaviors may influence their overall academic achievement score (See Figure 2 4) . When first beginning in the course, learners have an initial perceived career decision making self efficacy . Based on findings from Gaylor and Nicol (2016) , students will likely have a high career decision making self efficacy as they progress through the solving skills allow learners to ascertain challenges associated with their desired career. Based

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48 Figure 2 4. Conceptual Model : Self regulatory processes for car eer decision making in online coursework.

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49 on findings from Wright et al. (2016), low pe rceived barriers consequently lead to positive academic achievement . As students identify barriers associated with their chosen career, the learner is able to use self a ppraisal to overcome career challenges. decision making self efficacy while progressing through the course. Subequently, learners identify the necessary steps to overco me career barriers and achieve career goals (Cho & Shen, 2013). Learners with a high career decision making self efficacy will likely present a high motivation using various learning strategies that help to meet course assignments (Bandura, 1997 ; Gaylor & Nicol, 2016; Suh & Flores, 2017; Komarruju & Nadler, 2013 ). As students use self regulatory learning processes to accomplish meaningful career exploration coursework, students conversely improve their overall career de cision making self efficacy. Metacognitive processing may help students persist through coursework, as well as help students establish a high career decision making self efficacy (Fadlelmula et al., 2015; Sen, 2016; Wilson & Narayan, 2016 ). S tudents may use a comprehensive list of self regulated learning strategies, such as time management, help seeking from peers or the instructor, and the ability to persist through coursework . Thus, self regulated learni self efficacy towards deciding a career may d well in a course.

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50 Chapter Summary This study is guided by two important theories, which include self regulatory learning theory and social cognitive career theory (Lent et al., 1994; Zimmerman, 1981, 1983; Z immerman & Moylan, 2009) . Self regulatory learning theory explains cognitive and affective processes that learners may enact to successfully complete academic coursework ( Zimmerman, 1981, 1983; Zimmerman & Moylan, 2009) . Social cognitive career theory expl ains how personal influences and learning experiences may directly influence cognitive career decision making processes ( Lent et al., 1994) . Using both theories reveals social and cognitive influences on independent learner behaviors through individual car eer decision making self efficacy (Lent et al., 1994; Zimmerman, 1981, 1983; Zimmerman & Moylan, 2009) . Research has shown that using effective or multiple self regulatory behaviors have to higher academic achievement ( Gaylor & Nicol, 2016; Zimmerman & Moy lan, 2009) . L earners who possess a high self efficacy , particularly relating to their career decisiveness, may have a high academic per formance outcome ( Fadlelmula et al., 2015; Gaylor & Nicol, 2016; Ibrahim et al., 2010; Suh & Flores, 2017) . Previous rese arch suggests that demographic characteristics may influence both constructs, thus indirectly influencing academic achievement outcomes ( Lent et al., 1994) . The conceptual model presente d explains how this triadi c relationship that may either indirectly or directly influence academic achievement in online career and technical education coursework.

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51 CHAPTER 3 METHODOLOGY Chapter 1 described the rationale for conducting this study. Chapter 2 described the theoretical rese arch basis fo r this study using self regulated learning theory and social cognitive career theory . Previous research described in Chapter 2 provided the empirical literature perspective for this study. Using both theories outlined, the conceptual model defined in Chapt er 2 guides the framework for this study. Chapter 3 will discuss the methodology used to accomplish the purpose and objectives outlined in Chapter 1. Chapter 3 will address es the (a) research design, (b) population and sample, (c) procedures, (d) instrumen tation, (e ) data analysis, and (f) identified limitations t o the study . Research Design This study used a non experimental , descriptive correlational survey research design . Using recommendations from Creswell and Creswell (2018 ), this design was chosen to investigate the purpose of this study using a po stpositivist philosophical view . As previously mentioned, t he purpose of this study was to describe the influence of perceived self regulated learning strategies and career decision making self efficacy on academic achievement of learners in secondary career and technical education online learning courses. A postpositivist interpretation aims to determine the effects or outcomes among the variables of interest by examining the relationship among them (Cresw e ll & Creswell, 2018 ) . Particularly, a quantit ative methodology using a census provided the best data to determine relationships among selected variables (Creswell & Creswell, 2018 ). Therefore, this design was chos en to accurately accomplish the purpose an d objectives of this study.

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52 Population & Sample The population of this study was students (N = 3,859) enrolled in online career and technical education courses offered by Florida Virtual School (FLVS) in 2018 2019. The courses included (a) Digital Inform ation Technology, (b) Foundations of Web Design, (c) User Interface Design, and (d) Foundations of Programming. While FLVS offers additional career and technical education courses online, the students enrolled in the above mentioned courses were chosen by FLVS to participate in the study. FLVS does not allow researchers to collect samples from the population of interest. As such, the researcher attempted to collect from the entire population. From the entire population, approximately 347 students responded to some aspects of the study. From the 347 responses, 178 were removed due to students not completing all instruments. As a result, 169 responses were usable in this study. Based on the non disclosure agreement for this study outlined by FLVS, reducing non reponse bias was restricted. Since a census could not be obtained, the researcher treated the 169 usable responses as a sample for this study. Therefore, inferential statistics used in this study may not be generalized to the population (Field, 2018). T he respondents were dispersed throughout the state of Florida. Respondents ranged in age from 12 to 19 years old , with t he age of the respondents approximately sixteen years old ( M = 15.56; SD = 1.24) . A majority of the respondents were female ( 68.64%; f = 116 ) , white ( 72.19%; f = 122), and freshman ( 29%; f = 49). The majority of respondents ( 66.86% ; f = 113) reported that they had taken at least f i ve or more online courses, including the courses of i nterest sampled for this study . A n overwhelming majorit y of respondents (85.20%; f = 144) stated that they were unemp loyed, and

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53 reported that they had a current course grade of an A (100% to 94%) in their career and technical education course ( 42%; f = 71). Procedures The data collection period for this study was in the fall semester of 2018. Before data collection, the investigators received approval by the University Internal Review Board (IRB) based on the description and outline of the study (Appendix A ). The prinicipal investigator sought approval for the use of the SOL Q (Appendix B) and the CDSE SF (Appendix C). Concurrently, a pproval was sought by Florida Virtual School to conduct this study through a written resea rch proposal request (Appendix D ). Upon verbal clarification of the research request propo sal, an email was received from FLVS for approval to conduct the study based on t he proposal provided (Appendix E ). The Principal Investigator completed a non disclosure agreement form before conducting the study (Appendix F ). The Self Regulated Online Lea rning Questionnaire (SOL Q) (Appendix K) , Career Decision Making Self Efficacy Scale (CDSE SF) short form (Appendix L) , and Demographic Information Questio n naire (DIQ) (Appendix M) were given to students with the help of a research specialist employed by F lorida Virtual School. The frame of this study was students enrolled in a n online Career and Technical (CTE) course , and was provided by Florida Virtual School. The CTE courses included in this study were (a) Digital Information Technology, (b) Foundations of Web Design, (c) User Interface Design, and (d) Foundations of Programming . The students received an email containing a link to the inf ormed consent letter , as well as the survey, provided by the FLVS research specialist (Appendix G ). The letter explain ed (a) the nature of the study, (b) information regarding confidentiality and discreet use of data analysis, and (c)

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54 contact information to FLVS research staff and the IRB office pending further questions regarding the study. Before consent was received, s tudents were asked to identify their age. Students who were under the age of 18 were provided an informed consent letter for their parents t o agree to the study (Appendix H ) . Students who identified as 18 years of age or older were provided an informed co nsent letter that they could complete (Appendix I ) . Upon completin g the informed consent letter, respondents had access to take the instrument at any time of day for an approximate four week period. The three instruments took approximately 20 minutes total to complete. Each student in the population was provided a follow up email by the research specialist at approximately two weeks and four weeks after the initial survey was sent (Appendix J ). Instrumentation Three instruments were utilized for data collection. The three instruments were (a) the Self Regulated Online Learning Questionnaire (SOL Q ; Appendix K ), (b) the Career Decision M aking Self Efficacy Short Form assessment (CD SE SF ; Appendix L ) , and (c) the D em ographic Information Q uestion n aire (DI Q ; Appendix M ) that was developed by the Principal I nvestigator for this study. Self r egulated online learni ng questionnaire Due to the lack of instruments that measure self regulated learning online using a holistic approach, a questionnaire known as the Self Regulated Online Learni ng Questionnaire (SOL Q , Appendix K ) was used (Jansen, van Leeuwen, Janssen, Kester, & Kalz, 2017). The SOL Q is an adapted instrument that uses combined items from the Motivated Strategies fo r Learning Questionnaire (MSLQ; Pin trich et al., 1993 ) , Me tacognitive Awareness Inventory (MAI; Schraw & Dennison, 1994) , and Online Self -

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55 Re gulated Learning Questionnaire (OSLQ ; Barnard, Lan, To, Paton, & Lai, 2009 ). The instrument specifically measures lear ning strategies that respondents employ while engaging in online course work (Jansen et al. , 2017) . The SOL Q uses 36 items on a 7 never true to 7= always true ). The reliability estimates from previous research has shown e five sub scales relating to (a) (b) (c) environmental (d) (e) help (Jansen et al., 2017). A score of 5 or more indicated high use of self regulated learning behaviors relating to each construct (Jansen et al. , 2017) . Respondents who score in a range between 3 5 are considered to use some self regulated learning strategies, while respondents who report a score of 3 or lower w ere considered to not regularly use these strategies (Jansen et al. , 2017) . Career d ecision m aking self e fficacy short f orm The C areer Decision Making Self Efficacy Scale (CDSE SF , Appendix L ) short form was used for this study (Betz & Klein, 1996; Betz, Hammond, & Multon, 2005). The CDSE SF allowed for the investigator to evaluate students overall career decision making self efficacy. The instrument is 25 items using a 5 point , Li no confidence at all to 5 = complete confidence ). Thi s instrument uses 25 items to measure five sub scales to measure self efficacy expectations toward a future career. The reliability estimates from previo u sub scale of interest , which include (a) career goal orientation ( = .84), (b) career planning ( = .84), (c) problem solving ( = .80), (d) exposure to occupational information ( = .82), and (e) self appraisal ( = .81) (Paulsen & Betz, 2004). Betz et al. (2005) suggests that an overall score to measure career decision making self efficacy

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56 can be calculated by adding the total sum of the 25 items and dividing by 25 to return the score to the response continuum. Demographic i nformat ion q uestio n n aire Th e Demographic Information Questio n naire (DIQ , Ap pendix M ), was a researcher developed instrument . The demogr aphic items of interest included (a) age, (b) gender , (c) class year in school, (d) race, (e) ethnicity, (f) employment status, (g) number o f c ourses completed online, (h) household income, and (i) letter grade in the online course. The instrument used self reported information as the basis for data collection. These de m ographic items were chosen base d on previous research conducted in this ar ea on these selected demographics. Data Analysis Data was analyzed using IBM SPSS © version 24. Objective s one, two, and three will be accomplished by using descriptive statistics in SPSS. Descriptive statistics included composite mean, standard deviation, frequency, and percentages . Objective s four, five, and six will be measured us ing a Pearson product moment correlation r . Objectives seven and eight was attained by conducting multiple linear regressions , or a stepwise method, was used to create a predictive model of selected personal demographic variables. Objective seven will be accomplished by using data collected from the Demographic Information Questio n naire (DIQ ) and the Self Regulated On line Learning Questio n naire (SOL Q) . Objective eight will be completed by using data collected from the Career Decision Making Self Efficacy short form (CD SE SF ) instrument and the demographic information questionnaire (DIQ) . This demographic information was dummy coded and used as independent variables in the regression models (Field, 2009 ) .

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57 A post hoc Regulated Online Learning Questionnaire (SOL Q) and the Career Decision Making Self Efficacy short form (CDSE SF) instrument . The post hoc analysis revealed the Self Regulated Online Learning Questionnaire (SOL Q) on each of the five sub = .94 ), (b) time management ( = .65 = .83 = .8 4), and (e) help = .86 ). The post hoc analysis revealed alpha scores for the on Career Decision Making Self Efficacy Short F orm (CDSE SF) instrument for each of the five sub scales relating to (a) career goa ), 0), (d) exposure to o ccupational ), and (e) self ). Limitations The research design of this study led to several limitations of the study. These included: A non experimental descriptive correlational research design using a survey data collection methodology was used for this study . Therefore , it was impossible to draw a true cause and effect relationship. A sample could not be taken from the population as a result of FLVS policy and procedures. A third party collected the data via a survey at different points in time. The subjects in this study were enrolled in an online career and technical education course through Florida Virtual School. Therefore, results and conclusions cannot be generalized. Chapter Summary A descriptive survey research design was used to meet the research obj ectives outlin ed in C hapter 1 . From the population of an online career and technical education course from FLVS , a cons ensus was conducted to generate a cons ensus sample ( n =

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58 169 ). Respondents were given IRB approved consent f orms to complete before participating in the study. Respondents of the sample completed a combination of three instruments to measure the constructs of interest. The Self Regulated Online Learning Questionnaire (SOL Q), Career Decision Making Self Efficacy Scale (CDSE SF) short form, an d Demographic Information Questio n naire (DIQ ) were provided to respondents with the help of research specialists employed by Florida Virtual School. Data collected will be analyzed by using a combination of descriptive statistics, correlational coefficients, and linear regressions to meet the objectives outlined in Chapter 1.

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59 CHAPTER 4 RESULTS The purpose of Chapter 1 established the need for conducting this research. The purpose of this study was to describe the influence of perceived self regulated learning strategies and career decision making self efficacy on academic achievement of learners in secondary career and technical education online learning courses. The question addressed in this study was: what are the perceived self regulated learning s trategies and career decision making self efficacy and its influence on academic achievement of learners in online learning career and technical education cour ses ? Chapter 2 provided the theoretical and conceptual framework guiding this study based on emp irical research. Additionally, Chapter 2 provided a summary of relevant literature in this area of research. The purpose of Chapter 3 established the methodology used to meet the purpose and objectives of the study. Moreover, Chapter 3 discussed the instru mentation, population, and summary of data analysis used. Chapter 4 will present the findings of the study using the objecti ves outlined in Chapter 1. Results of this study will use a combination of descriptive statistics, multiple regressions, and correla tional coefficients. Objective One : Describe the Demographic Characteristics o f Onli ne Learners Enrolled in Career a nd Technical Education Courses. Objective one was analyzed using descriptive statistics. The respondents were primarily female ( f = 116; 6 9%) ; white ( f = 122; 72%) , and classified as freshman ( f = 49; 29%; Table 4 1). The respondents had previously enrolled in 5 or more online courses ( f = 113; 67%) , including the current onlin e course sampled in this study ( Table 4 1). A majority of the respondents were not employed at the time of this study ( f = 144; 85%) , and were unsure of their f amily income ( f = 98; 58%; Table 4 1). The majority of

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60 respondents had reported having an A at the time of the study ( f = 71; 42%; Table 4 1). The majorit y of respondents were approximately sixteen years old at the time of the study ( M = 15.56; SD = 1.24 ; Table 4 2). Objective Two: Identify the Self Regulated Learning Strategies Used by Online Learners Enrolled in High School Career and T echnica l Education C ourses. Self regulated learning behaviors were assessed using the Self Regulated Online Learni ng Questionnaire (SOL Q , Appendix K ). Respondents overall composite mean was interpreted as somewhat regular behaviors ( M = 4.99 ; SD = .98; Table 4 3). In particular, respondents had the highest c omposite mean score relating to environ ment structuring ( M = 6.24 ; SD = 1.13 ; Table 4 3) . Respondents in this study reported a s imil arly high composite mean towards persistence ( M = 5.30 ; SD = 1.26 ; T able 4 3) . Conversely, respondents reported the lowest composite mean relating to their ability to use help seeking strategies ( M = 3.02 ; SD = 1.56 ; Table 4 3) . Objective Three : Determine the Self Perceived Career Decision Making Self Efficacy of Online Learners Enrolled in High School Career and Technical Education Courses. The career decision making self efficacy of the sample was assessed using the Career Decision Making Self Efficacy short form (CDSE SF; Appendix L ) . Based on recommendations from Betz et al. (2005), it should be noted that an overall composite mean ( M = 3.83 ; SD = .76 ) was high (Table 4 4). Respondents in this study had the highest composite mean score relating to occupational information ( M = 4.07 ; SD = .77; Table 4 4) . Respondent s also reported a similarly high composite mean relating to self app raisal ( M = 3.93 ; SD = .83 ; Table 4 4) . T he lowest construct was problem solving skills ( M = 3.67 ; SD = 1.00 ; Table 4 4) .

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61 Objective Four : Examine the Relationship Between Perceived Self Regulated Learning Strategies and Academic Achievement of Online Learners Enrolled in High School Career And Technical Education Courses . There was no statistically significant relationship between self regulated learning strategies a nd their academic ac hievement of online instruction (Table 4 5 ). Objective Five : Examine Decision Making Self Efficacy and Academic Achievement of Online Learners Enrolled in High School Career and Tech nical Education Courses. There was no statistically significant relationship between career decision making self efficacy and academic ac hievement of online instruction (Table 4 6 ). Objective Six : Examine the Relationship Between Perceived Career Decision Making Self Efficacy and Perceived Self Regulated Learning Strategies of Online Learners Enrolled in High School Career And Technical Education Courses. The re was a statistical significant relationsh c areer decision making self e fficacy and their use of measured self regulated learning s trategies of online CTE instruction (Table 4 7). The direction and strength of the relationship was interpreted as a moderately positive relationship (r = .47 ; p = .00; Table 4 7). O bjective Seven : Determine if Demographic Variables of Students Enrolled in High School Career and Technical Education Courses Predict Self Regulated Learning Behaviors. The model explained approximately 21 % of the variance ( R 2 = .209 ; Table 4 8 ) . One predictor was statistically significant . The predictor was having a C+ (< 80% to 77%) .00). It should be noted that no other grade was found as a predictor of regulated learning strategies.

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62 Objective Eight : Determine i f Demographic Variables o f Students Enrolled in High School Career and Technical Education Courses Predict Career Decision Making Self Efficacy. The model expla ined app roximately 33% of the variance ( R 2 = .327 ; Table 4 9 ) . One predictor was statistically significant. The predictor was age 13 . It should be noted that no other age was found to be a statistically significant predictor of career decision making self efficacy. Chapter Summary Chapter 4 presented the results of this study based on the object iv es described in Chapter 1. The objectives of this study were to: (1) describe the demographic characteristics of online learners enrolled in career and technical education courses , (2) identify the self regulated learning strategies us ed by online learners enrolled in high school career and technical education courses , and (3) Determine the self perceived career decision making self efficacy of online learners enrolled in high school career and technical education courses. Objectives 1 3 were analyzed through descriptive statistical analysis. Subsequently, the following ob jectives were analyz ed using correlation coeffic i ents. The follow ing objectives analyzed using correlational coefficients included: (4) examine the relationship betwee n perceived self regulated learning strategies and academic achievement of online learners enrolled in high school career and technical education courses , (5) decision making self efficacy and aca demic achievement of online learners enrolled in high school career and technical education courses, and (6) e xamine the relationship between perceived career decision making self efficacy and perceived self regulated

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63 learning strategies of online learners enrolled in high school career and technical education courses. Lastly, objectives seven and eight were accomplished using a step wise method, or through multiple regression analysis of various demographic variables. Objective seven was to d etermine if d emographic variables of students enrolled in high school career and technical education courses predict self regulated learning behaviors. Objective eight was to d etermine if demographic variables of students enrolled in high school career and technical ed ucation courses predict caree r decision making self efficacy . The data used to meet objectives seven and eight were presented in Table 4 8 and Table 4 9. Chapter 5 will fu rther summarize these findings, draw conclusions based on reported data presented fro m this chapter , and offer various recommendations to researchers and instructors .

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64 Table 4 1. Select ed Characteristics of FLVS Online CTE C ourse T akers Variable f % Gender Male 51 30.18 Female 116 68.64 Prefer Not To Respond/ Other 2 1.18 Ethnicity White, Non Hispanic or Latino 122 72.19 Mixed Ethnicity or Other 24 14.21 Asian or Asian American 11 6.50 Black or African American 11 6.50 Hawaiian or Pacific Islander 1 .60 Hispanic/ Latino Yes 50 29.58 No 119 70.42 Grade Level Freshman 49 29.0 Sophomore 40 23.7 Junior 35 20.7 Senior 45 26.6 Enrolled Courses One 12 7.10 Two 21 12.43 Three 16 9.47 Four 7 4.14 Five or more 113 66.86 Income Levels $0 $9,999 7 4.14 $10,000 $19,999 5 2.96 $20,000 $29,999 7 4.14 $30,000 $39,999 7 4.14 $40,000 $49,999 10 5.92 $50,000 $59,999 13 7.70 $60,000 $69,999 4 2.36 $70,000 or more 18 10.65 Unsure 98 57.99 Employment Status Employed 25 14.80 Unemployed 144 85.20 Course Grade A (100% to 94%) 71 42.0 A (< 94% to 90%) 40 23.7 B+ (< 90% to 87%) 20 11.8 B (< 87% to 84%) 13 7.7 B (< 84% to 80%) 9 5.3

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65 C+ (< 80% to 77%) 5 3.0 C (< 77% to 74%) 2 1.2 C (< 74% to 70%) 0 0 D+ (< 70% to 67%) 0 0 D (<67% to 64%) 1 .60 D (< 64% to 61%) 1 .60 E (< 61% to 0%) 0 0 Unsure or Prefer Not To Say 7 4.1 Note. Data represents the number of courses that students have previously take n , including the current course that was sampled from the population; ( n = 169 ). Table 4 2 . Selected Age of FLVS Online CTE Course T akers Age M SD 15.56 1.2 4 Note . n = 169 . Table 4 3 . Self Regulatory Behaviors Construct M SD Interpretation Environment Structuring I choose the location where I study for this online course to avoid too much distraction. 5.66 1.70 I find a comfortable place to study for this online course. 5.92 1.57 I know where I can study most efficiently for this online course. 5.92 1.56 I have a regular place set aside for studying for this online course. 5.82 1.69 I know what the instructor expects me to learn in this online class. 6.24 1.13 Composite Mean 5.91 1.19 Usually true Persistence When I am feeling bored studying for this online course, I force myself to pay attention. 4.73 1.73 When my mind begins to wander during a learning session for this online course, I make a special effort to keep concentrating. 5.05 1.62 When I begin to lose interest for this online course, I push myself even further. 4.92 1.75 I work hard to do well in this online course even if I don't like what I have to do. 5.92 1.47 Even when materials in this online course are dull and uninteresting, I manage to keep working until I finish. 5.91 1.42 Composite Mean 5.30 1.26 Somewhat true Metacognition I think about what I really need to learn before I begin a task in this online course. 4.78 1.60 I ask myself questions about what I am to study before I begin to learn for this online course. 4.27 1.91

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66 I set short term (daily or weekly) goals for this online course. 5.44 1.73 I set long term (monthly) goals for this online course. 4.85 2.10 I set goals to help me manage my studying time for this online course. 5.34 1.65 I set specific goals before I begin a task in this online course. 5.12 1.82 I think of alternative ways to solve a problem and choose the best one for this online course. 5.35 1.60 I try to use strategies in this online course that have worked in the past. 5.62 1.57 I have a specific purpose for each strategy I use in this online course. 5.38 1.62 I am aware of what strategies I use when I study for this online course. 5.47 1.64 Although we don't have to attend daily classes, I still try to distribute my studying time for this online course evenly across days. 4.95 1.87 I periodically review to help me understand important relationships in this online course. 4.65 1.74 I find myself pausing regularly to check my comprehensi on of this online class. 4.66 1.80 I ask myself questions about how well I am doing while learning something in this online course. 4.94 1.73 I think about what I have learned after I finish working on this online course. 5.11 1.76 I ask myself how well I accomplished my goals once I'm finished working on this online course. 5.19 1.70 I change strategies when I do not make progress while learning for this online course. 5.15 1.67 I find myself analyzing the usefulness of strategies while I study for this online course. 5.02 1.76 I ask myself if there were other ways to do things after I finish learning for this online. 4.96 1.82 Composite Mean 5.06 1.22 Somewhat true Time Management I find it hard to stick to a study schedule for this online course. 3.52 1.71 I make sure I keep up with the weekly readings and assignments for this online course. 5.50 1.55 I often find that I don't spend very much time on this online course because of other activities. 3.33 1.85 Composite Mean 4.11 .99 Neutral Help Seeking When I do not understand something, I ask other course members in this online course for ideas. 2.64 2.04 I share my problems with my classmates in this online course so we know how to solve our problems. 2.24 1.83 I am persistent in getting help from the instructor of this online course. 4.37 2.01 When I am not sure about some of the material in this course, I check with other people. 3.64 2.08

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67 I communicate with my classmates to find out how I am doing in this online course. 2.20 1.80 Composite Mean 3.02 1.56 Infrequently true SOL TOTAL 4.99 .98 Somewhat true Note. n = 169 . Descriptive statistics were interpreted using recommendations from Jansen et al. (2017): 1 1.49 = Never true , 1.50 2.49 = Rarely true , 2.50 3.49 = Infrequently true , 3.50 4.49 = Neutral , 4.50 5.49 = Somewhat true , 5.50 6.49 = Usually true , 6.50 7.0 = Always true . Table 4 4 . Career Decision Making Self Efficacy Construct M SD Interpr etation Occupational Information Use the internet to find information about occupations that interest you. 4.47 .81 Find out the employment trends for an occupation over the next ten years. 3.86 1.13 Find out about the average yearly earnings of people in an occupation. 4.30 .93 Talk with a person already employed in a field that you are interested in. 3.60 1.34 Find information about undergraduate or professional schools. 4.09 1.13 Composite Mean 4.07 .77 Much confidence Self Appraisal Determine what your ideal job would be. 3.85 1.13 Accurately assess your abilities. 3.69 .98 Decide what you value most in an occupation. 4.04 .96 Figure out what you are and are not ready to sacrifice to achieve your career goals. 3.89 1.10 Define the type of lifestyle you want to live. 4.17 1.06 Composite Mean 3.93 .83 Much confidence Career Goal Selection Select one major from a list of potential majors you are considering. 3.78 1.13 Select one occupation from a list of potential occupations that you are considering. 3.80 1.11 Choose a career that will fit your preferred lifestyle. 3.94 1.15 Make a career decision and then not worry whether it was right or wrong. 3.18 1.28 Choose a major or career that will fit your interests. 4.13 1.03 Composite Mean 3.77 .98 Much confidence Career Planning Determine the steps to take if you are having academic trouble with an aspect of your chosen major. 3.75 1.02 Persistently work at your major or career goal even when you get frustrated. 4.10 .99 Change majors if you did not like your first choice. 3.43 1.17

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68 Change occupations if you are not satisfied with the one you entered. 3.54 1.16 Identify some reasonable major or career alternatives if you are unable to get your first choice. 3.82 1.10 Composite Mean 3.73 .86 Much confidence Problem Solving Make a plan of your goals for the next five years. 3.67 1.24 Determin e the steps you need to take to successfully complete your chosen major. 3.94 1.13 Prepare a good resume. 3.64 1.16 Identify employers, firms, and institutions relevant to your career possibilities. 3.71 1.21 Successfully manage the job interview process. 3.39 1.15 Composite Mean 3.67 1.00 Much confidence CDSE Total 3.83 .76 Much confidence Note. n = 169 . Descriptive statistics were interpreted using recommendations from Betz & Klein, (1996): 1 1.49 = No confidence at all, 1.50 2.49 = Very little confidence , 2.50 3.49 = Moderate confidence , 3.50 4.49 = Much confidence , 4.50 5.0 = Complete confidence . Table 4 5. Relationship b etween Regulated Learning Strategies and Academic A c hiev ement of Online I nstruction Variable p value Academic Achievement .103 interpretations: r = .00 to .09 = Negligible , r = .10 to .29 = Low , r = .30 to .49 = Moderate , r = .50 to .69 = Substantial and r .70 = Very Strong ; n = 162 . Table 4 Making Self Effica cy and Academic Achievement of Online I nstruction Variable p value Academic Achievement .758 interpretations: r = .00 to .09 = Negligible , r = .10 to .29 = Low , r = .30 to .49 = Moderate , r = .50 to .69 = Substantial Very Strong ; n = 162 . Table 4 7. Relationship between Making Self Efficacy and perceived Self Regulated Learning Strategies of online instruction Variable p value r value Effect size interpretation Self Regulated Learning Strategies .00 .47 Moderate

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69 interpretations: r = .00 to .09 = Negligible , r = .10 to .29 = Low , r = .30 to .49 = Moderate , r = .50 to .69 = Substantial Very Strong ; n = 169 . Table 4 8. Comparison of online Career and Technical Education student s ved Self Regulated Learning Strategies depending on thei r selected personal demographic characteristics Online Career and Technical Education Students ( n = 169 ) R 2 = .209 Constant p value Fem ale .0 4 .66 Hispanic .0 6 .4 8 Ethnicity Asian or Asian American .08 .7 7 Black or African American .1 7 .55 American Indian or Alaskan Native White, Non Hispanic .1 6 .76 Mixed Ethnicity of Other .19 .6 4 Grade Level Freshman Sophomore .05 .91 Junior .10 .53 Senior .0 9 .68 Enrolled Online Courses One .10 .34 Two Three .08 .42 Four .08 .3 9 Five or more .19 .10 Course Grade A (100% to 94%) A (< 94% to 90%) .11 .22 B+ (< 90% to 87%) .13 .1 3 B (< 87% to 84%) .00 .9 7 B (< 84% to 80%) .13 .14 C+ (< 80% to 77%) .2 5 * .0 0 * C (< 77% to 74%) .0 7 .42 C (< 74% to 70%) D+ (< 70% to 67%) D (< 67% to 64%) .13 .12 D (< 64% to 61%) .0 4 .6 3 E (< 61% to 0%) Unsure or Prefer Not To Say .0 2 .7 8 E mployed .03 .75 Age Twelve .0 0 .99

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70 Thirteen .0 2 .8 1 Fourteen .0 2 .87 Fifteen Sixteen .1 6 .25 Seventeen .03 .89 Eighteen .0 6 .71 Nineteen .0 3 .76 Note: p < .05 . Statistically significant predictors are denoted using an asterisk (*). Table 4 9. Comparison of online Career and Tech ved Career Decision Making Self Efficacy depending on their selected personal demographic characteristics Online Career and Technical Education Students ( n = 169 ) R 2 = .327 Constant p value Female .12 .12 Hispanic .15 .07 Ethnicity Asian or Asian American .24 .40 Black or African American .38 .18 American Indian or Alaskan Native White, Non Hispanic .52 .30 Mixed Ethnicity of Other .40 .32 Grade Level Freshman Sophomore .18 .17 Junior .24 .19 Senior .04 .88 Enrolled Online Courses One .14 .15 Two Three .00 .97 Four .02 .85 Five or more .09 .42 Income Levels $0 $9,999 .13 .17 $10,000 $19,999 .05 .54 $20,000 $29,999 .03 .71 $30,000 $39,999 .04 .59 $40,000 $49,999 .15 .06 $50,000 $59,999 .04 .63 $60,000 $69,999 .15 .06 $70,000 or more .06 .46 Unsure or prefer not to say

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71 Course Grade A (100% to 94%) A (< 94% to 90%) .05 .57 B+ (< 90% to 87%) .02 .81 B (< 87% to 84%) .03 .73 B (< 84% to 80%) .12 .15 C+ (< 80% to 77%) .10 .29 C (< 77% to 74%) .15 .06 C (< 74% to 70%) D+ (< 70% to 67%) D (< 67% to 64%) .10 .22 D (< 64% to 61%) .07 .38 E (< 61% to 0%) Unsure or Prefer Not To Say .07 .37 Employed .12 .18 Age Twelve .03 .75 Thirteen .28 * .01 * Fourteen .10 .39 Fifteen Sixteen .05 .72 Seventeen .31 .10 Eighteen .26 .09 Nineteen .12 .16 Note: p < .05 . Statistically significant predictors are denoted using an asterisk (*).

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72 CHAPTER 5 SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS The purpose of this study was to describe the influence of perceived self regulated learning strategies and career decision making self efficacy on academic achievement of learners in secondary career and technical education online learning courses . C hapter 1 established the need for understanding the relationship between student self making self efficacy for students enrolled in onlin e career and technical education courses. The purpose of Chapter 2 was to provide the theoretical framework guidi n g this st ud y, which was based on regulated learning t heory and career t heory. Addit i onally, Chapter 2 discussed the conceptual model guiding this study. Chapter 3 described the methods and procedures used to conduct the stu dy. Additionally, Chapter 3 outlined the research design, provide information on the population and instrumentation used, and provide a summary of how data was analyzed. Chapter 4 presented the findings of the study to answer the objec tives and purpose of the study. Chapter 5 will describe a summary of the findings, discuss conclusions based on empirical literature and findings presented in Chapter 4, and provide recommendations for practitioners and future research. Objectives T he following objectives guiding this study were: 1. Describe the demographic characteristics of onl ine learners enrolled in career and technical education courses. 2. Identify the self regulated learning strategies used by online learners enrolled in high school career and technical education courses.

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73 3. Determine the self perceived career decision making self efficacy of online learners enrolled in high school career and technical education courses. 4. Examine the relationship between perceived self regulated learning strategies and acade mic achievement of online learners enrolled in high school career and technical education courses. 5. making self efficacy and academic achievement of online learners enrolled in high school career and technical education courses. 6. Examine the relationship between perceived career decision making self efficacy and perceived self regulated learning strategies of online learners enrolled in high school career and technical education courses. 7. Det ermine if demographic variables of students enrolled in high school career and technical education courses predict self regulated learning behaviors. 8. Determine if demographic variables of students enrolled in high school career and technical education courses predict career decision making self efficacy. Summary of Findings Objective One Objective one was analyzed using descriptive statistics. The respondents were primarily female ( f = 116; 69%); white ( f = 122; 72%), and classified as freshman ( f = 49; 29%; Table 4 1). The respondents had previously enrolled in 5 or more online courses ( f = 113; 67%), including the current online course sampled in this study (Table 4 1). A majority of the respondents were not employed at the time of this study ( f = 144; 85%), and were unsure of their family income ( f = 98; 58%; Table 4 1). The majority of respondents had reported having an A at the time of the study ( f = 71; 42%; Table 4 1). The majority of respondents were approximately sixteen years old at the time of the study ( M = 15.56; SD = 1.24; Table 4 2).

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74 Objective Two Self regulated learning behaviors were assessed using the Self Regulated Online Learni ng Questionnaire (SOL Q, Appendix K). Respondents overall composite mean was interpreted as somewhat regular behaviors ( M = 4.99 ; SD = .98; Table 4 3). In particular, respondents had the highest composite mean score relating to environment structuring ( M = 6.24; SD = 1.13; Table 4 3) . Respondents in this study reported a similarly high composite mean towards per sistence ( M = 5.30; SD = 1.26 ; Table 4 3) . Conversely, respondents reported the lowest composite mean relating to their ability to use help seeking strategies ( M = 3.02; SD = 1.56 ; Table 4 3) . Objective Three The career decision making self efficacy of th e sample was assessed using the Career Decision Making Self Efficacy short form (CDSE SF; Appendix L ) . Based on recommendations from Betz et al. (2005), it should be noted that an overall composite mean ( M = 3.83 ; SD = .76 ) was high (Table 4 4). Respondents in this study had the highest composite mean score relating to occupational information ( M = 4.07 ; SD = .77; Table 4 4). Respondents also reported a similarly high composite mean relating to self appraisal ( M = 3.93 ; SD = .83; Table 4 4) . The lowest construct was problem solving skills ( M = 3.67 ; SD = 1.00 ; Table 4 4) . Objective Four There was no statistically significant relationship between self regulated learning strategies and their academic ac hievem ent of online instruction (Table 4 5 ).

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75 Objective Five career decision making self efficacy and academic achievement of online instruction (Table 4 6). Objective Six There decision making self e fficacy and their use of measured self regulated learning s trategies of online CTE instruction (Table 4 7). The direction and strength of the relationship w as interpreted as a moderately positive relationship (r = .47; p = .00; Table 4 7). Objective Seven The model explained approximately 21% of the variance ( R 2 = .209). One of self regulated learning strategies. Objective Eight The model explained approximately 33% of the variance ( R 2 = .327) . One career decis ion making self efficacy. Discussion and Implications Career and technical education coursework provides a number of benefits for students enrolled ( Alfeld , et al., 2006; Gottfried & Plasman , 2018; Kriesman & Stange, 2017; Parr et al., 2009; Young et al., 2009 ). Therefore , it is important to expose students

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76 from diverse demographics into various career and technical education fields. Using the findings as the basis for discussion, it can be concluded that learners enrolled in online career and technical ed ucation courses draws in students from similar demographics into career and technical education studies. In particular, online career and technical education cours ework at the secondary level draws in more females into career and technical education fields. Based o n this conclusion, it is recomm e nde d that administrators should attempt to reach more students from diverse demographic s to enroll in online career and technical education courses. Specificall y, efforts should be focused on recruiting online learners that are male, Hispanic, and other ethnic back g rounds. Some examples include students from African Ame rican or Latino demographics. It can be concluded th at learners relied more heavily on various self regulated learning strate rely more heavily on their ability to persist and their ability to find a conducive learning environment, as means to compensate for a lack of employing other self regulatory strategies. Additional ly, it can be concluded that learners enrolled in online secondary career and technical education courses do not regula r l y use time management skills and use help seeking strategies from peer to peer to excel in coursework. Thi s finding is similar to Iwamo to et al. ( 2017 ) , who stated that online learners may rely more heavily on internal motivational behaviors rather than cognitive dependent behaviors to complete learning tasks . In particular, persistence may be a self regulatory behavior that is easily emp loyable for learners ( Harackiewics et al., 2002; Komarruju & Nadler, 2013). Therefore, it is suggested that i nstructors should consider providing instructional materials for learners on how to better use various self -

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77 regulatory learning behaviors, such as time management skills and help seeking strategies through cooperative learning, to accomplish learning tasks . It can be concluded that learners enrolled in online secondary career and technical education courses have a high overall self efficacy towards t heir career decisiveness. Although students reported an overall high career decision making self efficacy, it should be noted that learners may have over reported their self efficacy due to sampling error (Creswell & Creswell, 2018) . However, eer decision making self efficacy can be continually improved based on various discrepancies of specific item constructs. I nstructors should consider encouraging more opportunities for students in online career and technical education courses to connect wi th leaders in various career and technical education fields. Yielding to the social cognitive career theory (Lent et al., 1994 ), learners learn best through social interactions from the learning experience . In particular, social intera c tions between learne rs and leaders decision making self efficacy. Additionally, it is recommended that administrators should consider incorporating more opportunities for students to practice so ft skills for career development. Specifically, this recommendation should be considered for all online career and technical education programs. Examples could be practicing for interviews, résumé s, and public speaking. B y further developing career explora tion thro ugh social interactions, by means of soft skill development, learners may have improved career readiness and career self efficacy. The findings revealed mixed results between online learners enrolled in career and technical education courses on th eir career decision making self efficacy, their use

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78 of self regulatory learning behaviors, and academic achievement outcomes. It can be concluded that academic achievement of learners enrolled in online career and technical education courses is not directly influence d by regulatory learning str ategies. This finding refutes Zimmerman (1981; 1983), who stated that learners are able to focus their thoughts, feelings, and actions in order to attain academic success. As mentioned by Zimmerman (1981; 1983), self regulatory learning behaviors are measurable processes for student motivation. Conversely, t his finding is supports Pintrich et al. ( 1993 ) and is similar to Fadlelmula et al. (2015), who each stated that self regulated learning strate gies that involve metacognitive processes are not highly correlated with academic achievement. Therefore, academic achievement outcomes for online career and technical education coursework is not an accurate representation for underlying student mot ivation . Additionally, it can be concluded that ecision making self efficacy is not statistically related to academic achievement outcomes of learners enrolled in online career and te chnical education courses. Therefore, academic achievement is not an indicator for individual career exploration and career development. Komarruju & Nadler (2013) and Wang et al., (2013) concluded similar finding s . Therefo re, instructors for online career and technical education courses may w ant to examine how academic achievement is evaluated through their assessment of instructional tasks to better meet the career development needs of learners . Instructors may want to encourage more opportunities for students in online career and technical e ducation courses to connect with leaders in various career and technical education

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79 fields. By adopting this strategy, instructors may better promote m ore opportunities for learners for social interaction and engage ment in various occupations. Betz et al. decision making self efficacy, and their use of self regulatory learning processes. It can be concluded making self efficacy is positively influenced use of self regulatory learning strategies. A number of empirical researchers ( Bandura, 1977, 1986; Lent et al., 1994 ) support this finding . Specifically, master y of learning leads to a learner perceiving a high career decsion making self efficacy is similar to previous research findings ( Gaylor & Nicol, 2016; Lent & Brown, 2013 ) . This conclusion indicates that student engagement in online career and technical edu cation courses, through the use of self reg ulatory learning strategies, influence s making self efficacy. Therefore, when career and technical education coursework is provided online as the main modality for instruction, increased learner engagement in the course may making self efficacy. Based on the conclusions of this study, it is recommended that instructors should consider assess ing r self efficacy by examining student use of self regulatory learning behaviors. Instructors an online career and technical education course. By instructors implementing thi s as part of their informal observation of stude nt learning, instructors may be better able to assess individual career development and career exploration .

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80 regulatory learning behaviors. It can be concluded that l with a low academic grade enrolled in an online career and technical education course are more likely to not use various self regulatory learning behaviors. Although only one statistically significant predictor was found betwee n students who had received a C+ and low u se of self regulatory behaviors. T his conclusion is best supported by self regulated learning theory (Zimmerman, 1981; 1983; Zimmerman & Moylan, 2009) . Therefore, instructors may want to consider course grades as m eans to determine learner motivation for online career and technical education courses. Subsequently, instructors may want to provide overcome learning tasks. Moreover, it can be concluded that l enrolled in online career and technical education course are more likely to have a low perceived career decision making self efficacy. This finding indicates that learners at a young age may have a lower career decision making self efficacy. This conclusion is best supported by the seminal work of Lent et al. (1994 ) . Learners draw their career interest based on their prior experience to shape career goals. Therefore, instructors may want to consider additional opportunities for career exploration for students at a young age. This may lead to recommendations for alternative teaching methods for career exploration and career d e velopment for learners, particularly for students thirt een years of age or less. Nonetheless, a longitudinal investigation should be conducted to compare individual student use of self regulated learning strategies, as well as compare student

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81 self efficacy towards career decision making. This study used a cro ss sectional research method using an aggregate data set . Thus, a longitudinal study comparing individual differences of selected variables may yield discerning results. Additionally, a longitudinal study should be conducted to further investigate how acad emic making self efficacy and regulated learning strategies. A longitudinal study in this area may lead to better understanding of how academic achievement is influenced. Subseque ntly, a study should be conducted to further examine academic achievement through other quantitative measurements. T his study should be conducted using other online career and technical education courses, for example online agriscience, health occupation, finance, and engineering. As mentioned, this study was conducted using four selected online career and technical ed ucation courses, which included students enrolled in (a) Digital Information Technology, (b) Foundations of Web Design, (c) User Interface De sign, and (d) Foundations of Programming. Furthermore, a n investigatio n should be conducted to variables besides their career decision making self efficacy, such as care er maturity and career indecisiveness. Yielding to social cognitive career theory (Lent et al., 1994; Betz et al., 2005 ) , these variables may better explain how career development is co created through online instruction. This study should be conducted to further investigate other demographic variables, such as an analysis of dual enrollment courses, academic extracurricular involvement, and enrollment in advanced placement courses. Involvement in

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82 extracurricular activities may influence online us e of self regulatory behaviors, making self efficacy. An investigation should be and technical education courses and traditional st udents enrolled in face to face career and technical education courses. Additionally, a qualitative piece should be considered making self efficacy and regulated learni ng strategies. This study was conducted using a pragmatistic view. Therefore, further investigation using an interpretivist philos o phical view may provide rich data (Creswell & Creswell, 2018) . Conclusions Based on the findings of the study , the following conclusions can be drawn: Learners enrolled in online career and technical education courses draws in students from similar demographics into career and technical education studies. Learners enrolled in online secondary career and technical educat ion cou rses rely heavily on their ability to persist through coursework and find a conducive learning environment in order to excel in coursework. This conclusion is similar to findings by Iwamoto et al. (2017) , Harackiewics et al. (2002), and Komarruju & Nadler (2013) . Learners enrolled in online secondary career and technical education courses do not regular l y u se time management skills and help seeking strategies from peer to peer to excel in coursework. This conclusion is similar to findings by Iwamoto et al. (2017) , Harackiewics et al. (2002), and Komarruju & Nadler (2013) . Learners enrolled in online secondary career and technical education courses have a high overall self efficacy towards their career decisiveness. regulatory learnin g strategies in online career and technical education courses is not statistically related to academic achievement outcomes. This conclusion is best supported Pintrich et al. (1993) and is similar to findings by Fadlelmula et al., (2015) . T his conclusion is also refuted by Zimmerman (1981; 1983). career decision making self efficacy in online career and technical education courses is not statistically related to academic achievement outcomes.

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83 This conclusion is similar to findings by Komarruju & Nadler (2013) and Wang et al. (2013) . making self efficacy is positively influenced by self regulatory learning strategies in online care er and technical education cour ses . This conclusion is best supported by Bandura (1977; 1986) and Betz et al. (1996). This finding is similar to previous research conducted by Gaylor & Nicol (2016), Lent et al. (1994 ), and Lent & Brown ( 2013 ). with a low academic grade enrolled in an online career and tec hnical education course are more likely to not use various self regulatory learning behaviors. This conclusion is best supported by the seminal work of Zimmerman (1981; 1983) . cal education course are more likely to have a low perceived career decision making self efficacy. T his conclusion is best supported by Lent et al. (1994 ). Recommendations for Practitioners This study was conduc t ed using students enrolled in specific onlin e career and technical education course through Florida Virtual School. Therefore, results and conclusions cannot be generalized. However, several recommendations are suggested for instructors in online secondary career and technical education courses. Bas ed on this study, the following recommendations may be considered : Administrators should attempt to reach more students from diverse demographic s to enroll in online career and technical education courses. Instructors should consider providing instructional materials for learners on how to better use various self regulatory learning behaviors, such as time management skills and help seeking strategies through cooperative learning, t o accomplish learning tasks. Instructors may want to encourage more opportunities for students in online career and technical education courses to connect with leaders in various career and technical education fields. Administrators should consider incorporating more opportunities for students to practice soft skill s for career development when developing online career and technical education courses . Examples may include practicing for interviews, résumé s, and public speaking.

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84 Instructors in online career and technical education fields should consider how academic achievement is evaluated through their assessme nt of instructional tasks. Instructors should consider assess ing efficacy by examining student use of self regulatory learning behaviors based on first hand observation, as well as throu gh observations on their engagement in an online career and technical education course. Instructors should consider course grades as means to determine learner motivation for online career and technical education course s , and address these students by mea ns of an intervention . Instructors should consider additional or alternative teaching strategies , through online instruction, for career exploration for students at a young age. Recommendations for Future Research This study served as an investigational research study based on previous studies that have been conducted related to online learners regulated learning making self efficacy. Specifically, this st udy examined students enrolled in online high school career and technical education courses. Future studies should be conducted in the following areas: A longitudinal investigation should be conducted to compare individ ual student use of self regulated learning strategies , as well as compare student self efficacy towards career decision making . A longitudinal study should be conducted to further investigate how academic making self efficacy and learn regulated learning strategies. A study should be conducted to further examine academic achievement through other quantitative measurements. This study should be conducted using other online career and technical education course s , for examp le online agriscience, health occupation , finance, a nd engineering. in career and technical education courses with other variables besides their career decision making self e fficacy, such as career maturity and career indecisiveness.

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85 This study should be conducted to further investig at e other demographic variables, s u ch as an analysis of dual enrollment courses, academic extracurricular involvement, and enrollment in advanced placement courses. An investigation should be conducted to compare the variables of interest with students enrolled in face to face career and technical education courses. A qualitative piece should be co nsidered to further investigate the formation of making self regulated learning strategies. Summar y The research objectives and methodology served as the basis fo r guiding this study. Chapter 1 served as the rationale for conducting this study. Chapter 2 presented the guiding theories directing this study, as well as empirical research conducted in this area. Chapter 3 presented the methodology of the study. Chapte r 4 presented the findings of this study, based on the objectives outlined in Chapter 1. In particular, Chapter 5 presented the discussion of the findings, drew conclusions from the data presented, and provided recommendations for practitioners and future research. The conclusions and recommendations were supported by relevant research in this area , as outlined in Chapter 2 . As opportun it ies for online career and technical education courses continue to expand for secondary students, there is a need to inves tigate outcomes from online career and technical education coursework.

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86 APPENDIX A IRB APPROVAL LETTER

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87 APPENDIX B APPROVAL FOR SOL Q

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89 APPENDIX C APPROVAL FOR CD S E SF

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91 APP ENDIX D FLVS RESEARCH PROPOSAL FORM

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101 APPENDIX E FLVS APPROVAL CORRESPONDENCE

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102 APPENDIX F NON DISCLOSURE AGREEMENT FORM

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1 05 APPENDIX G STUDENT ASSENT LETTER

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106 APPENDIX H INFORMED CONSENT FORM

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107 APPENDIX I INFORMED CONSENT FORM (18 AND OLDER)

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108 APPENDIX J STUDENT REMINDER EMAILS

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110 APPENDIX K SELF REGULATED ONLINE LEARNING QUESTIONAIRE (SOL Q)

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112 APPENDIX L CAREER DECISION MAKING SELF EFFICACY SHORT FORM (CDSE SF)

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113 APPENDIX M DEMOGRAPHIC INFORMATION SURVEY QUESTIONAIRE (DIQ)

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116 LIST OF REFERENCES Alfeld, C., Hansen, D. M., Aragon, S. R., & S tone, J. I. (2006). Inside the black box: exploring the value added by career and technical student organizations to students' high school e xperience. Career & Technical Education Research, 31 (3), 121 155. Association for Career and Technical Education (ACTE). (2018a). CTE today! What is career and technic al education? Retrieved from https://www.acteonline.org/wp content/uploads/2018/03/CTE_Today_Fact Sheet_January2018.pdf Association for Career and Technical Education (ACTE). (2018b). CTE works! CTE works for high school students. Retrieved from https:// www.acteonline.org/wp content/uploads/2018/03/CTE_Works_Research January2018.pdf Association for Career and Technical Education (ACTE). (2018c). CTE campaign video. Retrieved from https://www.youtube.com/watch?time_continue=136&v=nmmYOAUeoUM Bandura, A. (1985). Model of causality in social learning theory. In c ognition and psychotherapy (pp. 81 99). Springer US. Bandura, A. (1977). Self efficacy: Toward a unifying theory of behavioral change. Psychological Review , 84, 191 215. Bandura, A. (1997). Self eff icacy: the exercise of control. New York: W.H. Freeman, 1997. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall. Barnard, L., Lan, W. Y., To, Y. M., Paton, V. O., & Lai, S. L. (2009 ). Measuring self regulation in online and blended learning environments. Internet and Higher Education, 12 (1), 1 6. Betz, N. , Klein, K., & Taylor, K.M. (1996). Evaluation of a short form of the career decision making self efficacy scale. Journal of Career Assessment , 4, 47 57. Betz, N. & Klein, K. (1996). Relationships among measures of career self efficacy, generalized self efficacy, and global self esteem. Journal of Career Assessment, 4 , 285 298. Betz, N., Hammond, M., & Multon, K . (2005). Reliability and validity of response continua for the career decision self efficacy s cale. Journal of Career Assessment, 13 . 131 149.

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117 Bradley, R. L., Browne, B. L., & Kelley, H. M. (2017). Examining the influence of self effiacy and self regulation in online learning. College Student Journal , 51 (4), 518 530. Cavanaugh, C. (2007). Effectiveness of K 12 online learning. In M. Moore (ed.), Handbook of distance education. (2nd ed.). Mahwah, NJ: Lawrence Erlbaum. Cho, M., & Shen, D. (2013). Self regulation in online learning. Distance Education, 34 (3), 290 301. Creswel l, J. W. & Creswell, J. D. (2018 ). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Thousand Oaks, CA: SAGE Publications. Davis, J. A. (1971). Elementary survey analysis . Englewood Cliffs, NJ: Prentice Hall. Davis, D., Ch en, G., Hauff, C., & Houben, G. J. (2018 ). Activating learning at scale: A review of innovations in online learning strategies. Computers & Education , 125 , 3 27 344. https://doi.org/10.1016/j.compedu.2018.05.019 Dewey, J. (1938). Experience and education . New York: The Macmillan company . Digest of Education Statistics. (2017). Retrieved April 05, 2018, from https://nces.ed.gov/programs/digest/d17/tables/dt17_31 1.15.asp?current=yes Dinsmore, D., Alexander, P., & Loughlin, S. (2008). Focusing the conceptual lens on metacognition, self regulation, and self regulated learning. Educational Psychology Review, 20 , 391 409. Dutton, J., Dutton, M., & Perry, J. (2002). H ow do online students differ from lecture students? Journal of Asynchronous Learning Networks, 6 (1), 1 20. Edgar, D., Retallick, M., & Jones, D. Research priority 4: Meaningful Engagement Learning in All Environments. In Roberts, T. G., Harder, A., & Brashears, M. T. (Eds). (2016). American Association for Agricultural Education national research agenda: 2016 2 020. Fadlelmula, F. K., Cakiroglu, E., & Sungur, S. (2015). Developing a structural model on the relationship among motivational beliefs, self regulated learning strategies, and achievement in mathematics. International Journal Of Science & Math Education , (6), 1355. doi:10.1007/s10763 013 9499 4 Field, A. (2018). Discovering statistics using IBM SPSS statistics, (5 th ed . ). Thousands Oak, CA: Sage Publications Inc. Field, A. (2009). Discovering statistics using SPSS (3 rd ed.). Thousands Oak, CA: Sage Publ ications Inc.

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118 Florida Department of Education (2012). Online course graduation requirement. Helen Lancashire (2012 179). Tallahassee, FL. Florida Department of Education. (2017a). Students entering grade nine in the 2014 2015 school year and forward aca demic advisement flyer What students and parents need to know. Retrieved from http://fldoe.org/core/fileparse.php/7764/urlt/1415fowardflyer.pdf . Florida Department of Education. (2017b). Home ed ucation program. Retrieved from http://cdn.fldoe.org/core/file parse.php/5606/urlt/HomeEd_Sept_2017.pdf Florida Virtual School (FLVS). (2018). FLVS Flex Course Catalog 2018 19. Retrieved from https://www.flvs.net/docs/default source/counselor resources/course catalog flex.pdf Fraile, J., Panadero, E., & Pardo, R. (201 7). Co creating rubrics: The effects on self regulated learning, self efficacy and performance of establishing a ss essment criteria with students. Studies in Educational Evaluation 53 : 69 76. 10.1016/j.stueduc.2017.03.003 Gaylor, L., & Nicol, J. J. (2016). Experiential high school career education, self efficacy, and motivation. Canadian Journal Of Education , 39(2). Ghilay, Y. (2017). Online learning in higher education . Hauppauge, New York: Nova Science Publishers, Inc. Gottfried, M. A., & Plas man, J. S. (2018). Linking the timing of career and technical education coursetaking with high s c hool dropout and college going b ehavior. American Educational Research Journal, 55 (2), 325. doi:10.3102/0002831217734805 Harackiewics, J. M., Barron, K. E., Pintrich, P .R., Elliot, A. J., & Thrash, T. M. (2002). Revision of achievement goal theory: Necessary and illuminating. Journal of Educational Psychology, 94 , 638 645. Horn, M. B. (2013). Digital roundup: states legislatures scramble to boost, or in some cases block, online learning. Education Next, (4), 22. Ibrahim S., A., Was, C. A., & Randall M., I. (2010). Goals, efficacy and metacognitive self regulation: A path analysis. International Journal of Education, 2 (1), doi:10.5296/ije .v2i1.357 Imperatore, C. (2017). A brief history of CTE. Techniques, (2), 32. Iwamoto, D. H., Hargis, J., Bordner, R., & Chandler, P. (2017). Self regulated learning as a critical attribute for successful teaching and learning. International Journal For The Scholarship Of Teaching And Learning, 11 (2).

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123 BIOGRAPHICAL SKETCH and raised in Deland, Florida. His interest in agriculture was sparked after enrolling in agricultural education courses in high school. He became an active member of the National FFA Organization and his local 4 H club during his time in high school. Duri ng his time in both organizations, Tyler went on to serve as Vice President of his local FFA chapter and was slated as a Florida FFA State Officer Candidate. After graduating with honors from Deland High School (Deland, Florida) in 2013, he went on to purs ue a degree in agricultrual education from Abraham Baldwin Agricultur al College (ABAC). During his time at A Student Government A ssociation (SGA) and collegiate FFA organization. Tyler went on to serve as a Senator for the sch ool of agriculture and natural resources. He graduated magna cum laude in the spring of 2015. Upon completing his time at ABAC, Tyler transferred to the University of Florida an undergraduate student, Tyler was involved with many professional organizations. Tyler served as a National Teach Ag Ambassador , under the leadership of the National Association of Agricultural Educators, for the University of Florida. During his time as ambassador, where he worked to promote the agricultural education profession through his travels across the United States. Tyler completed his degree with honors after completing his student teaching internship at Lafayette County Middle High School in Ma yo, Florida. grad uate student, he worked to pro m o te the Florida Youth Institute as a camp counselor

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124 and staff member. He continued his work by volunteering with the Global Youth Institute in Des Moines, Iowa by serving on the Board of Reviewers . Tyler continued to expand his network by eventually working for Florida Ag in the Classroom as a facilitator. To date, Tyler serves as the lead teaching assistant for the AEC 3030 course, Effectiv e Oral Communication in Agricu l tur al and Life Sciences .