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The Relationships among Teacher Immediacy, Professor/Student Rapport, and Self-Regulated Learning

Permanent Link: http://ufdc.ufl.edu/UFE0044392/00001

Material Information

Title: The Relationships among Teacher Immediacy, Professor/Student Rapport, and Self-Regulated Learning
Physical Description: 1 online resource (206 p.)
Language: english
Creator: Estepp, Christopher M
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2012

Subjects

Subjects / Keywords: engagement -- immediacy -- motivation -- rapport
Agricultural Education and Communication -- Dissertations, Academic -- UF
Genre: Agricultural Education and Communication thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: The purpose of this study was to determine the relationships among teacher immediacy, professor/student rapport, and student self-regulated learning among selected undergraduate students in a college of agriculture.  The independent variables for this study were verbal and nonverbal immediacy and professor/student rapport.  The dependent variable in the study was self-regulated learning, which was a proxy for student motivation and academic engagement.  The motivation variable was made up of three constructs, including student expectancy for success, values and goals for the class, and test anxiety.  Engagement consisted of two constructs, which were cognitive/metacognitive strategy use and resource management strategy use.  The study utilized a non-experimental, correlational design.  The population for the study was students enrolled in large (50-100 students) classes in the College of Agricultural and Life Sciences at the University of Florida.  A convenience cluster sample was obtained for the study (n = 306).  Participants completed three survey instruments; the first measured their perceptions of their teachers’ verbal and nonverbal immediacy behaviors, the second measured students’ perceptions of their rapport with their instructor, and the third measured students’ motivation and engagement at the beginning and the end of the semester.  Results of the study revealed that students perceived that their instructors use verbal and nonverbal immediacy behaviors often and they additionally agreed that they have good rapport with their instructor.  Participants reported higher than intermediate scores for expectancy for success and values and goals for the class.  They reported intermediate levels of test anxiety, and slightly higher than intermediate levels of cognitive/metacognitive strategy use and resource management strategy use.  Mean increases were observed in expectancy for success, values and goals for the class, cognitive/metacognitive strategy use, and resource management strategy use, while the mean test anxiety decreased.  Verbal and nonverbal immediacy were found to have strong positive relationships with professor/student rapport, and students who reported higher levels of rapport were found to report higher expectancy for success, values and goals, and cognitive/metacognitive strategy use.  Additionally, the linear set of independent variables was found to be more predictive of motivation and change in motivation than of engagement or change in engagement.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Christopher M Estepp.
Thesis: Thesis (Ph.D.)--University of Florida, 2012.
Local: Adviser: Roberts, Thomas Grady.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2012
System ID: UFE0044392:00001

Permanent Link: http://ufdc.ufl.edu/UFE0044392/00001

Material Information

Title: The Relationships among Teacher Immediacy, Professor/Student Rapport, and Self-Regulated Learning
Physical Description: 1 online resource (206 p.)
Language: english
Creator: Estepp, Christopher M
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2012

Subjects

Subjects / Keywords: engagement -- immediacy -- motivation -- rapport
Agricultural Education and Communication -- Dissertations, Academic -- UF
Genre: Agricultural Education and Communication thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: The purpose of this study was to determine the relationships among teacher immediacy, professor/student rapport, and student self-regulated learning among selected undergraduate students in a college of agriculture.  The independent variables for this study were verbal and nonverbal immediacy and professor/student rapport.  The dependent variable in the study was self-regulated learning, which was a proxy for student motivation and academic engagement.  The motivation variable was made up of three constructs, including student expectancy for success, values and goals for the class, and test anxiety.  Engagement consisted of two constructs, which were cognitive/metacognitive strategy use and resource management strategy use.  The study utilized a non-experimental, correlational design.  The population for the study was students enrolled in large (50-100 students) classes in the College of Agricultural and Life Sciences at the University of Florida.  A convenience cluster sample was obtained for the study (n = 306).  Participants completed three survey instruments; the first measured their perceptions of their teachers’ verbal and nonverbal immediacy behaviors, the second measured students’ perceptions of their rapport with their instructor, and the third measured students’ motivation and engagement at the beginning and the end of the semester.  Results of the study revealed that students perceived that their instructors use verbal and nonverbal immediacy behaviors often and they additionally agreed that they have good rapport with their instructor.  Participants reported higher than intermediate scores for expectancy for success and values and goals for the class.  They reported intermediate levels of test anxiety, and slightly higher than intermediate levels of cognitive/metacognitive strategy use and resource management strategy use.  Mean increases were observed in expectancy for success, values and goals for the class, cognitive/metacognitive strategy use, and resource management strategy use, while the mean test anxiety decreased.  Verbal and nonverbal immediacy were found to have strong positive relationships with professor/student rapport, and students who reported higher levels of rapport were found to report higher expectancy for success, values and goals, and cognitive/metacognitive strategy use.  Additionally, the linear set of independent variables was found to be more predictive of motivation and change in motivation than of engagement or change in engagement.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Christopher M Estepp.
Thesis: Thesis (Ph.D.)--University of Florida, 2012.
Local: Adviser: Roberts, Thomas Grady.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2012
System ID: UFE0044392:00001


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1 THE RELATIONSHIPS AMONG TEACHER IMMEDIACY, PROFESSOR/STUDENT RAPPORT, AND SELF REGULATED LEARNING By CHRISTOPHER M. ESTEPP A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2012

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2 2012 Christopher M. Estepp

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3 To Joy, Cooper, Madison, and Ty

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4 ACKNOWLEDGMENTS Philippians 4:13 is well recognized on the University of Florida campus, as this was the verse displayed quite often by Tim Tebow during his time at UF. However, this verse has personal meaning for me in that I could not have completed this process withou do all things through Christ who strengthens I give all thanks and glory to Jesus I am forever grateful to my wife Joy and my three wonderful children ; Cooper, Madison and Ty. Thank you for allowing dad to up root his family and move to Florida. T hank you for being there during the stressful times and understanding when I had to leave for conferences or work late. Mostly, thank you for your prayers, support, love, and standing by me through this process. Joy, you have been my biggest cheerleader, and y ou will never know how much you mean to me, and I can only hope that I make you proud. Also, t hank you to my mom and dad for making me get up and go to school for all of those years, it finally paid off. Th ank you to my awesome committee, especially my chair, Dr. Grady Roberts. I I appreciate your advice, your friendship and that your door was always open, no matter what Also, t hank yo u for being my mentor and always treating me as a colleague even to the point of trying to make me call you Grady Dr. Kirby Barrick, thank you for always having some assignment for me to work on. You made sure that I did not spend my time at UF idly! I appreciate that you allowed me to be a part of so many of your projects; the experience I gained is invaluable. Dr. Ed Osborne, thank you for being the best department chair in the whole world! I know I can always count on you to ask the

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5 tough, thought provoking questions, but as a result I have grown immensely as a researcher and teacher. Thank you to Dr. Wendy Dahl for agreeing to be part of my committee. You brought a different perspective to the table and I know that my dissertation is much bette r for it. Turner. Thank you for the amount of work you put in helping me track down all of the data I needed. Thank you to Pastor Greg and Bev Evans and my entire River of Life family. You have al ways been there when my family or I needed someone to lean on. Leaving River of Life is bittersweet, but I know that we have made friendships that will last eternity. Also, now Greg has an excuse to come spend some time in the mountains of West Texas! Th ank you to my cohort buddies Dr. Kate Shoulders and Dr. Christopher Stripling Christopher, I have always been inspired by your faith. I thank you for your friendship and look forward to continuing it through the years. Kate, I know that I can always co unt on you for a laugh, especially when it involves searching for the elusive mullet. Thank you to all of the AEC graduate students, in particular Avery Culbertson and my officemates, Nathan Conner Jess Gouldthorpe Jessica Holt Angie Lindsey and Joy G oodwin Thank you, Dr. Andrew Thoron, for being a great mentor. I am glad you always responded when others who have helped me that I have forgotten, so t hank you to all my friends, family, pro fessors, mentors, and colleagues.

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6 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 9 LIST OF FIGURES ................................ ................................ ................................ ........ 10 LIST OF DEFINITIONS ................................ ................................ ................................ 11 ABSTRACT ................................ ................................ ................................ ................... 12 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 14 Statement of the Problem ................................ ................................ ....................... 23 Purpose ................................ ................................ ................................ .................. 24 Significance of the Study ................................ ................................ ........................ 25 Assumptions ................................ ................................ ................................ ........... 26 Limitations ................................ ................................ ................................ ............... 26 Chapter Summary ................................ ................................ ................................ ... 27 2 REVIEW OF LITERATURE ................................ ................................ .................... 28 Constr uctivism ................................ ................................ ................................ ........ 28 Social Cognitive Theory ................................ ................................ .......................... 31 Triadic Reciprocality ................................ ................................ ......................... 33 Enactive and Vicarious Learning ................................ ................................ ...... 36 Learning and Performance ................................ ................................ ............... 37 Conceptual Model Guiding the Study ................................ ................................ ..... 37 Motivational Processes ................................ ................................ ..................... 40 Self efficacy and student control of learning beliefs ................................ ... 41 Student values/goals ................................ ................................ .................. 45 Student affect/emotions ................................ ................................ ............. 48 Self regulatory Processes ................................ ................................ ................ 49 Regulating cognition ................................ ................................ .................. 50 Regulati ng motivation/affect ................................ ................................ ....... 54 Regulating behavior ................................ ................................ ................... 56 Regulating context ................................ ................................ ..................... 58 Conceptual Model of Motivation and Engagement ................................ ................. 61 Variables in the Conceptual Model of Motivation and Engagement ........................ 63 Teacher Immediacy ................................ ................................ .......................... 63 Nonverbal immediacy ................................ ................................ ................ 65 Verbal immediacy ................................ ................................ ...................... 66 Professor/student Rapport ................................ ................................ ................ 78

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7 Mo tivation ................................ ................................ ................................ ......... 84 Student Engagement ................................ ................................ ........................ 88 Teacher and Student Characteristics ................................ ............................... 90 Chapter Summary ................................ ................................ ................................ ... 94 3 METHODS ................................ ................................ ................................ .............. 97 Research Design ................................ ................................ ................................ .... 98 Design Validity ................................ ................................ ................................ .. 98 Statistical Conclusion Validity ................................ ................................ ........... 98 Internal Validity ................................ ................................ ............................... 100 Construct Validity ................................ ................................ ........................... 102 External Validity ................................ ................................ .............................. 102 Population ................................ ................................ ................................ ............. 103 Instrumentation ................................ ................................ ................................ ..... 105 Immediacy Be havior Scale ................................ ................................ ............. 105 Professor/Student Rapport Scale ................................ ................................ ... 107 Motivated Strategies for Learning Questionnaire ................................ ........... 108 Demographic Instrument ................................ ................................ ................ 110 Data Collection ................................ ................................ ................................ ..... 111 Data Analysis ................................ ................................ ................................ ........ 112 Research Objective One ................................ ................................ ................ 112 Research Objective Two ................................ ................................ ................ 113 Research Objective Three ................................ ................................ .............. 113 Research Objective Four ................................ ................................ ................ 113 Research Objective Five ................................ ................................ ................ 114 Research Objective Six ................................ ................................ .................. 114 Chap ter Summary ................................ ................................ ................................ 114 4 RESULTS ................................ ................................ ................................ ............. 116 Comparison of Sample and Population ................................ ................................ 117 Variability of the Independent Variables ................................ ............................... 119 Response Rates ................................ ................................ ................................ ... 120 Post Hoc Reliability of Instruments ................................ ................................ ....... 121 Description of the Sample ................................ ................................ ..................... 123 Objective One ................................ ................................ ................................ ....... 125 Objective Two ................................ ................................ ................................ ....... 128 Objective Three ................................ ................................ ................................ .... 134 Objective Four ................................ ................................ ................................ ...... 138 Objective Five ................................ ................................ ................................ ....... 139 Objective Six ................................ ................................ ................................ ......... 145 Summary ................................ ................................ ................................ .............. 150 5 SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS ................................ 151 Objectives ................................ ................................ ................................ ............. 151

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8 Summary of Findi ngs ................................ ................................ ............................ 151 Description of Sample ................................ ................................ .................... 152 Objective One ................................ ................................ ................................ 152 Objective Two ................................ ................................ ................................ 152 Objective Three ................................ ................................ .............................. 153 Objective Four ................................ ................................ ................................ 154 Objective Five ................................ ................................ ................................ 155 Objective Six ................................ ................................ ................................ .. 156 Conclusions ................................ ................................ ................................ .......... 158 Discussion and Implications ................................ ................................ .................. 159 Recommendations for Practitioners ................................ ................................ ...... 172 Recommendations for Further Research ................................ .............................. 173 APPENDIX A IMMEDIACY BEHAVIOR SCALE ................................ ................................ ......... 175 B PROFESSOR/STUDENT RAPPORT SCALE ................................ ...................... 177 C MOTIVATED STRATEGIES FOR LEARNING QUESTIONNAIRE ....................... 179 D IRB APPROVAL ................................ ................................ ................................ ... 186 E SCRIPT FOR INSTRUMENT ADMINISTRATION ................................ ................ 187 F IRB PROTOCOL AND CONSENT FORM ................................ ............................ 189 LIST OF REFERENCES ................................ ................................ ............................. 190 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 206

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9 LIST OF TABLES Table page 3 1 Reliability coefficients of sub scales of MSLQ ................................ .................. 109 3 2 Ti meline for data collection ................................ ................................ ............... 112 4 1 Expected and actual frequencies of race/ethnicities ................................ ......... 118 4 2 Instructor evaluation and immediacy scores ................................ ..................... 119 4 3 Instrument response rates ................................ ................................ ................ 121 4 4 Post hoc reliability of instruments ................................ ................................ ..... 122 4 5 Descriptive statistics of the sample ................................ ................................ ... 124 4 6 Verbal, nonverbal, total immediacy, and professor/student rapport means ...... 127 4 7 Beginning and ending student motivation and engagement means ................. 129 4 8 Correlations among variables ................................ ................................ ........... 137 4 9 Changes in motivation and engagement ................................ .......................... 139 4 10 Canonical correlation analysis testing motivation ................................ ............. 141 4 11 Follow up regression analyses for the motivation variables ............................. 142 4 12 Canonical correlation analysis testing engagement ................................ .......... 144 4 13 Follow up regression analyses for the engagement variables .......................... 145 4 14 Canonical correlation analysis testing change in motivation ............................. 147 4 15 Follow up regression analyses for the change in motivation variables ............. 148 4 16 Canonical correlation analysis testing change in engagement ......................... 149 4 17 Follow up regression analyses for the change in engagement variables ......... 150

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10 LIST OF FIGURES Figure page 2 1 Triadic reciprocality model ................................ ................................ .................. 34 2 2 A general model for student motivation and self regulated learning in the college classroom ................................ ................................ ............................... 39 2 3 Phases of the Self regulatory Process ................................ ............................... 50 2 4 Conceptual Model of Motivation and Engagement ................................ ............. 62 4 1 Distribution of participant age ................................ ................................ ........... 125 4 2 Distribution of verbal immediacy scores ................................ ........................... 126 4 3 Distribution of nonverbal immediacy scores ................................ ..................... 126 4 4 Distribution of professor/student rapport scores ................................ ............... 127 4 5 Distribution of beginning student expectancies scores ................................ ..... 129 4 6 Distribution of ending student expecta ncies scores ................................ .......... 130 4 7 Distribution of beginning values/goals scores ................................ ................... 130 4 8 Distribution of ending values/goals scores ................................ ....................... 131 4 9 Distribution of beginning affect scores ................................ .............................. 131 4 10 Distribution of ending affect scores ................................ ................................ ... 132 4 11 Distribution of beginning cognitive/metacognitive strategy use scores ............. 132 4 12 Distribution of ending cognitive/metacognitive strategy use scores .................. 133 4 13 D istribution of beginning resource management strategy use scores .............. 133 4 14 Distribution of ending resource management strategy use scores ................... 134

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11 LIST OF DEFINITIONS A GRICULTURAL E DUCATION Broadly defined, agricultural education in this study refers to study in the agricultural, food, fiber, and natural resources disciplines at t he undergraduate level. Broadly defined, agricultural education in this study refers to study in the agricultural, food, fiber, and natural resources disciplines at the undergraduate level. E NGAGEMENT aracterized by cognitive processes including increased mental effort, active (Estepp & Roberts, 2011a, p. 335). In this study engagement was operationalized as self regulated learning strategy use, which was found in the Motivated Strategies for Learning Questionnaire, and included the use of cognitive, metacognitive, and resource management strategies (Pintrich et al., 1991, 1993; Pintrich & Zusho, 2007). M OTIVATION uses us to action, pushes us in particular 2008, p. 452). In this study, motivation was operationalized by the motivation construct found on the Motivated Strategies for Learning Questionnai re, which included student expectancies, values/goals, and affect (Pintrich et al., 1991, 1993). P ROFESSOR / S TUDENT R APPORT al., 2010, p. 247). Professor/student rapport was operationalized through the use of the Professor/Student Rapport Scale (Wilson et al., 2010) S ELF REGULATED L EARNING n active, constructive process whereby learners set goals for their learning and then attempt to monitor, regulate, and control their cognition, motivation, and behavior, guided and constrained by their Zusho, 2007, p. 741). In this study, self regulated learning served as a proxy for student motivation and student engage ment combined. Self regulated learning was measured by the Motivated Strategies for Learning Questionnaire (Pintrich et al., 1991, 1993), which contained the two constructs of motivation and self regulated learning strategy use, the latter of which was co nceptualized as student engagement. T EACHER I MMEDIACY V erbal and nonverbal behaviors exhibited by teachers that enhance the perceived physical and/or psychological closeness between teacher and student (Andersen, 1978, 1979; Christophel, 1990; Mehrabian, 1981). Teacher immediacy was operationalized through the use of the Immediacy Behavior Scale (Christophel, 1990)

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12 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Require ments for the Degree of Doctor of Philosophy THE RELATIONSHIPS AMONG TEACHER IMMEDIACY, PROFESSOR/STUDENT RAPPORT, AND SELF REGULATED LEARNING By Christopher M. Estepp August 2012 Chair: T. Grady Roberts Major: Agricultural Education and Communication Th e purpose of this study was to determine the relationships among teacher immediacy, professor/student rapport, and student self regulated learning among selected undergraduate students in a college of agriculture The independent variables for this study were verbal and nonverbal immediacy and professor/student rapport. The dependent variable in the study was self regulated learning, which was a proxy for student motivation and academic engagement. The motivati on variable was made up of three constructs, including student expectancy for success, values and goals for the class and test anxiety Engagement consisted of two constructs, which were cognitive/metacognitive strategy use and resource management strate gy use. The study utilized a non expe rimental, correlational design. The population for the study was students enrolled in large (50 100 students) classes in the College of Agricultural and Life Sciences at the University of Florida. A convenience clus ter sample was obtained for the study ( n = 306). Participants compl eted three survey instruments; t i mmediacy behaviors, t with

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1 3 beginning and the end of the semester. Results of the study revealed that students perceived that their instructors use verbal and nonverbal immediacy behaviors ofte n and they additionally agreed that they have good rapport with their instructor Participants reported higher than intermediate scores for expectancy for success and values and goals for the class. They reported intermediate levels of test anxiety, and slightly higher than intermediate levels of cognitive/metacognitive strategy use and resource management strategy use. Mean increases were observed in expectancy for success, values and goals for the class, cognitive/metacognitive strategy use, and resour ce management strategy use, while the mean test anxiety decreased. Verbal and nonverbal immediacy were found to have strong positive relationships with professor/student rapport, and students who reported higher levels of rapport were found to report high er expectancy for success, values and goals, and cognitive/metacognitive strategy use. Additionally, the linear set of independent variable s was found to be more predictive of motivation and change in motivation than of engagement or change in engagement.

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14 CHAPTER 1 INTRODUCTION establi shed to train Puritan ministers it is no exaggeration to declare that higher Department of Education, 2006, p. ix). This was how the U.S. Department of Education (U SDE) opened its landmark report regarding higher education reform in America, A Test of Leadership: Charting the Future of U.S. Higher Education Albeit American colleges and universities have risen to a place of prominence through the years, many people are concerned about the future. Critics of higher education have argued that undergraduate students are increasingly disengaged in academic work (Hassel & Lourey, 2005; Taylor, 2006; Trout, 1997) and thus, graduating from colleges and universities lacking skills, such as critical thinking and problem solving (Arum & Roksa, 2011; Bok, 1996). Kuh, Kinzie, Schuh, and in college hinges upon student engagement and motivation, and Schunk (1989) posited that student eng agement and motivation have been identified as the two main characteristics associated with self regulated learning. Therefore, the goal of instructors in colleges and universities should be to develop students into self regulated learners. One possible way of increasing student motivation and engagement is through positive student instructor relationships. Astin (1993) and Chickering and Gamson (1987) determined that interactions between faculty and students have been a major predictor of s tudent success and development. McCombs (1991) further posited that, student relational

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15 va riables, such as teacher immediacy and professor/student rapport, might be the keys histo schools that existed three centuries ago. The emphasis of early colonial colleges, such as Harvard and Yale, was on preparing graduates for clerical service and civic lead ership, as opposed to training graduates for employment (Cohen & Kisker, 2010). The prevailing belief of the day was that occupational knowledge should not be disseminated in colleges, but instead learned through imitation and apprenticeships (Cohen & Kis ker, 2010). The advent of public land grant institutions in 1862, however, added practical training to the traditionally liberal education of colleges and universities (Stimson, 1919; True, 1929). The idea behind the land grant institution was that pract ical training would be made available to the common public in agriculture and the mechanical arts (Campbell, 1998; True, 1929). Land grant institutions provided greater access to a college degree for many students who previously were not afforded this op portunity. Campbell (1998) argued that the opportunity for more people to attend college created a better educated them to fulfill their personal and social responsibilitie s. One of these responsibilities was obtaining gainful employment, and postsecondary training in agriculture allowed many alumni of land grant institutions to enter the workforce in agriculturally related occupations. Even as more people left the farm fo r industrial jobs, the influx of

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16 graduates from land grant institutions helped the agricultural industry expand (Campbell, 1998). However, the dawn of twenty first century has brought many unexpected changes and presented greater challenges unknown to pre vious generations (National Research Council, NRC, 2009). Some challenges of this new era have included growing world populations, global integration and competitiveness, the need for greater scientific knowledge, health concerns, climate change and envi ronmental issues, and increased consumer pressures (Association of Public and Land grant Universities, APLU, 2009; NRC, 1992; NRC, 2009). Increased world populations have generated a need for more food production, leading to greater global integration (AP LU, 2009; NRC, 2009). The NRC (2009) argued that this global integration has increased the competitiveness in agricultural markets for both the inputs and outputs of production. What is more, health issues, such as obesity, diabetes, heart disease and ca ncer have become rampant in our society and environmental concerns, including the threat of global warming and changes in weather patterns have been of public concern as well (NRC, 2009). about food safety and agricultural sustainability continue to grow (N RC 2009). In the search for an answer, the National Research Council (2009) issued a challenge to colleges of agriculture; the NRC stated, Herein is the challenge to colleges and depa rtments of agriculture: to establish a place at the forefront of academe where students and scholars are prepared to learn about the complexities of agriculture and grapple with its evolution and change, and in so doing, find their opportunity to contribut e as leaders and participants in the agricultural enterprise (p. 3) The vision of the NRC in this challenge was to produce agricultural graduates capable of tackling the enormous problems society faces.

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17 Employers have an understanding of the curre nt societal climate as well and are looking for employees who can rise to meet the aforementioned challenges. For graduates to be competitive in the workforce they will need to possess 21 st century or soft skills, including flexibility, adaptability, self direction, leadership, responsibility (Young, 2011), critical thinking and problem solving skills (NRC, 2009), motivation, confidence, initiative, perseverance, the ability to work in teams, caring, and common sense (Kunkel, 1992), in addition to the disc ipline specific skills of agriculture. Boteler (2006) reported that agricultural employers indicated interpersonal skills as being of greater importance than experience in production agriculture. Over and above soft skills, the ability to work in interdi sciplinary teams has also emerged as a necessity (APLU, 2009; NRC, 2009). As a result, Kunkel and Thompson (1996) suggested that disciplines such as oceanography, geology, and ecology among others, will all become important for future agriculturalists. E dgerton (2001) further addressed this by stating that college graduates will need to be knowledgeable in science and technology because they will face issues where an understanding of these disciplines will be required to make informed decisions. In essen ce, the challenge for higher education and colleges of agriculture is to produce graduates who will be self regulated, lifelong learners. Unfortunately, the reality has been that many college graduates have not been prepared to enter society or the workfo rce. In an assessment of the state of learning in land appropriately educated citizenry graduates with sufficient skills to be effective workers 33). More recently, the National Conference of State

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18 ot preparing students for the 21 st century graduates are increasingly underprepared for the workforce (Association of American Colleges and Universities, AACU, 2002). The U.S. Department of Education (2006) reported that exceedingly large numbers of college graduates do not possess the skills students who earn degrees have not actually mastered the reading, writing and thinking skills we expect of college graduat graduate having accumulated whatever number of courses is required, but still lacki ng a coherent body of knowledge. all too o ften they graduate without knowing how to Reasons for the decline in the academic engagement (Arum & Roksa, 2 011; Hassel & Lourey, 2005; Taylor, 2006; Trout, 1997). In response to the growing problem of student disengagement and subsequent lack of learning, many have called for changes in the education of college of agriculture undergraduate students (APLU, 2 009, Jenkinson, 1994; Kunkel, Skaggs, & Maw, 1996; NRC, 1992 & 2009; Sprecker & Rudd, 1997). The National Research Council (2009, p. is needed in colleges of agriculture. An additional suggestion by The Kellogg Commission (1999) was that creating lifelong learners should be part of the core public mission of higher education, and indicated that this could be accomplished by creating

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19 new types of learning environments. The answer to solving the aforementioned problems will be an integrated approach that seeks to improve student academic engagement and motivation (Fredricks, Blumenfeld, & Paris, 2004; McLaughlin et al., 2005). Robert Barr and John Tagg (1995), in their in fluential piece titled From Teaching to Learning: A New Paradigm for Undergraduate Education suggested that the traditional that the mission of this paradigm is to delive r information to students. Freire (2009) referred to this as banking education, implying that teachers simply deposit information showing signs of becoming se lf satis fied most professors teach as they traditionally have, confident that the ways that have worked well enough in the past will continue to teaching in higher educa tion has become outdated and needs to change. Accordingly, many (e.g. Arum & Roksa, 2011; Barr & Tagg, 1995; Bok, 1996; Edgerton, 2001; Smith Sheppard, Johnson, & Johnson, 2005) have opined that a paradigm shift needs to occur in higher education from a n instruction paradigm to that of a student learning paradigm focused on engaging students academically. Barr and producing learning with every student by whatever mean 1995, p. 697). Volkwein and Cabrera (1998) suggested that the most important factors learning. Consequently, new approaches to educating colleg e students should focus on

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20 engaging students in the learning process (Kuh, Kinzie, Buckley, Bridges, & Hayek, 2006), and shifting the focus to student learning as the end with instruction as the means to get there (Barr & Tagg, 1995; Bonwell & Eison, 1991; Edgerton, 2001; Saroyan & Amundsen, 2004; Smith, Sheppard, Johnson, & Johnson, 2005). However, shifting the focus to student learning will require new expectations of learners in higher education as well as in colleges of agriculture, as learning center ed instruction involving active and interactive methods of instruction requires a great deal of effort on the part of the learner (Amundsen, Winer, & Gandell, 2004). Students involved in a learning paradigm would be required to take charge of their own le arning, because as Kuh et al. (2006) posited, This shift [to a learning paradigm] promises to have profound implications for setting higher expectations for students, for raising academic standards, for asking students to take more responsibility for thei r learning, for demonstrating competency through assessment, and for emphasizing and validating alternative ways of knowing, interdisciplinary methods, and p roblem focused learning. (p. 66) These factors would require that learners be engaged in the lear ning process, as academic engagement is a key to student learning (Fredricks, et al. 2004; McLaughlin, et al., 2005 ). McLaughlin et al. purported that in order for learning to occur learners must be engaged with the content. Therefore, a student learning focused approach in colleges of agriculture would mean that instructors need to strive for increased engagement in and out of the classroom (Furlong & Christenson, 2008). A result of increased student engagement might be that the passive learners once de pendent upon the professor to pour in information would become a thing of the past (Pintrich, 2004; Pintrich & Zusho, 2007).

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21 One possible way of increasing student engagement might be to increase student motivation (McLaughlin et al., 2005; Pintrich, 200 4; Pintrich & Zusho, 2007; Schunk, Pintrich (2004, Pintrich & Zusho, 2007) hypothesized that motivation plays a huge role regulated learning and self regulated learning has been equated with classroom academic engagement. According to Pintrich (2004), student motivation efficacy, goals, values, affect, and emotions. Because motivation includes this affective component, students will be more likely to engage in activities in which they feel more comfortable (Pintrich & Linnenbrink, 2004; Pekrun, Goetz, Titz, & Perry, 2002). Rodrig uez, Plax and Kearney (1996) suggested that increased levels of affective learning helped students expand motivation, thus increasing their will to learn. Therefore, the possibility of increasing motivation through the building of interpersonal relation ships between students and instructors might exist (Rodriguez, further sugge 34). Chickering and Gamson (1987) submitted that the principal factor contributing to student motivation and involvement is faculty s tudent interactions. If this contact between students and teachers is positive, students should feel more at ease in the classroom and enjoy the l earning environment (Rodriguez et al. 1996).

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22 Teacher immediacy represents one way instructors can improve the quality of teacher student relationships (Wilson, Ryan, & Pugh, 2010). Teacher immediacy is characterized by the verbal and nonverbal behaviors of teachers that bring about a psychological closeness between instructors and students (Andersen, 1978, 19 79; Christophel, 1990; Mehrabian, 1981). However, teacher student relationships go beyond immediacy behaviors. Wilson, Ryan, and Pugh (2010) used the phrase professor/student rappor t to describe the relationship between students and instructors and propo sed that immediacy and rapport are positively associated, and rapport is presumably built as a result of immediate behaviors. As a result, positive verbal and nonverbal immediacy behaviors exhibited by instructors should help the relationship building proc ess between students and instructors (Wilson et al. 2010). Velez (2008) suggested that creation of a positive classroom environment is in the hands of the instructor and that this positive environment can have a huge impact on student motivation. Theref professor/student rapport and improved student motivation. Walker, Greene, and Mansell (2006) suggested that increased student motivation might initiate greater use of self regulated learni ng behaviors by students. Pintrich and Zusho (2007) defined self whereby learners set goals for their learning and then attempt to monitor, regulate, and control their cognition, motivation, and beha vior, guided and constrained by their goals regulated learning with academic engagement. Self regulated learners tend to be motivated and engaged in their learning and pron e to use specific strategies such as

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23 goal setting, metacognition, self monitoring, and help seeking, all of which increase 2004; Pintrich & Zusho, 2007). All of the a forementioned self regulated learning behaviors are consistent with the characteristics found in lifelong learners (Knapper & Cropley, 2000), which are desired by agricultural employers. Consequently, the ultimate goal of a student learning focused paradi gm in colleges of agriculture should be to increase the number of self regulated learners. Statement of the Problem The National Research Council (2009) posited that, if agricultural graduates are to ulture must implement instructional Additionally, they indicated that graduates should be self motivated, lifelong learners possessing the aforementioned skills coupled with an appreciation of agriculture. However, the problem has been that students in higher education have become increasingly unmotivated and disengaged in the learning proces s (Arum & Roksa, 2011; Barr & Tagg, 1995; Bok; 2006; Edgarton, 2001; Kuh et al., 2006; National Research Council; 2009; Smith et al., 2005; Taylor, 2006; Trout, 1997). Therefore, critics have posited that college graduates have failed to acquire the skill s desired by employers (AACU, 2002; Bok, 2006; Kenny, 1998; NCSL, 2006; USDE, 2006). One way to increase student engagement and subsequently, learning, is by enhancing student motivation (Linnenbrink & Pintrich, 2003; McLaughlin et al., 2005; Pintrich, 2 003; Pintrich, 2004; Pintrich & Zusho, 2007; Walker, Greene, & Mansell, 2006). Perhaps, faculty student interactions involving teacher immediacy behaviors that

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24 lead to positive relationships between faculty and students might help establish professor/stud ent rapport, which could lead to higher levels of student motivation (Astin, 1993; Chickering & Gamson, 1987; Christophel, 1990; Velez, 2008; Wilson et al., 2010). Possession and utilization of high levels of motivation and engagement are typically charac teristics of self regulated learners (Pintrich, 1995; Pintrich, 2004; Pintrich & Zusho, 2007), and self regulated learners tend to demonstrate the qualities of lifelong learners (Knapper & Cropley, 2000) that employers are seeking in graduates of colleges of agriculture. The National Research Agenda ( Doerfert, 2011 ) recommended improving the success of students in colleges of agriculture as a Research Priority Area, more specifically indicating that research should examine the influence of faculty variab les immediacy and the development of professor/student rapport might serve as ways to increase the motivation and engagement of students, and ultimately academic achievement. Therefore, an examination into teacher immediacy, professor/student rapport, and self regulated learning and the relationship among these variables is warranted. Purpose The purpose of this study was to determine the relationships among teacher immediac y, professor/student rapport, and student self regulated learning among selected undergraduate students in a college of agriculture. The specific objectives of this research study were to: reported perceptions of teacher immediacy behaviors and professor/student rapport,

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25 reported measures of self regulated learning at two separate points in the semester, examine the relationships between selected undergraduate demographic s perceptions of teacher immediacy, professor/student rapport, and self regulated learning, regulated learning from the beginning of the semester to the end of the semester determine the predictive value of selected unde perceptions of teacher immediacy and professor/student rapport on self regulated learning, and determine teacher immediacy and professor/student rapport on the change in self regulated learning. Significance of the Study The findings of this research study hold several possibilities of s ignificance. First, a dearth of information exists in the agricultural education literature concerning teacher immediacy, professor/student rapport, and self regulated learning. The results of this study will contribute to the knowledge base in the agric ultural education discipline. Additionally, information obtained from the results of this study could have a bearing on college of agriculture instructors. If teacher immediacy and professor/student rapport can be identified as being strongly associated with student self regulated learning, then perhaps interventions can be created to help instructors increase their levels of teacher immediacy. Conceivably, helping instructors increase their immediacy could lead to higher levels of professor/student ra pport, and possibly higher levels of effectiveness in the classroom (Wilson et al. 2010). An implication of more effective teaching might be that students reach higher levels of achievement (Rosenshine & Furst, 1971), which could have the additional impa ct of increasing instructor efficacy (Guskfy, 1987).

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26 Administrators might also utilize the findings of this study to assess the efficacy of efforts to increase efficiency in higher education. Increasingly, administrators have been pressured to improve t heir efficiency (Barr & Tagg, 1995), and as budgets continually grow tighter this pressure will only become greater. One method of improving efficiency in higher education has been to increase class sizes (Arum & Roksa, 2011; Barr & Tagg, 1995). As the s tudent to instructor ratio increases, relationship building between instructors and students presumably becomes more difficult (Heppner, 2007) However, teacher immediacy behaviors might perhaps be a tool that professors can use to help increase the occur rence of relationship building in larger classes, thus hopefully leading to more student motivation, achievement and self regulated learning. Furthermore, teacher immediacy and professor/student rapport might be one piece in the larger puzzle of colleges of agriculture generating competent, capable, well prepared graduates, who will meet the challenges of the ever changing workplace. Assumptions The following assumptions were made for the purpose of this study: All participants responded in a truthfu l manner. Participants will be able to give an accurate assessment of their motivation and engagement from the beginning of the semester. Limitations The conclusions and implications drawn from this study were subjected to the following limitations: The d ata collected in this study are self reported measures of teacher immediacy, professor/student rapport, and self regulated learning behaviors. Other data collection methods could provide more accurate measures of the constructs.

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27 The sampling method for th is study limited the generalizability of the study. Chapter Summary As the result of a dynamically changing world, the National Research Council (2009) indicated that employers desire college graduates to possess soft skills such as critical thinking and problem solving among others. However, critics suggested that college graduates have increasingly become underprepared to fill the roles needed by employers (APLU, 2009; Campbell, 1998; Kunkel, Maw & Skaggs, 1996; NRC, 1992 & 2009; USDE, 2006). Indicatio preparedness has been a result of low levels of student academic engagement and motivation (Arum & Roksa, 2011; Bok, 1996; Hassel & Lourey, 2005; Taylor, 2006; Trout; 1997). Recommendations have been made that colleges of agriculture should change the ways that undergraduate students have been educated in order to help students engage more in the learning process (APLU, 2009; NRC, 2009), as student engagement has been identified as a precursor to learning (McLau ghlin et al., 2005). Student engagement can be improved through increased student motivation (McLaughlin et al., 2005; Pintrich, 2004; Pintrich & Zusho, 2007). One possible way to increase student motivation is by building relationships between instructo rs and students, which might be accomplished through the use of teacher immediacy behaviors (Christophel, 1990; Wilson, Ryan, & Pugh, 2010). Students who possess high levels of motivation are more apt to rely on self regulatory learning behaviors (Pintric h, 2004; Pintrich & Zusho, 2007; Walker, Greene, & Mansell, 2006) such as goal setting, use of metacognition, self monitoring, and help seeking, all of which have been associated with student engagement (Pintrich, 2004).

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28 CHAPTER 2 REVIEW OF LITERATURE Chapter 1 substantiated the need for this study by examining the literature associated with the current state of higher education. The consensus was that while graduates of colleges of agriculture have an abundance of opportunities, these graduates have b een overwhelmingly underprepared. The main problem has been that students in college classrooms have become academically unmotivated and disengaged. Therefore, the purpose of this study was to explore the relationships among teacher immediacy, professor/ student rapport, and self regulated learning as a means of helping students become more engaged Self regulated learning has been described as the learning that occurs as a result of motivated students engaging in self regulatory learning behaviors (Schun k, 1989). Thus for the purposes of this study, the measures of self regulated learning were used as a proxy to describe student motivation and student academic engagement. Chapter 1 also described the objectives of the study along with the definitions, assumptions and limitations relating to the study. Chapter 2 serves to first provide the theoretical bases for this study and then offer evidential support for the use of these theories through the presentation of empirical studies that relate the independ ent and dependent variables introduced in the conceptual framework. Constructivism The overarching theory that guided this study was constructivism. However, epistemology or this statement was that theories have been valid scientific explanations of a

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29 phenomenon by which one can test hypotheses and draw conclusions. Nevertheless, Schu nk purported that for the sake of consistency and the fact that constructivism allows researchers to make predictions that can be tested, the term constructivist theories has been deemed acceptable. Therefore, this study utilized the phrase constructivist theory Constructivist theory has posited that learners construct their knowledge based on their prior knowledge and experiences (Doolittle & Camp, 1999) and has operated on the assumption that learners must become active participants in the learning proc ess (Schunk, 1989, 2004). Doolittle and Camp additionally suggested that constructivism has not been a rigid idea, but instead has existed on a continuum ranging from cognitive, social, and radical constructivism. struction is a result of the cognitive processes in the brain has been called cognitive constructivism (Doolittle & Camp, 1999). Schunk (2004) concurred and deemed that cognitive processes have been a hermore, cognitive constructivist theorists conjectured that knowledge construction is based upon external realities and (Doolittle & Camp, 1999; Schunk, 2004; Spiro, Feltovi ch, Jacobson, & Coulson, 1995). The basis for cognitive constructivism came from the influence of the information processing model of learning (Doolittle & Camp, 1999; Schunk, 2004). s knowledge is constructed through social interactions using language and socially derived understandings of the world (Doolittle & Camp, 1999; Fosnot, 1996; Ormrod, 2008;

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30 Schunk, 2004; Vygotsky, 1978). Doolittle and Camp (1999) posited that social constr uctivism lies on the constructivist continuum between cognitive and radical sociocultural theory and has operated under the assumptions that learning is a result of informal and formal interactions with members in a society who convey cultural interpretations and responses to phenomena, and that cultures hand down the physical and cognitive tools needed by learners to interpret these phenomena (Ormrod, 2008). The third view of constructivism put forth by Doolittle and Camp (1999) was radical constructivism. Radical constructivism is the belief that learners create their own interpretations of the world around them (Doolittle & Camp, 1999; Schunk, 2004). The basis for this belief is that outside realities must first be filtered by the senses of the learner and then subjected to the interpretation of the learner (Doolittle & Camp, 1999). the radical constructivists believe that learners can never have an accurate representation of external realities because of the biases of the learner (Doolittle & Ca mp, 1999; Schunk, 2004). This study utilized an aggregated view of constructivism, in that all three views of constructivism were considered. This view of constructivism was taken because in accordance with Doolittle and Camp (1999), Estepp and Roberts ( 2 011b ) posited that three commonalities among the varying views of constructivism exist. Estepp and Roberts surmised that the first commonality among the various views of constructivism

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31 was that active cognition by the learner must take place. Next, they stated that learners must interpret reality, and lastly, the learning process must include an experience. The nature of the variables associated with this study combined well with the three tenets of constructivism put forth by Estepp and Roberts ( 2011b ) and therefore, the aforementioned combined view of constructivism provided a good theoretical basis. Teacher immediacy and professor student rapport provided a sociocultural context that students could draw upon to interpret reality. Additionally, self regulated learning requires that students actively, cognitively process the information to be learned, which is provided through classroom experiences. Social Cognitive Theory Social cognitive theory served as the mid level theoretical framework for this study (Bandura, 1986). The main tenet of this theory hypothesized that humans learn as a result of internal processes in conjunction with external influences (Bandura, 1986, 1989a, 1989b, 1999; Ormrod; 2008; Schunk, 2004). The main idea of social cogniti ve theory is supported by three assumptions: the triadic reciprocality among environmental, cognitive, and behavioral factors; learning is enactive and vicarious; and learning and performance are distinctly different processes (Bandura, 1986; Schunk, 2004) Each of these assumptions will be discussed in greater detail below. The aforementioned assumptions of the social cognitive theory have corresponded well with constructivist epistemology. As previously stated, constructivist theory has argued that act ive cognition, interpretation of reality, and experiences have all been required for a learner to acquire new knowledge (Estepp & Roberts, 2011b ). Accordingly, both theories agree that learners must be active participants in the learning process and that learning happens as a result of learner cognition. Additionally, both

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32 theories contain sociocultural aspects of the learning process (Bandura, 1986; Schunk, develo p from socially derived meanings and/or exchanges between learners (Doolittle & Camp, 1999), while social cognitive theory suggests that learning occurs as a result of th rough observation and modeling (Bandura, 1986). Albeit some learning experiences still have a role in the learning process, and this prior knowledge has been shaped by soc ial influences (Dewey, 1938). The last way constructivism and social cognitive theory are linked is through experiences. Constructivism requires that learners draw from their prior experiences to make sense of new experiences (Dewey, 1938), and social co gnitive theory entails that learners interact either actively or vicariously in learning experiences (Schunk, 2004). Constructivism and social cognitive theory complement each other well; consequently social cognitive theory provided a viable theoretical framework on which to base this study. Albert Bandura (1986), in his landmark piece Social Foundations of Thought and Action: A Social Cognitive Theory driven by inner forces nor automatically shape The social cognitive theory was founded in the main assumption that humans learn through interactions between the environment, personal factors, and behaviors (Bandura, 1986, 1989a, 1989b, 1999). Bandura (1986) proposed that learners are

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33 aforementioned interactions. According to Bandura (1986), the first of these capabilities is the symbolizing make generalizations about and react to environmental situations. Symbolizing allows learners to adapt to and alter their environment (Bandura, 1986). The second capability is the forethought capability, which allows learners to recognize possible consequences of actions beforehand and react accordingly. Furthermore, the forethought capability allows learners to set goals that can guide their motivation through the learning process (Bandura, 1986; Zimmerman, 1998). The vicarious capability is the third capability. This capability provides the basis for observational learning (Bandura, 1986). Next is the self regula tory capability, which helps students develop standards that guide behaviors (Bandura, 1986). The last capability is self reflective capability, which helps learners monitor, act upon, predict outcomes of, judge, and change behaviors (Bandura, 1986). The se five capabilities form the basis of the three abovementioned assumptions of the social cognitive theory : triadic reciprocality ; enactive and vicarious learning; and the differences between learning and performance (Schunk, 2004). Triadic Reciprocality O ne of the main assumptions of the social cognitive theory has been triadic reciprocality. The concep t of triadic reciprocality ( Figure 2 1) supposes that human learning is a product of the bidirectional interactions between environmental variables, person al factors, and behaviors (Bandura, 1986, 1989a, 1989b, 1997, 1999). Bandura (1989b) argued that many scholars have typically viewed learning as a unidirectional process where behaviors have been influenced by either cognitive processes or

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34 environmental v ariables. However, Bandura (1989b) refuted this unidirectional view of learning with his proposition of triadic reciprocality (Bandura, 1986), where the interactions in the triadic reciprocality model are the causative factors in human development. Howev er, Bandura (1986, 1989b) stated that reciprocal interaction does not mean equal interaction. The interactions between the three variables may be of varying strength and may not happen concurrently. The following paragraphs describe the possible interact ions among the variables. Figure 2 1 Triadic reciprocality model (Bandura, 1986, p. 24). The first interaction of interest was between personal factors (P) and behaviors (B). In the model of triadic reciprocality (Fig ure 2 1) personal factors ref er to physiological factors, as well as cognitive factors, such as affect, emotions, self efficacy, expectations, beliefs, goals, and thoughts (Bandura, 1989b; Schunk, 1989, 2004). In hink, believe, 3). Bandura (1989b) additionally posited that the consequences of behaviors can, in turn, have an effect on emotions and other cognitive processes. For example, perceived self e and implement actions necessary to attain Behaviors Personal Factors Environmental Variables

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35 84). Perceived self efficacy for academic achievement has been shown to affect the learning behaviors a person will 2001, 2004). In turn, subsequent academic achievement or failure, based on the outcomes of these behavioral choices, has the effect of increasing or decreasing self Furthermore, physiological factors can also affect the be haviors a person chooses to or processes, the outcomes of behavioral choices based on physical factors can, in turn, 1989b). The second set of interactions in the model is between environmental (E) variables behavior alters environmental conditions, and is, in turn, altered by the very conditions it can therefore be altered. In a classroom setting, environmental variables could include such factors as the classroom setting, academ ic tasks, and teacher variables (Pintrich & Zusho, 2007). Students in this setting can have an effect on the environment ; for instance, if students perform poorly on an assessment the teacher may choose to reteach the material (Schunk, 2004). In this exa mple, the behavior of the students, poor Conversely, the teacher, an environmental variable, can have an effect on student behavior. One way this can happen is through modeling ( Ormrod, 2008; Schunk, 1989). Schunk (1989) proposed that modeling has been a significant way students can

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36 acquire new behaviors. For example, teachers can model acceptable classroom The last interaction is between the environment (E) and personal (P) factors. cognitive competencies are developed and modified by social influences that convey info teacher might engage that student in class. Enactive and Vicarious Learning The second assumption of the social cognitive theory is that learning experiences can be enactive or vicarious (Schunk, 1989, 2004). According to Bandura (1986), enactive learnin g entails people learning from the consequences of their actions. Behaviors that produce desirable consequences tend to be adopted, while behaviors that produce undesirable consequences tend to be extinguished (Schunk, 1989). Vicarious learning, on the o ther hand, involves learning through observation (Schunk, 1989, 2004). Learners can observe by watching others model behaviors, reading, watching television, or listening (Schunk, 1989). Schunk (1989) posited that learning through observation can hasten the learning process because learners are able to the same behavior. Enactive and vicarious learning require interaction among the three variables of personal factors, e nvironmental variables, and behaviors. Therefore, the concepts of enactive and vicarious learning have been an important part of the social cognitive theory.

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37 Learning and Performance The third assumption of the social cognitive theory is that learning and performance are different (Schunk, 1989, 2004). Schunk (2004) posited that this notion of learning and performance being dissimilar differentiates the social cognitive theory from other learning theories, such as behaviorism. In light of this, Schunk (2 004) suggested that students may acquire knowledge, which has been synonymous with learning, but not put that knowledge into use until a later time, or perhaps never. The types of knowledge Schunk (1989, 2004) suggested students acquire are declarative kn owledge, the acquisition of facts; procedural knowledge, which consists of concepts and rules; and conditional knowledge, the knowledge of when and why to apply declarative and procedural knowledge. Schunk (2004) indicated that environmental variables, pe rsonal factors, and behaviors dictate to learners when to perform learned behaviors. Social cognitive theory is a robust theory that encompasses a myriad of factors. Human learning is not unidirectional in that learners are only affected by cognitive proc ess or the environment. Instead learning is a complex process that requires interaction among Conceptual Model Guiding the Study The conceptual model that framed this study was a researcher c onceived model, (Figure 2 2) model for student motivation and self regulated learning in the college classroom. The researcher conceived model will be discussed later, but first an ex planation of self regulated learning and its associated processes must be made. Pintrich and Zusho (2007) defined self

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38 learners set goals for their learning and then attempt to monitor, regulat e, and control their cognition, motivation, and behavior, guided and constrained by their goals and the 741). In line with this definition, Pintrich (2004) posited that self regulated learning has been establis hed on four general assumptions. The first assumption was that learning is active and constructive (Pintrich, 2004). This means that learners should be active participants in the learning process (McLaughlin et al., 2005; Pintrich, 2004; Pintrich & Zusho 2007) The second existed. This assumption suggested that learners have the ability to control, monitor, and regulate their cognition, motivation, behaviors, and e nvironmental context (Pintrich, 2004; Pintrich & Zusho, 2007). The next assumption was that students use goals, standards, or criterion to measure the value of learning and whether or not to engage in or continue with the learning process (Pintrich, 2004; Pintrich & Zusho, 2007). The last assumption was that self regulated learning, which has been described as learning that regulatory processes (Schunk, 1989), serves as a mediator between contextual or personal characteristics and student achievement (Pintrich, 2004; Pintrich & Zusho, 2007).

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39 Figure 2 2. A general model for student motivation and self regulated learning in the college classroom (Pin trich & Zusho, 2007, p. 735)

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40 In short, self engage in learning based on predetermined values and goals in conjunction with affective reactions, all of which should lead to subsequent achievement (Pintrich, 2004). As a result, motivation al processes work synergistically with self regulatory processes in the Pintrich and Zusho (2007) model, and this partnership of variables serves as a mediator between environmental/personal characteristics and achievement (Pintrich, 2004; Pintrich & Zusho, 2007). The following will describe the main variables of interest in self regulated learning, w hich include motivational processes and self regulatory processes Motivation al Processes One of the main areas of interest in self regulated learning has been motivation (Pintrich, 1988a, 1988b, 1989, 2004; Pintrich & Zusho, 2007; Schunk, 1989, 2004). Pintrich (2003) posited that motivational theories have consistently asked two basic motivation is a persistent and persuasive problem for faculty and staff at all levels of underst anding, and becoming active, self regulated learners in the right motivational classrooms need to understand what motivates students in order to promote optimal learnin g. Pintrich (1988a, 1988b, 1989, 2004; Pintrich et al., 1991, 1993; Pintrich & Zusho, 2007) proposed three motivational processes that interplay to create the larger

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41 construct of student motivation. These three motivational processes have consisted of stu Wigfield (2002) suggested that these constructs that make up motivation have been Consequently, the mo tivational processes investigated for the purpose of this study included these three constructs. Self e fficacy and student control of learning beliefs In the Pintrich and Zusho (2007) model (Figure 2 2) student expectanc ies consisted of self efficacy and control of learning beliefs. Examining each part of student expectancy more specifically, self about their capability to perform a task (Linnenbrink & Pintrich, 2003), while control o f have over their learning and effort (Ormrod, 2008). Self efficacy. Self capabilities to organize and execu te courses of action required to attain designated efficacy deals with specific situations (Pajares, 1996). Self efficacy has been s tudied extensively in the motivational literature and has been found to be an important predictor of student performance (Pintrich & Zusho, persistence, achievement, and c hoice of tasks (Bandura, 1986, 1997). Additionally, self efficacy has been compared with and found to be similar to the expectancy construct of the expectancy value theory of academic motivation (Wigfield & Eccles, 2000).

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42 While similar to the construct of expectancy, self efficacy has previously been confused with other constructs such as self concept and self esteem, however several differences exist (Linnenbrink & Pintrich, 2003). Self esteem has been described as the emotional feelings of worth that occur as a result of actual accomplishments or failures, whereas self efficacy perceptions precede a task and help determine accomplishments efficacy have been considered to be connected to past performances on tasks, they do not necessarily reflect the feelings of worth that accompany self esteem (Linnenbrink & Pintrich, 2003; Ormrod, 2008). Next, self efficacy differs from self concept in that self concept has been determined to be a fe eling of competence in a general area and self efficacy has dealt more with perceptions of ability in specific situations (Linnenbrink & Pintrich, 2003). For example, a student might have a high level of competence in reading, but when given a scientific research article their self efficacy to understand the content may be much lower than the self concept they have concerning their reading ability. Lastly, and similar to the previous point, self efficacy beliefs also have been associated with specific goa ls, while self concept has not been connected to specific goals (Linnenbrink & Pintrich, 2003). The development of self efficacy has typically been thought to be influenced by several factors, including past accomplishments and failures, communication of m essages by others, and the achievements and failures of others (Ormrod, 2008). Bandura (1986) indicated that students tend to feel more capable of performing tasks when they have experienced previous success in an academic area and that self efficacy shou

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43 adept at appraising their skills, thus creating discrepancy between actual ability and beliefs of competence (Schunk & Pajares, 2002). But, Schunk and Pajares also suggested that the capabilities of students to appraise their skill level in accordance with actual ability gets better as students grow older. Once students reach the point that they can accurately ascertain their self efficacy, Ormrod (2008) added that self efficacy leve ls should be high enough that small failures or setbacks should not cause students to lose motivation, a concept that Bandura (1989) termed resilient self efficacy A second factor that influences the development of self efficacy is communication of mess ages by others (Ormrod, 2008). Ormrod suggested that praise and efficacy levels to rise, thus increasing their persistence and effort. These messages can come from a variety of sources including in structors, peers, and family. Schunk and Pajares (2002) suggested that familial influences have the greatest effect on the building of self efficacy because these influences start early in life. However, Schunk and Pajares also indicated that as an indiv idual grows the influence of peers becomes increasingly important. Ormrod proposed that while messages from others can have effects on self efficacy, these effects can be short lived if the increase in self efficacy is not met with subsequent success. Orm rod (2008) proposed that the third factor that affects the development of self efficacy is the successes and failures of others. This idea has been directly connected Or mrod (2008) proposed that students often watch their peers, especially those of

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44 similar ability levels, and gauge their chances of success based on their peers successes or failures. Self vational processes (Pintrich, 2004). Hagen and Weinstein (1995) added that self efficacy plays a role in the types of goals that students set. Velez (2008) additionally reported a moderate positive relationship between self efficacy and the value that st udents place on academic tasks. Furthermore, Bandura (1986) indicated that low self efficacy is typically coupled with high test anxiety. Pintrich and Zusho (2007) proposed that efficacy typically perform better in courses, utilize more self regulatory strategies, and obtain higher levels of achievement. efficacy should have positive impacts on learning outcomes in the college classroom (Giglio & Lustig, 1987; Pintrich & Zus ho, 2007). Student c ontrol of learning beliefs. The second aspect of student expectancies is control of learning beliefs (Duncan & McKeachie, 2005; Pintrich, 2004; Pintrich & Zusho, 2007). Control of learning beliefs has been conceptualized in attributio n theory and has been explained in various ways, including locus of control, temporal stability, and controllability (Ormrod, 2008; Pintrich & Zusho, 2004; Weiner, 2000). Additionally, Deci and Ryan (1987) discussed the concept of self determination to de scribe the belief that students have autonomous control over their choices and achievement. Bandura (1986) further proposed the idea of outcome expectations to explain control over ernal environment would respond to actions taken by the student.

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45 Pintrich and Zusho (2007) posited that the beliefs which indicated an internal, student driven control have resulted in higher levels of student engagement and achievement, as opposed to th ose beliefs that have been characterized by external control. Pintrich and Zusho further suggested that students who believe a link exists between behaviors and performance tend to study more and engage in behaviors consistent with self regulated learning In summary, motivation can be driven by the control beliefs of students, in that control beliefs have an influence on future student behaviors (Pintrich & Zusho, 2007). Student v alues/goals Pintrich and Zush o (2007) model (Fig. 2 2) deals specifically with why students choose to engage in certain academic tasks and behaviors. The two main components h et al., 1991). oriented, while task value looks at the importance and utility students place on academic tasks. Each of these two components are discussed in greater detail below. Goal orientation. Pintrich and Zusho (2007) indicated that all theories of motivation rely on some sort of goal orientation aspect. Goal orientation describes the eferred to C

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46 Pintrich and Zusho (2007) reported that goal orientation research has typically focused on two types of goals : targe t goals and purpose goals. Target goals are described as being very specific and oriented towards student performance on one particular task, whereas purpose goals generally deal more with the reasons why students perform particular tasks (Pintrich & Zush o, 2007). Elliot (1997) suggested that the latter type of goals is more related to achievement motivation. Therefore, for the purpose of this study, the conceptual model of self regulated learning focused more on a purpose goal approach than on task goal s. intrinsic or extrinsic (Pintrich & Zusho, 2007). An intrinsic goal orientation suggests that ch as interest, engage in tasks for reasons, including competition, rewards, grades, and evaluation by others (Pintrich et al., 1991). Ames (1992) labeled these intrinsic an d extrinsic goal orientations, mastery and performance goal orientations, respectively. Schunk (2004) indicated that while both intrinsic and extrinsic goal orientations can lead to achievement and learning, intrinsic goal orientation tends to focus on self regulatory processes and strategies as well as learning and understanding, while extrinsic goal orientation appears to focus more on task completion, comparison with others, and grades. Pintrich and Zusho (2007) suggested that the adoption of an intr students who utilized intrinsic goal orientation should have increased levels of self

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47 efficacy a nd therefore should persist longer in academic tasks, exert more effort, and employ more effective learning strategies, thus leading to higher levels of achievement. Intrinsic goal orientation can have direct effects on student achievement as well as on t he use of self regulatory processes by students (Pintrich & Zusho, 2007). Therefore, Pintrich and Zusho recommended that college instructors encourage students to espouse an intrinsic goal orientation focused on learning and understanding instead of an ex trinsic goal orientation. Task value. refers to the value that an individual places on a particular task. More specifically in reference to achievement motivation, task value is related academic task, the importance of the task to students, and the utility value of academic ed as the level of enjoyment that a student attains from engaging in that task (Eccles & Wigfield, 2002). Pintrich and Zusho (2007) compared student interest to how much a student likes a task and posited that intrinsic interest is related to personal cha racteristics of the student and remains relatively stable over time. Additionally, Pintrich and Zusho (2007) suggested that in a classroom context student interest encompasses factors, such as interest in the course content and reactions toward the instru ctor. Eccles and Wigfield pointed out that intrinsic student interest in academic tasks benefits students, and Schiefele (1991) reported that student personal interest in academic tasks is related to increased use of self regulated learning behaviors. Ho wever, Pintrich and Zusho warned that while student interest is a product of personal and task characteristics, care should be taken

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48 to avoid the confusion of student interest with situational interest, which is the temporary interest in an academic task b rought about by environmental features such as an interesting guest lecturer, a fascinating topic, or other novel situations. Hidi (1990) indicated that situational interest is short lived and does not help establish deep intrinsic interest. Eccles et al. (1983) used the term attainment value to describe the importance of performing well on a task to an individual. Pintrich and Zusho (2007) posited that individual to indi vidual and from task to task. In accordance with this belief, Pintrich and a result, students may view success in a specific task differently according to their goal orientations and the importance students place on attaining that goal will drive how involved they become in the process (Pintrich & Zusho, 2007). The last task v alue according to Eccles et al. (1983) was utility value. Utility value Pintrich and Zusho (2007) suggested that students determine utility value by examining the perceived usefulness of an academic task in helping them reach their goals. In addition, Pintrich and Zusho posited that a high utility value of a task might outweigh other task value measures such as personal interest. Student a ffect /emotions According to Pintrich and Zusho (2007), student affect equates to student emotions. In an academic sense, student affect refers to how certain academic tasks make students feel. Pintrich and Zusho indicated that anxiety has been the most

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49 researched compo nent of student affect, and thus in the ir conceptual model student affect has been operationalized as test anxiety. Test anxiety has been described as having two distinct components, namely worry and an emotional response ( Cassaday & Johnson, 2002; Liebe rt & Morris, 1967). has been shown to be a detrimental emotion to students (Zeidn er, 1998), and Pintrich and Zusho (2007) generalized that test anxiety is generally associated with lowered cognitive processing, self regulatory behaviors, and achievement. Self regulatory Processes S elf regulatory processes were the next major variable p resented in the Pintrich and Zusho (2007) model Pintrich and Zusho stated that self regulatory processes used by students fall into the categories of regulating cognition, regulating motivation, regul ating behavior, and regulating context. For this stud y, these four regulatory process categories were operationalized on the Motivated Strategies for Lear ning Questionnaire as cognitive/metacognitive strategy use and resource management strateg y use (Pintrich et al., 1991). For this discussion on engagement the researchers will describe the four categories of self regulatory process es What is more, Pintrich and Zusho (2007) indicated that within each self regulatory process category f our phases exist, including goal setting and planning, monitoring, contr ol and regul ation, and self reflection ( Figure 2 3 ). The se four phases will be described for each self regulatory process category. Additionally, Pintrich and Zusho (2007) indicated that interaction occurs between phases and processes and that phases ma y occur simultaneously.

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50 Figure 2 3 Phases of the Self regulatory Process Regulati ng cognition cognitive and metacognitive activities that individuals engage in to adapt and change strategies is one of the key factors associated with regulation of cognition. In agricultural education, Filcher and Miller (2000) created a taxonomy of learning strategies for distance agricultural education students. They identified three types of lea rning strategies to help students in the regulation of cognition, including cognitive strategies, metacognitive strategies, and resource management strategies. Filcher and 93) self regulatory strategies measured on the MSLQ. The first phase during regulating cognition is goal setting. Three types of goal setting and planning are identified in regulating cognition, including target goal setting,

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51 retrieval of relevant prior knowledge, and initiation of metacognitive knowledge (Pintrich, 2004; Pintrich & Zusho, 2007). Target goal setting involves the creation of explicit goals that students can use to monitor and regulate their cognition (Pintrich & Zusho, 2007; Schunk, 1989 ; Zimmerman, 1989). More specifically, target goals include students setting strategic goals in areas such as learning, critical thinking, and metacognition (Pintrich, 2004; Pintrich et al., 1991). Pintrich and Zusho (2007) indicated that while target go als are typically set beforehand, goals can be set or adjusted during any phase of the regulatory process. Schunk (1989) further posited that setting target goals has a great benefit for students because these goals enable students to be able to easily mo efficacy for meeting their goals. The second aspect of the goal setting and planning phase during the regulating cognition is activation of relevant prior content knowledg e (Pintrich, 2004; Pintrich & Zusho, 2007). Pintrich and Zusho posited that activation of prior knowledge is necessary in all learning and that much of this activation of prior knowledge occurs unconsciously, and therefore does not constitute self regulat ed learning. However, in accordance with constructivist theory, Alexander, Schaller, and Hare (1991) suggested that learners who frequently regulate cognition actively seek out relevant prior knowledge to help them in their construction of new knowledge. As a result, Pintrich and Zusho recommended that purposeful activation of prior knowledge should be an integral part of the planning for regulating cognition process. The last piece of the goal setting and planning phase was activation of metacognitive kn owledge (Pintrich, 2004; Pintrich & Zusho, 2007). Schraw and

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52 Dennison (1994) stated that metacognitive knowledge is made up of three sub processes that help individuals reflect on their learning; the sub processes included declarative knowledge (knowledge knowledge (how to use learning strategies), and conditional knowledge (when and why to use strategies). Schunk (2004) echoed Schraw and Dennison adding that metacognitive knowledge describes the knowl edge of what strategies, skills, and resources academic tasks might require, as well as when and how to implement these strategies. Additionally, three variables that have typically been associated with influencing metacognitive knowledge were learner var iables, task variables, and strategy variables (Duell, 1986). Learner variables deal with the metacognitive capabilities of a learner (Schunk, 2004). Task variables refer to the difficulty of various learning tasks, and strategy variables connote a learn various strategies for regulating cognition (Schunk, 2004). Pintrich and Zusho (2007) indicated that strategy variables have been the most widely researched category of metacognitive knowledge. Goal setting and planning in regulating cognition requires that learners recognize each of the three metacognitive variables, develop an understanding of metacognitive knowledge, and learn how to utilize this knowledge to regulate cognitive processes (Pintrich & Zusho, 2007; Schunk, 2 004). The second phase in the self regulati ng process of cognition is cognitive monitoring (Pintrich, 2004; Pintrich & Zusho, 2007). The monitoring phase of regulating cognition is where the metacognitive knowledge developed during the planning phase is e mployed by the student (Pintrich & Zusho, 2007). Applying metacognitive knowledge to regulate cognition requires that students monitor their comprehension and

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53 performance, and also be aware of the progress of metacognitive strategies (Schraw, 1998). Pres sley and Ghatala (1990) found that the ability of students to monitor their cognition was generally poor even in older students, but Delclos and Harrington (1991) indicated that cognitive monitoring could be improved through instruction. Some examples of cognitive monitoring include tasks such as keeping track of attention while reading, self testing, and self questioning to check for comprehension (Pintrich et al., 1991). The third phase in regulating cognition ( control and regulation ) requires that learn ers take the information gained from the monitoring phase and utilize it to alter and adjust their cognition (Pintrich & Zusho, 2007). Pintrich et al. (1991) posited that a many regulation strategies such as rehearsal, elaboration, use of mnemonics, paraphrasing, summarizing, outlining, and note taking to control their learning. These regulat ion strategies have been classified as cognitive strategies ; however metacognitive knowledge is required on the part of the learner to know when and how to use these strategies (Pintrich & Zusho, 2007). As a result, proper selection of cognitive strateg ies is crucial for a student to successfully control and regulate their cognition (Pintrich & Zusho, 2007). The last phase in the regulating cognition process is self reflection (Pintrich, 2004; Pintrich & Zusho, 2007). Self reflection requires that learn ers assess and evaluate their academic performance, as well as determine where to attribute success or failure (Pintrich & Zusho, 2007). Zimmerman (1998) posited that attributions of success or

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54 failure relating to effort are much more adaptive than attrib utions related to student ability. Pintrich and Schrauben (1992) indicated that adaptive attributions are important in the self reflection process because adaptive attributions have been associated with higher levels of learning, achievement, and cognitiv e engagement. Regulati ng motivation /affect Regulating motivation consists of the regulation of motivational processes, including student expectancies, values, goals, and affect (Pintrich & Zusho, 2007). While Boekearts (1997) stated that regulation of stu dent affect and motivation is a critical piece of self regulated learning, regulating motivation has been researched much less than regulating cognition (Pintrich & Zusho, 2007; Wolters, 1998). The first phase in regulating motivation is the goal setting phase (Pintrich & Zusho, 2007). In the goal setting and planning phase, learners make judgments about themselves and academic tasks in relation to motivational processes (Pintrich & Zusho, 2007; Wolters, 1998). For instance, to ascertain student expecta ncy levels, learners must first examine their self efficacy to determine their degree of competence (Pintrich & Zusho, 2007), followed by the level of difficulty of a task, or what Nelson and Narens (1990) referred to as the ease of learning judgment. In addition, learners must establish how valuable an academic task might be before they pursue that task (Eccles et al., 1983; Pintrich & Zusho, 2007). Additionally, the goal setting and planning stage is where learners assess their interest in a subject (Pi ntrich & Zusho, 2007). The last motivational process that leaners must contend with in this phase is student affect in the form of test anxiety (Pintrich & Zusho, 2007). Students with high levels of anxiety before an exam tend to perform worse than stude nts with lower levels of anxiety (Bandura, 1997).

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55 The second phase in regulating motivation is monitoring. In the monitoring phase of motivational regulation, Pintrich and Zusho (2007) stated that a dearth of research has been conducted. However, they po sited that the general assumption among motivational researchers has been that motivational regulation follows the general pattern of cognitive regulation, in that learners must be aware of motivational beliefs and plans in order for monitoring and control to take place. The third phase during regulating motivation is control and regulation. Pintrich and Zusho (2007) indicated that many ways exist for individuals to control and regulate motivation. Some of the strategies used by students to control moti vation include positive self academic tasks (Wolters, 1998), trying to make tasks more interesting (Sansone, Weir, Harpster, & Morgan, 1992; Wolters, 1998), and trying to increase the tas k value or relevance of a task (Wolters, 1998). One additional motivational control strategy used by students that has had more negative results is self affirmation (Garcia & Pintrich, 1994). Self affirmation is when students lower the value of a task in order to maintain a better self worth. In addition to motivational regulation students can also regulate their affect (Pintrich & Zusho, 2007). Affective regulatory strategies have included self talk to help control anxiety (Zeidner, 1998), invoking s hame or guilt to motivate completion of tasks (Wolters, 1998), defensive pessimism where students use fear to help motivate themselves (Garcia & Pintrich, 1994), and self handicapping, when students decrease their effort or procrastinate on tasks in an att empt to save their self worth (Garcia & Pintrich, 1994).

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56 The last stage in motivational regulation is self reflection (Pintrich & Zusho, 2007). Pintrich and Zusho posited that when students reflect on their motivational processes, students will make attr ibutions for the outcomes, and that these attributions are important to the self regulatory process. Attribution theory suggests that the attributions made by learners can lead to intensified levels of emotions, such as pride, anger, humiliation, and guil t (Weiner, 1986). Emotional reactions by learners serve as efficacy beliefs, future expectancies for success, and interest (Pintrich & Schunk, 1996). Therefore, Pintrich and Zusho indicated the importa because the expectancies and values created in the self reflection phase are utilized by learners in the goal setting and planning phase of the next cycle of the motivat ional regulation pro cess. Regulati ng behavior Regulating behavior is the attempt by learner s to control their learning behaviors (Pintrich & Zusho, 2007). Pintrich and Zusho pointed out that some crit ics feel regulation of behavior does not belong under the category of self regulation, but instead behavioral control. However, Pintrich and Zusho argued that behavioral control is an important aspect of triadic reciprocality (Bandura, 1986), and behavior is a personal characteristic that can be controlled, regulated, and monito red. The first phase of regulating behavior is goal setting. Pintrich and Zusho (2007) indicated that the creation of a management plan for time and effort would be the main goal of the learner in this phase. Planning for time management would involve c reating study schedules and apportioning time for various academic and non academic activities (Pintrich & Zusho, 2007). Research has shown that time management

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57 strategies are commonly used by high achieving self regulated learners (Zimmerman & Martinez P ons, 1986). In addition to planning for time management, learners can also set goals that will determine the amount of effort to exert for different learning activities (Pintrich & Zusho, 2007). The second phase in regulating behavior is monitoring. I n the monitoring phase of regulating behavior learners examine their levels of effort and time management and make judgments accordingly (Pintrich & Zusho, 2007). Pintrich and Zusho stated that some learners might use formal observational procedures such as study logs or diaries to monitor their effort and time use. The information that learners gather during this phase allows them to assess their progress toward behavioral goals, which in turn will help learners during the control and regulation phase (P intrich & Zusho, 2007). The third phase in the regulatory processes of behavior regulation is the control and regulation phase (Pintrich & Zusho, 2007). Pintrich and Zusho indicated that the phases of self regulatory processes can happen concurrently, and control and regulation of behavior is one example where this is apparent. Many times the behavior monitoring phase and the behavior control phase happen simultaneously. The reason this happens is because behaviors are more easily monitored and adjusted than internal, cognitive processes (Pintrich & Zusho, 2007). Pintrich and Zusho gave the example of students regulating the time and effort to study textbook chapters. Depending upon the actual difficulty of the task, the students will either increase or decrease their effort and time spent studying. The difficulty of the task is easy to assess and the student can tell right away if his or her study habits and effort will be effective to meet learning goals.

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58 Certain strategies that learners may use to co ntrol and regulate their behavior are persistence and help seeking, which are positive strategies, or defensive pessimism and self handicapping, which decrease effort and are maladaptive (Pintrich & Zusho, 2007). However, Pintrich and Zusho warned that de pendent help seeking could be a negative practice because students seek help at the first sign of difficulty instead of persisting through difficult tasks. The last phase of regulating behavior is self reflection. Self reflection on behaviors is an imp ortant aspect of the behavioral regulation process (Pintrich & Zusho, 2007). During this phase students can judge their actions and strategies based on outcomes and make determinations about the effectiveness of those strategies (Pintrich & Zusho, 2007). effectiveness of behaviors will inform the goal setting and planning phase the next time the student faces a similar situation. Regulati ng context The last important self regulatory process is regulating context. Included in regulating context are attempts by the learner to monitor, control, and regulate the learning context (Pintrich & Zusho, 2007). As mentioned with behavior regulation, regulating context is not necessarily an internal s elf regulatory process; however in accordance with social cognitive theory (Bandura, 1986), Pintrich and Zusho concluded sonal self who is attempting to regulate the context, it seems important to include these activi ties in a model of self 762). The first stage in regulating context is goal setting and planning. Pintrich and

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59 perceptions of the learning environment and tasks. Students generate views about classroom norms, tasks, content, and other practices. In addition to environmental enthusiasm, and classroom and grading policies (Pintrich & Schunk, 1996). Pintrich and Schunk indicated that while many students will perceive classroom dynamics correctly there is the possibly that students may misinterpret the environmental context, thus leading to misconceptions concerning the ins tructor or classroom context. Nonetheless, these are the perceptions students will use throughout the subsequent phases of contextual regulation. The next stage in regulating context is monitoring. Pintrich and Zusho (2007) argued that monitoring is an important aspect of regulating the learning environment. They described the classroom context as a social system that operates with various constraints and opportunities, and that students must monitor and be aware of these constraints and opportunities to be successful (Pintrich & Zusho, 2007). Some of the areas students must monitor include rules, grading, task requirements, reward structures, and instructor behaviors (Pintrich & Zusho, 2007). Pintrich and Zusho posited that for a student to do well i n the classroom, they need to gain an awareness of the areas that need to be monitored and learn how to effectively monitor, because controlling the context is dependent upon successful monitoring. The third phase in the process of regulating context is control and regulation. Pintrich and Zusho (2007) stated that control and regulation of the context is the efforts by the learner to control, shape, or change factors in the learning environment. Pintrich and Zusho specified that contextual control is t he hardest of all of the self regulatory

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60 processes because oftentimes factors in the learning environment are not under the direct control of the learner. However, Doyle (1983) submitted that in many instances learners will negotiate with instructors abou t contextual features of the learning environment, such as students may ask to lessen assignments, change due dates, or use notes on an exam. Pintrich and Zusho additionally postulated that college students might have more control over their learning envi ronment because much of the learning that occurs in college takes place outside of a classroom. Examples of this type of contextual control might include students making sure their study environment is free of distractions, organized, and conducive to lea rning. The last phase in regulating context is self reflection. The self reflection phase is where learners can reflect upon and make generalizations about aspects of the learning environment, in addition to evaluating their efforts at monitoring and cont rolling the learning context (Pintrich & Zusho, 2007). Pintrich and Zusho postulated that the criteria learners use to evaluate their regulation of the learning environment can be based on comfort and enjoyment and/or cognitive factors such as learning an d achievement. Learners will find that some instructors will allow time for reflection and feedback about the learning environment and will make changes based on student feedback, while other instructors will not (Svinicki & McKeachie, 2011). Whatever th e regulatory process, and as with the prior self about contextual regulation provide valuable information to start over the regulator y process (Pintrich & Zusho, 2007).

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61 Conceptual Model of Motivation and Engagement Now that an introduction to self regulated learning has been given, the conceptual (Figure 2 2) can be discussed. Based on previous conceptualizations of engagement (e.g. Kuh et al., 2006; McLaughlin et al., 2005) regulated learning fully encompasses student motivation and academic engagement. In congruence with Social Cognitive Theory, t he triadic relationships investigated for the purpose of this study consisted of the environmental factor s of teacher immediacy and professor/student rapport the personal factor of motivation and the behavioral factors associated with student engagement, all of which relate to the variables in the Pintrich and Zusho model. For this study, motivation included student expectancies, values/goals, and affect, as measured on the MSLQ. Likewise the engagement behaviors which were in vestigated included cognitive/metacognitive strategy use and resource management strategy use Furthermore, the variable of professor/student rapport represented a hypothesized mediating variable between teacher and student characteristics and motivation and engagement. Alterations were made to the Pintrich and Zusho model based on an extensive literature review of teacher immediacy, professor/student rapport, and self regulated learning. For consistency throughout this study, this mo del will be referred to as the conceptual model of motivation and e ngagement (F igure 2 4 )

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62 Figure 2 4. Conceptual Model of Motivation and Engagement (Adapted from Pintrich & Zusho (2007))

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63 Variables in the Conceptual Model of Motivation and Engagement Teacher Immediacy been operationalized as the behaviors that facilitate the perceived feeli ng of closeness (Wilson et al. 2010). Teacher immediacy is a concept that has been researched extensively in the communication education field for many years, yet relatively little research has been conducted concerning teacher immediacy in an agricultur al education context. Gorham and Christophel (1992), communication education Vel ez (2008) suggested that teacher immediacy warrants investigation in agricultural education, as there has been a need to determine if teacher immediacy has an effect on 1981) work with communication behaviors and what was termed the implicit communication theory. Implicit communication theory posited that people constantly communicate using verbal hese communication behaviors led to his creation of the idea of approach avoidance eople approach 22). As a result, Mehrabian also produced three dimensions of emotional response that included pleasure displeasure, arousal nonarousal, and dominance submissiveness. These dimensions of emotional responses were based in the conception that people

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64 respond to different communication stimuli in different ways based on their emotions, prefer ences, likes, dislikes, and attitudes (Mehrabian, 1981). According to Mehrabian (1981) pleasure displeasure deals with emotional responses, such as satisfaction or dissatisfaction and happiness or unhappiness. Arousal nonarousal is specifically concerne d with the amount of activity and alertness that is generated through communication with another person. Lastly, the dominance submissiveness emotional response refers to feelings of control and influence in communication (Mehrabian, 1981). In addition t o the three emotional response dimensions, Mehrabian also indicated that relationships exist between these emotional responses and liking. In accordance with this, Mehrabian stated, W hen an object or person elicits pleasure, there is a positive correlati on between the arousing quality of that object or person and its liking that is, the more arousing a pleasurable entity is, the more it is liked. When an entity elicits displeasure, there is a negative correlation between the arousing quality of that en tity and its liking the more arousing an unpleasant entity is, t he less it is liked. (pp. 11 12) Consequently, if this concept were applied to a classroom context, teachers who elicit more pleasure in a student would be more liked and vice versa. One distinction that must be made is the difference between nonverbal behaviors and implicit communication behaviors (Velez, 2008). Mehrabian (1981) posited that nonverbal behaviors have been mostly associated with specific actions, such as facial expression s, nodding, gesturing, posturing of the body, and other body movements. These examples of nonverbal behaviors have been included with implicit communication, but implicit communication behaviors are inherently more subtle (Velez, 2008). Implicit communic ation behaviors include the aforementioned nonverbal

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65 behaviors, in addition to behaviors such as differentiation in vocal pitch, voice inflection, rate of speech and the like (Mehrabian, 1981). The contrast between nonverbal behaviors and implicit commu nication behaviors was made to account for cultural differences (Mehrabian, 1981), because as noted by understandable manner, yet still, by way of subtle nuances, convey inappro priate at the base of implicit communication theory can be recognized in cultures around the on a few Mehrabian posited that despite cultural differences, communication between people adheres to common principles, including the desire to approach what one likes, t he desire to be aroused to activity, and the feeling of powerfulness. According to Mehrabian, these principles lie at the core of all human interactions and help substantiate the implicit communication theory, which has served as the theoretical foundatio n for immediacy. Nonverbal i mmediacy Nonverbal interactions have been an important part of communication in the classroom (Velez, 2008). Andersen (1978) indicated that as much as 65% to 93% of the meaning of messages can be transferred by nonverbal commu nication, and as much as 81.6% of all teacher behaviors are nonverbal. Therefore, the importance of an examination of nonverbal communication by teachers in the classroom has become paramount.

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66 Andersen (1978) defined nonverbal teacher immediacy as those n onverbal posited that this distance reduction could be described in a physical or psychological sense. Richmond, Gorham, and McCroskey (1987) evidenced this by pointing ou t that nonverbal immediacy typically has been associated with affective feelings such as warmth, closeness, and belonging. Mehrabian (1981) further suggested that people tend to be drawn towards people or situations that promote feelings of liking, arousa l, and pleasure, and related this to non mplicit communication deals primarily with the transmission of information about feelings and like lev els of nonverbal immediacy should possess higher levels of affect and liking (Andersen, 1978). Examples of nonverbal immediacy behaviors exhibited by teachers that have helped increase student affect included eye contact, smiling, nodding, relaxed body po sition, leaning toward students, movement around the classroom, gesturing, and vocal variety (Witt, Wheeless, & Allen, 2004). In a study that investigated which immediacy behaviors contributed most to effective teaching, Crump (1996) found that students m ost preferred the nonverbal immediacy behaviors of dynamic delivery and vocal variation. Verbal immediacy Research has shown that nonverbal and verbal immediacy have been highly associated with one another (Edwards & Edwards, 2001; Velez & Cano, 2008, 2011), and verbal immediacy behaviors have been shown to increase cognitive, affective, and behavioral learning (Christophel, 1990; Gorham, 1988; Gorham & Christophel, 1990; Plax, Kearney, McCroskey, & Richmond, 1987; Rodriguez et al. 1996). Velez (2008)

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67 (p. 42). Examples of low inference verbal immediacy behaviors that have typ ically students, willingness to interact with students, and use of personal stories and examples (Edwards & Edwards, 2001; Gorham, 1988). Crump (1996) additionally found humor and the use of personal examples to be the most preferred verbal immediacy behaviors by students. In addition to the aforementioned examples, other verbal behaviors such as stylistic differences in expression can communicate like or dislike (Gorham, 1988). Specific examples of these verbal behaviors included past versus present tense of verbs, inclusivity (we vs. I), ownership (my vs. our class), probability (will vs may), and variations in adjectives (this vs. that person) (Gorham, 1988). Wilson et al. (2010) conducted a study with 195 undergraduate students in which they attempted to create a professor/student rapport scale. In their analysis, Wilson et al. inve stigated the relationships between teacher immediacy factors and professor/student rapport. Results of the study revealed that professor/student rapport correlated positively with the three teacher immediacy factors measured including a substantial relati onship with teacher friendliness moderate relationship with flexibility, and very strong relationship with nonverbal behaviors. Additionally, Wilson et al. indicated that professor/student rapport correlated with immediacy as a whole better than the corr elations between professor/student rapport and the three immediacy

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68 factors. They concluded that individual measures of immediacy were not as inclusive as professor/student rapport. In support of this, Wilson et al. additionally discovered that professor/ student rapport added to the variance of student outcomes explained by teacher immediacy with increases ranging from 9% to 27%. Wilson (2006) conducted a study ( n = 1,572) of college students to determine the relationships between verbal and nonverbal imme toward students, and desire of the instruc tor for students to succeed. These three attitudinal variables were part of Wilson (2010) professor/student rapport construct; therefore these variables gave a good indication of professor student rapport. Analysis of the data revealed positive correlations between verbal and nonverbal for students was substantially positively correlated with verbal immediacy and moderately positively correlated with nonverbal immediacy, students was similarly substantially positively correlated with verbal immediacy and substantially positively correlated with nonverbal immediacy and desire of instruc tor for students to succeed was additionally substantially positively correlated with both verbal and nonverbal immediacy Additionally, verbal and nonverbal immediacy added only miniscule amounts to the variance students, which was consistent with the findings by Wilson et al. (2010). Furthermore, both verbal instructors.

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69 Wilson and Taylor (2001) conducted a study to investigate th e relationship students to succeed. Correlations between the three variables of interest and the items on the immediacy scale ranged from slightly negative to moderately positive Substantial positive correlations were found between attitude toward students and using humor in class and praising st or comments. A moderate positive correlation was found between the professor showing genuine concern for students and professors use of humor in class along with a moderate positive relationship between professor concern and profe ssor smiling at the class. Lastly, substantial positive correlations between instructor wanting students to succeed ; smiling at the class as a whole; and comments were reported Additionally, Wilson and Taylor determi ned the correlations of concern for students, attitude toward students, and wanting students to succeed, and results showed that all three were moderately, positively co attitude toward the instructor. excessive use of immediacy behaviors. Students were given scenarios that described teachers using immediacy behaviors such as t students, and using eye contact in three different settings including the classroom, the instructor and the setting to be impo rtant variables in how students interpreted teacher

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70 immediacy behaviors. Rester and Edwards discovered that students viewed the use of immediacy by female instructors as caring ( F (1, 367) = 35.14, p < .001) while students viewed the same use of behaviors by male instructors as controlling ( F (1, 367), = 9.914, p = .002). Additionally, students viewed the use of immediacy behaviors more as sexual harassment in the office setting than in the classroom setting ( F (1, 367) = 5.22, p = .006). Most of the stud ies conducted in the immediacy literature that have addressed motivational processes have dealt with student state motivation defined by Christophel instructors. A dearth exists in the immediacy literature concerning the relationships between teacher immediacy behaviors, self efficacy, and goals. Furthermore, Williams (2010) indicated that virtually no studies have been conducted that examine the relationship betwe en immediacy and anxiety. In the existing immediacy literature, many studies have suggested a positive relationship between nonverbal immediacy and motivation (Christophel, 1990; Christophel & Gorham, 1995; Frymier, 1993,1994; Kalish, 2009; McCroskey & R ichmond, 1992; Richmond, 1990; Wilson & Locker Jr., 2008) as well as student affect (Andersen, 1978, 1979; Andersen, Norton, & Nussbaum, 1981; Chesebro, 2003; Chesebro & McCroskey, 2001; Kearney, Plax, & Wendt Wasco, 1985; Plax et al. 1986; Rodriguez et a l. 1996; Wilson & Locker Jr., 2008). Although not investigated as extensively as nonverbal immediacy, verbal immediacy has been found to be positively related to gains in student motivation (Frymier, 1993; Wilson & Locker Jr., 2008) and student affective learning (Gorham, 1988; Wilson & Locker Jr., 2008) as well.

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71 The few studies concerning teacher immediacy in agricultural education have been conducted by Velez (Velez, 2008; Velez & Cano, 2008, Velez & Cano, 2011). In his dissertation, Velez (2008) exa mined the relationships between immediacy and motivational processes. He specifically investigated the relationships among immediacy, self efficacy, and task value motivation. Results of the study revealed that verbal immediacy had a low positive associa tion with self efficacy and a low positive association with task value. Further, nonverbal immediacy had moderate positive correlations with self efficacy and low positive correlations with task value motivation. In a similar study, Velez and Cano (2008) found that nonverbal immediacy had a moderate significant relationship with expectancy value motivation and that the relationship between verbal immediacy and expectancy value motivation was slightly smaller. Christophel and Gorham (1995) investigated relationships between teacher immediacy and state motivation in college students ( n = 319). Measurements were taken at two different points in the semester and results showed low to modera te positive correlations between both nonverbal and verbal immediacy and student state motivation at both measurement points. Additionally Christophel and Gorham found that the difference in the correlations for nonverbal immediacy were significant, while the difference in correlations for verbal immediacy were not. Giglio and Lustig (1987) hypothesized that teacher immediacy would be positively correlated with student affect toward class. Their results revealed that teacher immediacy was substantially p ositively related with affect. In addition, 33% of the variance in student affect toward the class was explained by teacher immediacy. Giglio

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72 that teacher immediac y would still be positively correlated with affect towards the class. The hypothesis was supported. Giglio and Lustig further determined that a low positive relationship existed between teacher immediacy level and student expectations. In a study invol ving 195 undergraduate students, Wilson et al. (2010) investigated the relationships between teacher immediacy, professor/student rapport motivation. Results of the study indicated that teacher immediacy predicted 45% of the variability in student motivation. Additionally, results of the study revealed that simil ar study, Wilson (2006) found that both verbal and nonverbal immediacy were significant predictors of student motivation and that verbal and nonverbal immediacy also significantly predicted student enjoyment of a college course. Another study by Christense n, Curley, Marquez, and Menzel (1995) investigated communicate in class. Willingness to communicate has been considered a motivational variable related to apprehension, whic h might possibly be overcome by positive affect (Christensen et al., 1995). The investigations measured actual participation and attitudes toward participation and found that attitudes toward participation correlated more strongly with verbal and nonverba l immediacy than did actual participation in class communication. Results of the study indicated that a substantial positive relationship existed between attitudes about willingness to communicate and both verbal and nonverbal immediacy. However, results showed a low positive correlation between verbal immediacy and actual participation in classroom communication.

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73 Butland and Beebe (1992) conducted a study examining the relationships between teacher immediacy and affective learning. The affective learnin g measured by Butland and Beebe was attitude, behavioral intent, and total affect. Attitude dealt with attitudes toward content, recommended behaviors, and the instructor, while behavioral intent was concerned with intent for engaging in recommended behav iors, intent for enrolling in a similar course, and intent for enrolling with the same teacher. Results of study were that both verbal and nonverbal immediacy were significant predictors of total behavioral intent and total affect, while nonverbal immedia cy was a significant predictor of total attitude. In an experimental study conducted by Witt and Wheeless (2001), 400 college students watched an instructor deliver a lecture on videotape. Students were randomly assigned into one of five conditions, a c ontrol group or one of the four experimental groups. The experimental groups consisted of a high verbal immediacy with high nonverbal immediacy group, high verbal immediacy with low nonverbal immediacy group, low verbal immediacy with high nonverbal immed iacy group, and low verbal immediacy with low nonverbal immediacy group. A measure of affective learning was taken that consisted of students rating their affective reaction toward various aspects of the instructor and content. Results revealed that affe ctive learning was greater for the higher verbal immediacy groups than for the lower verbal immediacy groups. Furthermore, results indicated that affective learning was also greater for the groups with higher nonverbal immediacy and that nonverbal immedia cy in the high and low groups accounted for 26% of the variance in affective learning.

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74 Comstock, Rowell, and Bowers (1995) found a curvilinear relationship existed among the level of teacher immediacy and student motivation, attitudes toward content, and attitudes toward the teacher. Moderately high levels of immediacy were shown to be more motivating than were low or excessively high levels of motivation. Additionally, the same pattern was found was found with moderately high levels of immediacy signifi cantly predicting attitudes toward content and attitudes toward the instructor. For his dissertation, Mazer (2010) conducted a study with the purpose of creating a causal model of student engagement. Mazer utilized structural equation modeling to determin e the predictive relationships between immediacy, emotional interest, cognitive interest, and student engagement. The constructs of cognitive interest and emotional interest both overlapped with the construct of task value motivation as conceptualized in this present study (Duncan & McKeachie, 2005). Reported results indicated that teacher immediacy positively predicted both emotional interest and cognitive interest. Williams (2010) indicated the need for more studies examining the relationship between an xiety and immediacy. Therefore, Williams sought to investigate the association of immediacy behaviors with statistics anxiety in graduate students. Analysis of the data revealed that all six measures of statistics anxiety were significantly predicted by teacher immediacy behaviors. However, three subscales of the statistics anxiety scale were of particular interest; fear of the statistics instructor, computational self concept, and test anxiety. Twenty percent of the variance in fear of the instructor w as explained by teacher immediacy. Computational self perceived ability to work the statistics problems, which is closely related to the concept of self efficacy, and 11% of the variance in computational self concept was expla ined by

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75 teacher immediacy. Lastly, teacher immediacy accounted for 10% of the variance in test anxiety. Teacher immediacy was found to be a significant factor in the reduction of statistics anxiety. Chesebro (2003) investigated the association of nonverb al immediacy with student affect for the course material and the instructor as well as receiver apprehension. Results of the study found a significant main effect of nonverbal immediacy on student affect for the instructor and course material, but the eff ect of nonverbal immediacy on student receiver apprehension was not found to be significant. However, a similar study by Chesebro and McCroskey (1998), found that students with teachers expressing high levels of immediacy experienced less receiver apprehe nsion. Henning (2007) found that teacher verbal immediacy was a significant predictor of out of class contact with instructors and in class participation. Out of class contact was considered one of the factors of professor/student rapport (Alderman, 2008; Wilson et al. 2010). However, according to Duncan and McKeachie (2005), out of class interaction has also been associated with the student behavior of regulation of resources. In addition, nonverbal immediacy was found to be a significant predictor of in class participation. While achievement is not specifically addressed in the conceptual model for this study, the researcher hypothesized that increased motivation and engagement should lead to higher levels of achievement (Pintrich, 2004; Pintrich & Zus ho, 2007). Several studies have been conducted that have shown relationships between immediacy and achievement ; however, most of the studies have not indicated a direct link between the

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76 variables. In most cases, other variables were hypothesized as media ting the relationship between immediacy and achievement. Wilson et al. (2010) investigated the relationships between teacher immediacy and the outcome variables of amount learned and course grade. Analysis of the data revealed that teacher immediacy pre amount learned reported grades for the course. In a study by Butland and Beebe (1992) cognitive learning was measured through student self report and learning loss. Learning loss was the difference between report of learning and what students thought they would have learned from the ideal professor. Results of the study found that verbal and nonverbal immediacy were signific a nt predictors of both learning and learning loss. While the results of this study revealed that immediacy significantly predicted learning and learning loss, the limitation of the study was that learning was self reported by students and possibly not a r eliable measure. Additionally, Wilson (2006) found similar results ; verbal immediacy was a significant predictor of projected course grades. Giglio and Lustig (1987) proposed that teacher immediacy has an indirect relationship on cognitive learning. Resu lts of their study showed that students expected higher grades when an immediate teacher was present, but that the higher expectations were the influence on student achievement when teacher immediacy was controlled for. In the experimental study done by Wi tt and Wheeless (2001), they additionally measured cognitive learning in the form of recall and perceived student learning. Learning loss was additionally generated by calculating the difference between

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77 elt they would have learned if they had the ideal instructor. Results of the data analysis revealed that higher verbal immediacy did have a significant effect on recall, but the effect was not in the direction hypothesized by Witt and Wheeless. H igher ve rbal immediacy did not have a significant effect on learning loss. However, higher levels of nonverbal immediacy were found to have a significant, positive effect on recall. Additionally, higher levels of nonverbal immediacy were found to have an effect on learning loss in that less learning loss was experienced in the presence of higher levels of nonverbal immediacy. Comstock, Rowell, and Bowers (1995) investigated relationships between nonverbal immediacy and cognitive learning in the forms of short ter m recall and long term retention. Indications of the results were that a curvilinear relationship existed between immediacy and short term recall and that moderately high levels of immediacy were significantly better predictors of short term recall than l ow levels or excessive levels of immediacy. Frymier and Houser (1996) conducted an experimental study to determine the effects of nonverbal immediacy and relevance on cognitive learning. Students were randomly placed into either control or experimental g roups and the experimental groups were divided among high and low nonverbal immediacy in conjunction with high and low relevance. Recall of the information was used as the measure of cognitive learning. Results showed that regardless of the relevance lev el higher immediacy levels had Similarly, Kelley and Gorham (1988) conducted an experimental study with the purpose of investigating the effect of nonverbal immediacy on recall of information.

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78 Conditions of the exp eriment groups included high and low immediacy with either substantial amounts of eye contact or no eye contact. Kelley and Gorham reported that both eye contact and nonverbal immediacy had significant effects on recall. Additionally, immediacy accounted for 11.4% of the variance in recall and eye contact accounted for 6.9% of the variance. Nonverbal immediacy and eye contact also had a significant interaction that affected recall. Professor/student Rapport Wilson et al. (2010) defined professor/student rapport and stressed the importance of this concept to being an effective teacher. Lowman (1995) indicated that teachers who build r apport have been characterized as caring, welcoming, encouraging, positive, democratic, and have expressed a genuine interest in their students. Meyers (2009) posited that interpersonal relationships in the college classroom have been essential to the tea ching and learning process. One important aspect that has been associated with professor/student rapport has been interaction between students and instructors (Wilson et al. 2010). Alderman (2008) suggested that many positive student outcomes have been realized from faculty student interactions. Cox, McIntosh, Terenzini, Reason, and Lutovsky Quaye (2010) suggested that frequent personal contact between instructors and stud ents has had an (1997) synthesized the literature on effective teaching and reported that instructor relationships with quality out of class interactions involved four characteristics: instructors must be

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79 personable and approachable; instructors must show enthusiasm and passion for their work; instructors must care for students; and instructors must be mentors to students. Cox et al. (2010) indicated that casual instructor student contact was important, but not sufficient and that interactions needed to be substantive i n order to positively affect students. Consequently, teachers who utilize high levels of verbal and nonverbal teacher immediacy behaviors have been characterized as teachers who have been good at building professor/student rapport (Meyers, 2009; Wilson e t al. 2010). Stewart and Barraclough (1992) reported that teacher immediacy and enthusiasm, both components et al. (2010) concurred that teacher immediacy has been the variable most closely associated with professor/student rapport. However, Wilson et al. argued that teacher immediacy has not been robust enough of a construct to describe rapport. The argument has be en that immediacy dealt strictly with behaviors that teachers could utilize in the classroom to increase the closeness between teacher and student, but did not encompass all of the intricacies involved in interpersonal relationships (Benson, Cohen, & Buski st, 2005; Wilson, 2006; Wilson et al. 2010). However, instructors use of immediacy behaviors such as tone of voice, facial expressions and other nonverbal or facilitat e rapport building in college classrooms (Cox et al., 2010; Wilson et al. 2010). Chase (2 maintain positive levels of immediacy, they will likely develop positive connections with their students; this may lead to long term i

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80 13). The belief has been that frequent, substantive interaction between faculty and students (Cox et al., 2010), caring by faculty (Meyers, 2009), and relationship bui lding between faculty and students (Lowman, 1994, 1995; Wilson et al. 2010), including the use of teacher immediacy behaviors (Rodriguez et al. 1996) can have the effect of increasing student affect motivation, and achievement. In a study to establish a professor/student rapport scale, Wilson et al. (2010) investigated the relationship between professor student rapport and motivation. In the study, 195 undergraduate students were surveyed and results revealed that professor/student rapport explained 27 % of the variance in motivation over and above that explained by teacher immediacy. Benson, Cohen, and Buskist (2005) conducted a study investigating the relationship between professor/student rapport and student affect. Students reported the number of pr ofessors who had and had not established rapport in the classroom and students indicated their level of enjoyment and satisfaction with the class. Discrepancy scores were calculated by finding the mean difference between professors with and without rappor t for each affective response. A repeated measures ANOVA was used to determine if there was a statistically significant difference between professors who established rapport and those who did not. Results revealed that the presence of professor/student r apport made a significant difference in discrepancy scores. Benson et al. (2005) indicated that the presence of rapport significantly increased the likelihood of students taking another class from instructors, increased enjoyment of those instructors, and

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81 udents, and desire of the instructor for students to succeed were additionally variables used in Wilson (2010) professor/student rapport instrument. The three aforementioned variables provide a good measure of professor/student rapport. Results students to succeed were significant p redictors of student motivation. Furthermore, Wilson investigated the predictive value of instructor attitude toward students on student enjoyment in the course and found that attitude toward students, desire for students to succeed, and concern were all significant predictors of student enjoyment. The three variables also accounted for 42% of the variance in how much students enjoyed the course. Attitude of instructors toward students was also used to predict student attitudes toward the instructor. De sire for students to succeed, instructor concern, and instructor attitude toward students all emerged as significant predictors of student attitudes toward instructors and explained 60% of the variance in student attitudes toward instructors. Wilson and Locker Jr. (2008) investigated the associations between verbal immediacy and student motivational processes including motivation, course ratings, and attitudes toward instructors. However, Wilson and Locker Jr. broke verbal immediacy into components that correspond with attributes of professor/student rapport such as friendliness and flexibility. They found that friendliness was a significant predictor of

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82 motivation, course ratings and attitudes toward instructors. Additionally, Wilson and Locker Jr. fou nd that flexibility was a signi ficant predictor of motivation and course ratings. Wilson and Taylor (2001) found that certain professor/student rapport attributes had a positive association with increases in student motivation. Student motivation was ver y strongly positively correlated with professors showing genuine concern for students, substantially positively correlated with professors having a positive attitude toward students, and substantially positively correlated with instructors wanting students to succeed. In his dissertation, Henning (2007) indicated that student expectancies were a significant predictor of out of class contact with the instructor. Out of class contact was shown by Wilson et al. (2010) to be one of the factors involved in prof essor/student rapport. Out of class contact with the instructor can also be seen as a help seeking regulatory behavior depending upon the reason for the visit according to Duncan and McKeachie (2005). Therefore, the prediction of out of class contact by student expectancies might also provide a connection between student motivation and student engagement Benson, Cohen, and Buskist (2005) conducted a study involving 202 undergraduate students at Auburn University, in which the purpose was to determine th e relationships between professor/student rapport, student attitudes, and student behaviors. Paired sample t tests revealed mean differences on specific student measures for professors who exhibited and who did not exhibit rapport. Students who had profe ssors that had established rapport were significantly more likely to engage in

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83 behaviors such as attending class, paying attention in class, studying for class, attending office hours, and contacting the professor through email. In the study by Wilson et a l. (2010), they additionally investigated the relationship of professor/student rapport with the achievement measures of amount learned and course grade. For amount learned, professor/student rapport explained 10% of the variance over the 23% of the varia nce explained by immediacy. Likewise, professor/student rapport explained 9% of the variance in course grade over the 6% explained by teacher immediacy factors. Volkwein, King, and Terenzini (1986) investigated the predictive value of college transfer stu ( n = 231) relationships with instructors and the achievement variable s of student intellectual growth and academic content acquisition Results of the study revealed that academic content acquisition. Additionally, the faculty concern scale was a significant predictor of both academic content acquisition and student intellectual growth. Volkwein et al. indicated that the faculty relations scale was concerned with the qua lity of the relationships built between transfer students and instructors while the faculty concern scale dealt with how interested instructors have been about student growth in and out of the classroom, how available instructors have made themselves to st udents, and the extent to which instructors have been concerned with good teaching. The constructs of each scale were indicative of the construct of professor/student rapport (Wilson et al. 2010). In addition to her aforementioned findings, Wilson (2006 perceptions of instructor attitudes toward students accounted for 12% of the variance in

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84 projected course grades. Regression analyses indicated that concern for students and wanting students to succeed were significant predicto rs of projected course grades. Wilson and Taylor (2001) also investigated the relationships between certain professor/student rapport variables and projected grades. A moderate positive relationship was found between projected grades and professors showin g genuine concern for students. Additionally, a moderate positive relationship was found between p rojected grades and instructors having a positive attitude toward students, and a low positive association was found between projected grades and instructor desiring to see students succeed. Motivation For the purpose of this study, the variable of motivation was conceptualized as student expectancies, values/goals, and affect. Each of these three areas of motivation was broken down into smaller sub construc ts, which were operationalized on the Motivated Strategies for Learning Questionnaire (Pintrich et al., 1991, 1993). Student e xpectancies was operationalized by the sub constructs of self efficacy and control of learning beliefs. Values/goals consist ed o f goal orientation and task value, and affect was identified as test anxiety. In an investigation into causal factors of student engagement, Henning (2007) found student expectancies to be a significant predictor of student engagement skills and student en engagement skills construct proved to be similar to regulating cognition and behavior as described on the MSLQ, as were engagement preparation behaviors. Furthermore, Henning found that student in class participation was predicted by self efficacy and student expectancies.

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85 Mazer (2010) sought to determine the predictive relationship between cognitive ognitive and emotional interest matched the variable of task value as measured on the MSLQ (Pintrich, Smith, Garcia, & McKeachie, 1991, 1993). Cognitive interest was found to significantly predict student engagement as was emotional interest. Additionall y, the interaction between cognitive and emotional interest also served as a significant predictor of student engagement. Pintrich and Schrauben (1992) conducted a meta analysis of empirical literature to determine the relationships between motivational be liefs and the use of self regulatory behaviors. Results of their analysis revealed that higher levels of student self efficacy were associated with increased cognitive engagement. Pintrich and Schrauben found that students with high levels of self effica cy were more apt to utilize self regulatory behaviors and have increased levels of effort, monitor and regulate their learning, persist through difficult tasks, and manage their study time and environment. Pintrich and Schrauben also examined other motiva tional beliefs including control beliefs. They found that students with internal control beliefs who attributed successes or failures to effort and behavior were more likely to engage in the use of cognitive learning strategies. Furthermore, Pintrich and Schrauben examined the relationship between were associated with their cognitive engagement and students who reported higher task value levels tended to utilize critica l thinking and strategies to control cognition and effort more.

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86 Walker, Greene, and Mansell (2006) conducted a study with 171 college students to determine the role self efficacy played in cognitive engagement. Results of the study revealed that self effi cacy had a moderate positive relationship with meaningful cognitive engagement. Using path analysis, Walker et al. found that self efficacy and intrinsic motivation were positive predictors of meaningful cognitive engagement. Greene and Miller (1996) inve stigated the relationships between achievement, motivation, and cognitive engagement in college students. Motivational processes examined by Greene and Miller were perceived ability, which is similar to self efficacy, and learning goal orientation versus performance goal orientation. Results showed that perceived ability and adoption of learning goals had substantial positive associations with meaningful cognitive engagement. P ath analysis results showed that perceived ability had a total effect of .57 o n meaningful cognitive engagement and learning goal orientation had a total effect of .68 on meaningful cognitive engagement. Chesebro (2003) investigated the relationships between student apprehension and student learning. Chesebro proposed that student apprehension is inversely related to student learning, and results revealed a low negative relationship Scott and Wheeless (1977) additionally determined that receiver apprehension has been associated with lower student achievement outcomes. According to Chesebro and McCroskey (1998) receiver apprehension is the anxiety experienced by students that they will not be able to interpret or process information provided by the teacher. The concept of receiver apprehension mirrors a low self efficacy by studen ts concerning classroom performance. A meta analysis of 25 studies on receiver apprehension by Preiss, Wheeless, and Allen (1990) revealed that

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87 receiver apprehension is negatively related to information processing and listening effectiveness by students. In the Greene and Miller (1996) study mentioned earlier, the relationship between motivation and achievement was measured. Perceived ability was found to have a low positive association with achievement. In the path analysis, perceived ability did not have a direct effect on achievement, but had an indirect effect of .07, and learning goal orientation had an indirect effect of .11 on achievement. Chemers, Hu and Garcia (2001) found similar results. They found that self efficacy had significant, moder ate direct effects on academic performance in first year college students. Pajares and Miller (1994) investigated the role of self math performance. Results of the study revealed a very strong positive relationship between ma th self efficacy and math performance. Additionally, regression analysis indicated that self efficacy was a significant predictor of math performance. Pajares and Miller also conducted a path analysis to determine the direct and indirect effects between the variables. Results of the path analysis showed that math self efficacy had the largest effect on math performance. Giglio and Lustig (1987) conducted a study that investigated the relationships between teacher immediacy and student learning. They h expected grades in a class would be positively correlated with cognitive learning. Data analysis revealed that student expectations were moderately positively related with the actual grades received in the class. Henning (2007) found similar results to those of Giglio and Lustig (1987). Henning found that student expectancies and student self efficacy were significant predictors of predicted student cognitive learning.

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88 Giglio and Lustig (1987) concluded that students will compl ete the work required to receive the grade they expect in a class. Additionally, they concluded that since the relationship between expectations and actual grades exists, then instructors should try to raise the expectations of their students. This propo sal was possible, according to Giglio and Lustig, as they found that students of highly immediate teachers expected higher grades. Student E ngagement For the purpose of this study, student engagement was conceptualized by cognitive/metacognitive strategy use and resource management strategy use. Pintrich et al. (1991, 1993) operationalized cognitive/metacognitive strateg y use into five sub constructs, including rehearsal, elaboration, organization, critical thinking, and metacognitive self regulation. Pintrich et al. operationalized resource management strategy use as time and study environment, effort regulation, peer learni ng, and help seeking. Both cognitive/metacognitive strategy use and resource management strategy use were measured using the Motivated Strategies for Learning Questionnaire (Pintrich et al., 1991, 1993). Henning (2007) investigated cognitive learning us ing hierarchical regression analyses and found in the first model that student cognitive learning was predicted by student engagement skills, student engagement preparation behaviors, and in class participation. In a subsequent model when student motivati onal factors were added, in class participation dropped out of the model, but student preparation and student engagement skills remained significant predictors of cognitive learning. Similar results were found by Greene and Miller (1996). Their analysis showed that meaningful

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89 cognitive engagement had a low positive association with student achievement and that meaningful cognitive achievement had a direct effect of .31 on achievement. In a study that investigated the relationships between faculty student relationships and student intellectual growth, Volkwein, King, and Terenzini (1986) found that engagement was a significant predictor of intellectual skill growth in students. The classroom involvement scale used by Volkwein et al. consisted of three measurements, first was the motivational measurement of enjoyment of classes, second was how often students participated in expressing their views in class, and third was how often students have been intellectually stimulated in class. The results indicated that motivation and engagement in class both play a role in predicting student achievement. Schraw and Dennison (1994) tested the Metacognitive Awareness Inventory (MAI) to determine the validity of the instrument. In two experiments they found positive moderate to substantial, significant relationship s among knowledge of cognition and regulation of cognition. Schraw and Dennison subsequently used the MAI to determine the relationships between knowledge of cognition, regulation of cognition and performance on an academic test. Results showed that a significant relationship between knowledge of cognition and performance. Dunigan and Curry (2006) investigated the use of self regulatory behaviors by college students enrolled in a distance science education course. The instrument utilized by the researchers was the MSLQ. Results showed moderate positive relationship s between grades and elaboration and between grades and critical thinking. In addition, multiple regression analyses showed that grad es were significantly

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90 predicted by the learning strategies subscale. Elaboration and critical thinking were also significant predictors of grades. Additionally the resource management subscale, which included factors such as time and study environment, effort regulation, and help seeking, was found to be a significant predictor of grades Lastly effort regulation significantly, positively predicted grades. hypothesized rel ationships between personality fit (similar to professor/student rapport), student engagement, and student outcomes. The results of his analysis indicated that student engagement acts as a mediator between personality fit and student outcomes. The studen t outcomes that Lackey examined included course satisfaction, commitment, that self regulatory behaviors mediate the relationship between teacher variables and student o utcomes. Teacher and Student Characteristics In the Pintrich and Zusho (2007) model (Fig. 2 2) personal characteristics referred included age, gender, and ethnicity. H owever, for the purpose of this study, personal characteristics referred to student characteristics as well as instructor characteristics. Student characteristics were expanded to include major and year in school. The researcher included major in this se ction because of the diversity of disciplines in colleges of agriculture. Additionally, the rationale for including year in school was that students at the higher levels have had the opportunity to experience more instructors and therefore might have a di fferent perspective than students with a lower class

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91 ranking. Instructor characteristics included in the model were age, gender, ethnicity, years teaching, and professorial rank. In one of the few studies conducted in agricultural education, Velez (2008 ) investigated the relationships between student and teacher characteristics and teacher immediacy. Velez discovered that very low positive correlations existed between lo w and very low positive correlations existed between class size and reported verbal and nonverbal immediacy respectively Additionally Velez found that instructor age had low positive Velez and Cano (2008) sought to determine the difference between graduate showed that students perceived graduate students as having higher levels of immediacy than profes d were moderate for nonverbal and strong for verbal immediacy. Additionally, Velez and Cano immediacy immediacy scale. Results revealed that graduate students exhibited higher levels of verbal and nonverbal immediacy and that 9 out of the 14 verbal immediacy behaviors exhibited stro ng effect sizes with the remainder being moderate, and that all of the nonverbal immediacy behaviors had moderate effect sizes. Conclusions of the study were that graduate students possessed greater levels of both verbal and nonverbal immediacy than profe ssors, although Velez and Cano warned that other confounding variables such as age, gender, class size, and class type should be considered.

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92 ng to various student, classroom, and teacher variables. Velez and Cano (2011) found differences in mean verbal immediacy levels between late afternoon classes ( M = 2.92, SD = .82) and evening classes ( M = 2.29, SD d was found to be strong. They further found that verbal immediacy decreased as class size increased. A calculation of the effect size for class size was strong, but nonverbal immediacy remained relatively consistent throughout the different class sizes Mean scores for verbal immediacy increased as instructor age increased and were highest for instructors aged 50 59 ( M = 3.07, SD = .78), while means for professors aged 60 69 slightly decreased ( M = 2.93, SD = .59). Nonverbal immediacy mean scores followed the same trend for instructor age as verbal immediacy mean scores with instructors aged 50 59 being the highest ( M = 3.80, SD = .62). Verbal and nonverbal immediacy mean scores were slightly higher f or fe male instructors ( M verbal = 2.98, SD = .76; M nonverbal = 3.63, SD = .61) than for male instructors ( M verbal = 2.74, SD = .79; M nonverbal = 3.56, SD = .65). According to Velez and Cano (2011), the trend for verbal immediacy increased as students progressed from freshmen to seniors. Freshman reported a mean verbal immediacy score of 2.57 ( SD = .75) while seniors reported a mean verbal immediacy score slightly higher at 2.92 ( SD = .76). Nonverbal immediacy mean scores remained relatively the same. Addition ally, female students reported slightly higher mean verbal immediacy scores ( M = 2.87, SD = 2.77) and mean nonverbal immediacy scores ( M = 3.77, SD = .57) than did male students ( M verbal = 2.77, SD = .80; M nonverbal = 3.47, SD = .64).

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93 Torres and Cano (1995 ) investigated the cognitive levels of thinking by senior students enrolled in the College of Agriculture at The Ohio State University (n = 92). Torres and Cano utilized three measures of cognition, including basic cognitive abilities, application abiliti es, and critical thinking abilities. Results of the study showed that males scored higher on basic cognitive abilities ( M = 20.2) than females ( M = 19.4), but no significant difference was found. However, males scored higher on application abilities ( M = 20.9) than did females ( M = 19.3), and Torres and Cano found a significant difference. Both males and females scored relatively the same ( M = 16.7, M = 16.9, respectively) on critical thinking abilities. With regards to major, comparisons were not able to be made between groups. However, raw scores indicated that for basic cognitive abilities dairy science students scored the highest followed by agricultural mechanics, agricultural education, food science, animal science, agronomy, agricultural communic ations, agricultural economics, and horticulture. The mean scores for cognitive abilities ranged from 22.3 to 18.3. For application abilities, agricultural education students scored the highest followed by dairy science, agricultural mechanics, food scie nce, agronomy, horticulture, animal science, and agricultural economics, and agricultural communications. The mean scores for application abilities ranged from 22.6 to 17.5. For critical thinking agricultural mechanics students scored the highest follow ed by agricultural education, animal science, agricultural economics, food science, and agronomy. Critical thinking score means ranged from 15.1 to 18.5 Christensen, Curley, Marquez, and Menzel (1995) found that nonverbal immediacy very strongly positive ly correlated with willingness to participate when female students had male professors. In contrast, when the students and the professors were female,

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94 both verbal and nonverbal immediacy were moderately positively associated. Further, when male students had male professors nonverbal immediacy was substantially positively correlated with willingness to participate and verbal immediacy was moderately positively correlated with willingness to participate. However, w hen male students had female instructors the correlations between immediacy and willingness to participate decreased to low or negligible teacher immediacy behaviors and the relationship these perceptions have with age and gender. Chase found that females in the 18 24 year age group significantly reported more positive ratings of instructors than males in the same age group. Additionally, males in the 18 24 year age group r eported professors to be more controlling and challenging behavior than did older males who perceived more friendliness. Pajares and Miller (1994) investigated the role of self efficacy in math performance. Part of their analysis consisted of examining ge nder differences in self efficacy, math anxiety, and math performance. Men ( M = 77.1) were found to have more math self efficacy than women ( M = 71.8) and performed better than women ( M men = 14.7; M women = 13.7). Additionally, women ( M = 30.6) were found to have more math anxiety than men ( M = 34.1) (a higher score means less anxiety). Significant differences were found between men and women concerning each of these three variables. Chapter Summary Chapter 2 introduced the theories undergirding this study constructivism, social cognitive theory, and the conceptual model of motivation and engagement Additionally Chapter 2 described the main independent and dependent variables investigated in this

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95 study. The two independent variables were teacher immedi acy and professor/student rapport, while the dependent variable was self regulated learning operationally defined as motivation and engagement Furthermore, Chapter 2 presented the empirical literature to support the conceptual model of self regulated le arning. The review of literature indicated that both verbal and nonverbal teacher immediacy ha ve been found to be positively related to motivation, student affect, attitudes toward the instructor, attitudes toward the course, willingness to communicate, an d student interest, and negatively associated with student anxiety. Additionally, immediacy and professor/ student rapport appear t o be highly, positively related Further investigations showed that teacher immediacy has been linked to increased levels o f cognitive learning, but researchers have been unable to explain the cause for this relationship Professor/student rapport was found to be positively associated with motivation, student affect, and attitudes toward instructors. What is more, professor/ student rapport has been associated with higher levels of student engagement and achievement. The research literature further showed that increased levels of motivation including student expectancies, self efficacy, mastery goal orientation, and student interest have been positively related with high levels of engagement, more specifically, self regu lated learning behavior use In addition these higher levels of engagement have been related with higher levels of student achievement. While the literature showed that immediacy was positively related to motivation and motivation was positively related with increased engagement there is a paucity of literature that has shown how immediacy is related to the specific motivational factors of

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96 self efficacy, goa l orientation, task value, and affect. Additionally, no efforts have been made to test the relationship between immediacy and engagement Moreover, professor/student rapport is a fairly new construct that might help explain the relationship between immed iacy and motivation. This study will hopefully help fill in some of the blanks in the literature.

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97 CHAPTER 3 METHODS Chapter 1 of this study introduced the problem of the lack of student academic engagement in college classrooms. The constructs of teach er immediacy and professor/student rapport were introduced as possible pieces of the solution to help students in colleges of agriculture become more motivated and self regulated in their learning. Additionally, pertinent definitions of the key terms, the purpose of the study, the limitations and assumptions, and the objectives of the study were presented. Chapter 2 introduced constructivism, social cognitive theory, and the relational model of self regulated learning. The background of the theoretical an d conceptual frameworks was given, and empirical evidence was provided in an attempt to substantiate the relational model of self regulated learning. Chapter 3 will describe the methodology of the study. This description will include the research design, target population, instrumentation, data collection procedures, and data analysis procedures. The purpose of this study was to determine the relationships between teacher immediacy, professor/student rapport, and student self regulated learning (self regulated learning serves as a proxy for student motivation and engagement) among undergraduate students in a college of agriculture. The specific objectives of this research study were to: reported perc eptions of teacher immediacy behaviors and professor/student rapport, reported measures of self regulated learning at two separate points in the semester,

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98 examine the relationships between selected undergraduate demographics perceptions of teacher immediacy, professor/student rapport, and self regulated learning, regulated learning from the beginning of the semes ter to the end of the semeste r, determine the predictive value of selected unde perceptions of teacher immediacy and professor/student rapport on self regulated learning, and teacher immediacy and professor/student rapport on the change in self regulated learning. Research Design This was a quantitative study that used a descriptive/correlational research design. This research design allowed the researcher to describe the sample and v arious groups in the sample, compare the differences between various groups in the sample, investigate the relationships between the independent and dependent variables, and determine the predictive value of the independent variables on the dependent varia ble s (McMillan & Schumacher, 2010). This design was deemed appropriate by the researcher to fulfill the purpose of the study. Design Validity The validity of the design was assessed in accordance with Shadish, Cook, and ty. The four categories of threats to validity proposed by Shadish et al. (2002) were statistical conclusion validity, internal validity, construct validity, and external validity. Statistical Conclusion V alidity According to McMillan and Schumacher (2010 ), statistical conclusion validity is (p. 107). In essence statistical conclusion validity is concerned with the validity of the

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99 statistical procedures employed in the study. Seven threats to statistical conclusion validity were addressed by Shadish et al. (2002) including low statistical power; violated assumptions of statistical tests; fishing and error rate problems; unreliability of measures; restriction of ran ge; unreliability of treatment implementation; and extraneous variance in the experimental setting. Low statistical power. To combat the threat of low statistical power several steps were implemented. First, a standardized protocol was used to help assur e that conditions were the same each time a measurement was taken (McMillan & Schumacher, 2010). Additionally, reliable instruments were used in the study to help reduce measurement error (McMillan & Schumacher, 2010). Estimated reliability coefficients of instruments were reported from past studies, and further more, the researcher in this study conducted reliability tests. In addition the researcher in this study utilized a large sample size (McMillan & Schumacher, 2010). Violated assumptions of statistical tests. The researcher avoided violation of the assumptions of statistical tests in this study. Later, in the data analysis section the assumptions of the various statistical tests are listed and information is provided to indicate that assump tions were not violated. Fishing and error rate problem. The statistical tests which were utilized in this study were determined a priori therefore no unnecessary statistical analyses were run that might increase the risk of an error rate problem. Unreli ability of measures. As mentioned earlier, prior estimated reliability measures were reported for all instruments, as well as estimated reliability

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100 measurements from tests of reliability conducted by the researcher. All of the reliability estimates were found to be acceptable. Restriction of range. Restriction of range was not a threat in this study. Unreliability of treatment implementation. This study did not utilize a treatment intervention; therefore this threat was not an issue in this study. Extra neous variance in the experimental setting. This study did not utilize an experimental setting; therefore this threat was not an issue in this study. Internal V alidity ca internal validity is interested with the notion of the causality of the intervention on the dependent variable. Several categories of threats to internal validity exist, b ut some of the threats dealt only with experimental studies and were not a concern in this study. However, four categories of threats were considered legitimate, and these were selection, attrition (mortality), experimenter bias, and subject effects. Sele ction. Selection of the participants provided a slight limitation to this study. The sampling method utilized in this study was a cluster sampling method (McMillan & Schumacher, 2010). The classes included in the sampling frame were scheduled fall 2011 classes with between 50 and 100 students in the College of Agricultur al and Life Sciences (CALS) at the University of Florida. Classes that were sampled were randomly chosen from three groups which were based on inst ructor evaluation scores that were take n from the last time the instructor had taught the same course. The class evaluation scores were used as a proxy for perceived teacher immediacy, as Moore, Masterson, Christophel, and Shea (1996) found a positive relationship between teacher

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101 immediacy and student evaluation scores. An examination of the sampling frame revealed that some of the instructors had never taught the scheduled course, and therefore had no prior evaluation scores for that particular course. In this instance, this instructor and c ourse were removed from the sampling frame. Consequently, some classes were not included in the sampling, which is a limitation of the study. Attrition. According to McMillan and Schumacher (2010), attrition occurs when participants drop out of the study In this study, the researcher sampled the various classes on three separate occasions. Therefore, attrition and/or nonresponse were threats to the study The sample was limited to the students who attended class on the day of administration, as well a s the students who chose to participate. Additionally, the researcher was interested only in participants who completed all three instruments. To combat this threat, the researcher coded each response and eliminated respondents from the study who did not respond to all three instruments. Experimenter bias. McMillan and Schumacher (2010) posited that experimenter protocols were used in this study to try and prevent experim enter bias. The same person administered the instruments each time, and read the instructions verbatim to the participants. The proctor only answered questions pertaining to the instructions and IRB informed consent. When the participants were finished with the instrument, they placed them in a large envelope to ensure security. Subject effects. Subject effects were a slight threat to this study. The items

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102 attitudes. T herefore, the possibility of socially desirable answers was possible. However, the assumption was made that all participants answered truthfully. Construct V alidity the extent to which interventions and measured variables actually represent targeted, and Campbell (2002) created a list of 14 separate threats to construct validity, and McMilla n and Schumacher (2010) narrowed the list to the three most prominent threats in educational research. The three most common threats to construct validity are inadequate preoperational explication of constructs, mono operation bias, and mono method bias. Inadequate preoperational explication of constructs. In addition to using well documented constructs for this study, the researcher also operationally defined each construct according to the literature and preexisting instruments. As a result, the threat of inadequate preoperational explication of constructs was not a limitation. Mono operation bias. Mono operation bias was not considered a threat because this study was a non experimental design. Mono method bias. Mono method bias was considered a threa t in this study because the researcher utilized a self report method as the only means of data collection. Therefore, the researcher recognizes this as a limitation in the study. External Validity According to McMillan and Schumacher (2010), external vali

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103 McMillan and Schumacher (2010) additionally posited that two types of external validity exist, population and ecological. Population external validity. The sample chosen for this study was a convenience cluster sample (McMillan & Schumacher, 2010). Since this was a convenience sample the results of this study may not be generalized further than the sample However, McMillan and Schumacher stated that results may be generalizable to others if they possess similar characteristics as the sample. Therefore, the demographic information from the respondents was compared to the demographic information of the pop ulation to determine if the sample was representative of the population. Ecological external validity. The ecological external validity of this study only allows the results to be generalized to the population in the same context. The context of this stu dy was CALS classes with between 50 and 100 students in the fall semester of 2011 Population The population of interest for this study was undergraduate students enrol led in large CALS classes with between 50 and 100 students at the University of Florida during the f all 2011 semester The sample was comprised of students enrolled in large CALS classes during the fall 2011 semester who completed all three instruments The final sample for the study was 306 students. Since the assumption was made that ins tructors in large classes have a harder time interacting with students one on one thus making it more difficult to build rapport (Heppner, 2007), classes with 50 to 100 students were deemed appropriate for the study. Friedel (2006) reported that no standa rdized

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104 definitions of class size exist, but studies have considered a class size of 50 or more students to be a large class (e.g. Cuseo, 2007). The sample for this study was a convenience cluster sample McMillan and Schumacher (2010) described convenienc e sampling as the selection of participants based on availability. In this study, intact class groups were selected based on the willingness of the instructor to allow their class to participate. This method of sampling was chosen for two reasons. First the researcher desired to administer the instruments in a group setting, which random sampling would not allow. Second, cluster sampling allowed the researcher to increase the variability in the independent variable of teacher immediacy. The variabilit y of immediacy was increased because the classes sampled were chosen from groups based upon differing hypothesized levels of teacher immediacy. from student evaluations of teac hing were positively related with teacher immediacy. Therefore, the means of o verall teacher evaluation scores for CALS instructors who taught classes with between 50 and 100 students were examined and a distribution of teaching evaluation scores was crea ted. The scores were then divided into three categories high, intermediate, and low which were based on the natural breaks in the data The high category consisted of mean teacher evaluation scores ranging from 4.70 to 4.95. The intermediate category consisted of mean teacher evaluation scores ranging from 4.21 to 4.69, and the low category consisted of mean teacher evaluation scores ranging from 2.73 to 4.19.

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105 An initial invitation to participate was sent by email to 56 CALS instructors whose courses fit the desired criteria of enrollments between 50 and 100 students. Follow up emails were sent to non respondents. Eight instructors agreed to participate in the study. Two of the instructors allowed two of their classes to participate, giving a total of 10 classes. two five the high immediacy range. Instrumentation Three instruments were used to collect the data for this study. The following descriptions of the instruments provide information concerning constructs, validity, and reliability. Immediacy Behavior Scale The immediacy behavior scale was first utilized by Christophel (1990) ( Appendix A ) nonverbal immediacy scale. The immediacy behavior scale questionnaire consisted of 20 Likert type ver bal immediacy items and 14 Likert type nonverbal immediacy items. All item answer choices ranged from 1 ( Never ) to 5 ( Very Often ). The validity of the verbal and nonverbal immediacy scales has been established by many researchers over time. Gorham (1988) initially constructed the verbal immediacy scale and conducted analyses to determine the validity of the verbal immediacy items. However, critics (e.g. Robinson & Richmond, 1995) of the verbal immediacy scale suggested that the scale was not valid, and i n reality measured teacher effectiveness as opposed to verbal immediacy. In response, Wilson and Locker

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106 Jr. (2008) tested the validity of the verbal immediacy scale using factor analysis and a check of convergent validity with teacher effectiveness. Resu lts of the convergent validity test indicated that verbal immediacy slightly correlated with teacher effectiveness, as did nonverbal immediacy. Wilson and Locker Jr. concluded that a reasonable assumption is that immediacy and teacher effectiveness are so mewhat related. They indicated that an effective teacher does not have to exhibit immediate behaviors, but that an immediate teacher was most likely effective. Results of Wilson constructs measur ed on the verbal immediacy scale measures represent teacher immediacy. The validity of the nonverbal immediacy scale has been less contested than that of the verbal immediacy scale (Wilson & Locker Jr., 2008). The original nonverbal immediacy scale const ructed by Richmond, Gorham, and McCroskey (1987) has been well utilized in immediacy research and has been shown to be a valid measure of teacher immediacy (Richmond, McCroskey, & Johnson, 2003). Previous studies utilizing the immediacy behavior scale have typically reported high reliabilities for both the verbal and nonverbal items. Richmond et al. (1987) conducted two studies with the nonverbal scale and reported reliability coefficients of .87 and .80. Likewise, Christophel (1990) reported reliability coefficients of .83 and .80 for the nonverbal scale. What is more, Velez (2008) reported reliability coefficients of .82 and .85 for the nonverbal immediacy instrument in his pilot test and dissertation study, respectively. High reliabilities have also b een reported for the verbal immediacy instrument. Gorham (1988) found a reliability coefficient of .94, and Christophel (1990) reported

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107 reliability coefficient of .86 in his dissertation. The researcher additionally calculated post hoc reliability analyses for the verbal and nonverbal immediacy scales, the results of which appear in Chapter 4 Professor/Student Rapport Scale The instrument used to measure professor/stu dent rapport was the professor/student rapport scale created by Wilson et al. (2010) ( Appendix B ) The questionnaire contained 34 Likert type items that ranged from 1 ( Strongly disagree ) to 5 ( Strongly agree ). The professor/student rapport scale consisted of items that sought to pport. Sample items included, m y professor and I get along ; m y professor is thoughtfu l ; and my professor is approachable The professor/student rapport scale is a relatively new instrument and has not been extensively tested, but Wilson et al. (2010) found an internal consistency of .96 for the instrument. Additionally, Ryan et al. (2011) tested the professor/student rapport scale for internal consistency and test retest reliability. Ryan et al. reported a retest reliability of r = .72. Furthermore, the researcher conducted a post hoc reliability analysis on the professor/student rapport s cale, the results of which appear in Chapter 4 Ryan et al. (2011) conducted tests to determine the construct validity of the professor/student rapport scale with two scales that measure similar constructs, including the Working Alliance Inventory and a so cial support scale. Additionally, a measure of verbal aggressiveness was used as a divergent measure of validity. Results showed that the professor/student rapport scale correlated positively with the

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108 Working Alliance Inventory and the social support sca le, while the professor/student rapport scale correlated negatively to the verbal aggressiveness scale. Motivated Strategies for Learning Questionnaire The Motivated Strategies for Learning Questionnaire ( MSLQ ) was created by Pintrich et al. (1991) and consisted of two general constructs including, student motivation and self regulated learning strateg y use (Appendix C ) The self regulated learning strategy use construct was used to represent engagement in this study. M otivation contained three separate constructs, which included student expectancies value s/goals and affect, while engagement consisted of two separate constructs including cognitive /metacognitive strateg y use and resource management strateg y u se (Pintrich et al., 1993). The instrument consisted of 81 Likert type items that ranged from 1 ( N ot at all true of me ) to 7 ( Very true of me ). Because the MSLQ contains a large number of items, response and measurement error were a concern. The resear engagement at two points in the semester, the beginning of the semester and two thirds of the way through the semester. The researcher concluded that students would not be able to accurately assess their levels of motivation and engagement at the beginning of the semester, because the students did not have time to establish these perceptions and behaviors. There fore, the researcher measured the constructs of student motivation and engagement using a post then pre design (Rockwell & Kohn, 1989). Rockwell and Kohn indicated that a post then pre design was beneficial because the typical pre then post designs provid e an inaccurate assessment of the first set of behaviors or attitudes. The post then pre design consisted of modifying the MSLQ to measure motivation and engagement two thirds of the way through the semester.

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109 Using this procedure, students indicated thei r perceived ending level of motivation and engagement, as well as their perceived beginning motivation and engagement. The MSLQ has been utilized to measure motivation and self regulatory strategy use in many contexts by many researchers and has been sho wn to be valid over time (Duncan & McKeachie, 2005). Pintrich et al. (1993) reported validity measures for the MSLQ with college undergraduates and found the instrument to demonstrate predictive k examined the factorial, structural, and predictive validity of the MSLQ. Davenport found evidence to support the validity of the MSLQ as a measure of student motivation and self regulatory strategy use. Internal reliability coefficients were reported f or each sub scale of the MSLQ (Duncan & McKeachie, 2005; Pintrich et al., 1993). Table 3 1 shows the reliability coefficients reported for each sub scale. Table 3 1. Reliability coefficients of sub scales of MSLQ Motivational Scales Value s/Goals Intrinsic Goal Orientation .74 Extrinsic Goal Orientation .62 Task Value .90 Student Expectancies Control of Learning Beliefs .68 Self efficacy for Learning and Performance .93 Affect Test Anxiety .80

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110 Table 3 1 Continued Learning Strategies Scales Cogniti ve/ Metacognitive Strategies Rehearsal .69 Elaboration .75 Organization .64 Critical Thinking .80 Metacognitive Self regulation .79 Resource Management Strategies Time and Study Environment Management .76 Effort Regulation .69 Peer Learning .76 Help Seeking .52 Note. Source: (Duncan & McKeachie, 2005; Pintrich et al., 1993) The estimated reliability coefficients for several of the subscales on the MSLQ were less than desirable. However, the MSLQ is a well established instrument, and has been used in many studies (Duncan & McKeachie, 2005). Additionally, the researcher was c oncerned with the larger constructs of student expectancies values/goals, affect, cognitive/metacognitive strategy use, and resource management strategy use, as opposed to the smaller subscales shown in T able 3 1. Therefore, the reliability coefficients of the subscales were not a concern. Furthermore, the researcher conducted post hoc reliability analyses on the larger constructs of the MSLQ, the results of which appear in Chapter 4 Demographic Instrument Student d emographic information was measured us ing researcher created items that were included on the MSLQ instrument Demographics consisted of 5 items, including age, gender, ethnicity, year in school, and major. A similar instrument was

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111 created to collect instructor demographic data. The items instructor demographics measured were age, gender, ethnicity, years teaching, and instructor rank. All items were constructed according to recommendations by Dillman, Smyth, and Christian (2009). Data Collection This research project was approved by t he Institutional Review Board at the University of Florida ( Appendix D ) Upon IRB Approval, data were collected during the Fall semester 2011. Data were collected using a group administration technique (Dillman, Smyth, & Christian, 2009). The three instr uments were administered to students in 10 classes during the time period of October 13, 2011 through November 22, 2011 Each instrument was administered on a separate occasion to prevent any bias and a script of the directions was used to help with cons istency ( Appendix E ) On the first administration the researcher read aloud the IRB protocol ( Appendix F ) and informed students of the confidentiality of responses and the voluntary nature of the research. Participants were also informed that they would not receive any compensation for their participation in the study. Furthermore, participants were given time to read the IRB protocol and sign the consent form The IRB consent forms were collected at the end of the first administration period. During t he first administration participants completed the immediacy behavior scale Directions were printed at the top of the instrument, and the researcher also read aloud scripted instructions Participants were informed that they were to consider the current when filling out the immediacy behavior scale. The second administration consisted of participants completing the professor/student rapport scale. Procedures from the first administration were followed

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112 during administration of the professor/student rapport scale During the third administration, students completed the post then pre version of the MSLQ instrument. Once again, the same procedures were followed as with the first two administrations Participants were given as mu ch time as needed during all three administration periods. Table 3 2 shows the timeline of the administration of instruments. Table 3 2. Timeline for data collection Immediacy Behavior Scale Professor/student Rapport Scale MSLQ 10/ 1 3/11 10/ 20 /11 10/ 24 /11 10/ 27 /11 11 / 1/11 1 1 /22 /11 Data Analysis Data for the immediacy behavior scale and the professor/student rapport scale were entered into Microsoft Excel by the researcher while a third party individual entered the MSLQ data into Microsoft Excel Thirty percent of the data entered by the third party were examined by the researcher for accuracy, and any mistakes were corrected. T he researcher was interested in the data from participants who had completed all three instruments. Therefore each p articipant created an identification code that was included on each instrument at the time of administration. The data was examined and only the data from participants who completed all three instruments were included in the analysis. Research Objecti ve One Research objective one reported perceptions of teacher immediacy behaviors and professor/student rapport. Data used to accomplish this objective were taken from the immediacy behavior scale and th e professor/student rapport scale. Data were analyzed using descriptive statistics (measures of central tendency).

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113 Research Objective Two Research objective two reported measures of self regulated learni ng at two separate points in the semester. To accomplish this objective, data were taken from the MSLQ. Measures were taken of engagement using a post then pre design (Rockwell & Kohn, 1989). Motivation was represented by student expectancies, values/goals, and affect, while engagement was represented by cognitive/metacognitive strategy use and resource strategy use. Data for pre and post measures were analyzed using descriptive statistics (me asures of central tendency). Research Objective Three Research objective three was to examine the relationships between selected professor/student rapport, and self regulated learning. To accomplish this objective, the direction and strength of relatio nships between variables were calculated using the Pearson orrelation coefficient. Data were examined to determine if correlation coefficient are linear ity and equal variance. The relationships examined were between the demographic variables, perceived teacher immediacy, perceived professor/student rapport, and the beginning and ending constructs of motivation and engagement ( student expectancies, values /goals, affect, cognitive/metacognitive strategy use, and resource strategy use ). Research Objective Four Research objective four was to determine the change in selected undergraduate regulated learning from the beginning of the semester to the end of the

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114 semester. Mean differences were calculated between the pre and post measures of student motivation and engagement and paired sample t tests were used to check for significant differences. d was calculated to check the effect size of the changes. The assumptions for using paired samples t tests are randomization and normality. Tests were conducted to determine if these assumptions were met. Research Objective Five Research objective five was to determine the predictive value of selected rapport on self regulated learning. This objective was accomplished by using a canonical correlation analysis T o determine if a relationship exists between multiple interval independent variables and multiple dependent variables Hair, Anderson, Tatham, and Black (1998 ) stated that a canonical correlation is the proper method to use The main assumption associated with canonical correlation is multivariate normality ; however canonical correlation analyses tend to be robust against many violations of normality (Hair et al., 1998 ) Research Objective Six Research objective six was to determine the predictive value of selected rapport on the change in self regulated learning. A canonical correlation analysis was used to explain the changes in both s tudent motivation and student engagement based on perceived teacher immediacy, and perceived professor/student rapport. Chapter Summary Chapter 3 provided the details of the research methodology with regards to design of the study, population and sampli ng, instrumentation, data collection, and data

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115 analysis. The study utilized a descriptive correlational design to examine the relationships between student demographic characteristics, teacher immediacy, professor/student rapport, and self regulated learn ing. The population for the study was undergraduate students at the University of Florida enrolled in CALS classes with 50 to 100 students Cluster sampling was utilized to select participants enrolled in CALS classes having between 50 and 100 students. To increase the variance of teacher immediacy, instructor evaluation scores for instructors who taught classes with more than 50 students were examined and divided into three groups low, intermediate, and high. Classes were randomly selected from these t hree groups. 10 classes were sampled with a total of 306 students. Four instruments were used to collect data for this study. A researcher developed demographic instrument was used to collect demographic information from students. The immediacy behavior scale (Wilson, Ryan, & Pugh, 2010) was utilized to collect data about the perceptions of rapport between teach ers and students, and the Motivated Strategies for Learning Questionnaire (Pintrich et al., 1991, 1993) was used to collect data about student motivation and engagement Chapter 4 will present the results of the data analyses.

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116 CHAPTER 4 RESULTS Chapter 1 of this study introduced the problem which was the lack of academic engagement by undergraduate students T eacher immediacy and professor/student rapport were introduced as potential constructs that might help students in colleges of agriculture become more motivated and engaged in their learning. Additionally, pertinent definitions of the key terms, the purpose of the study, the limitations and assumptions, and the objectives of the study were presented. The purpose of the study was to determine the r elationships between teacher immediacy, professor/student rapport, and student self regulated learning (self regulated learning serves as a proxy for student motivation and engagement) among undergraduate students in a college of agriculture. The specific objectives of this research study were to: reported perceptions of teacher immediacy behaviors and professor/student rapport, reported measures of self regulated lea rning at two separate points in the semester, of teacher immediacy, professor/student rapport, and self regulated learning, determine the change in selected undergraduate studen regulated learning from the beginning of the semester to the end of the semester, teacher immediacy and professor/student rapport on self regulated learning, and det teacher immediacy and professor/student rapport on the change in self regulated learning. Chapter 2 introduced the grand and mid level theories, as well as the conceptual framework that guided the study The background of the theoretical and conceptual frameworks was given, and empirical evidence was provided to support the conceptual

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117 framework The review of literature contained research concerning verbal immediacy, nonv erbal immediacy, professor/student rapport, student expectancies, values/goals, affect, cognitive/metacognitive strategy use, and resource management strategy use. Chapter 3 provided the research methodology of this study; a description of the research des ign, population, instrumentation, data collection procedures, and data analysis were given Chapter 4 provides the results of the data analysis, which are listed by objective. Comparison of Sample and Population Statistical comparisons were made between the sample and population demographics to determine if the sample was representative of the population. Respondents were compared with the population on gender, College of Agricultural and Life Science (CALS) stud ents versus non CALS students, race/ethnicity, and age. According to McMillan and Schumacher (2010), results of a study are generalizable to the population if they possess similar characteristics as the sample. Chi squared tests were used to compare categ orical data, while independent t test s were used to compare the means of interval data. An alpha level of .05 was established a p riori Chi squared tests indicated that no significant difference existed between the respondents and population on gender, w ith a chi square statistic of 3.58 and a p value of .062. Therefore, the gender of the respondents was representative of the gender of the population. Likewise, no significant difference was found between respondents and the population on CALS versus non CALS students. The chi square statistic for CALS versus non CALS was 2.06 with a p value of .163. Consequently, the breakdown of CALS and non CALS students in the sample was representative of the proportion of CALS/non CALS students in the population. Additionally, an independent samples t

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118 test was run to compare the mean age of the respondents against the mean age of the population. Results showed t he mean age of the respondents was 21.17 (2.87) and the mean age of the population was 21.79 (2.57). Th ese means were found to be statistically significant ( p <.001) which would indicate that the sample was not representative of the population However, given the large number of respondents in the sample ( n =306) and population ( N =2033), the likelihood of finding significant results is high (McMillan & Schumacher, 2010). Therefore, d effect sizes were calculated according to conventions put forward by Kotr lik, Williams, and Jabor (2011) to determine practical significance of the r esults d value was .23, which according to Kotrlik et al., represents a small effect size. As a result, the sample is most likely representative of the population on the variable of age. Lastly, a chi squared test was conducted to determine if differences existed between the race/ethnicity of the respondents and population. A significant difference was found with race, with a chi square test statistic of 28.39 and a p value less than .001 ( Table 4 1). Table 4 1. Expected and actual freque ncies of race/ethnicities African American American Indian/Pacific Islander Asian Hispanic/Latino White Other Sample ( n =306) Count 46.0 0.0 13.0 38.0 194.0 15.0 Expected 38.1 1.6 20.9 47.8 192.2 5.5 Population ( n = 2033) Count 245.0 12.0 147.0 327.0 1275.0 27.0 Expected 252.9 10.4 139.1 317.2 1276.8 36.5

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119 Variability of the Independent Variables student evaluation scores and teacher immediacy. Thus, Chapter 3 described that instructors who taught large CALS classes at the University of Florida were divided into thr ee groups based on their past student evaluation scores. The three groups were high, intermediate, and low. The high category consisted of mean teacher evaluation scores ranging from 4.70 to 4.95. The intermediate category consisted of mean teacher eval uation scores ranging from 4.21 to 4.69, and the low category consisted of mean teacher evaluation scores ranging from 2.73 to 4.19 The rationale for creation of these groups was to hopefully increase the variability of teacher immediacy. Table 4 2 shows the mean scores for s total teacher immediacy which this sample was taken. One instructor was in the low group, two in the intermedia te group, and five in the high group. Additionally, Pearson Product Moment correlations were run to determine the relationships between these scores and teacher immediacy Teacher immediacy was found to be substantially, positively correlated with past s tudent evaluation scores ( r = .74) and current student evaluation scores ( r = .68). Table 4 2 Instructor evaluation and immediacy scores Instructor Past Evaluation Scores Current Evaluation Scores Total Teacher Immediacy a Mean SD Mean SD Mean SD Instructor 1 3.77 .87 4.03 .87 3.14 .25 Instructor 2 4.74 .50 4.49 .80 3.42 .2 8 Instructor 3 4.66 .58 4.80 .45 3.53 .27

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120 Table 4 2. Continued Instructor Past Evaluation Scores Current Evaluation Scores Total Teacher Immediacy a Mean SD Mean SD Mean SD Instructor 4 (1) N/A N/A 4.29 .98 3.54 .2 7 Instructor 6 4.70 .51 4.56 .77 3.55 .39 Instructor 4 (2) 4.34 .86 4.80 .46 3.74 .22 Instructor 7 4.70 .67 4.42 .96 3.76 30 Instructor 8 4.84 .41 4.66 .58 3.77 .2 9 Instructor 5 (2) 4.95 .22 4.80 .48 4.27 .34 Instructor 5 (1) 4.95 .22 4.72 .57 4.28 20 Note. Instructors 4 and 5 had two classes participate in the study, each represented by the 1 and 2 in parentheses. a Total teacher immediacy is the mean of verbal and nonverbal immediacy combined. Response Rates Each instrument used in this study was administered at separate times, approximately a week to two weeks apart and response rates were reported for each individual instrument. The immediacy behavior scale was administered between October 13, 2011 and October 20, 2011. A total of 555 students completed the immediacy behavior scale. The professor/student rapport scale was administered between Oct ober 24, 2011 and October 27, 2011, and 498 students completed this instrument. The MSLQ was administered between November 1, 2011 and November 21, 2011, and a total of 457 students completed this instrument. codes were used to deter mine which of the students completed all three instruments, and 307 students were found to have completed all three. However, out of these, one participant completed less than 25% of the MSLQ, and this participant was removed from the study, which left a total number of 306 participants. The percent usable

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121 responses for each instrument were 55% for the immediacy behavior scale, 61% for the professor/student rapport scale, and 67% for the MSLQ ( Table 4 3 ) Table 4 3. Instrument response rates Instrument Total Responses Usable Responses % Usable Response s Immediacy Behavior Scale 555 306 55% Professor/student Rapport Scale 498 306 61% MSLQ 457 306 67% Post Hoc Reliability of Instruments ost hoc reliability of the instruments used in this study ( Table 4 4 ) The immediacy behavior scale (Christophel, 1990) measured verbal immediacy, nonverbal immediacy, and total immediacy. The alpha coefficient for total immediacy was .87 while the reliability coefficients for verbal and nonverbal immediacy were .83 and .73, respectively. The professor/student rapport scale (Wilson et al., 2010) measured the construct of professor/student rapport. The alpha coefficient for the professor/student rapport instrum ent was .96. The MSLQ (Pintrich et al., 1993) was used to measure student motivation and engagement. This study utilized a post then pre design (Rockwell & Kohn, 1989) which measured cts that comprised student motivation were beginning and ending student expectancies, values/goals, and affect, while the constructs of student engagement were beginning and ending cognitive/metacognitive strategy use and resource management strategy use. Alpha coefficients for the motivation constructs were: beginning student expectancies ( = .88); ending student expectancies ( = .91); beginning values/goals ( = .86); ending values/goals ( = .86); beginning affect ( = .72); and ending affect (

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122 = .75). Alpha coefficients for the student engagement constructs were: beginning cognitive/metacognitive strategy use ( = .90); ending cognitive/metacognitive strategy use ( = .90); beginning resource management strategy use ( = .77); and ending resource coefficients that measure psychological constructs are considered acceptable at a range of above .70. All of the reliability coefficients in this study fell within this acceptable range. Table 4 4 Post hoc reliability of instruments Instrument Reliability Coefficient ( ) Immediacy Behavior Scale Verbal Immediacy .83 Nonverbal Immediacy .73 Total Immediacy .87 Professor/student Rapport Scale .96 Motivated Strategies for Learning Questionnaire (MSLQ ) Beginning Student Expectancies .88 Ending Student Expectancies .91 Beginning Values/Goals .86 Ending Values/Goals .86 Beginning Affect .72 Ending Affect .75 Beginning Cognitive/Metacognitive Strategy Use .90 Ending Cognitive/Metacognitive Strategy Use .90 Beginning Resource Management Strategy Use .77 Ending Resource Management Strategy Use .80

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123 Description of the Sample To describe the sample, ethnicity/race, gender, major, year classification, and age were taken into account. Table 4 5 gives an overview of the descriptive statistics of the sample. American, Asian, Hispanic/Latino, White/Caucasian, and Other. The majority of the participants wer e White/Caucasian (63.2%). The second largest group was African American (15.0%), followed by Hispanic/Latino (12.4%), Other (4.9%), and Asian (4.2%). Major was divided into 7 categories including, Food and Resource Economics ; Food Science and Human Nutr ition ; Family, Youth, and Community Sciences; Animal Science s; Agricultural Education and Communication; Biology; and Other. The O ther category consisted of majors outside of the College of Agricultural and Life Sciences or CALS majors that had a frequency of less than 10. Other represented the largest percentage of the sample at 40.1%, while the second largest major represented was Fam ily Youth and Community Sciences (26.4%). Food Science and Human Nutrition students made up 12.1% of the sample followed by Food and Resource Economics (8.5%), Animal Science s (5.5%), Biology (3. 9%), and Agricultural Education and Communication (3.3%). P Sophomore, Junior, and Senior. The majority of the participants were Seniors or Juniors, 45.3% and 40.1%, respectively, followed by Sophomores (7.5%) and Freshmen (6.8%). The m ajority of the participants in this study were female (63.7 %) and the mean age of participants was 21.17 ( SD = 2.86) ( Figure 4 1)

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124 Table 4 5 Descriptive statistics of the sample ( n = 306) Frequency ( f ) Percent (%) Ethnicity White/Caucasian 194 63.2 African American 46 15.0 Hispanic/Latino 38 12.4 Other 15 4.9 Asian 13 4.2 Major Other 123 40.1 Family, Youth, & Consumer Sciences 81 26.4 Nutrition 37 12.1 Food and Resource Economics 26 8.5 Animal Science 17 5.5 Biology 12 3.9 Agricultural Education/Communication 10 3.3 Year Classification Freshman 21 6.8 Sophomore 23 7.5 Junior 123 40.1 Senior 139 45.3 Gender Male 111 36. 3 Female 195 63. 7

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125 Figure 4 1. Distribution of participant age ( M = 21.17; SD = 2.86) Objective One Objective 1 was to reported perceptions of teacher immediacy behaviors and professor/student rapport ( Table 4 6 ) Students were asked to complete the immediacy behavior scale and the professor/stu dent rapport scale. There were a total of 306 usable responses. The immediacy behavior scale measured behaviors. The instrument measured the constructs of verbal immediacy, nonverbal immediacy, and total imme diacy on a Likert type scale of 1 to 5, with 1 being never and 5 being very often The mean for verbal immediacy was 3.57 ( SD = .54 n = 306) and the mean for nonverbal immediacy was 4.09 ( SD = .43, n = 306). The mean for total immediacy, which is verbal and nonverbal immediacy combined was 3.78 ( SD = .45, n =

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126 306). Verbal immediacy scores ranged from 2.2 5 to 4.75 ( Figure 4 2) and nonverbal immediacy score s ranged from 2.48 to 4.86 ( Figure 4 3) Figure 4 2. Distribution of verbal immediacy scores ( M = 3.57; SD = .54) Figure 4 3. Distribution of nonverbal immediacy scores ( M = 4.09; SD = .43)

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12 7 their rapport with instructors. The instrument consisted of 34 Likert type items ranging from 1 to 5, with 1 being strongly disagree and 5 being strongly agree There were a total of 306 usable responses. The mean for professor/student rapport was 4.36 ( SD = .53, n = 306). Professor/student rapport score s ranged from 1.65 to 5.00 ( Figure 4 4). Figure 4 4. Distribution of professor/student rapport scores ( M = 4.36; SD = .53) Table 4 6 Verbal, nonverbal, total immediacy, and professor/student rapport means ( n = 306) Construct Range Mode Mean SD Min Max Verbal Immediacy a 2.25 4.75 3.10 3.57 .54 Nonverbal Immediacy a 2.48 4.86 4.00 4.09 .43 Total Immediacy a 2.62 4.74 3.65 3.78 .45 Professor/student Rapport b 1.65 5.00 4.94 4.36 .53 a Likert type scale (1 = N ever to 5 = Very Often). b Likert type scal e (1 = Strongly disagree to 5 = S trongly agree).

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128 Objective Two Objective 2 of this study was to assess selected reported measures of self regulated learning at two separate points in the semester ( Table 4 7 ) For the purpose of this study, the term self regulated learning served as a proxy for student motivation and student engagement. Students were asked to assess their perceptions of their self regulated learning using the MSLQ. A post then pre design was used to collect this data ( Rockwell & Kohn, 1989) and a total of 306 usable responses were obtained. On the MSLQ the student moti vation consisted of the constructs of student expectancies, values/goals, and affect, while engagement consisted of cognitive/metacognitive strategy use and resource management strategy use. The means of the motivation constructs were: beginning student e xpectancies ( M = 5.77, SD = .83, n = 306) with scores ranging from 1.42 to 7.00 ( Figure 4 5) ; ending student expectancies ( M = 5.92, SD = .86, n = 306) with scores ranging from 1.42 to 7.00 ( Figure 4 6) ; beginning values/goals ( M = 5.06, SD = .96, n = 306) with scores ranging from 1.00 to 7.00 ( Figure 4 7) ; ending values/goals ( M = 5.27, SD = .93, n = 306) with scores ranging from 1.00 to 7.00 ( Figure 4 8) ; beginning affect ( M = 3.62, SD = 1.29, n = 306) with scores ranging from 1.00 to 7.00 ( Figure 4 9) ; a nd ending affect ( M = 3.53, SD = 1.35, n = 306) with scores ranging from 1.00 to 7.00 ( Figure 4 10) Additionally, the means of the engagement constructs were: beginning cognitive/metacognitive strategy use (M = 4.37, SD = .87, n = 306) with scores rangin g from 1.19 to 6.81 ( Figure 4 11) ; ending cognitive/metacognitive strategy use (M = 4.57, SD = .90, n = 306) with scores ranging from 1.19 to 6.81 ( Figure 4 12) ; beginning resource management strategy use (M = 4.39, SD = .77, n = 306) with scores ranging

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129 f rom 2.26 to 6.89 ( Figure 4 13) ; and ending resource management strategy use (M = 4.45, SD = .84, n = 3.06) with scores ranging from 2.26 to 6.89 ( Figure 4 14) Table 4 7 Beginning and ending student motivation and engagement means ( n = 306) Beginning Ending Construct Range Mode Mean Std Dev Range Mode Mean Std Dev Min Max Min Max Motivation Student Expectancies 1.42 7.00 5.67 5.77 .83 1.42 7.00 6.33 5.92 .86 Values/Goals 1.00 7.00 4.50 5.06 .96 1.00 7.00 5.57 5.26 .93 Affect 1.00 7.00 3.40 3.62 1.29 1.00 7.00 3.40 3.53 1.35 Engagement CMSU a 1.19 6.81 4.74 4.37 .87 1.19 6.81 4.68 4.57 .90 RMSU b 2.26 6.89 4.42 4.39 .77 2.26 6.89 5.00 4.45 .84 Note All items measured on a Likert type scale of 1 to 7 ( 1 = S trongly disagree and 2 = S trongly agree ) ; a CMSU = Cognitive/metacognitive strategy u se; b RMSU = Resource m anagem ent strategy u se Figure 4 5. Distribution of beginning student expectancies scores ( M = 5.77 ; SD = .83 )

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130 Figure 4 6. Distribution of ending stud ent expectancies scores ( M = 5.92 ; SD = .86 ) Figure 4 7. Distribution of beginning values/goals scores ( M = 5.06 ; SD = .96 )

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131 Figure 4 8. Distribution of ending values/goals scores ( M = 5.26 ; SD = .93 ) Figure 4 9. Distribution of beginning affect scores ( M = 3.62 ; SD = 1.29 )

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132 Figure 4 10. Distribution of ending affect scores ( M = 3.53 ; SD = 1.35 ) Figure 4 11. Distribution of beginning cognitive/metacognitive strategy use scores ( M = 4.37 ; SD = .87 )

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133 Figure 4 12. Distribution of ending cognitive/metacognitive strategy use scores ( M = 4.57 ; SD = .90 ) Figure 4 13. Distribution of beginning resource management strategy use scores ( M = 4.39 ; SD = .77 )

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134 Figure 4 14. Distribution of ending resource management strate gy use scores ( M = 4.45 ; SD = .84 ) Objective Three Objective 3 was to examine the relationships between selected undergraduate perceptions of teacher immediacy, professor/student rapport, and self regulated learning To accomplish this objective, s Product Moment Correlation s were calculated between the selected variables with continuous data and point biserial correlations were calculated for dichotomous data To describe the magnitude of the associations among variables, the terminology put forw ard by Davis (1971) was used. According to Davis, a correlation of zero denotes no association between variables, while a correlation of 1.00 signifies a perfect relationship. Additionally Davis submitted that correlations in the range of .01 to .09 are considered negligible, .10 to .29 are low, .30 to .49 are moderate, .50 to .69 are substantial, and any value above .70 is considered very high. Table 4 8 shows the matrix of the correlations.

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135 The independent variable of professor/student rapport was found to have a positive substantial relationship with all measures of the independent variable of immediacy, which included verbal immediacy ( r = .66), nonverbal immediacy ( r = .54), and total immediacy ( r = .68). A substantial positive relationship was also seen between professor/student rapport and the motivational construct of ending values/goals ( r = .54). Professor/student rapport was additionally moderately positively associated with the motivational constr ucts of ending student expectancies ( r = .43) and beginning values/goals ( r = .37), as well as the engagement construct of ending cognitive/metacognitive strategy use ( r = .34). Low associations were found between professor/student rapport and beginning c ognitive/metacognitive strategy use ( r = .28), beginning resource management strategy use ( r = .22), and ending resource management strategy use ( r = .29). Verbal immediacy was found to be moderately positively associated with the motivational constructs o f ending student expectancies ( r = .31), beginning values/goals ( r = .42), and ending values/goals ( r = .48), as well as the engagement construct of ending cognitive/metacognitive strategy use ( r = .32). Low positive associations were found between verbal immediacy and beginning student expectancies, beginning affect, ending affect, beginning cognitive/metacognitive strategy use, beginning resource management strategy use, and ending resource management strategy use, with correlation coeffici ents ranging f rom .11 to .27. Nonverbal immediacy and ending values/goals had a moderate positive relationship ( r = .38), while most other correlations of nonverbal immediacy with the dependent variables of motivation and engagement were either low or negligible. Tota l immediacy was found to be moderately positively

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136 related to ending student engagement ( r = .33), beginning values/goals ( r = .37), ending values/goals ( r = .49), and ending cognitive/metacognitive strategy use ( r = .32). Among the dependent variables, be ginning student expectancy measures were found to be substantially positively associated with beginning values/goals ( r = .59) and moderately positively associated with ending values/goals ( r = .37) and beginning cognitive/metacognitive strategy use ( r = 35). A substantial positive correlation was revealed between ending student expectancies and ending values/goals ( r = .53). Additionally, ending student expectancies was found to be moderately positively related to beginning values/goals ( r = .36). Beginning and ending values/goals were found to be moderately or substantially correlated with all aspects of engagement with correlation coeffic ients ranging from .35 to .55.

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137 Table 4 8 Correlations among variables Note. PSR=Professor/student Rapport; BSE=Beginning Student Expectancies; ESE=Ending Student Expectancies; BVG=Beginning Values/Goals; EVG=Ending Values/Goals; BA=Beginning Affect; EA=Ending Affect; BCSU=Beginning Cognitive/metacognitive Strategy Use; ECSU= Ending Cognitive/metacognitive Strategy Use; BRMSU=Beginning Resource Management Strategy Use; ERMSU=Ending Resource Management Strategy Use a Gender coded 0=male, 1=female. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1. Verbal Immediacy -.62 .95 .66 .27 .31 .42 .48 .11 .15 .27 .32 .15 .18 .18 .02 2. Nonverbal Immediacy -.83 .54 .15 .29 .18 .38 .03 .01 .14 .22 .05 .14 .10 .00 3. Immediacy -.68 .25 .33 .37 .49 .07 .10 .25 .32 .13 .18 .17 .01 4. PSR -.28 .43 .37 .54 .06 .05 .28 .34 .22 .29 .10 .02 5. BSE -.62 .59 .37 .06 .04 .35 .24 .23 .10 .01 .04 6. ESE -.36 .53 .20 .28 .25 .29 .23 .23 .02 .04 7. BVG -.74 .18 .17 .55 .45 .44 .35 .01 .01 8. EVG -.11 .09 .46 .53 .39 .45 .04 .04 9. BA -.90 .12 .08 .04 .07 .03 .01 10. EA -.10 .08 .06 .09 .07 .04 11. BCSU -.87 .65 .55 .01 .11 12. ECSU -.58 .67 .01 .10 13. BRMSU -.87 .06 .11 14. ERMSU -.05 .08 15. Gender a -.17 16. Age -

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138 Objective Four Objective 4 was to determine the change in selected regulated learning from the beginning of the semester to the end of the semester This objective was accomplished by calculating mean differences and utilizing paired sample t tests to determine significance of the changes. C d effect sizes were calculated for the changes according to recommendations put forward by Dunlap, Cortina, Vaslow, and Burke ( 1996 ) in order to correct for an overestimation of the change due to the correlation between the beginning and ending meas ures. Table 4 9 provides a summary of these findings. For the motivational construct s, the means of student expectancies changed from a beginning mean of 5.77 ( SD = .83) to an ending mean of 5.92 ( SD = .86), resulting in a mean gain of .15. A paired samp le t test was run to determine significance of the change, and results showed the change to be significant ( t = 3.63, p d was calculated and the effect size was small ( d = .18 ) The beginning mean for values/goals was 5.06 ( SD = .96) and the ending mean was 5.26 ( SD = .93), resulting in a mean gain of .20. The results of the paired sample t test revealed a significant change ( t = 5.06, p d ( d = .21) revealed a small effect size. For affect, the beginning mean was 3.62 ( SD = 1.29) and the ending mean was 3.53 ( SD = 1.35), which resulted in a .09 mean change. The paired samples t test showed the change to be significant ( t = 2.79, p d ( d = .07) indicated a less than small effect. For t he engagement constructs, the cognitive/metacognitive strategy use mean increased by .21 from 4.36 ( SD = .87) to 4.57 ( SD = .90). Paired sample t tests revealed that the change was significant ( t = 7.95, p <.001), but the effect size was small

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139 ( d = .23). Beginning resource management strategy use increased from 4.39 ( SD = .77) to 4.45 ( SD = .84), which was an increase of .06. The change was shown to be significant ( t = 2.49, p = .013), but the effect size was very small ( d = .07). Table 4 9 Changes in m otivation and engagement Construct Mean Change df t p d Motivation Student Expectancies .15 305 3.63 <.001 18 Values/goals .20 305 5.06 <.001 21 Affect .09 305 2.79 .006 07 Engagement Cognitive/metacognitive Strategy Use .21 305 7.95 <.001 23 Resource Management Strategy Use .06 305 2.49 .013 07 Objective Five Objective 5 of this study was to determine the predictive value of selected rapport on self regulated learning This objective was accomplished by utilizing canonical correlation analyse s Two separate canonical correlations were run for this objective. The first canonical correlation analysis included the independent variable set, whic h consisted of verbal and nonverbal immediacy, and professor/student rapport paired with the dependent variable set of student expectancies, values/goals, and affect. The second canonical correlation analysis used the same independent variable set paired with the engagement variable set that included cognitive/metacognitive strategy use and resource management strategy use. The researcher chose to group the dependent

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140 variables into two sets for analysis based on the division of the measured constructs int o motivation and engagement. Results of the first canonical correlation analysis revealed that the full model, which consisted of a linear combination of the independent variable set (verbal immediacy, nonverbal immediacy, and professor/student rapport) an d a linear combination of the dependent variable set (student expectancies, values/goals, and affect) was statistically significant ( = .611, F (9, 730.27) = 18.22, p <.001). According to Sherry and Henson (2005), since represents the variance unexplain ed by the model, the squared canonical correlation for the model ( R 2 c ) can be expressed by 1 which explains the variance shared between the variable sets across all canonical roots. For this model, R 2 c = .389, indicating that 38.9% of the variance was shared between the independent variable set and the motivation variable set. The model yielded three canonical roots, two of which were statistically significant. The first significant canonical root was the full model ( = .611, F (9, 730.27) = 18.22, p <.001), and the second significant root consisted of functions 2 to 3 ( = .930, F (4, 602) = 5.56, p <.001). The R 2 c values for the models were .343 and .070, respectively. Table 4 10 shows the canonical correlation analysi s, including the standardized canonical function coefficients (weights) and structure coefficients for the two significant canonical roots. The communalities ( h 2 ) across the two functions for each variable are also included According to conventions by Sherry and Henson (2005), structure coefficients above .45 are considered relevant variables for the root, and Warmbrod (2003) suggested that standardized weights greater than .30 are important. For canonical root 1, values/goals appears to be the most re levant criterion variable ( b =

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141 .804, r s = .967) followed by student expectancies ( b = .308, r s = .722). In the independent variable set, all three observed variables appear to be relevant according to their structure coefficients, however rapport emerged as the most relevant ( b = .724, r s = .966). While verbal and nonverbal immediacy appear relevant acco rding to their structure coefficients, b oth verbal immediacy ( b = .288, r s = .826) and nonverbal immediacy ( b = .094, r s = .666) have low to modest standardized weights. This occurred as a result of multicollinearity among the independent variables, which can be concluded from examining the b and r s values of each (Sherry & Henson, 2005). In the second canonical root, affect was the most relevant criterion variable in the motivation variable set ( b = .993, r s = .999). Student expectancies and values/goals had minimal r s values; however, one point worth noting is that student expectancies was inversely related to the dependent variable set when affect was the largest contributor. For the independent variable set, verbal immediacy was the most relevant cont ributor to the root ( b = 1.43, r s = .548). Nonverbal immediacy and professor/student rapport were not relevant contributors to root 2; however, they were both inversely related to the motivation variables of values/goals and affect in this root. The comm unality coefficients ( h 2 ) show the usefulness of the variable to the model, and Sherry and Henson (2005) suggested that communalities above 45% should be considered useful. All variables in the model met this criterion except nonverbal immediacy. Table 4 10 Canonical correlation analysis testing motivation Canonical Root 1 Canonical Root 2 Variable b r s b r s h 2 (%) Student Expectancies .308 .722 .024 .296 60.88 Values/goals .804 .967 .008 .081 94.17

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142 Table 4 10. Continued Canonical Root 1 Canonical Root 2 Variable b r s b r s h 2 (%) Affect .026 .009 .993 .999 99.8 0 Verbal Immediacy .288 .826 1.43 0 .548 98.26 Nonverbal Immediacy .094 .666 .433 .046 44.57 Professor/student Rapport .724 .966 .925 .212 97.82 Note. b = standardized canonical function coefficient (weight) ; r s = structure coefficient; h 2 = communality coefficient. Follow up multiple regression analyses were performed for each of the dependent variables to better explain the contribution of the independ ent variables. All regression models used the independent variables of verbal immediacy, nonverbal immediacy, and professor/student rapport ( Table 4 1 1 ). The first model included student expectancies as the dependent variable and only professor/student r apport ( R 2 Adj = .178, p <.001) was found to be a significant predictor of student expectancies The second model used values/goals as the dependent variable and two independent variables, verbal immediacy ( p = .006) and professor/student rapport ( p < .001), were statistically significant predictors. The adjusted R 2 for this model was .316. In the last model, both verbal immediacy ( p <.001) and professor/student rapport ( p = .002) were significant predictors of affect and the model had an adjusted R 2 v alue of .056 Table 4 1 1. Follow up regression analyses for the motivation variables B SE t p R 2 Adj Student Expectancies (DV) Professor/student Rapport .621 .117 .379 5.32 <.001 .178 Values/goals (DV) Verbal Immediacy .331 .119 .194 2.79 .006

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143 Table 4 11. Continued B SE t p R 2 Adj Professor/student Rapport .689 .114 .391 6.02 <.001 .316 Affect (DV) Verbal Immediacy .943 .202 .380 4.66 <.001 Professor/student R apport .618 .195 .241 3.17 .002 .056 Note. DV = Dependent Variable ; Adjusted R 2 for the entire model. Results of the second canonical correlation analysis revealed that the full model, which consisted of a linear combination of the independent variable set (verbal immediacy, nonverbal immediacy, and professor/student rapport) and a linear combination of the dependent variable set (cognitive/metacognitive st rategy use (CMSU) and resource management strategy use (RMSU)) was statistically significant ( = .846, F (6, 604) = 8.78, p <.001). Additionally, R 2 c = .154 indicating that 15.4 % of the variance was shared between the independent variable set and the engagement variable set The model yielded two canonical roots, which were both statistically significant, p <.001 and p = .028. The R 2 c values for roots 1 and 2 were .134 and .023, respectively. Table 4 1 2 shows the second canonical correlation analysis, including the standardized canonical function coefficients (weights), and structure coefficients for the two significant canonical roots. The communalities ( h 2 ) across the two functions are also included For canonical root 1 CMSU ( b = .907, r s = .995) was the most relevant criterion variable contributing to the dependent variable set. RMSU had a substantial r s value (.736), but a low standardized weight (.132), which could indicate multicollinearity between the criterion variab les (Sherry & He nson, 20 05). For the independent variable set in canonical root 1, professor/student rapport ( b = .687, r s = .951) was the most

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144 relevant contributor, and verbal immediacy ( b = .430, r s = .861) emerged as a secondary contributor to the independent variable set. A dditionally, while nonverbal immediacy was not a relevant contributor, an inverse relationship existed between nonverbal immediacy and both of the engagement variables for root 1. In canonical root 2, RMSU ( b = .1.33, r s = .677) was the most relevant crit erion variable contributing to the dependent variable set. For the independent variable set in root2, verbal immediacy ( b = 1.19, r s = .507) was the only relevant contributor to the independent variable set, and verbal immediacy had an inverse relations hip with RMSU. Communality coefficients revealed that all variables were useful to the model with the exception of nonverbal immediacy. Table 4 1 2 Canonical correlation analysis testing engagement Canonical Root 1 Canonical Root 2 Variable b r s b r s h 2 (%) CMSU 907 .995 987 098 99.96 RMSU .132 .736 1.33 .677 100.0 Verbal Immediacy 430 861 1.19 .507 99.83 Nonverbal Immediacy 038 601 .147 .252 42.47 Professor/student Rapport 687 951 1.18 .303 99.62 Note. b = standardized canonical f unction coefficient (weight); r s = structure coefficient; h 2 = communality coefficient. Follow up multiple regression analyses were performed for each of the dependent variables to better explain the contribution of the independent variables. All regression models used the independent variables of verbal immediacy, nonverbal immediacy, and professor/student rapport ( Table 4 1 3 ). In the first model cognitive/metacognitive strategy use was the dependent variable and both professor/student rapport ( p = .002)

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145 and verbal immediacy ( p = .027) were found to be significant predictors and the adjusted R 2 for the model was .127 The second model included resource management strategy use as the dependent variable and only profess or/student rapport was found to be a significant predictor ( p <.001) with an adjusted R 2 value of .080 Table 4 1 3 Follow up regression analyses for the engagement variables B SE t p R 2 Adj CMSU (DV) Professor/student Rapport 399 126 .233 3.17 .002 Verbal Immediacy .290 .131 .175 2.22 .027 .127 RMSU (DV) Professor/Student Rapport 488 120 307 4.06 <.001 .080 Note. DV = Dependent Variable ; Adjusted R 2 for the entire model. Objective Six Objective 6 of this study was to determine the predictive value of selected rapport on the change in self regulated learning. To accomplish this objective, canonical correlation analyses were performed. Two s eparate canonical correlations were run for this objective. The first canonical correlation analysis included the independent variable set, which consisted of verbal and nonverbal immediacy, and professor/student rapport paired with the dependent variable set of change in student expectancies, change in values/goals, and change in affect. The second canonical correlation analysis used the same independent variable set paired with the dependent variable set that included change in cognitive/metacognitive s trategy use and change in resource management strategy use. The researcher chose to group the dependent variables into two separate sets for

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146 analysis based on the division of the measured constructs into motivation and engagement. Results of the first can onical correlation revealed that the full model, which consisted of a linear combination of the independent variable set (verbal immediacy, nonverbal immediacy, and professor/student rapport) and a linear combination of the dependent variable set ( change in student expectancies, change in values/goals, and change in affect) was statistically significant ( = 867 F (9, 730.27) = 4.90 p <.001). Additionally, R 2 c = 133 indicating that 13.3 % of the variance was shared between the independent variab le set and the dependent variable set. The model yielded one canonical root that was statistically significant ( p <.001), and the R 2 c value for this canonical root was .115 Table 4 1 4 shows the first canonical correlation analysis, including the standardi zed canonical function coefficients (weights), and structure coefficients for the canonical root. Communalities are not included as in previous tables because only one canonical root emerged from this analysis. For the dependent variable set, change in v alues/goals emerged as the most relevant contributor to the dependent variable set ( b = .901, r s = .993). Additionally, change in student expectancies ( r s = .696) values would indicate that change in student expectancies should be a good contributor to the dependent variable set, but its low standardized weight ( b = .152) suggests that multicollinearity may exist in this canonical variate (Sherry & Henson, 2005). For the independent variables, nonverbal immediacy ( b = .876, r s = .755) surfaced as a rele vant contributor to the independent variable set, and professor/student rapport (b = .755, r s = .664) was a secondary contributor.

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147 Table 4 1 4 Canonical correlation analysis testing change in motivation Canonical Root 1 Variable b r s Student Expectancies 152 696 Values/goals 901 993 Affect .000 .095 Verbal Immediacy 855 190 Nonverbal Immediacy .876 755 Professor/student Rapport 755 664 Note. b = standardized canonical function coefficient (weight); r s = structure coefficient Follow up multiple regression analyses were performed for each of the dependent variables to better explain the contribution of the independent variables. All regression models used the independent variables of verbal immediacy, nonverbal immediacy, and professor/student rapport ( Table 4 1 5 ). The first model utilized change in student expectancies as the dependent variable. All three independent variables (verbal immediacy ( p = .019), nonverbal immediacy ( p = .029), and profess or/student rapport ( p = .003)) were significant predictors of the dependent variable and the adjusted R 2 for the model was .049 In the second model, change in values/goals was the dependent variable and all three independent variables verbal immediacy ( p <.001), nonverbal immediacy ( p <.001), and rapport ( p <.001), were found to be significant predictors. The adjusted R 2 for the model was .105. The last model used change in affect as the dependent variable and none of the independent variables were found to be significant predictors.

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148 Table 4 1 5 Follow up regression analyses for the change in motivation variables B SE t p R 2 Adj Student Expectancies (DV) Verbal Immediacy .263 .112 .192 2.35 .019 Nonverbal Immediacy .279 .127 .161 2.20 .029 Professor/student Rapport .322 .108 .229 2.99 .003 .049 Verbal Immediacy .361 .099 .290 3.64 <.001 Nonverbal Immediacy .480 .112 .303 4.28 <.001 Professor/student Rapport .316 .095 .246 3.32 .001 .105 Note. DV = Dependent Variable ; Adjusted R 2 for the entire model. Results of the second canonical correlation analysis revealed that the full model, which consisted of a linear combination of the independent variable set (verbal immediacy, nonverbal immediacy, and professor/student rapport) and a linear combination of the dependent variable set ( change in cognitive/metacognitive strategy use ( CMSU) and change in resource management strategy use ( RMSU)) was statistically significant ( = 944 F (6, 602 ) = 8.78, p = .008 ) Additionally, R 2 c = .056 indicating that 5.6 % of the variance was shared between the independent variable set and the dependent variable set. The model yielded one canonical root that was statistically s ignificant (p = .008), and t he R 2 c value for this root was .051 Table 4 1 6 shows the second canonical correlation analysis, including the standardized canonical function coefficients (weights), and structure coefficients for the two significant canonical roots. Communalities are not included as in previous tables because only one canonical root emerged from this analysis. For the dependent variables RMSU emerged as the one relevant contributor to the dependant variable

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149 set ( b = .796, r s = .984). Multicollinear ity between the dependent variables may have coefficient value and the standardized weight (Sherry & Henson, 2005). Among the independent variables results revealed tha t nonverbal immediacy was the most relevant contributor to the independent variable set for this canonical root ( b = .822, r s = .864), followed by professor/student rapport ( b = .668, r s = .772). Table 4 1 6 Canonical correlation analysis testing change i n engagement Canonical Root 1 Variable b r s CMSU 258 840 RMSU 796 984 Verbal Immediacy 516 437 Nonverbal Immediacy 822 864 Professor/student Rapport 668 772 Note. b = standardized canonical function coefficient (weight); r s = structure coefficient Follow up multiple regression analyses were performed for each of the dependent variables to better explain the contribution of the independent variables. All regression models used the independent variables of verbal immediacy, nonverbal immediacy, and professor/student rapport ( Table 4 1 7 ). For the first regression model, change in cognitive/metacognitive strategy use was the dependent variable, and nonverbal immediacy was found to be a significant predictor ( p = .027) and ha d an adjusted R 2 value of .030 Change in resource management strategy use was the dependent variable for the second model, and two of the independent variables, nonverbal immediacy ( p = .015) and professor/student rapport ( p = .037), were significant pre dictors with an adjusted R 2 value of .040

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150 Table 4 1 7 Follow up regression analyses for the change in engagement variables B SE t p R 2 Adj CMSU (DV) Nonverbal Immediacy .175 .079 .164 2.22 .027 .030 RMSU (DV) Nonverbal Immediacy .175 .071 .180 2.45 .015 Professor/student Rapport .127 .061 .161 2.10 .037 .040 Note. DV = Dependent Variable ; Adjusted R 2 for the entire model. Summary Chapter 4 presented the findings of the study. The findings were organized around the objectives that guided this research. The objectives of the study were to: ( a ) reported perceptions of teacher immediacy behaviors and professor/student rapport ; ( b ) assess selected undergraduate reported m easures of self regulated learning at two separate points in the semester ; ( c ) perceptions of teacher immediacy, professor/student rapport, and self regulated learning ; ( d ) determine the ch regulated learning from the beginning of the semester to the end of the semester ; ( e ) determine immediacy and professor/student ra pport on self regulated learning ; and ( f ) determine immediacy and professor/student rapport on the change in self regulated learning The findings presented in Chapter 4 will b e described in greater detail in Chapter 5, where conclusions, recommendations, and implications will also be presented.

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151 CHAPTER 5 SUMMARY, CONCLUSIONS AND RECOMMENDATION S The purpose of this study was to determine the relationships between teacher immediacy, professor/student rapport, and student self regulated learning (self regulated learning serves as a proxy for student motivation and engagement) among undergraduate students in a college of agricult ure. The independent variables for this study were teacher immediacy and professor/student rapport. The dependent variables for this study were motivation and engagem ent Objectives The specific objectives of this research study were to: assess reported perceptions of teacher immediacy behaviors and professor/student rapport, reported measures of self regulated learning at two separate points in the semester, exami of teacher immediacy, professor/student rapport, and self regulated learning, regulated learning from the beginning of t he semester to the end of the semester, teacher immediacy and professor/student rapport on self regulated learning, and determine the predictive value of selected undergradua teacher immediacy and professor/student rapport on the change in self regulated learning. Summary of Findings The findings of this study are organized by presenting a description of the sample and then listed by objective.

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152 Descr iption of Sample The sample in this study consisted of undergraduate students enrolled in large (50 100 students) College of Agricultural and Life Sciences (CALS) courses during the Fall 2011 semester ( n = 306). The majority of the sample was female (63.7 %), White/Caucasian (63.2 %), junior or senior classification (85.4%), and majoring in CALS (59.7%). Objective One reported perceptions of teacher immediacy behavior use and professor/studen t rapport. Results revealed that students perceive that teachers use verbal ( M = 3.57; SD = .54) and nonverbal ( M = 4.09; SD = .43) immediacy behaviors often To obtain the descriptor of often for the results, the means were rounded to the nearest whole number. The behavior use using a 1 to 5 Likert type scale, ranging from never to very often Additionally, s tudents agreed they have rapport with their instructors ( M = 4.36; SD = .53). Likert type scale and ranges from strongly disagree to strongly agree The same method of rounding the mean was applied to rapport to define the descriptor agree Objective Two reported measures of self regulated learning at two separate points in the semester. Students reported their beginning student expectancy at a mean of 5.77 ( SD = .83) and thei r ending student expectancy was 5.92 ( SD mean was 5.06 ( SD = .96) and their ending values/goals mean was 5.26 ( SD = .93).

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153 Affect, which was operationalized as test anxiety, had a beginning mean of 3.62 ( SD = 1.29) and an ending mean of 3.53 ( SD cognitive/metacognitive strategy use was 4.37 ( SD = .87) and the ending mean was 4.57 ( SD = .90). Lastly, the beginning mean for resource management strategy use was 4.39 ( SD = .77) an d the ending mean was 4.45 ( SD = .84). Objective Three Objective three was to examine the relationships between selected undergraduate of teacher immediacy, professor/student rapport, and self regulated learning Professor/student ra pport was found to have a positive substantial relationship with verbal immediacy ( r = .66), nonverbal immediacy ( r = .54), and total immediacy ( r = .68). A substantial positive relationship was also seen between professor/student rapport and ending value s/goals ( r = .54). Professor/student rapport was additionally moderately positively associated with ending student expectancies ( r = .43) and beginning values/goals ( r = .37), as well as ending cognitive/metacognitive strategy use ( r = .34). Low associat ions were found between professor/student rapport and beginning cognitive/metacognitive strategy use ( r = .28), beginning resource management strategy use ( r = .22), and ending resource management strategy use ( r = .29). Verbal immediacy was found to be mo derately positively associated with the ending student expectancies ( r = .31), beginning values/goals ( r = .42), and ending values/goals ( r = .48), as well as ending cognitive/metacognitive strategy use ( r = .32). Low positive associations were found betw een verbal immediacy and beginning student expectancies, beginning affect, ending affect, beginning cognitive/metacognitive strategy use, beginning resource management strategy use, and ending resource

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154 management strategy use, with correlation coefficients ranging from .11 to .27. Nonverbal immediacy and ending values/goals had a moderate positive relationship ( r = .38), while most other correlations of nonverbal immediacy with the dependent variables of motivation and engagement were either low or negligi ble. Total immediacy was found to be moderately positively related to ending student engagement ( r = .33), beginning values/goals ( r = .37), ending values/goals ( r = .49), and ending cognitive/metacognitive strategy use ( r = .32). Objective Four Objective four was to self regulated learning from the beginning of the semester to the end of the semester For the motivational constructs, the means of student expectancies changed from a beginni ng mean of 5.77 ( SD = .83) to an ending mean of 5.92 ( SD = .86), resulting in a mean gain of .15. A paired sample t test was run to determine significance of the change, and results showed the change to be significant ( t = 3.63, p d was ca lculated and the effect size was small ( d = .18 ) The beginning mean for values/goals was 5.06 ( SD = .96) and the ending mean was 5.26 ( SD = .93), resulting in a mean gain of .20. The results of the paired sample t test revealed a significant change ( t = 5.06, p d ( d = .21) revealed a small effect size. For affect, the beginning mean was 3.62 ( SD = 1.29) and the ending mean was 3.53 ( SD = 1.35), which resulted in a .09 mean change. The paired samples t test showed the change to b e significant ( t = 2.79, p d ( d = .07) indicated a less than small effect. For the engagement constructs, the cognitive/metacognitive strategy use mean increased by .21 from 4.36 ( SD = .87) to 4.57 ( SD = .90). Paired sample t tes ts

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155 revealed that the change was significant ( t = 7.95, p <.001), but the effect size was small ( d = .23). Beginning resource management strategy use increased from 4.39 ( SD = .77) to 4.45 ( SD = .84), which was an increase of .06. The change was shown to b e significant ( t = 2.49, p = .013), but the effect size was very small ( d = .07). Objective Five Objective five was to determine the predictive value of selected undergraduate self regulated learning. Two separate canonical correlations were run. In the first, a linear combination of the independent variable set (verbal immediacy, nonverbal immediacy, and professor/student rapport) and a linear combination of the dependent var iable set (student expectancies, values/goals, and affect) was statistically significant ( = .611, F (9, 730.27) = 18.22, p <.001). The squared canonical correlation ( R 2 c ) for the model was .389, indicating that 38.9% of the variance was shared between the independent variable set and the motivation variable set. The model yielded three canonical roots, two of which were statistically significant. The first significant canonical root was the full model ( = .611, F (9, 730.27) = 18.22, p <.001), and the second significant root consisted of functions 2 to 3 ( = .930, F (4, 602) = 5.56, p <.001). The R 2 c values for the models were .343 and .070, respectively. Follow up multiple regression analyses were perfor med for each of the dependent variables to better explain the contribution of the independent variables. The first model included student expectancies as the dependent variable and only professor/student rapport ( R 2 Adj = .178, p <.001) was found to be a si gnificant predictor of student expectancies. The second model used values/goals as the dependent variable and two independent variables, verbal immediacy ( p = .006) and professor/student rapport

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156 ( p <.001), were statistically significant predictors and had an adjusted R 2 value of .316 In the last model, both verbal immediacy ( p <.001) and professor/student rapport ( p = .002) were significant predictors of affect and had an adjusted R 2 value of .056 In the second canonical correlation, a linear co mbination of the independent variable set (verbal immediacy, nonverbal immediacy, and professor/student rapport) and a linear combination of the dependent variable set (cognitive/metacognitive strategy use (CMSU) and resource management strategy use (RMSU) ) was statistically significant ( = .846, F (6, 604) = 8.78, p <.001). Additionally, R 2 c = .154, indicating that 15.4% of the variance was shared between the independent variable set and the engagement variable set. The model yielded two canonical roots which were both statistically significant, p <.001 and p = .028. The R 2 c values for roots 1 and 2 were .134 and .023, respectively. Follow up multiple regression analyses were performed for each of the dependent variables to better explain the contributi on of the independent variables. In the first model cognitive/metacognitive strategy use was the dependent variable and both professor/student rapport ( p = .002) and verbal immediacy ( p = .027) were found to be significant predictors and had an adjusted R 2 value of .127 The second model included resource management strategy use as the dependent variable and only professor/student rapport was found to be a significant predictor ( p <.001) and had an adjusted R 2 value of .080 Objective Six Objective 6 of this study was to determine the predictive value of selected rapport on the change in self regulated learning. To accomplish this objective, two

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157 separate canonic al correlation analyses were performed. Results of the first canonical correlation revealed that the full model, which consisted of a linear combination of the independent variable set (verbal immediacy, nonverbal immediacy, and professor/student rapport) and a linear combination of the dependent variable set (change in student expectancies, change in values/goals, and change in affect) was statistically significant ( = .867, F (9, 730.27) = 4.90, p <.001). Additionally, R 2 c = .133, indicating that 13.3% of the variance was shared between the independent variable set and the dependent variable set. The model yielded one canonical root that was statistically significant ( p <.001), and the R 2 c value for this canonical root was .115. Follow up multiple regres sion analyses were performed for each of the dependent variables to better explain the contribution of the independent variables. The first model utilized change in student expectancies as the dependent variable. All three independent variables (verbal i mmediacy ( p = .019), nonverbal immediacy ( p = .029), and professor/student rapport ( p = .003)) were significant predictors of the dependent variable and had an adjusted R 2 value of .049 In the second model, change in values/goals was the dependent varia ble and all three independent variables were found to be significant predictors with an adjusted R 2 value of .105 The last model used change in affect as the dependent variable and none of the independent variables were found to be significant predictors Results of the second canonical correlation analysis revealed that the full model, which consisted of a linear combination of the independent variable set (verbal immediacy, nonverbal immediacy, and professor/student rapport) and a linear combination of the dependent variable set (change in cognitive/metacognitive strategy

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158 statistically significant ( = .944, F (6, 602) = 8.78, p = .008). Additionally, R 2 c = .056, indicating that 5.6 % of the variance was shared between the independent variable set and the dependent variable set. The model yielded one canonical root that was statistically significant (p = .008), and the R 2 c value for this root was .051 Follow up multiple regression a nalyses were performed for each of the dependent variables to better explain the contribution of the independent variables. For the first regression model, change in cognitive/metacognitive strategy use was the dependent variable, and nonverbal immediacy was found to be a significant predictor ( p = .027) and had an adjusted R 2 value of .030 Change in resource management strategy use was the dependent variable for the second model, and two of the independent variables, nonverbal immediacy ( p = .015) and p rofessor/student rapport ( p = .037), were significant predictors with an adjusted R 2 value of .040 Conclusions The sample used in this study was not randomly drawn from the population. With this limitation in mind, and based on the findings of this study, the following conclusions were drawn. 1) Participants in the study perceived that their instructors use verbal and non verbal immediacy behaviors often. Additionally participants agree d that they have good rapport with their instructor. 2) Participants in the study reported higher than intermediate levels of expectancy to do well in the course and high er than intermediate values and goals for the course at the beginning and end of the semester. In addition, participants indicated an intermediate level of strategy use and resource management strategy use were slightly higher than intermediate. 3) When students reported higher levels of immediacy, they tended to have higher levels of rapport with th eir instructor. Students who reported higher levels of

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159 rapport with their instructo r tended to have higher levels of expectancy for success and higher values and goals for the course at the end of the semester. Also, s tudents reporting higher levels of r apport with their instructor tended to use more cognitive/metacognitive strategi es at the end of the semester. 4) over the course of the semester, while their test anxiety sl ightly decreased. Additionally, the use of cognitive/metacognitive strategies and resource management strategies slightly increased throughout the semester. 5) and goals incre ased, while their test anxiety decreased. Likewise, as the use of anxiety increased as well An examination of the engagement variables showed that as rapport increased, so did the use of cognitive/metacognitive strategies and resource management strategies. Additionally, as verbal immediacy behaviors increased, cognitive/metacognitive strategy use increased. 6) for success and values and goals decreased. Conversely, as both nonverbal and their v alues and goals increased. Additionally, as nonverbal immediacy as rapport increased, the ch ange in resource management strategies increased Discussion and Implications Objectiv e One reported perceptions of teacher immediacy behaviors and professor/student rapport. Conclusion Participants in the study perceived that their instructors use verbal and nonverbal immediacy behaviors often. Additionally participants agreed that they have good rapport with their instructor. The expectation of the researcher was that participants in t his study would report high amount s of verbal immediacy, nonverbal immediacy, and professor/student rapport This reason for this was due to the sampling technique used in the study. Classes that were sampled were chosen based on the student evaluation s cores of the

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160 instructors, which according to Moore et al. (1996) is an indicator of immediacy. Of the classes sampled, one instructor came from the low immediacy group, two from the intermediate immediacy group and five from the high group. Additionally, the student evaluation scores of the instructors in this study were found to be substantially, T he conclusion was drawn that nonverbal immediacy was used by instructors more often than verbal immediacy. This finding is consistent with a similar study conducted by Velez and Cano (2008) where nonverbal immediacy use was more prevalent than verbal immediacy use in the college of agriculture at the Ohio State University. Nonverbal im mediacy consists of behaviors, such as smiling at students, moving around the classroom, gesturing while talking, and looking at the class while talking. The assumption can be made that nonverbal immediacy behaviors are easier for instructors to implement than verbal immediacy behaviors, which include behaviors and teaching style may play a role in the u tilization of verbal and nonverbal immediacy behavior use (Wilson & Taylor, 2001) One additional consideration with immediacy is that of culture. This study was conducted in the College of Agricultural and Life Sciences at the University of Florida, whi ch is a large land grant university in the Southeastern United States. Velez (2008) indicated that culture may play a role in how verbal and nonverbal behaviors are perceived by students. Consequently, the culture in CALS might be different than cultures in other colleges at the University of Florida, and culture in the Southeastern United States is most likely different than cultures in other parts of the

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161 country. Perhaps studies conducted in other locations might reveal different results concerning imm ediacy behavior use. Professor/student rapport was described by Wilson et al. (2010) as positive relationships built between instructors and students. Meyers (2009) suggested that teachers who utilize verbal and nonverbal immediacy behaviors should b e more effective at creating rapport than teachers who do not utilize immediacy behaviors. Wilson et al. reported significant positive relationships among verbal and nonverbal immediacy with professor/student rapport, and concluded that immediacy behavior s were not as inclusive as the rapport construct. This might explain why professor/student rapport was reported at higher levels than either of the immediacy behaviors, or total immediacy. Objective Two Assess repor ted measures of self regulated learning at two separate points in the semester Conclusion Participants in the study reported higher than intermediate levels of expectancy to do well in the course and high er than intermediate values and goals for the cou rse at the beginning and end of the semester. In addition, participants indicated strategy use and resource management strategy use were slightly higher than intermedi ate. Students participating in this study exhibited higher than intermediate levels of expectancy for success in their classes. According to Ormrod (2008), three factors contribute to this expectancy, past successes and failures, communication of messages by others, and accomplishments and failures of others. The classes utilize d in this

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162 study were upper level, major specific classes or lower level introductory courses in CALS and the students participating in the study were mostly juniors and seniors. Therefore, in accordance with motivation theory, students in these classes s hould have experienced prior success in classes in their major, as well as introductory CALS classes. Additionally, the instructors in these courses exhibited high levels of immediacy and rapport which should communicate positive messages to students, wh ich according to Ormrod CALS students typically build relationships with their peers, especially within majors, thus creating opportunities for dialoging about which classes are most challenging. In addition, much information is available about classes and instructors. The University of Florida website provides all student evaluation scores for every instructor, and many students also utilize websites such as Ratemyprofessor.com. Armed with thi s information, it is possible that students may feel more expectancy for success in certain classes. Participants in the study also reported having high levels of values and goals for their classes. The high goal levels reported by participants may be a f unction of the analysis of the MSLQ goal orientation data. The MSLQ measures both intrinsic and extrinsic goal orientation (Pintrich et al., 1991). According to Pintrich and Zusho (2007), students will either be intrinsically motivated (e.g. challenged t o master a concept) or extrinsically motivated (e.g. grades, rewards, competition). The analysis of the data in this study did not differentiate between intrinsic and extrinsic goal orientation, therefore, students reporting high levels of either type of goal orientation would appear to be more motivated.

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163 In addition, students reported higher than intermediate levels of value toward their classes. Wigfield and Eccles (2000) suggested that three components of value contribute to how much a student will v alue a class including, interest, importance, and future value of the information In this study, several of the courses were upper level classes that specifically pertain to that students have an inherent i nterest in courses belonging to their major. Additionally, students should place importance on learning the material in classes in their major and find value for future use of the information. Furthermore, Pintrich and Zusho (2007) posited that reactions toward an instructor can affect interest and value in a course. In light of reported immediacy use and rapport, perhaps reactions toward the instructor also contributed to the level of value students had for their courses. Participants in this study repo rted intermediate levels of test anxiety. While many studies have been conducted concerning test anxiety, no studies were found that delineated what amount of test anxiety could be considered normal. Cassaday and Johnson (2002) stated that test anxiety i s dependent upon the individual student and the situational factors. In the case of this study, measurements of test anxiety were self reports taken apart from a testing situation. Perhaps students in this study would report higher levels of test anxiety if measurements were taken at the time of testing. Students reported a slightly higher than intermediate use of cognitive/metacognitive strategies and resource management strategies. Pintrich and Zusho (2007) posited that students who have an intrinsic m otivation typically use more self regulated learning strategies. In this study, no distinction was made between intrinsic and extrinsic motivation, therefore, perhaps the use of these strategies was

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164 lower because, while students reported higher levels of motivation, some were extrinsically motivated and did not utilize self regulated learning strategies. Additionally, Pintrich and Zusho suggested that strategy use may not be instinctual among all students, and some students may need guidance in the use of self regulated learning strategies. Objective Three E xamine the relationships between selected undergraduate regulated learning Conclusion When students reported higher l evels of immediacy, they tended to have higher levels of rapport with their instructor. Students who reported higher levels of rapport with their instructor tended to have higher levels of expectancy for success and higher values and goals for the course at the end of the semester. Also, s tudents reporting higher levels of rapport with their instructor tended to use more cognitive/metacognitive strategies at the end of the semester. The expectation of this study was to find a strong positive relationshi p between immediacy and professor/student rapport. Wilson et al. (2010) posited that teacher immediacy is the variable most closely associated with rapport, although they concluded that rapport is a more robust measurement of relationships between teacher s and students. In light of this, instructors who utilize more verbal and nonverbal immediacy behaviors in their classroom should be more adept at building rapport (Chase, 2009; Cox et al., 2010; Meyers, 2009; Wilson et al., 2010 ). Students who reported higher levels of rapport with their instructors also had increased levels of expectancy for success and increased values and goals at the end

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165 of the semester This findi ng aligns with previous research, which found that higher levels of professor/student rapport were positively associ ated with student enjoyment, satisfaction with the class (Benson, Cohen, & Buskist 2005 ), and varying aspects of motivation (Wilson, 2006, Wilson et a l., 2010; Wilson & Locker Jr., 2008 ; Wilson & Taylor, 2001). In an agricu ltural education context, Velez and Cano (2008) found positive relationships among both verbal and nonverbal immediacy with expectancy value motivation. Additionally, student expectancy has been found to be a significant predictor of out of class interact ion between students and instructors, which has shown to be an important piece of rapport (Henning, 2007) Schunk (2004) would suggest that these higher levels of motivation created by relationships between instructors and students are a result of higher levels of student expectancy for success and greater values/goals for the course Because the relationships between rapport and motivation were stronger at the end of the semester than the beginning, this might suggest that students attribute more of thei r motivation to the relationship they build with their instructor throughout the course of the semester. Students who reported higher levels of rapport with instruc tors also reported using more cognitive/metacognitive strategies, as well as more r esource m anagement strategies. This supports research conducted by Benson, Cohen, and Buskist (2005) in which they found that students were more likely to engage in classes where the professor exhibited more rapport. However, not much research was found that inv estigates the relationships between rapport and engagement; most research has looked at rapport and motivation. O ne consideration in this study is that perhaps use of cognitive/metacognitive and resource management strategies is largely a function of

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166 stud ent motivation. Relationships were found in this study between motivation and engagement that support motivational theory, which posits that students with higher levels of expectancy for success and higher values and goals will use more cognitive/metacogn itive and resource management strategies (Ormrod, 2008; Pintrich & Zusho, 2007). Objective Four D regulated learning from the beginning of the semester to the end of the semester Conclusion Pa increased slightly over the course of the semester, while their test anxiety slightly decreased. Additionally, the use of cognitive/metacognitive strategies and resource management strategies s lightly increased throughout the semester. The expectation of the researcher was that expectancy for success, values and goals, cognitive/metacognitive strategy use, and resource management strategy use would all increase over the course of the semester, w hile test anxiety decreased. The findings of this study revealed that all changes were in the desired directions. However, little or no practical effects were seen from the changes. Results of this study were similar to results found in a study by Chris tophel and Gorham (1995), in which they reported an increase in student state motivation. Christophel and Gorham utilized a test retest procedure, unlike this study, which used a pos t then pre design (Rockwell & Ko hn, 1989). Perhaps the observed increase s were due to social desirability resulting from the data collection method used for the post then pre design.

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167 Objective Five D etermine the predictive value of selected undergraduate t on self regulated learning Conclusion expectancies and values and goals increased, while their test anxiety decreased. values and goals in the class increased. However, as verbal immediacy behavior use increased, showed that as rapport increased, so did the use of cognitive/metacognitive strategies and resource management strategies. Additionally, as verbal immediacy behaviors increased, cognitive/metacognitive strategy use increased. The expectation in this study was that immediacy and rapport would be significant predictors of motivat ion and engagement. Findings in this study revealed that immediacy and rapport account for a much larger portion of the variance in motivation than they do engagement. This is consistent with the previous findings in this study, which showed that both im mediacy and rapport are positively related to student expectancy for succe ss and student values and goals. Therefore, results showing that rapport significantly predicted student expectancy for success and values and goals came as no surprise. However, i t was surprising that nonverbal immediacy was not a significant predictor of any motivational vari ables and that increases in verbal immediacy predict increases in test anxiety These results reaffirm the premise that immediacy and rapport are highly rela ted and immediacy was masked by the

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168 multicollinearity between the variables, and that rapport might serve as a mediating variable between immediacy and motivation. Additionally as expected, rapport was found to be a significant inverse predictor of test an xiety. Because of the strong relationship between immediacy and rapport, this aligns with research by Williams (2010) where immediacy was found to be a significant factor in the reduction of test anxiety. However, inconsistent with Williams, in this stud y verbal immediacy was found to significantly positively predict test anxiety. This result is puzzling, especially since verbal immediacy is so strongly related to rapport. An examination of the correlation matrix shows that verbal immediacy is positivel y related to values and goals, and that value and goals are positively related to test anxiety. Since this study did not differentiate between intrinsic and extrinsic goal orientation, perhaps a large number of extrinsically motivated students participate d in this study. Extrinsically motivated students would be motivated by external factors such as verbal affirmation by instructors and grades (Pintrich & Zusho, 2007) which could explain why verbal immediacy positively predicts test anxiety Results sho wed that as rapport increased the use of cognitive/metacognitive and resource management strategies also increased also that as verbal immediacy increased the use of cognitive/metacognitive strategies increased These findings parallel findings by Benson Cohen, and Buskist (2005) where students were more engaged when the professor exhibited more rapport. However, an examination of the shared variance shows that only about 13% of the variance in engagement is explained by rapport, and only about 2% of t he variance in engagement is explained by immediacy, whereas about 34% of the variance in motivation is explained by immediacy

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169 and rapport. In light of this, and in conjunction with motivational theory (Ormrod, 2008; Schunk, 2004), perhaps student expecta ncy for success and values and goals contribute more heavily to student engagement than either immediacy, rapport, or a combination of the two. This would suggest that efforts by instructors to build rapport one possible way of increasing self regulated learning strategy use would be explicit teaching of cognitive/metacognitive and resource management strategies (Pintrich et al., 1991). Objective Six D etermine the predictive value of selected undergraduate s change in self regulated learning Conclusion As verbal immediacy behavior use increased, the change in rsely, as for success and their values and goals increased. Additionally, as nonverbal immediacy ategy use increased, the change in resource management strategies increased The expectation was that verbal and nonverbal immediacy, along with rapport, would be pred ictors of positive change in motivation and engagement. Similar to the findings in objective five, immediacy and rapport shared a much larger portion of the variance in motivational change than they did in the change in engagement. However, r esults revea led that as verbal immediacy increases that there is less of a change in both student expectancy for success and student values and goals. The thought is that

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170 perhaps the instructors in this study are engaging verbally more with students who are doing poo rly in class. Results of this study revealed that the instructors involved have a good amount of rapport with their students. Therefore, it is plausible that these instructors are proactive in intervening when students are having trouble. This would acc ount for the relationship showing that as verbal immediacy increases, the change in expectancy and values and goals goes down. Additionally, in light of the fact that as rapport increases the change in expectancy for success and values and goals increases, might i ndicate that students who are doing poorly do not take the step of building rapport with instructors, even with the presence of high verbal immediacy. Perhaps over a longer period of time and prolonged contact with the instructor, changes in expectancy an d values and goals may start to increase as poorer performing Conversely, results showed that as nonverbal immediacy and rapport increase, consistent with research conducted by Cox et al. (2010), where rapport was found to significa relationships. Compared to the previous finding where verbal immediacy predicted smaller changes in expectancy and values and goals, this finding illustrates that perhaps those students who increase their expectancy for success and their values and instructors. Additionally, because intrinsic and extrinsic motivation were not investigated separately in this study, the assumption might be made that students who

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171 build rapport with their instructors are intrinsically motivated and share some of the same interests as their instructors. Results revealed that as nonverbal immediacy behaviors increased the c hange in cognitive/metacognitive strategies increased, as did the change in resource management strategies. These findings were inconsistent with prior research by Christensen et al. (1995) and Butland and Beebe (1992). Christensen et al. found that nonv erbal immediacy was a significant predictor of attitudes about engagement, but not engagement itself. Likewise, Butland and Beebe found that nonverbal immediacy was a significant predictor of behavioral intent and attitudes Respondents in this study rep orted that instructors used nonverbal immediacy behaviors more than verbal immediacy behaviors. Perhaps the assumption could be made that teachers who use more nonverbal behaviors, such as voice inflection, gesturing, smiling, and moving around the classr oom, help keep students more alert and interested in the material being taught, which in turn, helps students be come actively engaged in the classroom instruction. Interested engaged students should tend to utilize more cognitive/metacognitive strategies and resource management strategies than students who are not as interested in the material (Pintrich & Zusho, 2007). Similarly, a s rapport increased the change in resource management strategies also increased. This could possibly be a function of the nature of the specific resource management strategies. The resource management strategies included on the MSLQ were, time and study environment, effort regulation, peer learning, and help seeking. Students who exhibit good use of their time and study en vironment and more effort regulation are likely students who have developed good rapport with their instructors.

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172 The conclusion could be made that students who feel that they have good relationships with their instructors might invest more time and effort in their classes. Likewise, both peer learning and help seeking involve relationships in the classroom. It is likely that classrooms where rapport is present are environments where students feel safer about seeking help from the instructor, and may feel that they can connect with other students No significant predictors of change in test anxiety emerged. This was probably due to the fact that the change in test anxiety was minimal. Ad ditionally, there are more than likely many other factors that contribute to changes in test anxiety. The assumption could be made that students might have more anxiety before the first test because they do not know what to expect, but on subsequent tests they might feel Recommendations for Practitioners Based upon the findings of this study, the following recommendations were made for practitioners: 1) Students who reported having higher levels of rapport with their instructors also reported higher levels of motivation and engagement. Therefore, because immediacy and rapport are strongly related, instructors should consider utilizing more verbal and nonverbal immediacy behaviors, along with bui lding rapport with their students 2) To build rapport with students, instructors could implement the following practices: (a ) encourage more i nstructor student interaction; (b ) invite students to visit during office hours; (c ) use personal examples in teachi ng; (d) call students by name; (e ) get to know students and show genuine concern for students; (f ) show en thusiasm for the subject; (g) try to connect with all students, especially those who may not normally seek out a relationship with an instructor; and ( h ) show respect for all students. 3) Instructors in colleges of agriculture might benefit from professional development that emphasizes the use of immediacy behaviors and rapport building.

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173 4) Because motivation might play an instrumental role in engaging studen ts, instructors should develop an understanding of student motivation and the factors that help motivate students, both intrinsic and extrinsic. 5) Use of several of the self regulated learning behaviors may not be instinctive to all students, therefore stude nts might benefit from instruction in self regulated learning behavior use. Recommendations for Further Research Based upon the findings of this study, the following recommendations for further research were made: 1) Although the researcher attempted to increase the variability in immediacy, the sample for this study was a convenience sample. This study should be replicated using procedures that allow for randomization of the sample and ultimately more generalizability. 2) Because this study was a post then pre design, perhaps responses may have not been as accurate for the pre account of motivation and engagement. It is plausible that perhaps respondents either could not fully remember their motivation and engagement, or responded in a socially desirab le manner. Therefore, further studies should be conducted that investigate the variables of motivation and engagement using a pre/post design. 3) This study was conducted in the College of Agricultural and Life Sciences at the University of Florida, which is a large land grant university in the Southeastern United States. Velez (2008) indicated that culture may play a role in how verbal and nonverbal behaviors are perceived by students. Consequently, the culture in CALS might be different than cultures in o ther colleges at the University of Florida, and culture in the Southeast United States is most likely different than cultures in other parts of the country. Therefore, further research should be conducted across the university setting and in other geograp hic areas to determine the role culture plays in the relationships among immediacy and rapport and self regulated learning. 4) Further research should be conducted investigating the variables of immediacy and professor/student rapport, in order to determine the extent to which immediacy contributes to professor/student rapport. 5) Further studies should be conducted investigating the variables of motivation and engagement, in order to determine the extent to which motivation contributes to student engagement. 6) Be cause the differentiation between intrinsic and extrinsic motivation was not made, further studies might investigate the relationship of each type of motivation with immediacy and rapport.

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174 7) Path analyses should be conducted to determine which variables medi ate in the conceptual model of motivation and engagement 8) Hierarchical Linear M odeling should be conducted to investigate relationships among all the variables on a class by class basis. 9) Quasi experimental studies investigating the effects of rapport and i mmediacy on motivation and engagement should be conducted. 10) Observational studies should be conducted. This would allow researchers to quantify the amount of immediacy behaviors being used in the classroom instead quency of immediacy behavior use. 11) Student achievement was not addressed in this study. Prior research has been conducted that shows relationships between immediacy and achievement. Further studies to determine relationships of the current variables wit h achievement should be conducted. 12) Qualitative inquiries should be conducted with students to gain a deeper understanding of how immediacy behaviors and rapport influence motivation and student engagement. 13) Because this study was conducted over the course o f only one semester, perhaps motivation and engagement did not have enough time to significantly change. Longitudinal studies could be conducted that might help researchers understand how motivation and engagement change over time.

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175 APPENDIX A IMMEDIACY BEHAVIOR SCALE ____ For each statement please indicate the frequency with which your professor in this class demonstrates the described behaviors Never Rarely Occasionally Often Very Often 1. Uses personal examples or talks about experiences she/he has had outside class. 1 2 3 4 5 2. Asks questions or encourages students to talk. 1 2 3 4 5 3. Gets into discussions based on something a student brings up even when this part of his/her lecture plan. 1 2 3 4 5 4. Uses humor in class. 1 2 3 4 5 5. Addresses students by name. 1 2 3 4 5 6. Addresses me by name. 1 2 3 4 5 7. Gets into conversations with individual students before or after class. 1 2 3 4 5 8. Has initiated conversations with me before, after, or outside class. 1 2 3 4 5 9. 1 2 3 4 5 10. 1 2 3 4 5 11. Provides feedback on my individual work through comments on papers, oral discussions, etc. 1 2 3 4 5 12. Calls on students to answer questions even if they have not indicated they want to talk. 1 2 3 4 5 13. Asks how students feel about an assignment, due date, or discussion topic. 1 2 3 4 5 14. Invites students to telephone or meet with him/her outside of class if they have questions or want to discuss something. 1 2 3 4 5 15. Asks questions that have specific, correct answers. 1 2 3 4 5 16. Asks questions to solicit viewpoints or opinions. 1 2 3 4 5 17. comments. 1 2 3 4 5 18. 1 2 3 4 5 19. Will have discussions about things unrelated to class with individual students or with the class as a whole. 1 2 3 4 5 20. Is addressed by his/her first name by the students. 1 2 3 4 5 21. Sits behind desk while teaching. 1 2 3 4 5 22. Gestures while talking to the class. 1 2 3 4 5 23. Uses monotone/dull voice when talking to the class. 1 2 3 4 5 24. Looks at the class while talking. 1 2 3 4 5 25. Smiles at the class while talking. 1 2 3 4 5 26. Has a very tense body position while talking to the class. 1 2 3 4 5 27. Touches students in the class. 1 2 3 4 5 28. Moves around the classroom while teaching. 1 2 3 4 5

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176 29. Sits on a desk or in a chair while teaching. 1 2 3 4 5 30. Looks at board or notes while talking to the class. 1 2 3 4 5 31. Stands behind podium or desk while teaching. 1 2 3 4 5 32. Has a very relaxed body position while talking to the class. 1 2 3 4 5 33. Smiles at individual students in the class. 1 2 3 4 5 34. Uses a variety of vocal expressions when talking to the class. 1 2 3 4 5

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177 APPENDIX B PROFESSOR/STUDENT RAPPORT SCALE ID Code (Last 4 digits of phone # and first 3 lette Instructions: For each item, please circle the number that best represents your answer. Please think of the professor from this course Strongly Disagree Neutral Strongly Agree 1. My professor and I get along. 1 2 3 4 5 2. My professor is not helpful. 1 2 3 4 5 3. My professor is inconsiderate. 1 2 3 4 5 4. My professor is understanding. 1 2 3 4 5 5. My professor is thoughtful. 1 2 3 4 5 6. My professor is disrespectful. 1 2 3 4 5 7. I understand what my professor expects of me. 1 2 3 4 5 8. My professor is aware of the amount of effort I am putting into this class. 1 2 3 4 5 9. I respect my professor. 1 2 3 4 5 10. My professor is a mentor to me. 1 2 3 4 5 11. My professor encourages questions and comments from students. 1 2 3 4 5 12. My professor is not friendly. 1 2 3 4 5 13. My professor is approachable. 1 2 3 4 5 14. 1 2 3 4 5 15. My professor makes class enjoyable. 1 2 3 4 5 16. I want to take other classes taught by my professor. 1 2 3 4 5 17. 1 2 3 4 5 18. My professor maintains eye contact with me. 1 2 3 4 5 19. I really like to come to class. 1 2 3 4 5 20. My professor and I communicate well. 1 2 3 4 5 21. My professor is eager to help students. 1 2 3 4 5 22. My professor is compassionate. 1 2 3 4 5 23. My professor encourages me to succeed. 1 2 3 4 5 24. I feel I have learned much less from this professor compared to others I have had in the past. 1 2 3 4 5 25. My professor is confident. 1 2 3 4 5 26. My professor enjoys his or her job. 1 2 3 4 5 27. My professor cares about students. 1 2 3 4 5 28. My professor is enthusiastic. 1 2 3 4 5 29. My professor is a role model. 1 2 3 4 5 30. My professor wants to make a difference. 1 2 3 4 5 31. My professor is receptive. 1 2 3 4 5

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178 32. My professor is reliable. 1 2 3 4 5 33. My professor is unfair. 1 2 3 4 5 34. My professor will spend extra time going over a concept if students need it. 1 2 3 4 5

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179 APPENDIX C MOTIVATED STRATEGIES FOR LEARNING QUESTIONNAIRE ID Code For each statement please indicate how well this described you at the beginning of this semester and how well it describes you now for this course 1= Strongly Disagree (SD); 7=Strongly Agree (SA) Beginning of the Semester Now SD SA SD SA 1 2 3 4 5 6 7 In a class like this, I prefer cours e material that really challenges me so I can learn new things 1 2 3 4 5 6 7 1 2 3 4 5 6 7 If I study in appropriate ways, then I will be able to learn the material in this course 1 2 3 4 5 6 7 1 2 3 4 5 6 7 When I take a test I think about how poorly I am doing compared with other students 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I think I will be able to use what I learn in this course in other courses 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I believe I will receive an excellent grade in this class 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I'm certain I can understand the most difficult material presented in the readings for this course 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Getting a good grade in this class is the most satisfying thing for me right now 1 2 3 4 5 6 7 1 2 3 4 5 6 7 When I take a test I think about items on other parts of the test I can't answer 1 2 3 4 5 6 7 1 2 3 4 5 6 7 It is my own fault if I don't learn the material in this course 1 2 3 4 5 6 7 1 2 3 4 5 6 7 It is important for me to learn the course material in this class 1 2 3 4 5 6 7 1 2 3 4 5 6 7 The most important thing for me right now is improving my overall grade point average, so my main concern in this class is getting a good grade 1 2 3 4 5 6 7

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180 1 2 3 4 5 6 7 I'm confident I can learn the basic concepts taught in this course 1 2 3 4 5 6 7 1 2 3 4 5 6 7 If I can, I want to get better grades in this class than most of the other students 1 2 3 4 5 6 7 1 2 3 4 5 6 7 When I take tests I think of the consequences of failing 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I'm confident I can understand the most complex material presented by the instructor in this course 1 2 3 4 5 6 7 1 2 3 4 5 6 7 In a class like this, I prefer course material that arouses my curiosity, even if it is difficult to learn 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I am very interested in the content area of this course 1 2 3 4 5 6 7 1 2 3 4 5 6 7 If I try hard enough, then I will understand the course material 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I have an uneasy, upset feeling when I take an exam 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I'm confident I can do an excellent job on the assignments and tests in this course 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I expect to do we1l in this class 1 2 3 4 5 6 7 1 2 3 4 5 6 7 The most satisfying thing for me in this course is trying to understand the content as thoroughly as possible 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I think the course material in this class is useful for me to learn 1 2 3 4 5 6 7 1 2 3 4 5 6 7 When I have the opportunity in this class, I choose course assignments that I can learn from even if they don't guarantee a good grade 1 2 3 4 5 6 7 1 2 3 4 5 6 7 If I don't understand the course material, it is because I didn't try hard enough 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I like the subject matter of this course 1 2 3 4 5 6 7

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181 1 2 3 4 5 6 7 Understanding the subject matter of this course is very important to me 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I feel my heart beating fast when I take an exam 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I'm certain I can master the skills being taught in this class 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I want to do well in this class because it is important to show my ability to my family, friends, employer, or others 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Considering the difficulty of this course, the teacher, and my skills, I think I will do well in this class 1 2 3 4 5 6 7 1 2 3 4 5 6 7 When I study the readings for this course, I outline the material to help me organize my thoughts 1 2 3 4 5 6 7 1 2 3 4 5 6 7 During class time I often miss important points because I'm thinking of other things 1 2 3 4 5 6 7 1 2 3 4 5 6 7 When studying for this course, I often try to explain the material to a classmate or friend 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I usually study in a place where I can concentrate on my course work 1 2 3 4 5 6 7 1 2 3 4 5 6 7 When reading for this course, I make up questions to help focus my reading 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I often feel so lazy or bored when I study for this class that I quit before I finish what I planned to do 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I often find myself questioning things I hear or read in this course to decide if I find them convincing 1 2 3 4 5 6 7 1 2 3 4 5 6 7 When I study for this class, I practice saying the material to myself over and over 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Even if I have trouble learning the material in this class, I try to do the work on my own, without help from anyone 1 2 3 4 5 6 7

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182 1 2 3 4 5 6 7 When I become confused about something I'm reading for this class, I go back and try to figure it out 1 2 3 4 5 6 7 1 2 3 4 5 6 7 When I study for this course, I go through the readings and my class notes and try to find the most important ideas 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I make good use of my study time for this course 1 2 3 4 5 6 7 1 2 3 4 5 6 7 If course readings are difficult to understand, I change the way I read the material 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I try to work with other students from this class to complete the course assignments 1 2 3 4 5 6 7 1 2 3 4 5 6 7 When studying for this course, I read my class notes and the course readings over and over again 1 2 3 4 5 6 7 1 2 3 4 5 6 7 When a theory, interpretation, or conclusion is presented in class or in the readings, I try to decide if there is good supporting evidence 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I work hard to do well in this class even when I don't like what we are doing 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I make simple charts, diagrams, or tables to help me organize course material 1 2 3 4 5 6 7 1 2 3 4 5 6 7 When studying for this course, I often set aside time to discuss course material with a group of students from the class 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I treat the course material as a starting point and try to develop my own ideas about it 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I find it hard to stick to a study schedule 1 2 3 4 5 6 7 1 2 3 4 5 6 7 When I study for this class, I pull together information from different sources, such as lectures, readings, and discussions 1 2 3 4 5 6 7

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183 1 2 3 4 5 6 7 Before I study new course material thoroughly, I often skim it to see it is organized 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I ask myself questions to make sure I understand the material I have been studying in this class 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I try to change the way I study in order to fit the course requirements and the instructor's teaching style 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I often find that I have been reading for this class but don't know what it was all about 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I ask the instructor to clarify concepts I don't understand well 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I memorize key words to remind me of important concepts in this class 1 2 3 4 5 6 7 1 2 3 4 5 6 7 When course work is difficult, I either give up or only study the easy parts 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I try to think through a topic and decide what I am supposed to learn from it rather than just reading it over when studying for this course 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I try to relate ideas in this subject to those in other courses whenever possible 1 2 3 4 5 6 7 1 2 3 4 5 6 7 When I study for this course, I go over my class notes and make an outline of important concepts 1 2 3 4 5 6 7 1 2 3 4 5 6 7 When reading for this class, I try to relate the material to what I already know 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I have a regular place set aside for studying 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I try to play around with ideas of my own related to what I am learning in this course 1 2 3 4 5 6 7 1 2 3 4 5 6 7 When I study for this course, I write brief summaries of the main ideas from the readings and my class notes 1 2 3 4 5 6 7

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184 1 2 3 4 5 6 7 When I can't understand the material in this course, I ask another student in this class for help 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I try to understand the material in this class by making connections between the readings and the concepts from the lectures 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I make sure that I keep up with the weekly readings and assignments for this course 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Whenever I read or hear an assertion or conclusion in this class, I think about possible alternatives 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I make lists of important items for this course and memorize the lists 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I attend this class regularly 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Even when course materials are dull and uninteresting, I manage to keep working until I finish 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I try to identify students in this class whom I can ask for help if necessary 1 2 3 4 5 6 7 1 2 3 4 5 6 7 When studying for this course I try to determine which concepts I don't understand well 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I often find that I don't spend very much time on this course because of other activities 1 2 3 4 5 6 7 1 2 3 4 5 6 7 When I study for this class, I set goals for myself in order to direct my activities in each study period 1 2 3 4 5 6 7 1 2 3 4 5 6 7 If I get confused taking notes in class, I make sure I sort it out afterwards 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I rarely find time to review my notes or readings before an exam 1 2 3 4 5 6 7 1 2 3 4 5 6 7 I try to apply ideas from course readings in other class activities such as lecture and discussion 1 2 3 4 5 6 7

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185 What is your age? ___________ What is your gender? M or F What is your major? ____________________________________ What is your race/ethnicity? African American American Indian or Alaska Native Asian Hispanic/Latino White/Caucasian Other What is your classification? Freshman Sophomore Junior Senior Other

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186 APPENDIX D IRB APPROVAL

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187 APPENDIX E SCRIPT FOR INSTRUMENT ADMINISTRATION Good afternoon. My name is Chris Estepp and I am a doctoral student in the Department of Agricultural Education and Communication. I am here today because I am conducting the research for my dissertation. The aim of my study is to look at instructor practices and the relationship these have with student learning. Today I will be asking you to answer a short survey instrument, but first I need to go over the informed consent with you. If you flip to the side of the paper that says informed consent on the top, you will see the information about your rights as a participant in this study. Basically what this form sa ys is that participation in this study is not mandatory. You have the right to not participate, or to stop participating at any point. Additionally, you are not being compensated in any way to participate in this study. This form also informs you that y our information will remain confidential at all times. You will be asked to create a user ID on the survey, but neither I nor your instructor will be able to link you to your user ID. Also, no one except me will ever see I will not know that it belongs specifically to you. If you flip over the paper you will see the words ID code at the top. Please take a second to create your ID code. It will consist of the last four digits of your telephone number followed by the fir Now that you have created your ID code, here are the instructions. For each statement, please indicate the frequency with which your professor in this class demonstrates the described behaviors (please indicate th e level to which you agree/disagree with the statement for P/SR and MSLQ). If you notice on the right hand

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188 side there are numbers from 1 to 5 (1 to 7 for MSLQ). These numbers correspond to the terms at the top of the column, never to very often. When you are finished please bring your survey and place it _________. I appreciate your willingness to participate in this study. I hope to find some interesting information and be able to make some conclusions about undergraduate student learning. O nce again, thank you and have a great day.

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189 APPENDIX F IRB PROTOCOL AND CONSENT FORM

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190 LIST OF REFERENCES Alderman, R. V. (2008). Faculty and student out of class interaction: Student perceptions of quality interaction. (Unpublished doctoral dissertation). Texas A&M University, College Station. Alexander, P. A., Schallert, D. L., & Hare, V. C. (1991). How researchers in learning and literacy talk about knowledge. Review of Educational Research, 61 (3), 315 343. Ames, C. (1992). Class rooms: Goals, structures, and student motivation. Journal of Educational Psychology, 84 261 271. Amundsen, C., Winer, L., & Gandell, T. (2004). Designing te aching for student learning. In A. Saroyan & C. Amundsen (Eds.). Rethinking teaching in higher education. Sterling, VA: Stylus Publishing LLC. Andersen, J. F. (1978). The relationship between teacher immediacy and teaching effectiveness (Doctoral dissertation). Retrieved from ProQuest. (Order No. 7900859) Andersen, J. F. (1979). Teacher immediacy as a predictor of teaching effectiveness. In D. Nimmo (Ed.), Communication yearbook 3 (pp. 543 559) New Brunswick, NJ: Transaction Books. Andersen, J. F., Norton, R. W., & Nussbaum, J. F. (1981). Three investigations exploring relationships betwe en perceived teacher communication behaviors and student learning. Communication Education, 30 377 392. Arum, R., & Roksa, J. (2011). Academically adrift: Limited learning on college campuses Chicago, IL: University of Chicago Press. Association of A merican Colleges and Universities. (2002). Greater expectations: A new vision for learning as a nation goes to college. Washington, DC: Association of American Colleges and Universities Association of Public and Land grant Universities. (2009). Human capacity development: The road to global competitiveness and leadership in food, agriculture, natural resources, and related services (FANRRS). Washington, DC: Association of Public and Land grant Universities Astin, A. (1993). What matters in college ? Four critical years revisited. San Francisco, CA: Jossey Bass. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.

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191 Bandura, A. (1989a). Human agency in social cognitive theor y. American Psychologist, 44 (9), 1175 1184. Bandura, A. (1989b). Social cognitive theory. In R. Vasta (Ed.), Annals of child development. Vol. 6. Six theories of child development (pp. 1 60). Greenwich, CT: JAI Press. Bandura, A. (1997). Self effica cy: The exercise of control. New York, NY: Freeman. Bandura, A. (1999). Social cognitive theory of personality. In L. Pervin & O. John (Eds.), Handbook of personality (2nd ed., pp. 154 196). New York, NY: Guilford Publications. (Reprinted in D. Cervon e & Y. Shoda [Eds.], The coherence of personality. New York, NY: Guilford Press.) Barr, R. B., & Tagg, J. (1995). From teaching to learning: A new paradigm for undergraduate education. Change, 27 (6), 697 710. Benson, T. A., Cohen, A. L., & Buskist, W. (2005). Rapport: Its relation to student attitudes and behaviors toward teachers and classes. Teaching of Psychology, 32 (4), 237 239. Boekaerts, M. (1997). Self regulated learning: A new concept embraced by researchers, policy makers, educators, teachers, and students. Learning and Instruction, 7 (2), 161 186. Bok, D. (2006). Our underachieving colleges. Princeton, NJ: Princeton University Press. Bonwell, C. C ., & Eison, J. A. (1991). Active learning: Creating excitement in the classroom ( ERIC No. ED340272). Retrieved from http://www.oid.ucla.edu/uni ts/tatp/old/lounge/pedagogy/downloads/active learning eric.pdf Boteler, F. E. (2006 July ). Response to recommendations of the national food and agribusiness management commission. Presentation at the American Agricultural Economics Association Meeting, Long Beach, CA. Butland, M. J., & Beebe, S. A. (1992 May ). A study of the application of implicit communication theory to teacher immediacy and student learning. Paper presented at the Annual Meeting of the International Communication Associat ion, Miami, FL. Campbell, J. R. (1998). Reclaiming a lost heritage: Land grant and other higher education initiatives for the twenty first century. East Lansing, MI: Michigan State University Press. Cassaday, J. C., & Johnson, R. E. (2002). Cognitive t est anxiety and academic performance. Contemporary Educational Psychology, 27 270 295.

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192 Chase, R. (2009). behaviors. (Unpublished doctoral dissertation). Walden University. Chemers, M. M ., Hu, L., & Garcia, B. F. (2001). Academic self efficacy and first year college student performance and adjustment. Journal of Educational Psychology, 93 (1), 55 64. Chesebro, J. L. (2003). Effects of teacher clarity and nonverbal immediacy on student learning, receiver apprehension, and affect. Communication Education, 52 (2), 135 147. Chesebro, J. L., & McCroskey, J. C. (1998). The relationship of teacher clarity and Comm unication Quarterly, 46 (4), 446 456. Chickering, A. W., & Gamson, Z. F. (1987). Seven principles for good practice in undergraduate education. AAHE Bulletin. Retrieved from http://www.aahea.org/bulletins/articles/sevenprinciples1987.htm Christensen, L. J., Curley, K. E., Marquez, E. M., & Menzel, K. E. (1995). Classroom situations which lead to student participation (ERIC No. ED391207). Retrieved from http://eric.ed.gov/PDFS/ED391207.pdf Christophel, D. M. (1990). The relationships among teacher immediacy behaviors, student motivation, and learning. Communication Education, 39 323 340. Christophel, D. M., & Gorha m, J. (1995). A test retest analysis of student motivation, teacher immediacy, and perceived sources of motivation and demotivation in college classes. Communication Education, 44 292 306. Cohen, A. M., & Kisker, C. B. (2010). The shaping of American higher education: Emergence and growth of the contemporary system (2nd ed.). San Francisco, CA: Jossey Bass. Comstock, J., Rowell, E., & Bowers, J. W. (1995). Food for thought: Teacher immediacy, student learning and curvilinearity. Communication Educ ation, 44 251 266. Cox, B. E., McIntosh, K. L., Terenzini, P. T., Reason, R. D., & Lutovsky Quaye, B. R. (2010). Pedagogical signals of faculty approachability: Factors shaping faculty student interaction outside the classroom. Research in Higher Educa tion, 57 (8), 767 788. Crump, C. A. (1996). Teacher immediacy: What students consider to be effective teacher behaviors (ERIC No. ED 390099). Retrieved from http://www.eric.ed.gov/PDFS/ED390099.pdf

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206 BIOGRAPHICAL SKETCH Chris grew up in rural Central Texas, where his family was involved in a small part time farming operation. He was actively involved with his high school FFA chapter, which helped develop his love of agricultural education. Chris rec degree in animal s cience from Texas A&M University in 1997 and worked in the meat industry, first as a meat processor for HEB Grocery Company and then as a production manager for Readfield Meats Inc In 2005, Chris went back to Texas A&M to work on ducation. Upon completion, he taught high school agriscience at G reenwood High School in Midland, Texas. In 2009, Chris enrolled in the Department of Agricultural Education and Communication at the University of Florida to work on his PhD. After graduation, Chris will start his faculty career as an assistan t professor of agricultural e ducation at Sul Ross State University in Alpine, Texas.