Science, Irving K. Barber Faculty of (Okanagan)
DSpace
UBCO
Hay, Tristyn N.
Doctor of Philosophy - PhD
University of British Columbia
The fundamental question this study aims to answer is one that many educational researchers are trying to understand: How can we best improve the academic success of our students? Students are most successful academically and otherwise when they are intrinsically motivated. Intrinsic motivation can be increased and maintained when student autonomy, competence and relatedness are nurtured (Deci & Ryan, 2000). As goal setting, particularly SMART goals, may help to embed these psychological requirements, this research sought to determine whether SMART goal setting will result in an increased level of intrinsic motivation and thus academic success. Levels of intrinsic motivation, metacognition and self-control along with grades were assessed in both science and non-science majors in first year post-secondary students utilizing pre- and post- self-reporting questionnaires and a single online goal setting intervention. When the intervention was received earlier in the term, science majors’ grades increased; however, no other responses were observed. In the non-majors there was an increase in self-control, control of learning beliefs, metacognition and intrinsic motivation three weeks following the intervention. Across both majors and non-majors, students’ self-control was also observed to be correlated with goal usage and goal completions. This study suggests that goal setting may be a worthwhile endeavor in the struggle to improve students’ academic success and persistence in first year post-secondary students.
THE USE OF GOAL SETTING IN IMPROVING FIRST-YEAR SCIENCE AND NON-SCIENCE MAJOR STUDENTS’ ACADEMIC SUCCESS AND INTRINSIC MOTIVATION By Tristyn N. Hay M.Sc., The University of British Columbia (Okanagan), 2013 B.Sc., The University of British Columbia, 2005 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY In THE COLLEGE OF GRADUATE STUDIES (IGS) (Interdisciplinary Graduate Studies) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) July 2021 ÓTristyn Hay, 2021 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: The Use of Goal Setting in Improving First-Year Science and Non-Science Major Students’ Academic Success and Intrinsic Motivation Submitted by Tristyn Hay in partial fulfillment of the requirements of the degree of Doctor of Philosophy. Dr. Richard M. Plunkett, Irving K. Barber School of Arts and Sciences Supervisor Dr. Peter Arthur, School of Education Supervisory Committee Member Dr. W. Stephen McNeil, Irving K. Barber School of Arts and Sciences Supervisory Committee Member Dr. Tamara K. Freeman, Irving K. Barber School of Arts and Sciences University Examiner Dr. Alison B. Flynn, Department of Chemistry and Biomolecular Sciences, University of Ottawa External Examiner iii Abstract The fundamental question this study aims to answer is one that many educational researchers are trying to understand: How can we best improve the academic success of our students? Students are most successful academically and otherwise when they are intrinsically motivated. Intrinsic motivation can be increased and maintained when student autonomy, competence and relatedness are nurtured (Deci & Ryan, 2000). As goal setting, particularly SMART goals, may help to embed these psychological requirements, this research sought to determine whether SMART goal setting will result in an increased level of intrinsic motivation and thus academic success. Levels of intrinsic motivation, metacognition and self-control along with grades were assessed in both science and non-science majors in first year post-secondary students utilizing pre- and post- self-reporting questionnaires and a single online goal setting intervention. When the intervention was received earlier in the term, science majors’ grades increased; however, no other responses were observed. In the non-majors there was an increase in self-control, control of learning beliefs, metacognition and intrinsic motivation three weeks following the intervention. Across both majors and non-majors, students’ self-control was also observed to be correlated with goal usage and goal completions. This study suggests that goal setting may be a worthwhile endeavor in the struggle to improve students’ academic success and persistence in first year post-secondary students. iv Lay Summary In the search for ways to help indirectly reduce attrition rates and best help first year post-secondary students succeed academically, we studied whether a SMART goal setting intervention in can build student levels of intrinsic motivation. Both science majors’ and non-majors’ levels of intrinsic motivation, self-control, metacognition and grades were assessed before and after the intervention. We found that following the goal setting intervention there was an increase in final grades in science majors and an increase in metacognition, self-control, control of learning beliefs and intrinsic motivation in non-majors. This study suggests that goal setting may be an important part in helping students achieve academic success via an increase in their level of intrinsic motivation. v Preface The research described in this thesis contributes to the overall understanding of how best to promote academic success in first year post-secondary students. The research was designed using assessment of students’ intrinsic motivation, self-control and metacognition, before and after a single goal setting intervention for both science majors and non-majors. Increases in metacognition, self-control, control of learning beliefs and intrinsic motivation were observed in the non-majors while final grades increased in response to the early intervention in science majors. vi Table of Contents Abstract ............................................................................................................................. iii Lay Summary ..................................................................................................................... iv Preface ................................................................................................................................. v Table of Contents ............................................................................................................... vi List of Tables ...................................................................................................................... ix List of Figures ...................................................................................................................... x Acknowledgements ............................................................................................................xii Chapter 1 Introduction ....................................................................................................... 1 1.1 Introduction ................................................................................................................. 1 1.1.1 Nature of the Problem ........................................................................................... 1 1.1.2 Significance of study ............................................................................................. 3 1.1.3 Research Questions ............................................................................................... 6 1.1.4 Limitations to the study ......................................................................................... 9 1.2 Definition of key terms ............................................................................................... 13 1.3 Literature Review ...................................................................................................... 16 1.3.1 The Research on Motivation ............................................................................... 16 1.3.2 Goal Orientation ................................................................................................. 17 1.3.2.1 Achievement Motivation .............................................................................. 17 1.3.2.2 Goal orientation precursors .......................................................................... 20 1.3.2.3 Goal orientation outcomes ............................................................................ 21 1.3.3 Self-Determination Theory.................................................................................. 24 1.3.3.1 Needs ........................................................................................................... 25 1.3.3.2 Intrinsic motivation ...................................................................................... 27 1.3.3.3 Autonomy .................................................................................................... 27 1.3.3.4 Competence ................................................................................................. 28 1.3.3.5 Relatedness .................................................................................................. 29 1.3.3.6 Extrinsic motivation ..................................................................................... 30 1.3.3.7 External regulation ....................................................................................... 31 1.3.3.8 Introjection ................................................................................................... 32 1.3.3.9 Identification ................................................................................................ 32 1.3.4.0 Integration .................................................................................................... 33 1.3.4 Academic impacts of motivation ......................................................................... 33 1.3.5 Other academic influences .................................................................................. 35 1.3.5.1 Metacognition .............................................................................................. 35 1.3.5.2 Self-Control ................................................................................................. 38 1.3.5.3 Self-Efficacy ................................................................................................ 42 1.3.5.4 Mindset ........................................................................................................ 43 vii 1.4 Background ............................................................................................................... 45 1.4.1 Goal Setting Theory ............................................................................................ 47 1.4.2 Goal setting as a concept ..................................................................................... 47 1.4.3 Increasing self-determination through goal setting .............................................. 49 1.4.4 SMART Goals .................................................................................................... 52 1.4.5 Goal setting in academics .................................................................................... 53 2.0 Chapter 2 ..................................................................................................................... 57 2.1 Synopsis..................................................................................................................... 57 2.2 Methods ..................................................................................................................... 58 2.2.1 Format ................................................................................................................ 58 2.2.2 Assessment Scales .............................................................................................. 59 2.2.2.1 Motivational Strategies Learning Questionnaire (MSLQ) ............................. 60 2.2.2.2 Brief Self-Control Scale (BSCS) .................................................................. 61 2.2.2.3 Metacognitive Awareness Inventory (MAI) .................................................. 61 2.2.3 SMART Goals .................................................................................................... 62 2.3 Analysis ..................................................................................................................... 63 2.4 Results ....................................................................................................................... 63 2.4.1 Science Majors ....................................................................................................... 63 2.4.2 Non-Science Majors............................................................................................ 66 2.5. Discussion: ............................................................................................................... 70 2.5.1 Science Majors: low performers .......................................................................... 70 2.5.1.1 Constructs .................................................................................................... 70 2.5.1.2 Academics ................................................................................................... 71 2.5.2 Science Majors: high performers ......................................................................... 72 2.5.2.1 Constructs .................................................................................................... 72 2.5.2.2 Academics ................................................................................................... 72 2.5.3 Non-Science Majors: low performers .................................................................. 73 2.5.3.1 Constructs .................................................................................................... 73 2.5.3.2 Academics ................................................................................................... 74 2.5.4 Non-Science Majors: high performers ................................................................. 75 2.5.4.1 Constructs .................................................................................................... 75 2.5.4.2 Academics ................................................................................................... 75 2.6 Conclusion ................................................................................................................ 76 2.6.1 Low performers versus high performers .............................................................. 77 2.6.2 Non-Majors versus Majors .................................................................................. 78 2.7 Intervention Implementation ...................................................................................... 79 2.8 Future Research ........................................................................................................ 80 3.0 Chapter 3 ..................................................................................................................... 81 3.1 Synopsis..................................................................................................................... 81 3.2 Methods ..................................................................................................................... 81 viii 3.2.1 Format ................................................................................................................ 81 3.2.2 Assessment Scale ................................................................................................ 82 3.2.2.1 Brief Self-Control Scale (BCSC) .................................................................. 82 3.3 Analysis ..................................................................................................................... 83 3.4 Results ....................................................................................................................... 84 3.5 Discussion ................................................................................................................. 85 3.5.1 Goal Completion ................................................................................................. 86 3.5.2 Time Spent ......................................................................................................... 87 3.6 Conclusion ................................................................................................................ 89 3.7 Future Research ........................................................................................................ 89 4.0 Chapter 4: Conclusions ............................................................................................... 91 4.1 Discussion ................................................................................................................. 91 4.1.1 Science Majors ................................................................................................... 91 4.1.2 Non-Science Majors ......................................................................................... 93 4.1.3 Self-Control ........................................................................................................ 95 4.1.4 Science majors versus Non-Science majors ......................................................... 97 4.1.5 Low performers versus High performers ............................................................. 99 4.1.6 Student perception ............................................................................................ 101 4.1.6.1 Student Attitudes ........................................................................................ 101 4.2 Implications ............................................................................................................. 103 4.3 Limitations .............................................................................................................. 104 4.4 Future Research ...................................................................................................... 107 4.4.1 Pre-assessment .................................................................................................. 107 4.4.2 Not all created equal ......................................................................................... 108 4.4.3 Integration ........................................................................................................ 109 4.4.4 Student “Buy-In” .............................................................................................. 110 4.4.5 Faculty “Buy-In” .............................................................................................. 111 4.4.6 Creating a “New Tool”...................................................................................... 112 Appendices ....................................................................................................................... 113 Appendix A .................................................................................................................... 113 Appendix B .................................................................................................................... 114 Appendix C .................................................................................................................... 118 Appendix D.................................................................................................................... 121 Appendix E .................................................................................................................... 129 ix List of Tables Table 2.1 Metacognition of regulation, self-control, control of learning beliefs, intrinsic goal orientation and extrinsic goal orientation and SMART goal intervention timeline………………………………………………... 65 x List of Figures Figure 1.1 The spectrum of motivation adapted from Deci and Ryan’s Self-Determination Theory (Deci & Ryan, 2000) SDT and the facilitation of intrinsic motivation, social development and welfare (Ten Cate et al., 2011)……………………………………………………………… 39 Figure 1.2 SMART Goal intervention used for both science majors and non-science majors first year post-secondary students……………………………. 59 Figure 2.1 Metacognition of regulation (a), self-control (b), control of learning beliefs (c), intrinsic goal orientation (d) and extrinsic goal orientation (e) averages across three surveys for group A (early intervention), low and high performers, and group B (late intervention), low and high performers…….............................................................................. 71 Figure 2.2 Mean grade averages across three surveys for group A (early intervention), low and high performers, and group B (late intervention), low and high performers……………………………………………………………. 72 Figure 2.3 Intrinsic goal orientation and control of learning beliefs averages across three surveys for group A (received intervention), low and high performers, and group B (no intervention), low and high performers……………………………………………………………. 73 Figure 2.4 Self-control averages across three surveys for group A, low and high performers, and group B, low and high performers.………………...... 74 Figure 2.5 Metacognition regulation averages across three surveys for group A, low and high performers, and group B, low and high performers……. 74 Figure 2.6 Mean grade averages across three surveys for group A (early intervention), low and high performers, and group B (late intervention), low and high performers………………………………. 75 Figure 3.1 Correlation between time spent completing the goal setting intervention and average self-control score…………………………... 99 Figure A.i Cohort designation pre-screening tool………………………………... 118 Figure A.ii. Cohort Treatment Package Summary………………………………… 118 Figure B Modified SMART goal setting intervention…………………………. 122 xi Figure C Consent Form. Note: This consent form was completed immediately prior to survey 1…………………………………………………... 123 Figure D Qualtrics assessment scale used for survey 1, 2 and 3. Note: Consent form included at the beginning of survey 1…………………………... 126 Figure E As grade point average increases with increased goal setting usage in first year post-secondary science students……………………………. 134 xii Acknowledgements Firstly, I must give thanks to all those researchers who provided the foundations of this work. Without their persistence in the study of understanding why we do what we do I would not have had the insight to create the intervention used in this study. As someone who has been a student, teaching assistant, an instructor and now a program coordinator I have had the opportunity to see students through various lenses. Over the last decade this lens has helped me to notice a problem exists in how students are learning but also ways in which we can help. This combined with the literature discussed in this thesis lead me to ask the questions asked and to create an intervention in which to best able to help students learn in a more beneficial way. Secondly, I would like thank my committee, especially Dr. Richard Plunkett, my supervisor. Dr. Plunkett was available when I was needing to mull things over and helped me to refocus when I often went down the many rabbit holes I did. Dr. Stephen McNeil was great at keeping me grounded and realistic of what endeavors I could actually take on which is likely how I am actually finishing this degree in a timely manner. Dr. Peter Arthur was a great sounding board for hashing out the best way to assess my students’ levels of motivation, metacognition and self-control and was also paramount in helping me understand the completely unanticipated results that I saw. Within the three of them I was able to attain both broad and specific knowledge, stay on course and all while maintaining my own sense of autonomy in my project. xiii Thirdly I would like to say thanks to Dr. Michael Deyholos, my employer. Absolutely none of this would have been possible if he not only allowed me to take on this venture while working full time but also believed that I could do it. Not many employers would provide the level of flexibility and support he has provided and thus I must acknowledge him here. Lastly, I would like to acknowledge my family for being patient. With an adoption process underway, two children, a global pandemic and a large move for our family this was a trying couple of years to say the least. My parents were also a huge support providing me the right push when it felt as though I wanted to give up or that I would not be able to complete the project. No matter what, my parents maintained a consistent support network during the entire process. 1 Chapter 1 Introduction 1.1 Introduction 1.1.1 Nature of the Problem High attrition in post-secondary science, technology, engineering and mathematics (STEM) education is a global problem (Farrar & Mathieson, 2013; Knapp et al., 2007; Ray, 2009; Wilson, 2015). At the university of Witwatersrand (WITS) in South Africa over four years there was a 17% decline in student registration in the BSc program and a completion rate of only 50% over 13 years, measured as graduation within four years (Ray, 2009). In a study conducted in the United States, only 35% of science undergraduates completed their degree within four years with 57% completing within six years and 25% not completing at all (Knapp et al, 2007). Similarly, the University of Health Sciences in Bethesda, Maryland, had a 35% attrition rate in one year with 40 of 180 students being dismissed or disenrolled over a six-year period (Wilson et al., 2015). From 2009-2012 the University of British Columbia Okanagan also observed high attrition rates of 19.5% among the BSc domestic students and a 29.5% attrition rate for BSc international students (Farrar & Mathieson, 2013). High attrition has broad negative impacts (Trowler, 2010). Society is also negatively impacted by high attrition rates, as those individuals may not continue to pursue education, and as a result be less marketable (Perry et al., 2005). In a study conducted in the UK, wages were less for those who did not complete their degrees and these students had longer periods of unemployment compared to their graduate counterparts, with graduates earning up to 70% more (Johnes & Taylor, 1991). 2 Attrition rates have many influencing factors, including engagement/motivation of students. Low intrinsic motivation may be caused by working for grades. There is a considerable adjustment phase for students starting post-secondary education. Students often have to leave family and friends when starting their post-secondary education as well as learning how they will fit into a new social dynamic; all while coping with more intensive course work and at a faster pace (Pennebaker et al., 1990). This shift is often associated with a high degree of loneliness, health issues and depression (Pennebaker et al., 1990). Students will often not persist in their education due to financial hardships and relational issues they face within their first year of college (Kuh et al., 2008). However, when accounting for these factors and others, it has been shown that a student’s engagement is positively associated with the degree to which a student will persist in their educational endeavors from first year to second year (Trow, 2007). Student engagement is the degree of interest and focus a student displays when learning and this dictates the level of motivation the student has in their learning and progress in their education. Though adjustment to university life (Pennebaker et al., 1990) and financial and relational difficulties (Kuh et al., 2008) all have effects on a student’s persistence in their education, it is a student’s engagement and thus motivation (Trow, 2007) which we need to look hard at when trying to remedy these high attrition rates. Why are engagement and motivation problematic in our students? The low intrinsic motivation may be responsible for the high attrition rates in first year students seen today. 3 The focus on grades in higher education in North America is a recent phenomenon. Changes to delivery, such as larger class sizes and an emphasis on lectures, have resulted in changes to assessment methods (Rüegg, 2003). Student-teacher relationships were informal with conversations among peers continuing on into pubs and markets prior to the 20th century. We saw much critical evaluation of classroom recitations and debates at Harvard in 1636 followed by more applied experience of learning by Bennet in the 1830s. Around 1889 class sizes began increasing and in response we saw a decrease in discussion (Thelin, 2004). This trend continued into the 1960s where lecture halls began to grow until we were at mass delivery in the 1970s where knowledge was now attained only through lecture (Cohen & Kisker, 2010). Currently many first-year post-secondary lectures have student numbers in the hundreds, with a single instructor leading the way. As a result, the opportunity for student discussion and collaboration decreased. To deal with the ever-growing numbers, educators had to assess students more efficiently and this led to fewer assignments, and grades resting on one to two exams. These types of assessment may not accurately evaluate deeper cognitive learning but rather the more superficial learning associated with performance goal-oriented learning. 1.1.2 Significance of study In order to increase deeper learning in first year students we need to help shift students away from a performance goal orientation and towards a learning goal orientation. To facilitate this change, this study used a goal setting intervention built to increase student’s self-determination in order to shift students towards intrinsic motivation. 4 The purpose of an educational institution is, I believe, to educate the students in order to best prepare them for becoming responsible and contributing members of society. Thus, I feel as educators we have both a required and moral responsibility to ensure that not only are we teaching our students what they will need to know, but that they are in fact learning what we are teaching. With the uncertainties of the future needs of our society it is not enough for educators to just teach but rather we need to ensure students are in fact learning. As has already been discussed, the assessment by which we determine “learning” may not be accurately reflective of the deeper cognitive learning which educators often strive to attain and may in fact be causing students to shift away from learning for learning. And as it is unlikely that educational institutions will change how they assess students’ “learning” we need to find another way to ensure students are really learning, even within this grade centric system. As it is highly unlikely that these educational institutions will change their methods of assessments, due to many logistical challenges they face, we must find another way to help students truly succeed in their acquisition of knowledge and pursuit of their education. What makes students learn out of a true interest in learning? How can we encourage students to be motivated to learn (for their own good), as lifelong learners, rather than to “chase the grade?” These are questions that I believe to be extremely important in increasing our students’ academic and lifelong success. The positive relationship between intrinsic motivation and lifelong success can be seen in Deci and Ryan’s (2000) review of related research. In particular it has been shown that intrinsic motivation has a positive relationship with academic performance, mental health, and healthy habitats—all contributing towards lifelong success (Deci & Ryan, 2000), and that when we nurture and enhance intrinsic 5 motivation we shift students towards a learning goal orientation (Eison et al., 1986). Eison (Eison et al., 1986) noted that students who tended to show a learning goal orientation were motivated to learn for the sake of learning and showed better academic performance compared to students with a more grade goal orientation (Eison et al., 1986). From Dweck and Elliot’s (Dweck & Elliot, 1988) research in achievement motivation, Dweck (Dweck, 1986) defined these orientations as learning versus performance goal orientation. In a meta-analytic examination, Payne et al. (2007) reported that learning goal-orientated post-secondary students display positive personality traits such as higher self-efficacy, self-esteem, an increased need for achievement, higher degree of the conscientiousness personality types of the Big Five personality traits, and tend to have a higher implicit theory of intelligence or growth mindset (GMS)—all of these are associated with academic success. (Payne et al., 2007). The same study describes students with learning goal orientation compared to performance goal orientation, concluding they tend to also show greater task mastery, meaning they are motivated to master the task for the sake of the task, and thus have higher job performance (Payne et al., 2007). Thus, students with learning goal orientations tend to show higher task mastery and consequently do better with respect to job performance than those showing more performance goal orientations. They also show deeper learning strategies and increased feedback-seeking behaviour, which also increases their academic success (Payne et al., 2007). 6 Learning goal orientation is associated with positive outcomes including academic success, task mastery, and job performance (Payne et al., 2007); however, our current grade-centric modality often makes it challenging for students to maintain a learning goal orientation, assuming they had one when they started, or to shift students towards a learning goal orientation. The literature supports the notion that when the environment is focussed on performance students will maintain their performance goal orientation and, in the case where students had a learning goal orientation the environment, will cause them to shift towards a more performance goal orientation. Arguably, if we change the environment to encourage a learning goal orientation we can cause students to shift towards a learning goal orientation. As indicated above, having a learning goal orientation benefits student and thus as educators, we should promote a learning goal orientation in performance goal orientated students and encourage and support our learning goal orientated students who already have it. The issue is how to do this in such a way without overburdening educators. This project aims to create an intervention that will encourage a learning goal orientation, in an easy-to-implement, time sensitive approach. The intervention that was used was a relatively short and simplistic task which is intended to increase students’ level of intrinsic motivation. An increase in intrinsic motivation should result in an increase in learning goal orientation, deeper learning and increased lifelong success. 1.1.3 Research Questions When the SMART goals intervention was being employed, using the principles outlined in the next few sections, I was looking to see if there would be any changes in the constructs 7 such as intrinsic motivation, metacognition, self-control, control of learning beliefs and academic performance using a quantitative pre and post assessment format. The first assessment was done looking at the effects, if any, of the intervention on first year science majors’ level of intrinsic motivation, self-control, metacognition and academic success, determined by grades. First year science majors were assessed together and also as two groups: low and high academic performers. Students were grouped into these categories based on their final grades from previous academic term class for the science majors and their overall grades for the non-biology majors. The same assessment was done for first year non-science majors. In order to get an idea of the students’ levels of self-control, goal usage and overall intervention buy-in, I looked at the correlation between self-control, goal usage, time spent on, and completion rates of the intervention within both the majors and non-majors’ groups. Overarching Goal 1. Increase deeper cognitive learning and lifelong success in first year post-secondary students by maintaining learning goal orientations in learning goal orientated students and shift performance goal orientated students towards learning goal orientation 2. Understanding the relationship between students’ self-control and learning goal orientation. a. academic success b. goal usage c. goal commitment 8 Research Questions 1. What affect does this SMART goal curricular intervention have on intrinsic motivation, self-control, metacognition and control of learning beliefs for both science and non-science majors first year post-secondary students? • The Motivational Strategies Learning Questionnaire, Metacognitive Awareness Inventory and the Brief Self-Control Scale were used to assess changes in these constructs. • My hypothesis is that the SMART goals curricular intervention will change first-year post-secondary students’ levels of intrinsic motivation, self-control, metacognition and control of learning beliefs for both science and non-science majors. • I predicted that the SMART goals curricular intervention will increase first-year post-secondary students’ levels of intrinsic motivation, self-control, metacognition and control of learning beliefs for both science and non-science majors. 2. Will first year post-secondary students’ level of self-control be correlated with goal usage and goal commitment? • The Brief Self-Control Scale were used to assess students’ levels of self-control and time spent on the SMART goals curricular intervention and completion rate were used to determine goal usage and goal commitment. 9 • My hypothesis is that students’ levels of self-control would be correlated with student’s goal commitment and goal usage. • I predicted that students’ high levels of self-control will be correlated with high goal commitment and goal usage. 1.1.4 Limitations to the study Over the next few chapters I will provide much evidence to support the notion that intrinsic motivation is key to improving our students’ academic success and that through SMART goals we can facilitate this increased intrinsic motivation; however, it is important to acknowledge that there are numerous variables that are both directly and indirectly involved in a student’s ability to be academically successful. The research presented here may provide compelling arguments and results to support this notion, but there are confounding variables which may also play a role which need to be considered. As this research did not focus on assessing the response of other variables including; self-efficacy, self-esteem, growth mindset, personalities, anxiety and need for achievement, to name a few, we cannot say with any level of confidence that these are not also playing a direct or indirect role in our results. Another limitation to consider in this study is that it was relatively a short-term study. It is very likely that if we could carry out a longitudinal study and follow these students over the course of their degrees we may see more statistically significant changes in intrinsic motivation, self-control, metacognition and control of learning beliefs in response to the intervention. An intervention of the kind used in this research may require more than a few weeks for the full impact to be seen. One could also argue that a single intervention may not 10 be enough to elicit a more permanent change in goal orientation. However, this remains to be seen as this study did not follow the students throughout their program. With more time this would have been something I would have liked to pursue. The goal of this research was to find a suitable intervention that would be simple in nature and able to be implemented in a short period of time; however, most things worth learning or changing require more than a single intervention. Habits take time and repetition and thus it is likely that by only administering the intervention at one time we may not have seen its true potential. Secondly, students may have been ill equipped to have tackled such an intervention. We are trying to shift students away from performance goal orientation and towards learning goal orientation. Those with performance goal orientations may not be as well equipped to complete the goal setting intervention. Generally speaking, students with learning goal orientations tend to use goals regularly and show high levels of self-control while performance goal-oriented individuals do not. Although this paper has a clear focus to motivate students to having learning goal orientations, this by no means suggests there is not a place for performance goal orientation in academics. Rather I believe we are too heavily focused on performance goals and thus the goal of this research is to create a shift. Consequently, it is possible that the shift may be too slight to see statistically significant results, and thus the results of this study may be unable to truly reflect the outcome of this intervention. 11 There is still much debate in the literature about what characteristics students must have to be the best learners; however, we have now recognized that intelligence is not the be-all end-all. For decades we believed a student’s intelligence was fixed and that it was the best measurement we had to determine a student’s fate. Though there is some literature to support this notion (Gottfried, 1990) recent literature recognizes there are many other criteria which can predict a student’s academic success, with one of these being motivation (Dweck et al., 2007). Due to time and logistical constraints I was not able to assess the impacts this intervention may have indirectly or directly had on other variables outside of motivation, metacognition, self-control and control of learning beliefs. As I have a strong desire to see positive change in my students there may be a cognitive bias in my willingness to see change where perhaps there is none. Due to my dual nature as both the researcher and coordinator of these courses, it would have been better to elicit the help of other collaborators in order to eliminate that bias. However as other faculty are often overworked it would be challenging, at best, to ask an instructor to restructure their course in order to facilitate such a study as this would be too taxing. In order to make the most of my dual role and avoid potential biases, I had teaching assistants implement the intervention and one of my technicians was responsible for ensuring all the survey information went out on time. Any questions or concerns students or teaching assistants had were directed to this technician. I had no access to any course grades or survey responses until after the course closed. 12 As the argument can be made that the use of grades is not always a true reflection of a student’s learning the use of this imperfect metric in order to assess changes in students’ academic performance in response to the curricular intervention is also a limitation to the study. That being said this was the only metric available at that time and was easily attainable. It was also the case that the experimental design required us to offer the SMART goals curricular intervention to all students and thus by offering it to both groups we did not have a comparison group which did not receive the intervention. When there are many variables at play, as is the case when working with humans, we cannot conclude with any degree of certainty that the responses we are seeing are “caused” by the intervention itself but rather we can only infer there may be a correlation between the intervention and the results seen. This must always be considered when interpreting these results. Another consideration is the fact that the analysis for this study did require an abundance of t-tests and thus there is always a risk that we may in fact being seeing statistical significance due to chance. That is, we are assuming there is something happening in our results but rather it is just chance due to the number of statistical test which were conducted. 13 1.2 Definition of key terms Achievement motivation: this can be viewed as an individual’s need for success or strong performance. Autonomy: As defined by Deci and Ryan (Ryan & Deci, 2000), autonomy is the degree to which an individual feel that they are the origin of their behaviour. That is, the individual has choses this behaviour or perceives the choice. Conscientiousness – Conscientiousness has been used as a way to predict an individual’s ability to be successful and is one of the Big Five personality traits which are typically associated with constructs such as accountability, goal orientated, tenacious and exact (Tanksale, 2015). Extrinsic motivation: an individual is extrinsically motivated when they are motivated by some external force such as a reward, deadline or evaluation. Goal Orientation: an individual’s disposition towards a person’s individual goal. That is an individual with a performance goal orientation may be motivated more towards ensuring they get an A on an exam whereas someone with a learning goal orientation would be more motivated to ensure they are learning as much as they can. 14 Growth mindset: growth mindset is a construct created by Dweck (2006) as a way to describe an individual who believes their mind is malleable. That is a person’s intelligence is not fixed such as would be in an individual with a fixed mindset. Implicit theories of intelligence: the belief an individual has of the malleability of their intelligence. Intrinsic motivation: an individual who is intrinsically motivated means they are motivated to complete a task or pursue some learning for purely interest sake and no external forces. Locus of Control: How one believes their actions can determine a particular outcome. Individuals with a high locus of control believe their actions have a large control over the outcomes and those with a low locus of control believe the outcomes are not the result of their own actions but of that which is uncontrollable. Metacognition: often described as “thinking of one’s thinking.” Metacognition is an individual’s ability to think about the knowledge they have and the strategies involved in attaining further knowledge. Perceived Locus of Causality: the perception an individual has about how much they are at the origin of their behaviour. Someone with an intrinsic perceived locus of causality will believe that they have much autonomy, that is that the individual is the origin of 15 the behaviour, and/or are directing their motivation and behaviour whereas someone with an extrinsic perceived locus of causality perceives a lack of autonomy and/or perceives their behaviour or motivation is being driven by an external force. Self-control: the ability to defer short-term or instant gratification for a more long-term gain in the face of temptation. Self-determination theory: The self-determination theory, created by Deci and Ryan (2000) describes a condition in which an individual becomes more self-determined and thus more intrinsically motivated when their three innate psychological needs including; autonomy, competence and relatedness are satisfied. Self-efficacy: Bandura (1993) coined the term self-efficacy in order to describe an individual’s belief about their ability to be successful, which will often dictate how one behaves and is motivated. SMART Goals: Smart goals is a well utilized tool designed in order to help plan and achieve an individual’s goals by creating proximal goals required to achieve the more distal or overarching goal(s). There is considerable variability in the acronym of SMART goals; however, they all assure that goals are clear and reachable. 16 1.3 Literature Review 1.3.1 The Research on Motivation Both Murray (1938) and Hull (1945) attempted to explain what drives human behaviour. Though both researchers looked at motivation from the perspective of needs, they had very different views on what “needs” looked like. Hull believed the needs which drove behaviour were those that constituted innate physiological needs, later named the “drive theory”, while Murray (1938) argued that it was psychological learned/acquired needs which influenced the motivation of the individual, described as “personology”. However, Hull’s “drive theory” did not explain behaviours such as curiosity, experimentation, etc. and Murray’s theory was extremely broad and thus could really be substituted with many other similar terms (Deci & Ryan, 2000). This “Needs Theory” was the way in which researchers looked at motivation until the 1960s, when there was a shift from the “Needs Theory” to a more “Cognitive Theory”. That is, researchers began looking less at needs in order to explain the motivational behaviours and started to look towards goals and the pursuit of those goals (Dweck & Elliot, 1988; Hull, 1945; Murray, 1938; Nicholls, 1975). The idea that two equally-valued goals with equal expectancies would result in the same effort led to the development of differentiating types of goals (Dweck & Elliot, 1988). 17 1.3.2 Goal Orientation 1.3.2.1 Achievement Motivation In the 1940s David McClelland had begun looking at the reason for which an individual was motivated to perform well at a task, and established that there were two: either they wanted to succeed, or wanted to avoid failing. Around the 1950s John Atkinson expanded on McClelland’s research in order to answer the question of why some people are more successful compared to others. In order to explain this phenomenon Atkinson developed the achievement motivation theory (Atkinson, 1964). This theory holds that individuals are motivated in their drive to attain success or their drive to avoid failure. According to Atkinson, there are three factors which determine an individual’s tendency to succeed: the drive they have for success, the expectation of their success, and the amount of incentive they have to succeed in the task. Atkinson’s achievement motivation spawned numerous ideas around goal orientation in the 1970s and 80s beginning with Eison’s (Eison et al., 1986) development of the Learning Orientation-Grade Orientation Scale. In this scale, students with a more learning orientation are those that see their education as an opportunity to acquire knowledge and cognitively develop whereas those students with a more grade-oriented attitude saw the attainment of the grade as the focus. Over time, Eison came to the realization that this was less of a scale and more two independent measures. (Eison et al., 1986). In order to make sense of why school-age children set high or low academic expectations of themselves, Nicholls was looking at the achievement motivation theory (Nicholls, 1975) and emphasized that it was the way in which children defined success. In this case he developed two categories: task involvement 18 and ego involvement. Students who exhibit task involvement are generally individuals who are constantly comparing their progress and success with their own past performances. Thus, in this situation students are constantly assessing whether or not they have improved. However, with ego involvement the student is focused on comparing their performance with that of other students (Nicholls, 1975). From this research Nicholls established the ability-development goal orientation and ability-demonstration goal orientation (Nicholls, 1984). Looking at achievement motivation more from a development perspective, Dweck and colleagues (Dweck, 1986) wanted to understand why high ability children would make negative comments about the task or their own abilities in the face of challenge. Dweck (1986) established that in this situation, students with a learning goal orientation approach their learning with the focus on learning for learning’s sake while those with performance goal orientations are focused on attaining some grade or positive appearance and/or to avoid the appearance of failure (Dweck, 1986). So according to the models of Dweck and Nicholls, individuals focused on performance goals and/or ability-demonstration goals are concerned with attaining positive judgements for their abilities while learning goal- and/or ability-development goal-focused individuals are looking to increase their ability for the interest of it. Though these theories appear very similar they differ in why individuals have the orientations they have. Carver and Schierer (1998) differentiate goals into approach goals and avoidance goals. In this model, those individuals who fall into the approach goal category generally do things out of pleasure while the avoidance goal focused individuals try to avoid anything which will or might have a negative impact. 19 All of the goal orientation theories described here agree that learning goal orientation results in the increased academic success educators want to see in students while the more performance focused orientations result in poorer academic achievement (Dweck, 1986; Dweck & Elliot, 1988; Eison et al., 1986; Nicholls, 1975). In a meta-analysis by Utman (1997) it was seen that in complex learning situations, the learning goal orientation resulted in higher academic performance compared to performance goal orientation and another meta-analysis conducted by Rawsthorn and Elliot (1993) demonstrated that performance goals tend to undermine intrinsic motivation in students. Thus, shifting students towards having more learning focused goal orientation would be more academically beneficial overall. Something to consider when thinking about a student’s goal orientation is that often goal orientation can manifest as both a state and trait (Payne et al., 2007). In the case of state goal orientation, the type of goal orientation exists for a specific time in which the “state” exists. For example, an athlete who is preparing for a race may maintain a certain exercise regime during this time but once the race is over the more performance focused goal orientation will change. We often think of “traits”, such as someone’s personality, as more fixed. That is, regardless of the state of our situation or what we are doing at the time our personality does not shift much. In the case of goal orientation, the stability of goal orientation as a trait in academics has shown to be moderately stable over the course of a college term, but the longer the time lapse the less stable it becomes (Payne et al., 2007). Consequently, if we want to maintain a student’s learning goal orientation as a trait we must consider how to ensure its stability over a student’s academic career and not just over one term. 20 In re-assessing the assumption that students had to be either one orientation or another Button et al. (1996) proposed that individuals may actually have competing goals. A good example of this would be the race example provided earlier. In this situation someone who is training for a race is trying to beat their competitors, while at the same time they are trying to improve on their own performance. The idea of the multidimensionality of goal orientations came about with VandeWalle (1997) who defined two performance goal orientations: prove performance goal orientation and avoid performance goal orientations. In the prove performance goal orientation individuals are trying to attain positive feedback or gain favorable judgements; in the avoid performance goal orientation individuals are trying to avoid negative feedback of negative judgements. Although there is still much debate about the negative/positive effects of performance goal orientation Brophy deems that the avoidance performance goal orientation is the most dysfunctional (Brophy, 2004). 1.3.2.2 Goal orientation precursors There are a number of variables which are believed to contribute to an individual’s goal orientation development. These include cognitive abilities, growth mindset (the belief that your mind does not have a fixed capability), self-esteem, need for achievement, personality characteristics, and general self-efficacy (as opposed to specific self-efficacy about a specific task) (Payne et al., 2007 and references therein). Some sources suggest that cognitive ability plays a role in how an individual’s goal orientation develops, but not. Although Eison (Eison et al., 1986) found there to be a link between cognitive ability and development of goal orientation, Dweck (1986) and Payne et al. (2007) did not, so it is not clear whether this is an important consideration. Individuals with an implicit theory of intelligence or growth 21 mindset, that is students who believe their intelligence is not fixed but instead malleable, are more likely adopt a learning goal orientation as opposed to a performance-based goal orientation (Dweck, 1986; Payne et al., 2007). Need for achievement was also found to be positively correlated with learning goal orientations, as was self-esteem (Payne et al., 2007), self-efficacy (Dweck, 1986; Payne et al., 2007) and conscientiousness (Elliot & Church, 1997; Hough, 1992; Payne et al., 2007). If we are to try and make changes in our students’ goal orientation it is important to consider any important variables that may contribute to this change. 1.3.2.3 Goal orientation outcomes 1.3.2.3.1 Proximal outcomes Proximal outcomes, or short-term outcomes, are those which generally facilitate the success of attaining more distal outcomes, or long-term outcomes. These include outcomes such as self-efficacy, learning strategies, feedback seeking, state anxiety, implicit theories of intelligence, need for achievement and, openness and conscientiousness (Payne et al., 2007 and references therein). Understanding the potential outcomes of altering a student’s goal orientation is important in order to predict whether a change or intervention will yield predictable results, and to help confirm the success or failure of an intervention. Learning goal orientations have been found to result in a number of proximal consequences which impact the more distal consequences (Payne et al., 2007). Individuals with learning goal orientations are generally found to show higher specific self-efficacy compared to individuals with avoidance performance goal orientations (Payne et al., 2007). Specific self-efficacy is defined as an individual’s belief in their ability to perform well on a specific task (Bandura, 22 1982), contrary to general self-efficacy. Learning strategies have been seen to enhance an individual’s overall learning process (Pintrich & DeGroot, 1990) and shown to be positively associated with learning goal orientations. Learning strategies have been shown to be unrelated to prove performance goal orientation and negatively associated with avoidance performance goal orientations (Payne et al., 2007). Research by VandeWalle and Cummings (VandeWalle, 1997) hypothesized that the type of goal orientation would dictate the level of feedback an individual may seek. They propose that an individual with a strong prove performance or avoidance performance goal orientation may not want to seek feedback as this may result in a blow to the ego. However, those whose goals are focused on learning would actively seek out feedback in an attempt to master the task and or improve their learning and/or learning process. Payne et al. (2007) demonstrated the hypothesis of VandeWalle and Cummings (VandeWalle, 1997) to be accurate. In their study, feedback seeking was positively associated with learning goal orientation, not associated with prove performance goal orientation and was negatively associated with avoidance performance goal orientations. Looking at state anxiety Payne et al. (2007) demonstrated a negative relationship between state anxiety and learning goal orientations and a positive association with prove performance goal orientation and avoidance performance goal orientation. 1.3.2.3.2 Distal outcomes Some of the distal goals impacted by the proximal goals include learning, academic performance, task and job performance. Often learning and academic performance are viewed as the same thing; however, this is not the case. As defined by Payne et al. (2007), learning is the acquisition of knowledge which is assessed by a task such as a test or exam. 23 Academic performance is often measured by grade or GPA, and these metrics are often based on assignments, tests, or exams; but they may also reflect a student’s motivation or even a student’s ability to prioritize and manage their time. It is not a surprise that Payne et al. (2007) found that learning goal orientations are associated with higher learning and academic performance. It is apparent that learning goal orientation is generally associated with traits which tend to give rise to strong learning as measured by persistence on a task in the face of challenge, high self-efficacy and high goal setting (Sujan et al., 1994). As learning goal orientations are positively correlated with self-regulatory processes, which are beneficial to even non-academic settings (Locke et al., 1981), it is also not surprising to see that learning goal orientations tend to be positively associated with both task and job performance (Payne et al., 2007). 1.3.2.3.3 Goal Commitment or “Buy-In” Goals need to include some form of feedback so that individuals can see their progress and reevaluate their goals (Morisano & Locke, 2013). Individuals must show strong goal commitment or “buy-in” towards their goals. Ways in which one can build in “buy-in” is to ensure the goals are seen to have value and interest, and are viewed by the individual as attainable. Creating attainable goals will increase an individual’s self-efficacy which in turn will help increase the “buy-in” of the goals, and increase their willingness to create more challenging goals (Morisano & Locke, 2013). Properly created goals will also facilitate an individual’s choice in proper strategy selection, how they respond to failure, and increase their overall enthusiasm for the goal (Morisano et al., 2010). If a student experiences goal 24 attainment this will perpetuate an increase in perceived self-efficacy, which will impact an individual’s level of persistence towards goals in general (Morisano & Locke, 2013). Within the social-cognitive learning model it is easy to see how goal setting has a significant impact on academic achievement (Morisano et al., 2010). The more the student experiences goal attainment, the more their self-efficacy increases, which propagates further goal commitment and motivation towards other goals. Consequently, it is not hard to see why goal setting theory has been shown to be so successful in predicting student academic success (Locke et al., 1981; Locke & Latham, 2002). As both self-control and goal setting have been shown to be positively correlated with academics (Locke et al., 1981; Locke & Latham, 2002; Morisano et al., 2010), and the relationship between self-control and goal setting appears to be reciprocal, this study sought to determine whether students with high self-control do in fact use goals and is there a correlation between self-control and goal commitment. 1.3.3 Self-Determination Theory The theories of Dweck, Elliot, Nicholls, Carver and Schierer fall under the more “Cognitive Theory” approach leaving behind the “Needs Theory” of Hull and Murray. The Self-Determination Theory (SDT) (Deci & Ryan, 1985) still differentiates the concept of goals, however the goal orientation in this theory is determined by the required psychological needs being met. Deci and Ryan’s SDT (Deci & Ryan, 1985) uses the concept of innate needs, but not the physiological needs seen in Hull’s (Hull, 1945) “drive theory”, and the psychological 25 needs, but not the acquired/learned concept of Murray’s (Murray, 1938) “personology theory” (Deci & Ryan, 2000). The goal is to attain the “innate” needs seen in Hull’s drive theory (Hull, 1945) and the “psychological” needs of Murray’s “personology theory” (Murray, 1938). The self-determination theory is on attaining the psychological innate needs of autonomy, competence and relatedness (Deci & Ryan, 2000). 1.3.3.1 Needs According to the self-determination theory, all three of these needs; autonomy, competence and relatedness must be satisfied, and although the degree of need for each of these constructs may vary with the individual, Deci and Ryan (2000) argue this is not as important as accepting that these are the universal needs that all humans have. The self-determination theory agrees with the drive theory in that they consider “needs” to be innate however with the self-determination theory these needs are psychological, similar to Murray’s theory, rather than physiological (Deci & Ryan, 2000). Another significant difference between these two theories is that the physiological needs theory is based on the human organism lacking some physiological requirement, and accordingly the overall equilibrium is being disturbed resulting in actions/behaviours involved in mitigating the feeling of deficiency. This is very different from the self-determination theory where the human organism is working in a more developmentally focused approach. Another significant difference occurs in how people respond to their physiological versus psychological needs. When a physiological need is thwarted people will focus their energies towards removing the deficit as this need will become progressively more consuming. For example, when people continue to get hungry more and more of their attention or energy will be spent focusing on reducing this need and 26 often resulting in other needs becoming less of a priority (Deci & Ryan, 2000). The inability to meet a psychological need, however, often results in people making other accommodations that help to lessen the impact that psychological deficit may be having rather than pursuing action to remove the deficiency entirely. The personality is yet another needs theory which is hard to distinguish from other needs. The difference between these theories is that the personality theory holds that these needs are learned compared to the self-determination theory viewing them as psychologically innate, the drive theory indicating they are physiologically innate (Hull, 1945) and the “personology theory” holds that needs are simply psychological in nature (Murray, 1938). White (1959) argues that the drive motivation theory must be accompanied by an individual’s psychological need for competence. This theory is very much in line with Deci and Ryan’s self-determination theory as this theory also holds that individuals have an innate need for competence (Deci & Ryan, 2000). We also see that the field of attachment, which argues the need or strong desire for relatedness (Ainsworth et al., 1978), also parallels Deci and Ryan’s self-determination’s theory in its quest for innate relatedness. Interestingly the innate need, and arguably Deci and Ryan’s (2000) most influential need, autonomy, is much less predominant in empirical psychology. Often the construct of autonomy is equated with constructs such as individualism, an internal locus of control and independence which is incorrect (Deci & Ryan, 2000). 27 1.3.3.2 Intrinsic motivation The concept of these psychological needs came about in Deci and Ryan’s (2000) attempt to understand the research in the area of intrinsic motivation. The physiological needs theories could not explain what was being seen in the research and thus the self-determination theory was born. The exploration of the concept of intrinsic motivation began around the 1970s at a time when Skinner’s “Operant Theory” was still a strong force (Skinner, 1953). This theory holds that an individual will make an association between an action and a consequence, be it a reward or punishment, thus reinforcing the behaviour. However, this theory could not explain why humans would often be involved in behaviour in which there was no reinforcement, but rather individuals were engaging in these behaviours for interest’s sake. Consequently, Deci (1975) proposed the idea of that these intrinsically motivated behaviours were done out of a need to feel competent and self-determined. 1.3.3.3 Autonomy It has been demonstrated that extrinsic rewards can undermine intrinsic motivation (Deci & Cascio, 1972) as people start to feel controlled by said reward resulting in a shift to extrinsic motivation. This has been observed in a meta-study which included 128 studies across 3 decades (Ryan & Deci, 2000 and references therein). Along with rewards, evaluations (Harackiewicz et al., 1984) and deadlines (Amabile et al., 1976) have also been observed to result in a shift towards students having a more external perceived locus of causality, meaning they believe they are not in control of their actions but rather their actions are 28 determined by external forces. Along with this, Deci and Ryan (Deci & Ryan, 2000 and references therein), show that with reward and evaluation there is a decline in an individual’s level of creativity, the ability to solve complex issues and deeper cognitive processing. There is a clear relationship between an individual’s feeling of autonomy and the level of intrinsic motivation they feel for a particular task or process. As we have more reward, evaluations or deadlines, our perceived locus of causality becomes more external and thus our sense of autonomy. Said another way, the more we feel external forces the less control we feel we have and consequently the less interest there is likely going to be in the activity or process we are doing. It is not surprising then that when individuals are provided with choice, and thus the feeling or perception of autonomy, their level of intrinsic motivation increases (Zuckerman et al., 1978). 1.3.3.4 Competence We know that external sources, such as grades and deadlines, decrease an individual’s sense of autonomy, and thus intrinsic motivation, and that by building in choice and/or the perception of choice and autonomy and/or the perception of autonomy into the curriculum, we can help individuals to increase their level of intrinsic motivation (Deci, 1975). Another way we can facilitate increased intrinsic motivation is to provide positive feedback (Deci, 1975), which Deci and Ryan linked to an individual need for competence initially described by White (1959). Contrary to positive feedback, negative feedback will reduce an individual’s feeling of intrinsic motivation due to a decrease in feelings of competence (Deci & Ryan, 2000). This relationship has been confirmed in a study by Vallerand and Reid (1984). This relationship only holds true when the individual actually feels competent for the 29 performance or task (Fisher, 1978) and as long as it is not at the expense of their sense of autonomy (Ryan, 1982). The most optimal situation for intrinsic motivation is when both autonomy and competence needs are satisfied fully (Deci & Ryan, 2000). Thus, we can see that actions such as rewards, deadlines and evaluations tend to create an external perceived locus of causality while activities such as choice create a more intrinsic perceived locus of causality (Deci & Ryan, 2000) and thus intrinsic motivation. Similarly, we see that negative feedback tends to undermine intrinsic motivation by raising perceived incompetence while positive feedback helps to augment intrinsic motivation. 1.3.3.5 Relatedness We have seen that autonomy and competence are very important in creating and increasing an individual’s level of intrinsic locus of causality and thus intrinsic motivation. Although both of these constructs are found to be most pivotal in the development of intrinsic motivation, relatedness is also important (Deci & Ryan, 2000). This relationship can be observed when considering Bowlby’s (1988) attachment theory and in a study looking at children who showed low levels of intrinsic motivation when engaging in an activity alone versus with an adult (Anderson et al., 1976). This relationship can also be seen in a study showing that students displayed higher levels of intrinsic motivation when they were taught by instructors who demonstrated a more nurturing disposition (Ryan et al., 1994). Though these relationships exist, the self-determination theory suggests relatedness is a more distal need as individuals are often found to engage in intrinsically motivated behaviours in isolation (Ryan & Deci, 2000). Thus, it is important to recognize this construct as having a 30 degree of influence on one’s intrinsic motivation it is however, one which holds less influence when compared to competence and autonomy. In summary, in order to achieve high levels of self-determination and/or intrinsic motivation, all psychological needs—autonomy, competence and relatedness—must be met, with relatedness having the least direct impact. Ryan and Deci (2000) indicate that autonomy holds the strongest influence over a student’s feeling of self-determination with competence coming in at a close second (Ryan & Deci, 2000). Though relatedness has been seen to impact students’ levels of self-determination, its influence is somewhat low relative to both autonomy and competence. An important point to consider is that the attainment of any of these individual’s needs, but more specifically autonomy and competence, must not occur at the expense of another, thus we shift away from self-determination and thus intrinsically motivated behaviours, and towards less self-determined and thus extrinsically motivated behaviours (Deci & Ryan, 2000). 1.3.3.6 Extrinsic motivation The more these psychological needs are met, the more intrinsic motivation an individual will feel, and the less these psychological needs are met, the more extrinsically motivated an individual will feel. In some extreme scenarios individuals may show no motivation at all; this is called amotivation. Thus, SDT can be viewed as a spectrum with amotivation, or no motivation, being on one end, followed by extrinsic motivation where individuals are motivated solely by some external source, and then intrinsic motivation, doing for the sake of interest, on the other end. However, within extrinsic motivation Deci (Deci et al., 1999) 31 there lay three types of regulations: external, introjected and identified. In figure 1.1, the spectrum of Ryan and Deci’s motivation can be seen with amotivation, no motivation, at the far left followed by the most extrinsic form of motivation, external regulation. Introjected regulation follows with the more self-determined motivations of identified and integrated to the right. At the right end, an individual’s behaviour is completely self-determined and thus the individual has become fully intrinsically motivated. Figure 1.1: The spectrum of motivation adapted from SDT (Deci & Ryan, 2000) and the facilitation of intrinsic motivation, social development and welfare (Ten Cate et al., 2011). 1.3.3.7 External regulation External regulation is the more classic case of extrinsic motivation where individuals are motivated purely by some tangible reward or to avoid some negative consequence. This parallels the “operant theory” described by Skinner (1953). In SDT this type of regulation can be seen as the most controlling, with behaviours being contingent on some external consequence administered by others. These behaviours will have a low degree of maintenance unless continuously propagated by an external motivation and thus will cease 32 once the reward and/or external motivator is removed (Deci & Ryan, 1985). Thus, this form of regulation requires no self-determination. 1.3.3.8 Introjection Introjection can be thought of as when you perform a task and/or action which you do not truly want to do but you are motivated to do it as you are concerned how you might be perceived by others if you do not perform the task and/or action. With external regulation the regulation is the consequence which comes solely from an external source while introjected regulation is regulation in which the individual is motivated externally but out of an internal concern for how they will be judged if they do not perform the task. Examples of these are constructs such as guilt and pride (Deci & Ryan, 2000). Thus, introjection involves individuals whose behaviour is somewhat externally controlled but the consequence is administered by the individual rather than others. Because the consequence comes from within there is some internalization here however it has not been integrated in the self and thus is not self-determined (Deci & Ryan, 2000). That being said, because there is some level of internalization here, introjected regulation has a higher chance of being maintained compared to external regulation but is still more unstable compared to some of the more self-determined constructs to be discussed next. 1.3.3.9 Identification Identification becomes more self-determined as the individual begins to recognize and accept the value of the behaviour. It is the individual’s identification of the value of the behaviour that makes this behaviour more internalized, and as such they are accepting the behaviour as 33 their own (Deci & Ryan, 2000). Compared to introjection, where the individual is responsible for creating the consequence, with identification they have more fully internalized the behaviour and consequently, the behaviour resulting from identified regulation is more autonomous than the previously mentioned but is still somewhat externally motivated as the behaviour is not being done on the individuals own volition (Deci & Ryan, 2000). 1.3.4.0 Integration At the fullest level of intrinsic motivation is integration. Here an individual has identified the importance of the behaviour and has fully integrated the value of the behaviour in the self. The once-externally regulated behaviour has now fully integrated into the self and is thus self-determined. Individuals can shift within between these and find themselves along the continuum at different regions depending on the activity being asked of them. In general, as individuals’ internalization increases (self-determination increases) and they become more intrinsically motivated, so too does their perseverance and level of engagement (Deci, & Ryan, 1985; Deci & Ryan, 2000). 1.3.4 Academic impacts of motivation It has been well documented that intelligence only partially accounts for students’ academic success; one study suggesting that as little as 25% of the variance in academic outcomes can be attributed to intelligence (Kuncel & Hezlett, 2004). Motivation, however, has been found to be a major predictor of academic success (Gose et al., 1980; Schicke & Fagan, 1982; Spinath et al., 2006). Consensus among the literature is that students are most successful 34 academically when they are motivated—specifically, intrinsically motivated (Deci & Ryan, 1985; Ryan & Deci, 2000). Research in the area of motivation has been ongoing for decades. The term motivation can be described as a state which excites, propels and engages an individual in a specific task (Ormrod, 2008). A student’s competency or belief in their own abilities to complete a task is often not enough to ensure the task is initiated, nor does it guarantee completion of the task, but rather it is the motivation that determines the effort put in when dealing with a challenging task (Ormrod, 2008). Ryan and Deci (2000) describe those with motivation, unlike those without, to “be moved to do something” with the level and type of motivation varying among individuals. More intrinsically-motivated individuals tend to perform well overall, have less anxiety and stress, have higher self-esteem, strive for a true understanding of knowledge and are able to apply said knowledge to reality—and do all this without some tangible reward (Lei, 2011). However, to be completely intrinsic may not always serve those individuals well, as highly intrinsically-motivated individuals can often be too focused on the activity/knowledge of interest and thus become distracted from the other items that need to be completed but may not be as enjoyable (Lei, 2011). Those who are solely extrinsically motivated do tend to get things completed but this is only done when there is a reward and/or some recognition. These individuals tend to only put in a minimal effort and often require frequent and indefinite rewards or the effort will cease. These individuals tend to be motivated for the wrong reasons and often have very high stress and anxiety and suffer from low self-esteem (Lei, 2011). 35 1.3.5 Other academic influences When assessing motivation, it is important to examine some of the other motivational influential constructs. Both metacognition (Pintrich & de Groot, 1990) and self-control (Paschke et al., 2016) have been shown to have some level of influence, be it direct or indirect, on motivational levels. 1.3.5.1 Metacognition Scholars struggle to have a uniform definition of metacognition in the literature but the description by Gilbert (2005) refers to the construct as “thinking of one’s thinking”. It is often described by many as being a concept which lacks coherence and has a multitude of meanings to different people (Anderson et al., 2009). It is also considered a highly elusive cognitive phenomenon. As described in more detail below, the ability of “thinking of one’s thinking” is something that is critical to student’s ability to self-regulate. In order to see the influence of metacognition and the and an individual’s level of intrinsic motivation we must first dive into the concept of the self-regulated learner (Boekaerts, 1996; Zimmerman, 2000). Within the self-regulated learner there are multiple phases, including those occurring before, during and after task execution. At each stage the individual is involved in thought, motivation and activity (Efklides, 2011). Personal traits, which influence the individual’s involvement, metacognition and self-reflection are all involved. The Efklides (2011) model proposes a modification of the SRL to focus metacognition and affective aspects of the individual’s interaction with the task. This is termed the metacognitive and affective model of self-regulated learner, or MASRL model. Within this 36 framework, Efklides (2011) looks at the individual’s experiences in response to the learning task as the cognitive processing is taking place. The MASRL model proposes that within the SRL process one must consider what the individual brings into the learning task via personal traits, which is not a function of the task, and the person to task traits, which is what the person does and feels during the task. There are three facets of metacognition: 1) the metacognitive knowledge, which constitutes the individual declared knowledge; 2) the metacognitive skill, the strategies involved; and 3) the metacognitive experience, which is the feelings, judgements and or perceived effort required of the task, during the task (Efklides, 2011). The metacognitive experience can be either negative or positive (Winkielman & Cacioppo, 2001). There are two feedback loops involved. The first is the outcome of the cognitive process, that is how close the individual is to completing their task (Efklides, 2011), while the second relates to the rate of progress towards completing the task. The larger the discrepancy from the goal and/or the slower the progression, or interruption of the progression (Carver & Scheier, 1998), the more negative the feedback. Thus, metacognitive experience has a direct influence on feelings such as confidence (Efklides, 2011). According to Bandura (1982) an individual’s confidence has a direct influence on their self-efficacy which results in their attributing negative or positive inference to their abilities. Thus, the metacognitive experience can significantly impact the individual overall feelings and perceived confidence. As described earlier, competence is a required psychological need in increasing self-determination and thus shifting towards intrinsic motivation (Deci & Ryan, 2000). Therefore metacognition, specifically the metacognitive experience, has an indirect 37 influence on intrinsic motivation through a direct effect on competence. Thus, understanding students’ metacognition can inform us of their level of intrinsic motivation. Due to its complex nature, our inability to “observe” metacognition, and the development of assessments within a laboratory-like environment, this construct is challenging to assess (Lai, 2011). Behavioural observations, “think aloud” (in which the student verbally describes their cognitive processes), interviews and self-reporting questionnaires are some of the assessments used currently in metacognitive research. Behavioural observational methods have the advantage of being able to record the learner’s actual behaviours and thus one can view information that would otherwise be lost in verbal or questionnaire methods (Whitebread et al., 2009). However, Schraw and Moshman (1995) suggested verbal reports were more advantageous as they allow researchers to “see” aspects of thinking that would otherwise be hidden; however, this requires that the individuals have strong verbal ability. Interviews allow for the one-on-one time, but they require a vast amount of time per individual to administer the questions and record answers, and thus it is challenging to assess larger numbers (Anderson et al., 2009). Self-reporting questionnaires tend to show a high degree of convergent and discriminate validity (Sperling et al., 2002); however; they require a strong level of verbal ability which arguably could become a consideration when looking at ESL (English Second Language) students, as is done in this study. In an ideal world where time and funds are not issues the use of two or three types of assessment tools would provide an exceptional overview of an individual’s metacognitive abilities, however this is just not the case. As this study aimed at assessing the metacognitive abilities of more than 600 students in a relatively short period of time the questionnaire was the soundest option. 38 1.3.5.2 Self-Control Since the time of Dewey (1938), educators have held the belief that self-control is a significant factor in a student’s academic success. So, what exactly is self-control? According to Duckworth and Gross (2014) self-control is the ability of an individual to forgo more immediate gratification in the pursuit of enduring goals by regulating one’s feelings, focus and behaviour. As an example, a student has a self-control conflict when trying to decide between an activity such as studying for a math test, which is needed in order to attain the longer-term goal of performing well in math, and scrolling through a Facebook page which is a fun activity that has no long-term value. As it would be impossible to do the two activities simultaneously the student has to choose one over the other. A student with high self-control would forgo the immediate fun of viewing Facebook, instead allowing the needed time to study for the math test, while the student with low self-control would select the more immediate gratification at the expense of their math test. Similar to the term metacognition, there is a substantial amount of variability when attempting to describe self-control (Duckworth & Kern, 2011). For example, Freud and Plato describe self-control as something related more towards impulse control (Freud, 1965; Plato, n.d.); others describe it as strategies used to reduce conflicts which arise in attaining goals (Duckworth et al., 2016). Some terms used to describe the presence of self-control include delay of gratification, control of cognition and inhibition control (Duckworth et al., 2019). Zimmerman’s (1990) term “self-regulation” also contributes to the confusion around self-control, particularly outside the realm of educational psychology, as it is used synonymously with self-control (Carver & Scheier, 1998). Self-control is also a component of the Big Five: 39 Conscientiousness in the context of academics however it can also be part of the Big Five: Agreeableness when looking at interpersonal relationships and again we see the term self-control is often used interchangeably with executive functioning in the cognitive neuroscience literature (Duckworth et al., 2019). There are two features of self-control that help to distinguish it from the constructs mentioned above. To truly engage in self-control, a student must initiate the behaviour themselves. For example, in the situation described above the student would have to actively decide to study rather than go on Facebook for this action to be considered self-control. Comparatively, if the educator removed the student’s cell phone which resulted in the student studying, this would not be considered self-control, as the student is just submitting to the authority figure of their instructor (Duckworth et al., 2019). Another way we can distinguish self-control from the previously mentioned constructs is that the individual has to be able to set aside their immediate desires, for example checking their Facebook page, for a more longer-term goal, such as doing well academically. One reason students need self-control is that they see the value in their education for future purposes, but may not enjoy the work required in order to attain their academic goals (Duckworth et al., 2019). In a study of over 1,000 middle and high school students, Duckworth et al. (2019) found that students saw school work as less enjoyable compared to many other daily activities but recognized their school work as important for their future. Therefore, it is a student’s self-control that helps them to continue to do a task they find unenjoyable in lieu of something they do enjoy. Clearly, if students had no self-control they would not choose to complete their academic work over doing something fun. Consequently, 40 it seems intuitive that self-control is not only needed for students to pursue their academic goals, but the degree of self-control exhibited can predict a student’s academic performance (Duckworth et al., 2019 and references therein). From even the early years of childhood development, kindergarten teachers have noted that self-control is the most important concept determining a student’s academic success (Blair & Raver, 2015). Self-control predicts attrition rates in high school and university, and students with low self-control are associated with high degrees of unemployment, drug abuse and illegal activity (Duckworth et al., 2019 and references therein). It has also been shown to predict course grades in primary to post-secondary students (Duckworth et al., 2019 and references therein). In a longitudinal study of 140 eighth-grade students by Duckworth and Seligman (2018), self-control was shown to predict final grades and, in another study, they showed that self-control predicted differences in final grades, attendance and even time spent on homework more than twice as much as IQ in 164 eighth-grade students. Duckworth et al. (2010) found that over a 4-year study changes in self-control over time significantly reflected changes seen in grades reported, while changes in how students were reporting self-esteem levels and grades had no influence on levels of self-control. Similarly, the Big Five: Conscientiousness, which is strongly affiliated with the self-control construct under the education regime (Park et al., 2017), shows a strong association with grades in a meta-analysis of over 70,000 students from elementary to post-secondary (Poropat, 2009). Last but not least we also see a predictive ability of self-control with standardized achievement scores (Duckworth et al., 2019 and references therein). Clearly the research supports a strong relationship between student level of self-control and academic success. 41 There are essentially four stages within the cycle of self-control: situation, attention, appraisal and response (Duckworth et al., 2019). The “situation” would be when a student enters their room and sees their cell phone, with notifications which they desperately want to check, and their biology text book. In the case of the student with high self-control the student will direct their “attention” towards the textbook and disregard the cell phone with notifications. Then they will “appraise” the situation. That is, the student will assess the outcomes of their choices. In this example a student would then recognize that by studying their biology textbook they will do better academically which will help them attain their long-term goal of becoming a surgeon. The last part of the cycle is the “response” component where the student will then begin the work (Duckworth et al., 2019). This cycle follows the academic goal congruent cycle as it is consistent with the requirements of the students’ academic goals. Of course, in a situation where the student has lower self-control we would see an academic goal incongruent cycle, where the student places focus on the cell phone and decides to read the notifications rather than the textbook (Duckworth et al., 2019). Throughout these cycles we see that students are regularly assessing the value of these tasks. The value the student places on the tasks will determine the level of motivation the students has for the task which will dictate which course of action the student is likely to take. In either case students are constantly experiencing conflict between their long-term academic values and their nonacademic goals they enjoy doing more (Duckworth et al., 2019). In looking at academic goal cycles in Duckworth et al. (2019), we can see how important the value placed on the goal is. Those goals deemed to have a higher value will be more likely to be attained in the face of distraction and temptations. 42 Self-control can be described as the ability to pursue and attain long term goals in the face of proximal distractions (Tangney et al., 2004). This includes the ability of one to alter both behaviour and thoughts, thus overriding impulse (Tangney et al., 2004). It seems intuitive that those with high self-control would likely display better academic performance and generally make healthier lifestyle decisions and this has been observed (Hofer et al., 2011; Kreutz et al., 2009; Tangney et al., 2004). There is also evidence (Paschke et al., 2016) that self-control has a high degree of overlap with emotional regulation—the ability to up-regulate or down-regulate emotions in order to adapt to situations (Koole, 2009). Both concepts show a high degree of overlap and it is assumed that emotional regulation is itself a form of self-control (Gross & Thompson, 2007). Both constructs are also highly correlated with the same outcomes including academic success (Tangney et al., 2004), improved health (Kreutz et al., 2009) and overall well-being (Hofer et al., 2011). Of course, as both constructs require some level of cognitive strategies, it is possible that intelligence and self-efficacy may also be involved in this relationship: one study showed that the ability to down-regulate emotions was significantly correlated with high levels of self-control. (Paschke et al., 2016). 1.3.5.3 Self-Efficacy Bandura was one of the pioneers in the study of self-efficacy, which is the perception one has about their abilities to succeed. There are three main constructs which are involved in developing one’s self-efficacy and these include a person’s individual qualities, their past experiences, and the level of support they have overall (Bandura, 1982). Qualities that may impact an individual’s level of self-efficacy can include whether an individual approach a 43 situation with or without confidence (Schunk, 1995). An individual’s “experiences” are not limited to their own direct, personal experiences, but can also be experiences they have attained vicariously through observation, and this is a second factor affecting self-efficacy. A third is the feedback that is received via their support or social network, which could include people such as teachers or peers (Schunk, 1995). Across a multitude of studies, it can be shown that a student’s self-efficacy can have a significant impact on a student’s academic performance (Chemers et al., 2001; Elias & Loomis, 2002; Galyon et al., 2012). Komarraju and Nadler (2013) discovered that this indirect relationship exists due to the monitoring and self-regulation students with high self-efficacy are able to maintain which increases their academic success. As study created an intervention that was geared towards building autonomy and competence it is very likely that through competence, more specifically, the intervention may have indirectly impacted a student’s level of self-efficacy and consequently this may explain any trends seen in this research. 1.3.5.4 Mindset Dweck (2006) argues that students can be placed in one of two categories, those that have a growth mindset and those that have a fixed mindset. Those with a growth mindset are individuals who believe their intelligence is malleable, that is it can continuously develop with no real set ceiling, while those with a fixed mindset believe their intelligence is fixed and thus no amount of development will increase their intelligence. There are significant differences within these individuals’ overall beliefs systems because of their mindset. For 44 example, those with the growth mindset will continue to work through challenge and obstacles while those with a fixed mindset may not even attempt something let alone persevere through it (Dweck, 2006). In the field of education Dweck (2010) has demonstrated that individuals with a growth mindset compared to a fixed mindset generally perform better academically. This seems intuitive as those with a growth mindset are much more likely to believe they can do something with a bit of effort and thus will be that much more likely to put the effort in. Dweck (2010) also indicates that instructors can actually foster a growth mindset in their students through the right type of praise and encouragement. By providing positive feedback about the student’s effort, strategy or even the choices they are making we can create a growth mindset in that individual. Another way to foster a growth mindset is to teach about how to adopt a growth mindset. The creation of goals is one-way educators can help students do this (Dweck, 2010). It is possible that any changes we see in academic performance may be an indirect result of reshaping the mindset of our students rather than specifically increasing their intrinsic motivation or self-determination. Similarly, to self-efficacy, it is possible that changes may result from the building of our goals and the positive feedback students may indirectly receive through the increased competence the intervention aims to create. 45 1.4 Background In both pre- and post-secondary education, motivation has been observed to be correlated with academic performance (Shanti, 2016; Steinmayr & Spinath, 2009; Suárez-álvarez et al., 2014). At the end of their first year, 327 university students’ academic performance in Indonesia was positively correlated to a motivation to learn (Shanti, 2016). Similarly, in a study by Suárez-álvarez et al. (2014), a sample of 7,729 students in a high school institution in Spain had a high and statistically significant correlation between motivation and academic achievement. More specifically, consensus among the literature is that students are most successful academically when they are intrinsically motivated (Deci & Ryan, 1985; Ryan & Deci, 2000). This may be in part due the deeper cognitive learning, such as the ability to master a task, that is associated more with intrinsic motivation (Brahm & Gebhardt, 2011) compared to the more superficial type of learning, such as mastery of a test, that is associated with extrinsic motivation (Ã et al., 2007). Research indicates that overall, students are less intrinsically motivated than extrinsically (Brahm & Gebhardt, 2011; Busse & Walter, 2013; Jacobs & Newstead, 2000). For example, Brahm and Gebhardt (2011) showed that intrinsic motivation was low during a student’s undergraduate years but increased during graduate studies. This is likely because graduate students are more interested in their coursework. As students progress through their post-secondary years they begin to enter into courses more specific to their focus, thus facilitating higher levels of interest, which would lend itself to more intrinsically motivated students. Jacobs and Newstead (2000) found that interest, which is linked to intrinsic motivation, was low in the first two years of an undergraduate degree with interest increasing in the last couple of years, while Busse and Walter (2013) also found that there was a substantial loss of intrinsic motivation for students over the course of their 46 first year of study. Though there are some differences in when students become more intrinsically motivated throughout their educational career, it is clear that within, and following, students’ first year of education we see very low levels of intrinsic motivation. We are aware that autonomy is key to building intrinsic motivation; however, it is directly undermined by such things as evaluations (Harackiewicz et al., 1984) and deadlines (Amabile et al., 1976) as it shifts towards a more external focus and thus away from self-determination (Deci & Ryan, 2000). As deadlines are a regularly-enforced part of the post-secondary institutionary structure, and grades are a direct form of evaluation, it is not surprising that this grade-centric modality has shifted students in this way. Today’s students tend to be even more extrinsically motivated, and we see that purely extrinsically-motivated students tend to do poorly academically compared to more intrinsically-motivated students. Deci and Ryan (2000) point out that students must have their three psychological needs met in order to develop that intrinsic motivation. To shift students in this way they must integrate their learning into their own values: they must truly “buy in”. At the first and often second year level, most students are taking courses which are considered more general and potentially not of direct interest, nor do students feel they have any real value. In order to shift students’ motivation back from grades, we need to help students tie their seemingly-uninteresting and/or irrelevant tasks to something which has value, interest and relevancy to the student. The establishment of goals, in this case, educational goals, can help facilitate the shift towards a more self-determined student. 47 1.4.1 Goal Setting Theory In 1997, Bandura first outlined the overall system individuals have when creating goals. There are essentially two levels to this hierarchical system; the proximal and distal goals. Here the proximal goals are those goals which an individual strives to attain over a short time period and thus are responsible for motivating our more recent behaviours while distal goals can be viewed as the longer, overarching goals, in which proximal goals are built towards. Though there may be instances where individuals build proximal goals without distal goals in mind, in general it is the creation of proximal which help the attainment of the more distal or overarching goals, with these goals requiring more time or work to achieve (Bandura, 1997). 1.4.2 Goal setting as a concept Locke and Latham (1990) first began to examine the idea of goal setting in the mid 1960s. Goal setting is the creation of a plan focused on attaining an individual’s goal. The action plan is created in a way to help motivate the individual to ensure higher success in goal attainment. But creating a goal on its own does not ensure success of attaining said goals but rather goals must be specific and challenging in order to ensure improvement (Locke & Latham, 2002). The level of commitment an individual has towards their goal is dependent on the importance of their goal, that is how meaningful the goal is, their belief in their own ability to attain the goal and accountability (Locke & Latham, 2002). If the goal is not meaningful enough, it the level of commitment towards the goal will decrease in the face of obstacle and challenge. Similarly, if the individual does not perceive themselves as capable of achieving the goal, rightly or wrongly so, they will not persist in attaining the goal. It is 48 important however that the goals are achievable but yet challenging enough. Latham and Piccolo (2012) showed that having challenging goals is far better for guaranteeing success than having no goals or easy goals. The positive impact that goal setting has on a student has been shown time and time again (Rader, 2005). The process of goal setting allows students to take time to reflect on what is important to them and why, in order to help them be motivated to achieve their goal. The difficulty in goal setting arise when students are not properly equipped to create goals successfully. For example, students may set goals which are too easily attainable rather than challenging. This will not facilitate the needed motivation that comes with creating goals with are challenging (Locke & Latham, 1990). When one sets challenging goals there is more motivation to succeed in attaining these goals. It is not overly rewarding to attain a goal that was easy to attain. A study by Locke et al. showed that students who succeeded in accomplishing more previously set challenging goals displayed higher levels of self-efficacy and motivation (Locke et al., 1984). Goal setting should be done by the students, and it requires a balance of being both challenging and attainable. As stated previously, the concern when having students create their own goals is that they may create goals which are not challenging enough to build competence; however, if we were to set goals for our students, no autonomy would be created and consequently there would be less goal commitment. Creating goals clearly has a positive effect on students as it allows students to reflect on their motives and goals, and really examine themselves (Rader, 2005). However, having students create their own goals 49 can be problematic as they may take the opportunity to create goals which are not challenging enough. Goals need to be created so they are not only challenging but attainable. Locke and others (Locke et al., 1984) demonstrated that when students were initially given challenging goals they took on even more challenging goals when prompted, and showed higher levels of both commitment and self-efficacy. Consequently, this study created a goal setting intervention which helps guide students in creating their own goals in order to facilitate both the autonomy and competence that is needed. Because this study was focused on creating self-determination in our students in order to increase our students’ level of intrinsic motivation this was deemed the better route than administering our own goals for the students. It is possible that, if students do take the “easier way” and create easier goals we may not see the other benefit of building self-efficacy in our students. 1.4.3 Increasing self-determination through goal setting Goal setting encourages autonomy and competence, both of which lead to increased levels of intrinsic motivation. Many studies have reported that goal setting positively affects academic performance and augments achievement (Boekaerts, 2002; Emmons & Diener, 1986; Schunk, 2003). Over 400 experimental and correlative studies have examined the validity of goal setting in academics (Latham & Locke, 2007; Locke & Latham, 1990). It has been posited that every individual has some form of hierarchical goal system which includes distal and proximal goals (Bandura, 1977). It has been shown that using subordinate (proximal) goals significantly increases the motivation and success of attaining the distal goals (Bandura & Schunk, 1981). This seems intuitive, as a large overarching goal can feel intimidating and out of grasp for many, and as such may cause individuals to feel discouraged in their ability 50 to not only attain it but also to pursue it. Having subordinate goals allows for more frequent rewards and positive feedback which can further instill motivation and competence. To ensure the highest academic performance among students, these goals must not be instructor-created goals, but rather student-created as indicated by the research conducted by Mento et al. (1987). In creating goals, students may find intrinsic value where they did not have it before, providing them with a sense of ownership and further contributing to their sense of autonomy (Cross & Steadman, 1996) and thus increasing their intrinsic motivation. Goal setting also contributes to a student’s sense of competence. It has been reported that goal setting acted as a catalyst to perceived competence found in goal attainment (Brunstein, 1993). This perceived competence would consequently increase overall well-being and autonomy which will arguably increase levels of intrinsic motivation. Dweck (1986) argues that setting these goals is not enough to motivate and guarantee success, but setting proximal and distal goals is better than having no goals at all. Two types of goals fall under the context of achievement motivation, that is an individual’s need to accomplish a task: mastery goals and performance goals (Dweck & Elliot, 1988). Nicholls (1984) proposes a similar scheme in which ability development goals parallels Dweck’s mastery goals, and ability demonstration goals parallels performance goals. Those with mastery goals or ability development goals tend to want to increase their competence, or to really master something. These individuals generally show adaptive patterns of achievement, that is the ability to alter their behaviour/actions when needed, and focus on the pursuit of knowledge (Dweck, 1986). Individuals with performance goal orientation or ability 51 demonstration goals tend to focus on gaining some positive feedback for their competence and they will avoid tasks which may result in negative feedback of their competence. Often these individuals will avoid or withdraw from a challenge that they perceive may result in potential negative feedback, and will select “challenges” which are easy and will guarantee their success. This type of behaviour is termed maladaptive patterns of achievement (Dweck, 1986). Children exhibiting performance goal orientation need a level of certainty before pursuing a challenge, regardless of their cognitive ability (Dweck & Elliot, 1988). Any failure was interpreted by the child as an inability and was believed to predict their failure in attempting any future challenges. Thus, those students with maladaptive patterns of behaviour may face huge challenges in acquiring and displaying their cognitive skill. In contrast, children with learning goal orientation chose the more challenging option even when they had assessed themselves as cognitively low (Dweck & Elliot, 1988). Locke and Latham’s (1990) research demonstrated that individuals who set challenging and specific mastery goals were much more successful in completing their goals. Students need to have autonomy and competence to build towards intrinsic motivation (Deci & Ryan, 1985). Students thus need to be able to take ownership of their goal setting, but to avoid non-challenging goals being created, the process needs to be guided. As this thesis is framed in the context of improving students’ academic success, which under the current pedagogical dogma is determined by grades, students will inevitably set more performance- 52 based goals; however, the task of this intervention is to try and tie in the student’s internal values. 1.4.4 SMART Goals SMART is an acronym for principles provided to help guide goal setters to ensure their success in attaining their goals. In the most common use of this term, these goals need to be Specific, Measurable, Attainable, Relevant and Time-bound (SMART). There is considerable variation in how these terms are defined, depending on the individual. For example, the term “specific” has been changed to “strategic” while “results-based” has been used instead of “relevant” in some cases when creating goals directed at student learning (O’Neil & Conzemius, 2006). Similarly, we see “achievable” has been exchanged for “aligned with corporate goals” in goal setting used for employers in management when trying to implement new policy (Piskurich, 2011). The first use of the term SMART goals began in management with George T. Doran (Doran et al., 1981) in the attempt to create objectives that were clearly outlined and which also provided the opportunity for individuals to evaluate their progress. Doran’s use of the acronym included the more generally used objectives including; Specific, Measurable, Assignable, Realistic and Time-related (Doran et al., 1981). As can be seen there is variation between Doran’s (1981) and the more general version mentioned above, and this is due in part to whether the goal and/or goals need to be quantifiable or if they can remain more abstract. Regardless of the terminology selected in general there are many similarities to the ideas represented by each letter. Both “specific” and “strategic” are after the same result, in 53 that there needs to be a clear plan of attack or objective. “Achievable”, “assignable” and “attainable” are also driving at something similar in that they are looking for a goal which has a clear end or some way to define that the goal has been completed. And of course, “time-bound”, “time-sensitive” and “timeframe” are all focused on creating some type of deadline. Though there is much literature for the use of SMART goals in management, this acronym has also been used in areas including planning (Ryals & McDonald, 2007), disability and rehabilitation (Siegert & Taylor, 2004), coaching (Atkinson & Chois, 2012) and student learning (O’Neil & Conzemius, 2006). In this study, SMART goals were used in an attempt to create intrinsically motivated and thus self-determined post-secondary students. 1.4.5 Goal setting in academics The use of specific learning goals will increase the likelihood of students academically succeeding (Schunk, 2012). Based on the self-determination theory model, goals would need to be constructed in a way that satisfies the individual’s three psychological needs of autonomy, competence and relatedness. Through the rules required of SMART goals (Doran et al., 1981) this can be facilitated. According to Weinberg & Gould (2015) goals must be specific, measurable, attainable, relevant and time-bound. Specific goals provide a clear path with no ambiguity thus increasing the chances of success. As an example, we can consider a person who would like to be a stronger runner. More specifically, they would like to increase the distance they can run from 5 km to 10 km. In order to support autonomy and thus shift individuals towards intrinsic motivation, individuals need to create goals which they are 54 meaningful to them. By creating meaningful goals, which thus reflect personal interest and value, individuals discern the reason they will pursue the goal and maintain the pursuit in the presence of challenges and maintain a harmonious balance with the self (Deci & Ryan, 2000). In order to facilitate the second psychological need of the SDT model of competence, the goals must also be attainable. Having goals which are attainable will help maintain levels of motivation as it will build individual competence. This inherently makes sense as one continues to pursue tasks they enjoy, and one enjoys tasks they feel they are good at. By ensuring that the goals set are in fact attainable, individuals will ensure their required psychological need of competence is met and enhanced, thereby maintaining and/or shifting towards more intrinsic motivation. Our third psychological requirement of the SDT model of relatedness can be facilitated by our fourth SMART goal of relevance. Students want to know, what does this have to do with them? How are they connected? Creating goals that result in students understanding the manner in which they are connected will help facilitate the sense of relatedness needed in order to maintain and/or increase intrinsic motivation. Arguably, the fifth SMART goal can be viewed as undermining our three psychological needs as it requires goals to be time-bound. Often this is facilitated using deadlines and or a minimum or maximum time requirement designated to a task. Deadlines undermine intrinsic motivation and increase extrinsic motivation as individuals become less self-determined. If, however, the deadline was set by or in collaboration with the student, it would be less undermining. However, as our current educational system operates nearly exclusively under this paradigm and life, students would suffer academically if this last rule was not fulfilled. As the three psychological needs of autonomy, competence and relatedness are met by 55 applying SMART goals, it could be argued that SMART goals are still in line with facilitating increased self-determination/intrinsic motivation. For the purposes of this study in the SMART goal setting intervention I have replaced Doran’s (1981) measurable goals with meaningful goals, assignable goals with achievable goals, and realistic goals with relevant goals in order to better facilitate increased opportunity for autonomy and competence. The goal setting intervention itself included a section which helps students to begin thinking about their life long vision or more overarching goals. They are then asked to build both distal goals, goals which may take four to five years to achieve, which would align with their larger overarching goal and their more proximal goals, shorter term goals, which will enable them to get to their more distal goals. Following the completion of the creation of these different tiered goals students are then asked a series of questions pertaining to their goals. These questions are helping to prompt them to revisit their goals to ensure they align with the requirements of the SMART goals. Students are not able to move forward with the completion of the goal setting activity until they are able to meet these requirements built directly into the intervention. Levels of metacognition, self-control and intrinsic motivation were assessed three times during the fall 2017 using self-reported questionnaires. From a first-year science class, 465 students out of a total enrolment of 477 competed the first of three surveys, with 215 completing the third survey. 110 students had to be omitted from the survey as they were unable to complete their anonymous identification. No changes in metacognition, self-control, or intrinsic motivation were observed throughout the term, in low and high 56 academically performing students combined, or when assessed separately. No changes in academic performance were observed for any students in response to the SMART goal setting intervention; however, when students were assessed separately as high (B- and higher, or ≥68% on the University of British Columbia’s standard grading scale) and low performers (C+ and lower, or <68%), an increase in academic performance was seen in the higher academic performing students when receiving their SMART goal setting intervention earlier in the term. From a first-year non-majors’ class with a total of 127 students, all completed at least one survey, with 31 completing all three surveys. Only those who completed all three surveys were included in the analysis. An increase in intrinsic motivation, control of learning beliefs, metacognition and self-control were seen among low performers following the goal setting intervention. 57 2.0 Chapter 2 Does goal-setting in first year post-secondary science majors and non-majors’ students increase student’s intrinsic motivation, metacognition, self-control and academic success in first year science students? 2.1 Synopsis The focus in this chapter was to ascertain whether or not a single goal-setting intervention would increase first-year post-secondary science major and non-science major students’ levels of intrinsic motivation, and thus their academic success as other studies have shown an individual goal-setting exercise may result in shifts in goal setting orientation (Shivetts 2006) and academic success (Dotson 2016). Students levels of intrinsic motivation, self-control, metacognition, control of learning beliefs and grades were assessed at the beginning and end of term, and post-intervention during the term. Results of this study indicate that when the intervention was received earlier in the term science majors’ grades increased; however, no other responses in self-control, metacognition, control of learning beliefs, intrinsic motivation or extrinsic motivation were observed in the major’s group. In the non-majors there was an increase in self-control, control of learning beliefs, metacognition and intrinsic motivation three weeks following the intervention, but no change in mean grade. There were significant differences in the responses between both science majors and non-science majors and between low and high performing students. This study suggests that goal setting may be a worthwhile endeavor in the struggle to improve students’ academic success and persistence in first year post-secondary students but that differences in discipline and academic performance must be considered when creating these interventions. 58 2.2 Methods 2.2.1 Format 465 of 471 students in first-year science (BIOL 125 Biology for Science Majors) were assigned to two groups, A and B (Table 2.1). In BIOL 117 (Biology for Non-Science Majors) 127 first year students were divided into two groups, A and B. Both groups completed the assessment scale online during their laboratory session at the beginning of term and before their first lecture exam. In both majors and non-majors’ classes, Groups A and B received the intervention at different times (Table 2.1). Group A received the intervention just prior to their first lecture exam. Following the first lecture exam and before the second lecture exam, both groups completed the assessment scale during their laboratory session, after which group B was given the intervention. Following the second lecture exam and before the final, both groups completed the assessment scale in their laboratory session. The goal setting guide, used to create the goal setting questionnaire, can be found in the supplemental information. Ethics approval was conducted by the Behavioural Research Table 2.1 Metacognition of regulation, self-control, control of learning beliefs, intrinsic goal orientation and extrinsic goal orientation and SMART goal intervention timeline for both Biology 125 and 117. Week Group A Group B 1 Assessment scale administered Assessment scale administered 2 SMART Goal setting intervention 3 4 5 Midterm 1 Midterm 1 6 Assessment scale administered Assessment scale administered 7 8 SMART Goal setting intervention 9 10 11 Midterm 2 Midterm 2 12 Assessment scale administered Assessment scale administered 59 Ethics Board of the University of British Columbia Okanagan certificate number H18-01501. All surveys were administered using Qualtrics XM software 2019. Qualtrics is a user-friendly online survey tool which allows the user to build surveys in order to collect and evaluate data (Qualtrics, 2005). 2.2.2 Assessment Scales For the purpose of this study, the level of intrinsic motivation in first-year science majors was assessed using a combination of subscales from three existing assessment scales. These included three subsets of the Motivated Strategies for Learning Questionnaire (MSLQ) (Pintrich et al., 1991), including intrinsic goal orientation, extrinsic goal orientation and control of learning beliefs, the Brief Self-Control Scale (BSCS) (Tangney et al., 2004), and the regulation subset of the Metacognitive Awareness Inventory (MAI) (Schraw & Dennison, 1994). This combination was used because each of these sub scales was believed to provide information on intrinsic motivation, either indirectly or directly, and thus would better inform us as to whether this intervention was steering students towards a more intrinsically-motivated position. Within the context of metacognition, it has been shown that the experience the learner has in relation to the task being completed has a direct impact on the learner’s feeling of confidence (Efklides, 2011). Confidence has a direct effect on the learners’ sense of self-efficacy 60 (Bandura, 1982) and thus feeling of competence. A learner’s level of metacognitive regulation can inform us indirectly of the learner’s level of intrinsic motivation. As learners with high levels of self-control tend to make healthier lifestyle choices (Tangney et al., 2004) and show a stronger ability to emotionally regulate (Paschke et al., 2016)—and thus adapt to new situations more easily (Koole, 2009)—they will be much better at metacognitive regulation. Thus, we can use students’ levels of self-control to indirectly assess their levels of intrinsic motivation. Arguably, students who have higher levels of self-control and thus make better choices can also better emotionally regulate. They therefore may be happier overall and find more interest in tasks, and thus have higher levels of intrinsic motivation compared to those with lower levels of self-control. Due to these indirect effects of metacognitive regulation and self-control on intrinsic motivation, these scales were used alongside the motivation scale when assessing overall intrinsic motivation. The metacognitive knowledge component of the MAI scale would have offered information about the students’ awareness of their abilities and knowledge which could have potentially helped inform the study on their level of competence. However, with survey fatigue being a serious concern we felt that the regulation component would offer more insight. 2.2.2.1 Motivational Strategies Learning Questionnaire (MSLQ) The MSLQ is made up of 96 questions that assess students’ overall motivation. For the purpose of this study only 3 of the 15 subscales of the MSLQ were used to assess the level of intrinsic motivation of our students. These three subscales include Intrinsic Goal Orientation, Extrinsic Goal Orientation, and Control of Learning Beliefs (Pintrich et al., 1991). Both the Intrinsic Goal Orientation and the Extrinsic Goal Orientation scales assess the value students 61 place on the current activities and learning they are doing, while the Control of Learning Beliefs assesses how confident students are that their actions will result in attaining their goal. Taylor and others conducted a study to assess the validity and reliability of the MSLQ (Taylor, 2012). The results indicated that the reliability coefficients fall within a good range and confirmatory factor analyses indicate the MSLQ show reasonable factor validity. This scale and its subscales have already undergone multiple revisions, are well cited within the psychological community and demonstrate high psychometric quality (Taylor, 2014). Consequently, this scale will be able to accurately answer the questions posed in this study. 2.2.2.2 Brief Self-Control Scale (BSCS) The brief self-control scale (BSCS) (Maloney et al., 2012) exists in both a long format consisting of 36 questions, and a short format with 11 questions, and is used to assess the self-regulating ability and the ability to pursue a plan without reward. As psychometric responses are similar for both, meaning they both measure the same thing and are equally reliable, the short version was used for the purposes of this study in order to reduce participation fatigue. There are two subscales within this scale, including impulsivity and restraint. 2.2.2.3 Metacognitive Awareness Inventory (MAI) The MAI instrument has eight subcomponents including: procedural knowledge, declarative knowledge, conditional knowledge, information management strategies, debugging 62 strategies, planning, comprehension monitoring, and evaluation (Schraw & Dennison, 1994). These all fall under the umbrella of two subsets: knowledge about cognition and regulation of cognition. This assessment tool has been vetted multiple times (Schraw & Dennison, 1994; Young & Fry, 2008) resulting in strong reliability and consistency. MAI correlates very well with academic achievement with respect to both grades and GPA, and lends itself well to assessing classes where student-teacher interaction is low, such as larger and online classes (Young & Fry, 2008). Thus, it can be used to identify low-performing students and help guide instructors as to which cognitive strategies are lacking within the student body. In order to reduce participation fatigue and maintain a focus on motivation through regulation, this project only utilized the regulation of cognition subset. 2.2.3 SMART Goals Following the MAI, MSLQ and BCSC assessment, a strategically-created goal-setting intervention termed SMART goals (Doran et al., 1981) was carried out. In our study the acronym SMART was modified slightly in order to facilitate the three requirements of the SDT model (Deci & Ryan, 2000): goals which are Specific, Meaningful, Achievable, Relevant, and Time-bound. This intervention was modified and implemented to help guide students to create both their proximal and distal goals under the umbrella of their own lifelong vision. This was done by asking students specific questions in order for them to tie their interest and values into an overarching goal. From their students had to build smaller or proximal goals they would need in order to achieve this distal goal within the required parameters outlined in the SMART goals. The focus of the goal-setting strategy was to force students to reflect and to create specific, meaningful, attainable and time-bound goals, all of which would help them connect 63 their current academic situation to their own valued goals in order to facilitate increased autonomy and competence, thereby increasing their intrinsic motivation. (see Appendix A). 2.3 Analysis The software package (SPSS) statistical package for the social sciences (SPSS Statistics for Windows, 2009) was used to analyze the data for this study. Data were transformed for items within the scales which were reversed. Means were created for all motivational subscales, the self-control scale, and the metacognition regulation scale. Independent sample t-tests were conducted to compare those in the first intervention group A to those in the second intervention group B. For analysis in SPSS, the data were grouped by survey (1, 2 and 3) and grade (high performers (A-B) versus low performers (C+ and lower). Students were separated into low and high performers as the research indicates there is considerable variability in how these students operate (Carpenter et al. 2016). 2.4 Results 2.4.1 Science Majors 465 students completed survey one, 215 completed all three surveys. 110 of these students did not complete the student ID section and thus we were unable to associate them with a survey, section or grade. 209 of these students completed the goal setting intervention from group A and 148 from group B. Those that did not complete the survey or goal setting intervention were not included in the analysis. 64 As shown in Figure 2.1, no significant differences (i.e. p<0.05) in intrinsic goal orientation, extrinsic goal orientation, control of learning beliefs, self-control or metacognition of regulation were observed in response to the intervention in either the high or low performing students of group A or group B. No significant differences were found in students’ mean grades from the previous term, mean grades for the first or second exam. High performing students in group A which had received the intervention earlier (M = 78.82 SD = 6.73) did show a significant increase in their midterm 3 grades compared to high performing students in group B who received the intervention later in the term (M = 76.80 SD = 6.52) as seen in Figure 2.2. First language influenced meta-cognition averages but only for survey 1 with English as a first language students reporting less metacognition (M = 3.62) than those who reported English not as their first language (M = 3.76). 65 a. b. c. d. e. Figure 2.1 In the science majors, Metacognition of regulation (a), self-control (b), control of learning beliefs (c), intrinsic goal orientation (d) and extrinsic goal orientation (e) averages across three surveys for group A (early intervention), low and high performers, and group B (late intervention), low and high performers. No error bars represent no variability. Data were collected using the (MSLQ) Motivated Strategies Learning Questionnaire, (BSCS) Brief Self-Control Survey and (MAI) Metacognitive Awareness Inventory. Data are shown as mean values and standard deviation (error bars). 66 Figure 2.2. In the Science Majors, mean grade averages across three surveys for group A (early intervention), low and high performers, and group B (late intervention), low and high performers. Data are shown as mean values and standard deviation (error bars). * denotes statistical significance. The p-values are indicated where statistically significant differences were observed. 2.4.2 Non-Science Majors 127 students completed survey one and 31 students completed all three surveys. 43 students in group A and 33 students from group B completed the intervention. Only those students who completed all three surveys and interventions were included in the analysis. No significant differences were observed in metacognition of regulation, self-control, control of learning beliefs, intrinsic goal orientation and extrinsic goal orientation in either the high or low performers in the first survey or the high performers in the second survey (Figure 2.3). Low performers who had received the intervention earlier in the term (group A), showed higher levels of intrinsic motivation (M = 3.64, SD = 0.35) and control of learning beliefs (M = 3.82, SD = 0.24) compared to those students who had just received the intervention (group B), intrinsic goal orientation (M = 3.06, SD = 0.64) and control of learning beliefs (M = Group 0.04 * * 67 3.41, SD = 0.42), while no statistically significant differences were observed in the high performers, an increase in intrinsic goal orientation was observed in those students who had received the intervention (group A) compared to those who had not (group B). Figure 2.3. In the non-science majors intrinsic goal orientation and control of learning beliefs averages across three surveys for group A (received intervention), low and high performers, and group B (no intervention), low and high performers. Data were collected using the (MSLQ) Motivated Strategies Learning Questionnaire, (BSCS) Brief Self-Control Survey and (MAI) Metacognitive Awareness Inventory. Notes: * denotes statistical significance. Data are shown as mean values and standard deviation (error bars). The p-values are indicated where statistically significant differences were observed. The third survey showed an increase in self-control in the low performing students who had just recently received the intervention (group B) (M = 3.12, SD = 0.17) compared to the students who had received the intervention towards the beginning of the term (group A) (M = 2.86, SD = 0.2) (Figure 2.4). * * 0.05 0.03 * * 68 Figure 2.4. In the non-science major’s self-control averages across three surveys for group A, low and high performers, and group B, low and high performers. Data were collected using the (MSLQ) Motivated Strategies Learning Questionnaire, (BSCS) Brief Self-Control Survey and (MAI) Metacognitive Awareness Inventory. Notes: Data are shown as mean values and standard deviation (error bars). * denotes statistical significance. The p-values are indicated where statistically significant differences were observed. Metacognition regulation was observed to be higher in the high performing students who had received the intervention earlier in the term (group A) (M = 3.87, SD = 0.32) compared to the high performing students who had just recently received the intervention (group B) (M = 3.57, SD = 0.27) (Figure 2.5). No significant difference in mean grades were found across all students from the previous term, the first midterm or the midterm 3 in the lower performers (Figure 2.6). Mean grades were higher in high performing students who had not received the intervention (group B) (M = 78.53, SD = 8.55) compared to those high performing students who had (group A) (M = 70.94, SD = 16.84) (Figure 2.6). 0.03 * * 69 Figure 2.5 In the non-science major’s metacognition regulation averages across three surveys for group A, low and high performers, and group B, low and high performers. Data were collected using the (MSLQ) Motivated Strategies Learning Questionnaire, (BSCS) Brief Self-Control Survey and (MAI) Metacognitive Awareness Inventory. Notes: Data are shown as mean values and standard deviation (error bars). * denotes statistical significance. The p-values are indicated were differences were statistically significant differences were observed. Figure 2.6. In the non-science majors mean grade averages from the midterm assessments for group A (early intervention), low and high performers, and group B (late intervention), low and high performers. Notes: Data are shown as mean values and standard deviation (error bars). * denotes statistical significance. The p-values are indicated where statistically significant differences were observed. First language influenced meta-cognition averages but only for survey 1, with English as a first language students reporting less metacognition (M = 3.62) than those who reported English not as their first language (M = 3.76). 0.015 0.05 * * * * 70 2.5. Discussion: 2.5.1 Science Majors: low performers 2.5.1.1 Constructs This study did not show any statistically significant changes in metacognition, self-control, control of learning beliefs, intrinsic or extrinsic motivation in response to the goal setting intervention in the low performing science majors’ group. It was predicted that metacognition, self-control, control of learning beliefs, intrinsic motivation would increase in response to an early goal setting intervention, while extrinsic motivation would decrease in this group. These results were surprising as, generally speaking, lower academically performing students tend to show a more performance goal orientation and thus tend to be less intrinsically motivated (Rawsthorne & Elliot, 1993). Consequently, it was believed that an intervention built to increase autonomy and competence, and thus intrinsic motivation, would help increase the construct of intrinsic motivation along with the above-mentioned relevant constructs. Many studies have reported differences in science majors compared to non-majors such as academic performance (Knight & Smith, 2010), motivation (Glynn et al., 2011) and interests (Knight & Smith, 2010). In general, the academic performance in science majors is higher compared to their non-major counterparts (Knight & Smith, 2010) which could be due to their interest and motivation being higher in the course (Glynn et al., 2011; Knight & Smith, 2010). Non-majors may enroll in an introductory science course in order to fulfill a requirement rather than for interest, which might negatively affect their motivation (Pintrich 71 et al., 1991) thus it is feasible that who we identify as “low performers” in the majors group would not necessarily behave in the same way as the “low performers” of our non-majors. That is the “low performers” of our majors’ group may still be more learning goal orientated than the “non-major” thus we would not likely see as much change, if any, in the constructs mentioned above, as these students may already be relatively high for the above constructs. Another consideration is that the science majors are more likely to already know and/or believe they understand their academic pursuits and their academic goals. Completing a goal setting intervention and/or using goals does require that the individual has a sense of their interests, what motivates them, and where they are heading. The majors’ group may already be using goals that are facilitating higher levels of some of the constructs mentioned early. Consequently, it would be unlikely to see a change in these constructs in response to a goal setting intervention geared at increasing construct levels which are already heightened in this group. 2.5.1.2 Academics This study did not show any statistically significant changes in mean grade in response to the goal setting intervention in the low performing science majors’ group. It was predicted that mean grade would increase in response to an early goal setting intervention as goal setting has been shown to increase students’ academic success (Rader, 2005). It is possible, as indicated earlier, that these students may already be using goals setting strategies of some kind and thus the current goal setting strategy may not have had the benefit academically that we were expecting to observe. 72 2.5.2 Science Majors: high performers 2.5.2.1 Constructs This study did not show any statistically significant changes in metacognition, self-control, control of learning beliefs, intrinsic or extrinsic motivation in response to the goal setting intervention in the high performing science majors’ group. As high academically performing students are associated with constructs such as high metacognition (Gul & Shehzad, 2012) high intrinsic motivation (Rawsthorne & Elliot, 1993) and high self-control (Tangney et al., 2004) we predicted that this group would not benefit from the goal setting intervention implemented in this study. 2.5.2.2 Academics Contrary to the response seen in the low-performing science majors, this study did show a statistically significant increase in final mean grade in the high academically performing science major students in those groups receiving an early goal setting intervention. It was predicted that this group would likely already be exhibiting high levels of the more learning goal orientated constructs and thus would not benefit much from the intervention. However, it is possible that the intervention acted as more of a reinforcement and or reminder of students’ previously set goals which thus increased their academic success. In a study by Schunk (2003) it was shown that frequently re-evaluating and monitoring of one’s goals increases the effectiveness of said goal. Consequently, it is likely that the single goal intervention implemented, created the opportunity for goal re-evaluation and monitoring which may have facilitated the academic success we saw in the high academically performing science major students receiving the early intervention. 73 2.5.3 Non-Science Majors: low performers 2.5.3.1 Constructs This study did not show any statistically significant changes in metacognition or extrinsic motivation in response to the goal setting intervention received three weeks previous in the low performing non-science majors. As mentioned previously, low performers are generally associated with low levels of metacognition and higher levels of extrinsic motivation (Rawsthorne & Elliot, 1993). Metacognition is a process which requires much reflective time in order to assess and determine what learning strategies and resources would be most beneficial to the individual (Schraw et al., 2006) thus it is likely that three weeks following the intervention students may not have shifted enough towards becoming more self-determined to see an increase in metacognition. The lack of change in levels of extrinsic motivation observed may be due, in part, to the degree to which these students are extrinsically motivated and thus performance goal orientated, accordingly it may require more than a single intervention to promote the shift in motivation we are looking for. Interestingly, in the survey taken three weeks following the intervention, we saw an increase in intrinsic motivation, control of learning beliefs and self-control (Fig. 2.4), all of which are associated with a more learning goal orientation (Rawsthorne & Elliot, 1993). As this goal setting intervention was designed to build autonomy and competence and shift students towards a more learning goal orientation, the increases we observed were expected for the lower academically performing students. The increase in self-control seen following the goal setting intervention is inherent as one requires goals in order to exhibit self-control and conversely without self-control one cannot attain their goals. We would therefore predict 74 that the goal setting intervention would result in an increase in self-control. Students with more learning goal orientations tend to have a stronger belief that they are responsible and have control over their outcomes, thus an intervention that shifts students towards a more learning goal orientation would also result in an increase in control of learning beliefs (Payne et al., 2007). 2.5.3.2 Academics Although we saw an increase in the constructs of intrinsic motivation, self-control and control of learning beliefs, all of which are proximal outcomes of a learning goal orientation, we did not see the increase in mean grade that is generally associated with having a learning goal orientation (Eison et al., 1986; Payne et al., 2007). From this study we see that some of the constructs associated with a learning goal orientation increased in response to the goal setting intervention however some did not. It is likely that we would have seen the latter constructs increase if the study had continued over a longer period of time. This would have provided the time necessary for students to shift from a performance goal orientation to learning goal orientation. As discussed above, it is also likely that the intervention would have been more effective if done repeatedly throughout the term allowing for re-evaluation and monitoring. A longer period of time may have allowed for the above described goal orientation shift and the increased evaluation time necessary in order to see the expected positive impact in mean grade. 75 2.5.4 Non-Science Majors: high performers 2.5.4.1 Constructs This study did not show any statistically significant changes in self-control, control of learning beliefs, intrinsic or extrinsic motivation in response to the goal setting intervention in the high performing non-science majors’ group. This result was expected, as high academically performing students are generally associated with constructs such as high intrinsic motivation (Rawsthorne & Elliot, 1993), control of learning beliefs, and high self-control (Tangney et al., 2004). Conversely, an increase in metacognition, measured by the metacognitive awareness inventory, in the group receiving the early goal setting intervention. As indicated, some of the other learning goal orientation constructs increased three weeks following the intervention while metacognition increased following six weeks. The data here support the notion that metacognition may require longer periods of time to develop compared to some of the other constructs described above. The literature supports the idea that metacognition is a construct associated with high performers (Gul & Shehzad, 2012) and it is not surprising then that we did not see any change in metacognition in the low performers however we did see an increase over the same six-week period in the high performers. 2.5.4.2 Academics The second survey showed a statistically significant change in mean grade for midterm exam 1 in group B. This observation was not anticipated as this group had not yet received any intervention; however, there are often significant grade differences between the various lab sections within a course. It is possible that when the lab sections were randomly combined, 76 the lab sections making up group B were already a higher achieving group. Although access to this specific data is unavailable for the term analyzed, this is a trend I have seen in the labs over the past decade. The academic distinction described here is often observed in the science majors’ course; however, with the sheer number of lab sections involved, academic differences within each lab section are less observable compared to the non-major’s course where there are only six lab sections in total. 2.6 Conclusion The results reported in this chapter indicate that our goal setting intervention did facilitate an increase in academic success, represented here as mean grade, in the high performing science majors however no other changes in learning goal orientation constructs in the science majors’ groups were observed. The same intervention in the non-majors’ groups did result in a shift towards a learning goal orientation in the lower performing non-science major students represented by an increase in intrinsic motivation, control of learning beliefs and self-control and in the higher performing non-science majors with an increase in metacognition however we saw no increase in academic success in either the low or high performers. There was considerable variability in the responses of the science majors and non-science major groups and the low and high academically performing students to the goal setting intervention which indicates an important finding in how we must proceed when creating interventions for our students. It is understood that the Likert data are not interval data and as such we did not pull any quantitative information from this data set but only the differences between values were discussed. 77 2.6.1 Low performers versus high performers The results of this study indicate clear differences between the low performers and high performers in both the majors and non-majors’ students. The same intervention yielded academic improvement in our majors’ students but no response was observed in the lower academically performing students. Similarly, in our non-science majors we saw most of our learning goal orientation constructs respond favourably to the goal setting intervention with very little occurring in the higher academically performing students. Clearly these groups do not operate, educationally speaking, in the same way. We see a similar trend in the Carpenter et al. (2016) study where they split their students in to low-, mid- and high-academically performing students. Though they did not see a differentiation between the low- and mid-academically performing students there was a significant difference observed between the low-/mid- and high-academically performing students in how they utilize retrieval-enhanced learning, Often studies looking at developing strategies to help support students’ academic success do not separate students based on academic success. The results of this study and that of (Carpenter et al., 2016) suggest that there are many differences between the low and higher performing students. Though our results suggest there are differences in how these two groups respond to a single goal setting intervention, it has also been demonstrated (Carpenter et al., 2016) that there are significant differences in how these groups of students perceive their own abilities, the amount of previous knowledge they bring to their learning and the effectiveness of different instructional methods. Consequently, we must be cautious when 78 interpreting findings from studies where this variable has not been considered as these findings may guide us astray and/or this may result in trends being missed. 2.6.2 Non-Majors versus Majors This study demonstrates another key finding: not all first-year post-secondary students are “created equal”. That is, although both these groups see science as having value and believe they can “do” science, these cohorts of students experience science in differently in terms of their level of motivation and interest in doing science, their ability to connect science to the rest of the world, their perceived competence in doing science and in their own study habits (Cotner et al., 2017; Glynn et al., 2011; Knight & Smith, 2010). In the current research our majors and non-majors group responding entirely different for each facet of this research. The non-majors responded favourably to the intervention with respect to constructs associated with learning goal orientation but did not respond in terms of academic success. Meanwhile, our majors’ students only responded favourably in terms of academic success. Both groups got the same intervention implemented in the same way at the same time and yet yielded entirely different results. Similarly, these two groups show significant performance differences (Knight & Smith, 2010), levels of motivation in science (Glynn et al., 2011) interest in science (Knight & Smith, 2010), demographics (Cotner et al., 2017), how they identify themselves as a “scientist” (Cotner et al., 2017), studying habits (Knight & Smith, 2010), appreciation and success with group work (Knight & Smith, 2010), confidence to do science (Cotner et al., 2017) and their ability to connect science to other academic fields (Knight & Smith, 2010). Yet both groups feel they can do science and that science is of value (Cotner et al., 2017). It’s not surprising that a student majoring in science would likely have 79 more confidence in their ability to do science and/or be more interested in the topic compared to non-science major who is likely taking the course for credit rather than for interest. This is an important consideration when developing academic interventions as these two groups are not beginning their science at the same starting line if you will. 2.7 Intervention Implementation Schunk (1995) indicates a need for the re-evaluation and monitoring of goals in order for goals to be effective, which is also supported by this study. In our non-majors we observed increases in learning goal orientation constructs such as intrinsic motivation, self-control and control of learning beliefs three weeks following the intervention but not at six weeks. This trend was observed for all three constructs which may suggest that the frequency of an intervention is as important as the intervention itself. That is, a single goal setting intervention may have some short-term positive impacts but in order to attain long term results, such as creating a shift towards learning goal orientations in students we may need to build in opportunities for repeated goal evaluation and monitoring. We also observed that the students in this study who performed well academically tend to already use some form of goal setting (Figure A). Students who answered high on the Likert scale to the question “I ask myself periodically if I am meeting my goals” on the MAI, showed a higher mean grade point average compared to those that answered lower on the scale. This suggests that students with high self-reporting (typically high academically standing students), already monitor and regulate their own goals while those lower 80 academically standing students do not. This provides further evidence to support the need for an ongoing and/or term long goal setting intervention rather than a single event. 2.8 Future Research This information sheds light on a few modifications that would need to be made in order to better help our students’ academic success. We cannot assume one intervention will work for all, but rather these interventions must be customized to account for the differences we have seen in our students’ academic success, and also with respect to their major. Obviously, it would be overly burdensome to try and meet the needs of each individual but as evidenced by this research, at the very least we must try create an intervention with these two main distinctions in mind. Another assumption we must not make when implementing goal setting as an educational tool is that our students know how to set goals, or even that they know what their goals are. Providing students a goal setting intervention without the proper guidance to help them create and implement these goals would likely set our students up to fail. And as mentioned above we must ensure that any goal setting intervention is designed with regular evaluation and monitoring in mind. It would be substantially more feasible if a single intervention would provide the most effective strategy for our students’ academic success but this is just not the case. Consequently, if we are really motivated to help our students we have their best chance of success we are going to need to be with them every step of the way. 81 3.0 Chapter 3 Will first year post-secondary science majors and non-science majors with high self-control spend more time on the goal setting intervention and complete it? 3.1 Synopsis The focus in this chapter was to ascertain whether students with high self-control use goals and what relationship exists between self-control and goal commitment in first year post-secondary students. Levels of self-control and intrinsic motivation were assessed throughout the term three times using self-reported questionnaires. 465 first year science majors and 127 first year non-science majors were assessed together. We saw a weak correlation with students with lower levels of self-control spending more time on the goal setting intervention, and a marginal difference in those low self-control students having a higher completion rate of the goal setting intervention. Thus, it appears likely that students with higher levels of self-control tend to use goals already and are thus much quicker at working through a goal setting intervention or, alternatively, they do not feel the need to invest the time at all. 3.2 Methods 3.2.1 Format 465 first-year science majors and 127 first year non-science majors were assigned to two groups, A and B. Both groups A and B completed the Brief Self-Control Scale (BSCS) (Tangney et al., 2004) during their lab section online three times: at the start of term, immediately following midterm 1, and immediately following midterm 2. Group A received the goal-setting intervention three weeks before the first midterm and group B received the goal-setting intervention three weeks before midterm 2 (Table 3.1). The goal setting guide, 82 used to create the goal setting questionnaire, can be found in the supplemental information. Ethics approval was conducted by the Behavioural Research Ethics Board of the University of British Columbia Okanagan certificate number H18-01501. Table 3.1. Metacognition of regulation, self-control, control of learning beliefs, intrinsic goal orientation and extrinsic goal orientation and SMART goal intervention timeline. Week Group A Group B 1 Assessment scale administered Assessment scale administered 2 SMART Goal setting intervention 3 4 5 Midterm 1 Midterm 1 6 Assessment scale administered Assessment scale administered 7 8 SMART Goal setting intervention 9 10 11 Midterm 2 Midterm 2 12 Assessment scale administered Assessment scale administered 3.2.2 Assessment Scale 3.2.2.1 Brief Self-Control Scale (BCSC) The Brief Self-Control Survey (BSCS; Maloney et al., 2012) can be seen in both a long format, consisting of 36 questions and a short format with 11 questions, is used to assess the self-regulating ability and the ability to pursue a plan without reward. As psychometric responses are similar for both, the short version was used in this study in order to reduce participation fatigue. There are two subscales within this scale including impulsivity and restraint. Following the BCSC assessment, a strategically-created goal-setting intervention termed SMART goals (Doran et al., 1981) was implemented. This intervention was modified to help 83 guide students to create both their proximal and distal goals under the umbrella of their own lifelong vision. The focus of the goal-setting strategy was to force students to reflect, create specific, meaningful, attainable and time-bound goals. This would help students connect their current academic situation to their own valued goals in order to facilitate increased autonomy and competence, thereby increasing their academic success via increased intrinsic motivation, control of learning beliefs, metacognition and self-control (see Appendix 1.1). The focus of this chapter was to determine if higher self-control values were correlated with time spent on the goal setting intervention and whether those that completed the intervention had higher levels of self-control. 3.3 Analysis Surveys were administered, and data were collected and reported using Qualtrics XM software 2019. Qualtrics is user-friendly online survey tool which allows the user to build surveys in order to collect and evaluate data (Qualtrics, 2005). The statistical package for the social sciences (SPSS) software package was used to analyze the data for this study (SPSS Statistics for Windows, 2009). Due to the differences in the scales of the questionnaires data had to be transformed. Means were created for the brief self-control scale from the third survey and groups A and B were combined to ensure all students had the opportunity to complete the goal-setting intervention. The amount of time spent on the goal setting intervention and completion rate were collected via Qualtrics. Pearson’s correlation coefficients were used to look at whether the amount of time a student spent on completing the goal setting intervention was related to their level of self-control 84 determined from survey 1. For self-control averages an independent sample t-test was done to compare scores for those who did and did not do the intervention. Figures in this chapter were created using R software environment. 3.4 Results Though not statistically significant, there appears to be a correlation (r (241) = −0.12; p = 0.063; 95% CI (−0.24, 0.006)) between the levels of self-control and the amount of time students spent on their goal setting task, with the students with higher levels of self-control spending less time on the goal setting task (Figure 3.1). There was a marginal (p = 0.11) difference in self-control in those groups who completed the intervention and those groups who did not. Those groups higher completion rates for the goal setting intervention showed lower self-control values (mean = 2.77) while the groups that did not do the goal setting intervention displayed higher levels of self-control (mean = 2.85, t (398) = 1.61, p = 0.11) 85 Figure 3.1: Correlation between time spent completing the goal setting intervention in seconds and average self-control score. R is the Pearson’s correlation coefficient. This graph represents the both science and non-science majors’ students. Each dot represents an individual student and their combination of self-control and time spent on the goal setting intervention. The line represents the relationship between the variable of self-control and time taken on goal setting intervention. 3.5 Discussion Both goal setting and self-control have been shown to be positively associated with a student’s level of academic success (Locke et al., 1981; Locke & Latham, 2002; Morisano et al., 2010). One needs self-control in order to attain goals, and without goals we would not see self-control. Consequently, we hypothesized that we would see a correlation between self-control and goal commitment, with goal commitment defined as time spent on the goal setting intervention. More specifically, it was hypothesized that with higher levels of self-control there would be a higher degree of goal commitment. Thus, as student’s levels of self-control increase so too would the amount of time have spent on the goals setting intervention. It was also hypothesized that students with high levels of self-control would have higher 86 completion rates for the goal setting intervention, as this to was thought to represent a student’s level of goal commitment. Though not statistically significant, results from this study indicate a correlation between self-control and time spent on goal setting however the opposite relationship was observed. Results from this study suggest that as students’ levels of self-control increased, they spent less time on the goal setting intervention. Similarly, there was a slight difference in completion rates of the goal setting intervention, with students reporting high levels of self-control less likely to complete the intervention. Both these findings were surprising. This could be interpreted as evidence that students with high levels of self-control are less likely to commit to goal setting interventions. A few explanations are possible for the trend observed in this study. 3.5.1 Goal Completion One possible explanation for the relationship seen here is that those students with high levels of self-control are already using some type of goal setting. In order to test whether students with high levels of self-control are in fact already using some type of goal setting, we considered the item “I am able to work effectively towards long term goals” in the BSCS, and compared it with students’ midterm three grades. Students who rated themselves high on this item had significantly higher grades than those who ranked themselves lower (P = 0.02) (See Appendix 3). Though we are only considering a single question from this scale, which results in losing construct validity and reliability of the scale as a whole, it does inform us about the likelihood that students who use goals would likely have high levels of self-control; 87 both are associated with high grades. Similarly, Tangney et al. (Tangney et al., 2004) reported that postsecondary students with higher levels of self-control had higher grade point averages. This suggests that as high self-control is associated with higher grade average, and higher grade average is associated with goal setting usage, that high self-control students would already be using goals and thus would not believe it was time well spent to work on an activity which they have already done. Arguably, most people do not like to do extra work or to do things twice when there is no apparent gain. However, those students with lower levels of self-control, and thus less likely to be using goals, may see the value in pursuing the goal setting intervention, which would explain why the completion rate for this group was higher. 3.5.2 Time Spent Though we see a possible justification for why students with high levels of self-control would be less likely to complete the goal setting intervention, this does not explain our observation that some students with high levels of self-control did in fact complete the goal setting intervention, but spent significantly less time on the intervention than those with low levels of self-control. Though students were not required to complete the goal setting intervention, the reminders about the intervention were provided by the teaching assistants for each laboratory section. As students with high levels of self-control tend to be high academic achievers (Duckworth et al., 2019), it is possible that those students may have been in a mindset to complete anything asked of them by their teaching assistant, regardless of whether that intervention was described to them as being voluntary. 88 Students with high levels of self-control are likely already using goal setting and may have already seen the benefits of this in their academics thus far. As high self-control individuals are generally high achievers (Duckworth et al., 2019), they may have decided that doing a slightly different goal setting intervention may still offer some benefit and thus it would be worth doing. Some high achieving students might consider anything which may give them an academic edge to be worth the time. The decrease in time spent on the intervention that accompanied increased self-control levels may provide more evidence to support the notion that students with higher levels of self-control are already using goals. High self-control individuals who already use goals would be better prepared to complete the goal setting intervention compared to those with lower levels of self-control. Similarly, a student who has already completed an assignment and is asked to complete a similar assignment would likely complete the assignment at a much faster pace having the experience from the first assignment. Thus, students in this study with high levels of self-control, who may already be using goal setting as a strategy, would be better equipped to complete the goal setting intervention and consequently would have needed much less time to complete it than the students with lower levels of self-control. These students who are unfamiliar with the goal setting process will thus require much more guidance and will likely require more built-in accountability and rewards to increase goal commitment. Teaching students about goal setting and building in a sense of accountability would be important for students with low levels of self-control as they would not have seen the benefits of such an exercise and thus would not have a built-in positive feedback loop. 89 3.6 Conclusion The results of this study indicate that students with high levels of self-control had less buy-in to the intervention, likely because they are already using goals and thus would be able to complete the intervention quickly or alternatively would not bother completing the intervention as it would be redundant, while those with lower levels of self-control do not. Thus, in order to help our students with lower levels of self-control achieve higher levels of academic success through goal setting we must create goal setting interventions which provide guidance and support and have a built-in accountability and rewards system to ensure goal commitment. For those students less experienced in goal setting a more step by step approach may be of more benefit rather than a single goal setting intervention as they would need more practice and experience as one needs when learning anything new. 3.7 Future Research The information found in this study sheds some light on the differences that exist in students’ levels of self-control, goal usage and goal commitment. In order to develop and implement a goal setting intervention that will have the highest academic impact, we need to have a more complete understanding of the differences that exist in our students. Consequently, if we could answer questions surrounding our current student cohorts’ levels of self-regulation, self-control, goal usage and goal commitment we would be better able to understand how to help them. Though this research does inform us that there is a relationship between goal usage and goal commitment relative to a student’s level of self-control, the mechanism behind this is not clearly evident. Having a better understanding of what specific mechanism 90 is responsible for this correlation may help to create more beneficial goal setting interventions for this particularly high-risk group. 91 4.0 Chapter 4: Conclusions 4.1 Discussion 4.1.1 Science Majors One hypothesis of this study was that the SMART goals curricular intervention would change first-year post-secondary students’ levels of intrinsic motivation, self-control, metacognition, control of learning beliefs and grades for science majors. It was predicted that the SMART goal curricular intervention would increase first year post-secondary students’ levels of intrinsic motivation, self-control, metacognition, control of learning beliefs and grades however only grades were observed to increase in response to the SMART goal setting intervention in science majors. The results from this study suggest that a single goal setting intervention may influence improved academic success in first year science major students. The results of our study of first-year science majors revealed no significant differences in intrinsic motivation, extrinsic motivation, metacognition, self-control or control of learning beliefs associated with our goal-setting intervention. This was seen in both low and high academically performing students. We did not see any changes in midterm or final mean grade when the high and low academically performing students were grouped together. However, we did see an increase in mean grade in the high academically performing students in response to the intervention, but not in the low-performers, when low and high academic performers were assessed separately. 92 When looking to see what differences, if any, there were in goal usage between the high academic performing students and the low academic performing students we used the item “I am able to work effectively toward long-term goals” from the BSCS and the item “I ask myself periodically if I am meeting my goals” from the MAI. For both items we saw a statistically significant positive relationship in high academically performing students for both of these items compared to the lower performing students. Consequently, we suggest that high academically performing students already use some form of goal setting and monitor and regulate their own goals, while lower academically performing students do not. Thus, the high performing students would have been better equipped to complete the goal setting activity more easily and would have likely created more successful goals and/or been better equipped to work through the intervention. As this group of students would have already reaped the benefits of goal setting in the past they would have also exhibited higher goal commitment to these goals and/or the goal setting activity and thus would have been more likely to internally value these goals. Another consideration is that the high academically performing group of students likely already know and/or believe they know with more certainty where they are going with their academic careers, what strategies work and/or don not work for them, and what they value. This type of knowledge generally comes more easily for individuals who have a good sense of who they are and what their interests and strengths are. It would very challenging to ask someone to complete a goal setting activity intended to connect students values with their academic goals if they do not have a grasp on who they are and where they are going. It could be argued that we did not see the academic benefit we anticipated in the lower 93 performing students as they were not equipped to complete the goal setting intervention provided due to a lack of understanding of themselves. Alternatively the use of grades as our determination of academic performance may have presented us from seeing changes in learning that may have actually transpired as grades are not always a true representation of as students learning. Non-Science Majors Another hypothesis of this study was that the SMART goals curricular intervention will change first-year post-secondary students’ levels of intrinsic motivation, self-control, metacognition, control of learning beliefs and grades for non-science majors. It was predicted that the SMART goals curricular intervention would increase first-year post-secondary students’ levels of intrinsic motivation, self-control, metacognition, control of learning beliefs and grades for the non-science majors. Though differences were observed in students’ levels of intrinsic motivation, self-control, metacognition and control of learning beliefs no differences in grades were observed. No differences were observed in intrinsic motivation, extrinsic motivation, control of learning beliefs, metacognition, self-control or mean grade when high and low academically performing non-major students were grouped together. Intrinsic motivation and control of learning beliefs were found to be higher in students who received the intervention compared to those who had not in the lower academically performing students while self-control was higher in students who received the intervention later in the term. In both cases the intervention was received exactly three weeks prior to the assessment tool. Though it was 94 hypothesized that only those students who received the intervention early in the term would see positive results, this research supports the idea that goal setting needs to be something that is facilitated more regularly in order to ensure it is in the forefront of the student’s mind. This “three-week effect” was not seen in the science majors. There is evidence among the literature to suggest that a single intervention alone is not enough to impact students, but rather goal setting which is frequently re-evaluated and reviewed is more effective (Schunk, 1995). We observed that those students who regularly monitor their goals performed better academically than did those who did not monitor their goals. Simply creating a goal did not appear to be enough to facilitate positive change; regular reinforcement may improve those outcomes. The increased level in reported metacognition of the high academically preforming students observed at the end of the term is not surprising as high metacognition is generally associated with higher academic success. Metacognition, or “thinking about thinking” (Flavell, 1979), may include activities such as planning what strategies and resources will be required for a task, awareness of an individual’s comprehension of a subject and how well a task is being performed and even appraisal of the end result and/or process (Schraw et al., 2006). Clearly in order to “think about thinking” an individual would need time to process all of the above factors. It seems reasonable that there would be a delay in increased metacognition in response to the intervention. As this increase in metacognition in the high academically performing students was seen only seen in the later part of the term, this may give us some information as to how much time is required for this process to develop, at least in non-majors. Based on the information collected from this research, it seems students needed 95 almost the entire term to begin to start thinking metacognitively. This information is very helpful in that it informs us that some of these constructs we are measuring really need to be monitored over the course of a term and/or over an entire academic year to confirm whether a response occurs. Of course, for some students who already have this in their practice, this process may come more naturally and thus more quickly; however, for others this may require more time. Researchers may want to ensure they have built in enough observational time in order to see the full picture. Thus, in our study, in order to develop better levels of metacognition it is reasonable that group A would have had higher levels of metacognition compared to group B, as they received the intervention earlier on and would have then had much more time to reflect, process and internalize the intervention. 4.1.3 Self-Control • My hypothesis is that students’ levels of self-control would be correlated with student’s goal commitment and goal usage. • I predicted that students’ high levels of self-control will be correlated with high goal commitment and goal usage. With regards to self-control it was hypothesized that students’ self-control levels would be correlated with student’s goal commitment and goal usage. It was predicated that students’ high levels of self-control would be correlated with high goal commitment and goal usage however this study indicates that high levels of self-control were correlated with less time invested in completing the goal setting intervention activity and that students with high levels of self-control had less goal setting completion rates. It was theorized that those with more self-control would have had stronger goal commitment measured by time spent on the goal 96 setting intervention and goal setting intervention completion. However, it was those students with reported low self-control who tended to spend more time working through their goal setting intervention and who were more likely to complete the intervention. These observations tell us a great deal about the processing time of our students. This goal intervention was likely more labor intensive for students with lower levels of self-control and thus they likely struggled with the process more so than the other students with higher levels of self-control. They may have even found the task intimidating and overwhelming. Reinforcing the idea that when implementing goal setting strategies for students, in particular those with little or no goal-setting experience, we need to spend the time to teach them how to work through these, just as we would with any other assignment. Educators generally do not give an assignment to students without any information regarding content needed for the assignment, or the instructions on how to complete the assignment and how it will be evaluated. This is no different than any educational strategy and consequently, we as educators need to administer the intervention in much the same way. The smaller amount of time spent on the intervention by the high academically performing students is likely due to the more regular goal monitoring and reflecting that this group has been shown to do, based on the previously described analysis. This suggests that they would have a better understanding of their goals and how to make goals which would make them more equipped to complete the goal setting intervention compared to the low academically performing students. 97 The results of the second question, whether high academically performing students will be more likely to complete the goal setting intervention, were not anticipated. It was expected that due to stronger goal commitment those high academically performing students would be more likely to complete the intervention, because they would have found it important. However, it was the lower academically performing students who were more likely to complete the intervention. This result agrees with the results described earlier. In this case the high self-control students may have already completed some form of goal setting on their own and thus did not see any reason to recreate the task. However, those lower self-control students would likely not have defined goals or perhaps have goals that are not designed in such a way to benefit them; consequently, they likely saw the value in pursuing the goal setting task to fill this gap. In both cases the results of our self-control study combined that data from both majors and non-majors. It is possible that had these been teased apart we might have seen more statistically significant results. 4.1.4 Science majors versus Non-Science majors Both non-majors and majors have been shown to be able to “do” science (Knight & Smith, 2010), however many studies have shown tremendous differences between these two groups in performance (Knight & Smith, 2010), motivation (Glynn et al., 2011), interest (Knight & Smith, 2010), demographics (Cotner et al., 2017), science identity (Cotner et al., 2017), studying habits (Knight & Smith, 2010), group work (Knight & Smith, 2010), confidence (Cotner et al., 2017), and their ability to connect science to other academic fields (Knight & Smith, 2010). Similarly, and not surprisingly, in our study we saw a significant difference in how our majors and our non-majors responded to a goal-setting intervention which aimed at 98 increasing students’ intrinsic motivation. In the majors we did not see a change in intrinsic motivation, extrinsic motivation, control of learning beliefs, self-control or metacognition while the non-majors showed increased intrinsic motivation, control of learning beliefs and self-control. As Smith et al. (2004) discuss, non-majors generally register for their introductory science courses in order to fulfil a requirement and not out of interest and/or motivation for the subject. They typically see science as a separate entity, a subject on its own that does not relate or transfer to any other subjects outside of itself (Cotner et al., 2017), and in the study by Knight and Smith (2010), they failed to connect their genetics course to their own careers. Thus, it is unlikely that non-majors would have high levels of intrinsic motivation and/or high self-determination for the course. The results observed in this study can thus be explained by the low internal value non-majors place on their science coursework. In thinking about the differences between these groups described above, it makes sense that we would observe a larger “value-gap” in the non-majors than in science students, with more room for improvement in the measures that correlate to internal value. By providing the goal setting intervention, which was specifically designed in order to help students connect their course work with their own values, we might have helped students to view the course as something more connected to themselves than they had initially thought. It could be expected that we would not see any change in these constructs with our majors, as this group of students generally has higher levels of motivation (Glynn et al., 2011) and interest (Hebert & Cotner, 2019). This group already sees themselves as “science people” and they generally show higher levels of confidence in the sciences, and have likely already connected their 99 course work with their future careers (Hebert & Cotner, 2019). Thus, they would have already connected their course to their own values resulting in increased intrinsic motivation and/or self-determination and so we would not be likely to see a change in these constructs in response to the goal setting intervention. The fact that both groups have been shown to be capable of “doing” science (Hebert & Cotner, 2019) supports the idea that it is the motivation and attitudes of the students that really determine their overall success in the course. Hebert and Cotner (2019) demonstrated that both groups were capable of working through an inquiry-based curriculum, however the differences arose in the level of experimental detail used, where non-majors used significantly fewer details when designing their experiments. It is likely that the lack of confidence and interest in the subject within this group may have more to do with this difference than their abilities. From this study and those referenced above it is clear that what works for one group does not necessarily work for the other. Consequently, when building strategies, interventions and curriculum we need to ensure we have considered the audience we are targeting if we are truly interested in helping students succeed as best they can. 4.1.5 Low performers versus High performers When making decisions regarding the implementation of learning strategies, the literature supports the notion that there are significant differences between the high academic and low academic standing students in their prior knowledge, and thus the success of the type of 100 instructional method used, students’ attitudes and their perception of their academic success (Carpenter et al., 2016). The expertise of the learner has a significant influence on the success of the type of instructional method used. When looking at a large post-secondary science course, Carpenter et al. (2016) found that significant difference was observed between high and low knowledge students when using a retrieval-enhanced learning strategy. Students who had a high prior knowledge generally performed significantly better when using retrieval-enhanced learning compared to those students who had less prior knowledge. In an undergraduate level statistics course (Leppink et al., 2012), it was observed that students who scored high on a pre-test performed better when they were required to come up with their own learning to support their arguments while those scoring lower on the pre-test learned better from simply reading the examples. Similar trends were seen in middle school students studying chemistry (Lee et al., 2006) and high school students studying computerized spreadsheets (Cooper et al., 2001). Based on these findings, Carpenter et al. (2016) suggested that because retrieval is a more active process of searching memory, those students having higher academic standing—and thus higher prior knowledge—would be better equipped when using retrieval-enhanced learning as they would have the knowledge base to draw from compared to the lower academic standing students. Alternatively, tasks which require processing of information that are already present, such as a worked example, has no need to “retrieve’ information and 101 this can result in high knowledge students disengaging from the process as it is deemed redundant and non-active as was seen in the study by (Lee & Kalyuga, 2014). 4.1.6 Student perception Students’ ability to predict their academic outcome has also been shown to significantly differ between the high academic and low academic standing students. In a post-secondary psychology course, the lower performing students overpredicted their success by 18%, while the high performing students tended to under predict (Miller & Geraci, 2011). In a similar study of an undergraduate education course, low performing students tended to overpredict their scores by almost double as much as the high performing students (Bol et al., 2005). 4.1.6.1 Student Attitudes Along with prior knowledge and student perception, the attitudes students have towards their learning has also been shown to be different between low and high academically performing students (Jensen & Moore, 2008). In this study, Jensen and Moore saw a steady academic improvement in exam scores over the course of the term in post-secondary students and both an increase in attendance and submission of extra work in high performing students while lower performing student’s exam grades decreased across the term and students generally became less involved. Student goal commitment or “buy-in” is paramount to the success of any educational intervention. As was discussed in chapter three, students with high levels of self-control spent less time completing the goal setting intervention and had a lower rate of completion. 102 This group likely already does some form of goal setting and thus would have found this to be a waste of their time. Students who exhibited low self-control appeared to embrace the goal setting intervention and consequently spent more time on the intervention and had a higher rate of completion. It seems highly intuitive that one would not want to put energy and time into working through something they do not see as having any value thus it is important that we are able to convey the value to the students for whichever educational intervention we plan to administer. Consequently, “understanding our audience” is the first step we must make in order to determine how we create and administer any intervention. Similarly, this study saw differences in responses of low versus high performing students in both the major and non-majors’ group. For example, we saw an increase in academic success of the higher performing students in our majors in response to the goal setting intervention but did not see this in the lower performing students. These higher academically performing students already had some prior knowledge and usage of goals and consequently the goal setting intervention resulted in the academic improvement we saw in this study. In looking at the constructs including self-control, intrinsic value and control of learning beliefs we saw in increase in the low performers in response to the goal setting intervention but did not see any of this in the high performers while only metacognition was observed to increase in response to the goal setting intervention in the high performers. This study, along with those mentioned earlier, suggests that in order to assist students in attaining their best academic self we cannot use a “one size fits all” approach. Students’ differences in their academic achievement, their major discipline, their own perceptions, prior knowledge and 103 attitudes would ideally need to be considered when appraising the most effective pedagogical interventions. 4.2 Implications The results of this study along with some of the other research mentioned here informs educators that we must try and account for the high diversity that occurs within our student body. We must first develop a broad understanding of our students and differentiate our majors from our non-majors when creating any educational intervention. We have seen that non-majors are capable of doing science and do see value in it; however, they will need more emphasis placed in showing the relevancy of science. As first year non-majors’ courses are likely students one and only opportunity to convey this information this makes it that much more significant to do. Thus, the approach we take to teach non-majors and majors may need to look a little different. We also need to consider students prior knowledge when creating and evaluating new interventions. As this paper and others have described there are significant differences in how lower academically performing students and higher academic performing students learn “new” information, perceive their success in learning it and their attitudes towards it which will all influence their overall success. Thus, the better understanding we have of these differences in these groups will help to better inform us when trying to enhance our students learning. Unfortunately, with this information in mind we cannot take the “one size fits all” approach when it comes to evaluating the effectiveness of our interventions. 104 4.3 Limitations One of the biggest issues facing researchers utilizing these self-reporting questionnaires is survey fatigue. Researchers are often torn with trying to get as much information as we can while still mitigating the amount of time students have to spend completing these surveys. If we overburden students with questionnaires we may actually start to lose information as students will develop a more “autopilot” method of answering. It is key that we are able to walk that line to get the most information without burnout. Though piloting of the survey was conducted with both undergraduate and graduate students to determine if the length of this survey was too long it is possible that undergraduates who are likely dealing with other surveys in other courses may have experienced survey fatigue and consequently this would have skewed our results. Survey fatigue is one possible explanation for the decrease in participation we saw between the first survey and the third. That combined with student schedules being more hectic towards the end of the term as students begin to face the realities of final exams around the corner may have resulted in the reduced sample size we saw in both the majors and non-majors. It is also possible that teaching assistants may have played a role in the diminished participation, as they may have also become overly busy as the term progressed and either forgot to initiate the survey with their students, or even dismissed the importance of continuing to complete all three surveys. The high variation in caliber of our teaching assistants is also a serious limitation to the study. We have seen from grade differences and from student evaluations that there is a high 105 degree of variability within this group. Hiring both undergraduate and graduate level students mostly from science but also from other disciplines does reduce the continuity of the study. Though teaching assistants were given a specific script to read and a sequence of events to follow, there was no one else in the room with the student to ensure the script was read and that students were given the time needed in class to complete the surveys. The lack of control during survey and intervention intake is also another limitation of the study. As the researcher was not permitted in the lab during these sessions due to the potential conflict that may occur the responsibility for collection of data fell to this diverse group of students. Sample size is generally an issue, and as described earlier we saw a significant decline in survey participation as the term progressed. Of course, this plays a role in the power we get from our statistical analysis and more specifically, this was an issue with our non-majors as we went from 127 to 31. It is highly possible we would have seen more statistical significance across these studies if our sample size had stayed higher in the non-major’s group. The significant difference in overall sample size, both at the beginning and end of term, between the non-majors and majors does negatively affect confidence when making conclusions regarding the differences that exist between these two groups. Ideally, if both the non-majors and majors started off with a similar sample size and finished with a similar sample size we would have much more confidence in our results. Our results were impacted by the lack of participant “buy-in” in two key areas: the time they spent on the intervention, and whether they completed all the surveys. The internalization of the goal setting intervention may also have been affected by lack of “buy-in”. Because we 106 collected less information than we might have, we cannot conclude whether or not this goal setting intervention actually had a significant influence on students if they did not truly “buy-in” to the intervention. We lacked information about our students at the beginning of this study in terms of their previous goal usage, exposure and knowledge of goals, training of goals, and current use of goals; it would have been extremely beneficial to have had that information at the beginning. We then could have then inferred whether or not students were truly already setting goals (and thus they did not bother investing in the intervention), or in the case of students who have had no exposure to goal setting we might have inferred they were struggling in working through the intervention, but still saw it is valuable. It would be worth asking students as part of the overall survey about their perception of the intervention as to whether they saw value in this exercise or not. A pre-examination of our student body with respect to whether they already use goals, if they have a true understanding of what they are pursuing, whether they have ever been taught goal setting in the past and their overall motivation for starting their program of choice, would have really helped to answer many of the questions which have come out of this study. Something else to consider is that students may suffer from participation bias, in which they may inflate their responses because they want to be perceived a certain way. For example, students intending to go to medical school, as would describe many in our majors’ group, may not want to admit to any shortcomings, regardless of the anonymous nature of the study, and might answer slightly differently thus skewing our results. Students may also be in denial about some of their own skill sets (or lack thereof) and consequently respond less honestly to the questions being asked of them. As was discussed earlier there is a difference 107 in how students perceive their own competence based on whether they are high or low academic performers. In either case this would result in our results being skewed in one direction. We have discussed the discrepancies in low performers’ and high performers’ ability to predict their academic success, so it is not a far stretch to assume they may also have challenges being able to perceive their own shortcomings or even strengths. Due to the requirements of the experimental design we were unable to create a control group and as such we must be careful when interpreting results. Unfortunately building in a control group was not possible for this project. 4.4 Future Research As this study has shown there is no “one size fits all” solution for our students. Future work should focus on identifying some of the key differences that exist within our first-year post-secondary student populations that will better inform educational interventions and how best to create the “buy-in” needed to ensure success of any educational pedagogical implementation. Alternatively, we may unnecessarily waste much time, money and other resources in attempting to increase our students’ academic success. 4.4.1 Pre-assessment The development of a tool which can assess differences in students’ majors, academic standing, prior-knowledge, perception, motivation, attitude and background is key. These factors have been shown, both here and in studies mentioned, to impact the success of any educational intervention. Having such a tool can better inform educators when creating their 108 pedagogical interventions to ensure the best possible outcome. A meta-analysis of some of the differences in student responses to interventions which have already been attempted may help to inform the development of this tool. 4.4.2 Not All Created Equal As this research has shown, it is imperative that we implement strategies tailored to our students. When developing future pedagogical interventions, majors and non-majors cannot be treated equally. As we have seen, both groups respond entirely differently from each other, with majors only showing changes in grades and non-majors shifting in various constructs. When evaluating different constructs, we must do so within these groups and not by combining them. To do the latter would result in much information being missed and consequently would get us no further ahead. Similarly, it is uninformative to assess students’ responses to interventions when high and low academically performing students are grouped together. This study provides a clear example of this, as when high and low performing students were assessed together we saw no response to our intervention at all but differences were only seen when they were split. Arguably, had a pre-assessment test been done, we may have separated our students even further, and potentially seen other trends and/or we would have been able to cater the intervention towards the different groups of students. 109 4.4.3 Integration Though this study has shown a single intervention can have some influence on students’ academic success, intrinsic motivation, metacognition, control of learning beliefs and self-control, future research should focus on creating more integrated educational interventions in order to attain the best overall outcome. This will likely mean the creation of multiple versions of an intervention specifically targeted to the groups of students outlined from the pre-assessments. In developing pedagogical interventions researchers should focus on a slow integration of the intervention with much scaffolding and feedback for students. As described earlier, students need processing time when asked to do something that for many of them may very well be the first time. As with anything new we ask of our students, we must take the time to teach them how to do it, give them the opportunity to do it, and make changes where applicable from the feedback we receive. If left on their own, they will be much more likely to become frustrated and even discouraged from participating and/or will put little effort into completing it. It is also imperative that future projects focus on integrating the intervention, where possible, into their curriculum. First year students tend to compartmentalize everything and unless we show them how this is, in fact, integrated they will continue to see it as “just something else they have to complete”. If it is integrated within a course, for example, it will help them to see the relevancy and thus be better able to internalize and see value in the task thus increase their level of intrinsic motivation and self-determination. Ideally an intervention would be built to be integrated within a particular course but would also have a high degree of transferability. The intervention must be built, in such as a way that is obvious to the student 110 and not just the researcher to really drive the transferability point home. In order to facilitate a more generalizable intervention, the intervention must not be content specific but rather focus on building those traits seen in students with learning goal orientations, which will be beneficial in any course a student is pursuing. Instructors can then modify the intervention to make it more or less content specific. 4.4.4 Student “Buy-In” This study, along with others, inform future research that any pedagogical interventions must build in student “buy-in”. In order to most easily facilitate this, researchers must ensure the interventions have some direct academic influence. That is, the intervention should contribute in some way to a student’s overall grade in order to ensure, at the very least, that students complete the exercise. I do not know any educator who has not experienced students not making an effort to do something voluntary because it was not worth marks, or putting very little effort into something worth very few marks. Consequently, educators have to strategize how best to weight their assignments, projects and exams in such a way to reflect the expected work level they anticipate the student putting in. Thus, we need to be fully aware of this when trying to get students to commit to taking on something which may look to the student like additional work. Of course, the issue here is that we are creating a more extrinsic motivator once again, at least initially, however it can be argued that the intervention becomes completely useless if we cannot convince the students to complete it at all. For the most part students will complete an exercise if they have deemed that there is some instant academic gain from the 111 use of their limited time and resources. The first step is just to get them in the door. Once we have externally-motivated “buy-in” researchers can then start to work on shifting towards intrinsic motivators by building in autonomy, competence and relevancy within the intervention. Of course, this will look different for different groups of students as it will be informed from the information attained in the pre-assessment tools. 4.4.5 Faculty “Buy-In” Though we have talked about student “buy in”, faculty perception is also something to consider and may even be as important, as without faculty “buy-in”, students will not even have the opportunity to make improvements. As with the students, faculty may generally may feel stretched too far already may not want to take on an additional task unless they can be sure it is worth it. Someone who feels they have no additional time or resources may have little incentive to attempt something new when there is no guarantee of its success. To compound this issue there are also faculty who have been doing things a certain way for a very long time and who, unless they see a significant reason to modify things are not going to bother putting the effort in. Consequently, if over time researchers can show the development of an accurate pre-assessment tool, the customized educational interventions, determined by the pre-assessment tool, and the overall student academic success in response to these interventions we will be much more likely to get the faculty “buy-in” needed. Though this would be a large undertaking and require multiple terms of data collecting across multiple disciplines without it, regardless of how well intended we are, the interventions are rendered useless if they remain unused. 112 4.4.6 Creating a “New Tool” Although this research suggests that we need to look at students at a more individual level when determining the best pedagogical approach this is not something that is logistically feasible. The results from this research suggests we may be able to separate our students into four distinct cohorts (see Figures A(i) and A(ii) in Appendix A). It is likely that each student would fall within one of these cohorts and, although students in each cohort will likely show a high degree of variability, this would allow us to focus our pedagogical approaches to those four cohorts in order to help the majority of our students. The next steps would be to test the accuracy of this pre-screening tool to ensure the majority of our students do in fact land somewhere within this scale. Following this the “Treatment Tool Kit” would then be created and adapted for use across all four cohorts. 113 Appendices A Figure A.i: Cohort designation pre-screening tool Figure Aii: Cohort Treatment Package Summary Cohort BHigh GradesLow Self-RegulationHigh Goal UsageLow Self-DeterminationCohort AHigh GradesHigh Self-RegulationHigh Goal UsageHigh Self-DeterminationCohort DLow GradesLow Self-RegulationLow Goal UsageLow Self-DeterminationCohort CLow GradesHigh Self-RegulationLow Goal UsageHigh Self-DeterminationGradesGoalUsageSelf-Regulation (Metacognition/Self-Control)Self-DeterminationCohort B: argue they aren’t really learning and don’t know how to make or use goals properly• Step by step goal instruction• Increased proximal goals• Build accountabilityCohort A: All good but can still help• Increase number of proximal goalsCohort D: Target cohort• Understand themselves• Step by step goal instruction• Build high number of proximal goals within distal goals• Build in accountabilityCohort C: Don’t know about goals and/or how to use them• Step by step goal instruction• Build proximal goalsGradesGoalUsageSelf-Regulation (Metacognition/Self-Control)Self-DeterminationAppendix 114 Appendix B 115 116 117 Figure B: Modified SMART goal setting intervention 118 Appendix C 119 120 Figure C: Consent Form. 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Thesis/Dissertation
eng
Interdisciplinary Studies
Vancouver : University of British Columbia Library
Attribution-NoDerivatives 4.0 International
Graduate
Plunkett, Richard
The use of goal setting in improving first-year science and non-science major students' academic success and intrinsic motivation
Text