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Motives and methods: motivation, learning approaches, and academic achievement of students during first year transition to medical school

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Motives and methods: motivation, learning approaches, and academic achievement of students during first year transition to medical school

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MOTIVES AND METHODS:
MOTIVATION, LEARNING APPROACHES, AND ACADEMIC ACHIEVEMENT
OF STUDENTS DURING FIRST YEAR TRANSITION TO MEDICAL SCHOOL

by

Jane Lynn Rosenthal

______________________________________________________

A Dissertation Presented to the
FACULTY OF THE USC ROSSIER SCHOOL OF EDUCATION
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF EDUCATION

August 2012

Copyright 2012 Jane Lynn Rosenthal
ii
Dedication

This dissertation is dedicated to my family - I would not be who I am without
you. To my dad, my mom, and my brothers that I have lost: You have inspired and
encouraged me, and I could not have done it without you. I wish you were here to share
with me. And to my brothers Reed and Tom, and my lovely sisters-in-law Dollie and
Valerie: thank you for always making me feel at home, even when I was off doing crazy
things like getting a doctorate.

iii
Acknowledgements
A special thank you to my committee Drs. Kimberly Hirabayashi, Maura
Sullivan, and Helena Seli. I owe a debt of gratitude to Dr. Nik Gorman, Dr. Roger Girard
and Ms. Vicki Young for helping me collect and analyze my data – you have saved me
numerous times in your always helpful and cheerful ways. And a very special thanks for
my friends and colleagues in the Department of Medical Education at the Keck School of
Medicine who offered encouragement and support during the entire process: Drs.
Guadalupe García Montaño, Joe York, Win May, Cha-Chi Fang, Henri Ford, Donna
Elliott, Dixie Fischer, Joel Schechter, Denise Souder, Jan Trial, Raquel Arias, and
Maurice Hitchcock. I have felt encouraged by your insights and feedback, and I feel
blessed to work with so many kindred spirits.
And to my friends and extended family who are far too numerous to mention
here. You have been there for me through all my highs and lows, and this is something I
can never repay. It has been a long, strange journey made all the better by a wonderful
menagerie of friends. And to Oscar – thanks for always being there.

iv
Abstract
The transition into the first year of medical school can be challenging for many
students. Students may have difficulty adjusting their learning strategies in the fast-paced,
high stakes medical school environment. Medical students may also experience changes
in their expectations and motivation for learning in the medical school learning
environment. The purpose of this study is to identify approaches to learning and
motivational factors reported by students and how these relate to academic achievement
outcomes as the students transition through their first year of medical school.
This study had three overarching goals. First, this study explored the learning
approaches reported by the students, how these strategies change over the course of the
first year, the correlation between these approaches and academic achievement outcomes,
and whether there are differences among underrepresented groups determined by age,
gender, and ethnicity. The second part of the study focused on the motivation of medical
students in the areas of achievement goals, self-efficacy, task value, test anxiety, and self-
regulation strategies, how these variables related to overall academic performance, and
whether differences exist among groups of underrepresented students.
The findings indicated that students shifted to decreased alertness to assessment,
desire to achieve, self-monitoring, but increased in rote memorization strategies and fear
of failure. Effective time management, organizational strategies, and desire to achieve
were most significantly related to achievement and to deep and surface learning
approaches were inversely related to achievement. Underrepresented minority students,
particularly Hispanic and African-American students were most at risk for adopting rote
v
memorization strategies and experiencing higher levels of fear of failure, which appeared
to impact exam performance. Recommendations for admissions, curricular innovations,
and student support are described.
vi
Table of Contents
Dedication ........................................................................................................................... ii
Acknowledgements ............................................................................................................ iii
Abstract .............................................................................................................................. iv
List of Tables & Figures .................................................................................................. viii
Chapter 1: Introduction ....................................................................................................... 1
Background of the Problem ............................................................................................ 1
Statement of the Problem ................................................................................................ 4
Purpose of the Study ....................................................................................................... 4
Research Questions ......................................................................................................... 5
Methodology ................................................................................................................... 6
Assumptions .................................................................................................................... 7
Definition of Terms......................................................................................................... 8
Organization of the study .............................................................................................. 11
Chapter 2: Literature Review ............................................................................................ 13
Transition to the Medical School Environment in the First Year ................................. 13
Approaches to Learning and Study Skills ..................................................................... 19
Motivation and its Influence on Learning ..................................................................... 21
Task Value ................................................................................................................ 22
Self-Efficacy ............................................................................................................. 22
Self-Regulation ......................................................................................................... 24
Achievement Goals ................................................................................................... 24
Interaction of Learning Orientation and Motivation ..................................................... 30
Medical School as a Socio-Cultural Context ................................................................ 33
Underrepresented Minority Students in Medical Education ..................................... 33
Gender in Medical Education ................................................................................... 35
Student’s Age in Medical Education ........................................................................ 35
Chapter 3: Research Methodology.................................................................................... 39
Research Questions ....................................................................................................... 39
Population and sample .................................................................................................. 40
Instrumentation ............................................................................................................. 44
ASSIST Questionnaire .............................................................................................. 44
Motivation Questionnaire ......................................................................................... 48
Data collection .............................................................................................................. 53
Institutional Review Board & Ethics Consideration ..................................................... 55
Data analysis ................................................................................................................. 55

vii
Chapter 4: Results ............................................................................................................. 58
Correlations Amongst Motivation and Learning Approaches ...................................... 58
Findings......................................................................................................................... 61
Research Question 1 ................................................................................................. 61
Research Question 2 ................................................................................................. 63
Research Question 3 ................................................................................................. 69
Research Question 4 ................................................................................................. 74
Research Question 5 ................................................................................................. 79
Summary ....................................................................................................................... 81
Chapter 5: Discussion ....................................................................................................... 84
Discussion of Findings .................................................................................................. 84
Implications and Recommendations ............................................................................. 92
Student Learning and Motivation ............................................................................. 92
First-Year Student Transition ................................................................................... 95
Underrepresented Minorities in the Medical School Context................................... 97
Limitations .................................................................................................................... 99
Delimitations ............................................................................................................... 100
Recommendations for Future Research ...................................................................... 101
Conclusion .................................................................................................................. 102
References ....................................................................................................................... 105
Appendix A: ASSIST Questionnaire .............................................................................. 136
Appendix B: Coding scheme for ASSIST questionnaire ................................................ 141
Appendix C: Motivation Questionnaire .......................................................................... 145
Appendix D: Coding Scheme for Motivation Questionnaire ......................................... 148
Appendix E: Participant Recruitment Letter................................................................... 150
Appendix F: Information Sheet ...................................................................................... 151

viii
List of Tables & Figures

Table 1: Number of Students who Completed surveys…….……………..…….……….41
Figure 1: Histogram comparing the cut-points dichotomizing student ages
based on upper quartile versus upper 5% of scores………………………..….....43

Table 2: Means and Cronbach’s alpha of study participants on ASSIST Assessment..…47
Table 3: Reliability Coefficients of Motivation Questionnaire…………………….……51
Table 4: Student Performance Measures……………………………………………….. 53
Table 5: Timeline for Data Collection…………………………………………………...54
Table 6: Correlations of Learning Orientations and Motivational Constructs………..…59
Table 7: Summary of Paired Samples t-tests Comparisons of Learning Approaches...…62
Table 8: Summary of Stepwise Regression Modeling of the Learning Approaches
on Performance on the Core Exam……………………………………………....64

Table 9: Summary of Stepwise Regression Modeling of the Learning Approaches
on Performance on the Skin Exam……………………………………………….65

Table 10: Summary of Stepwise Regression Modeling of the Learning Approaches
on Performance on the Hematology Exam………………………………….…...65

Table 11: Summary of Stepwise Regression Modeling of the Learning Approaches
on Performance on the Neuroscience Exam………………….……………...…..66

Table 12: Summary of Stepwise Regression Modeling of the Learning Approaches
on Performance on the Musculoskeletal Exam………………………………......67

Table 13: Summary of Stepwise Regression Modeling of the Learning Approaches
on Performance on the Comprehensive Exam………….….………..….……..…68

Table 14: Summary of Between-Subjects Effects for Follow-Up ANOVA Analyses
of Ethnicity on Exam Scores………………………………..……………………70

Table 15: Summary of Mean Differences (Row Mean – Column Mean) by Ethnicity
and Exam………………………………………………….……………………..71

ix
Table 16: Summary of Between-Subjects Effects for Follow-Up ANOVA Analyses
of Age, Ethnicity, and Gender on Learning Approaches…………….…………..73

Table 17: Summary of Mean Differences by Age and Gender by Learning Approaches.74
Table 18: Summary of Stepwise Regression Modeling of Motivational Constructs
on Performance on the Core Exam………………………………………………75

Table 19: Summary of Stepwise Regression Modeling of Motivational Constructs
on Performance on the Skin Exam……………………………………………….76
Table 20: Correlations between Self-Assessment and Academic Achievement……..….78
Table 21: Summary of Between-Subjects Effects for Follow-Up ANOVA
Analyses of Gender on Motivation…………………………...………………….80
Table 22: Summary of Between-Subjects Differences by Gender and Motivation……..81
Table 23: Summary of Findings, by Course……………………………………………..82

1
Chapter 1: Introduction
The transition into medical school is academically and emotionally challenging as
the students cope with fast-paced rigorous studies, high-stakes testing situations, and
meeting high expectations. These challenges may have an impact on the student’s
motivation, goals, study strategies and approaches to learning, which in turn can impact
academic achievement. Evidence suggests that underrepresented minority students may
be particularly at risk for challenges of academic difficulty, which may stem from
ineffective study skills and undirected goals. The present study sought to gain
understanding of these issues.
Background of the Problem
Educational research has demonstrated the importance of academic achievement
goals, value of learning tasks, learning strategies, self-regulation strategies, and self-
efficacy in the process of learning and academic achievement (Elliott & McGregor,
2001). However, these aspects of learning have not been explored systematically in
medical school students, who often struggle with the academic rigor of medical school.
Many medical students struggle to adjust to the academic rigor and seem to be
most vulnerable to adverse academic events and transitional challenges during the first
year of medical school (Ball & Bax, 2002; Parkerson, Broadhead & Tse, 1990; White,
2006). Research has shown that many students begin their medical school studies
academically unprepared, which may challenge their ability to cope with the transition,
(Dyrbye, Thomas & Shanafelt, 2005; Rohe et al., 2006), their motivation, and the study
approaches that they adopt (Chemers, Hu & Garcia, 2001). These challenges often lead to
2
academic struggles, including lower test scores, course failure, or other academic
difficulties (White, 2006).
Generally, medical students are assumed to already have strong academic skills
prior to entry into medical school, and overall these students have much higher
graduation rates than students in other graduate-level or professional programs in the U.S.
(Garrison, Mikesell & Matthew, 2007). However, the complexity of the expectations for
learning, clinical reasoning, self-reflection, professionalism and responsibility in the
medical school environment present unique challenges for medical students (Cooke, Irby
& O’Brien, 2010; Epstein & Hundert, 2002; Hays, Lawson & Gray, 2011; Hendricson &
Kleffner, 2002; Hilton, 2008). In addition to mastering basic science content, students are
also evaluated on applying content to clinical vignettes through complex decision
making, reflecting and evaluating their decisions, which tends to be quite different
compared to previous undergraduate science course. Additional demands of taking
responsibility for patients and team members can add complexity to academic and skill
development, particularly in clinical settings in which even first year students participate
(Cooke, et al., 2010). Thus, the transition process is an important stage to examine in
order to better understand student learning, motivation, and adaptation to the medical
school environment to predict and address difficulties.
Research has suggested that the rigorous schedule and examination demands of
medical school may shift students from deep, meaningful learning strategies to surface,
memorization-based strategies (Bengtsson & Ohlsson, 2010; Linblom-Ylänne and Lonka,
2001). These superficial approaches to learning have been shown to lead to less long-
3
term retention of material and decreased grades at the end of the course (Ward, 2011).
Implicated in this shift to superficial learning strategies, medical students may face
threats to self-efficacy. Papinczak, Young, Groves and Haynes (2008) found a significant
link between high self-efficacy and deep learning approaches, but also found that
students made substantial shifts to surface learning approaches focused on rote
memorization and experienced a decrease in self-efficacy during the course of their first
year of medical school studies. Thus, research suggests that threats to motivation and a
shift to surface learning approaches may have serious consequences to students’ self-
efficacy and long term retention of knowledge.
Although medical students have a high completion rate overall, significant
disparities in achievement exist for underrepresented minority students (Garrison,
Mikesell & Matthew, 2007; Lumb & Vail, 2004). Specifically, Black/African American,
Hispanic/Latino, and American Indian/Alaska Natives have significantly higher non-
completion rates and those that graduate take much longer than their White or Asian
counterparts, often taking as much as ten years to graduate from a program that takes the
majority of White students complete in four years (Garrison et al., 2007). Degree non-
completion and delays in graduation have resulted in significantly increased educational
costs for these students (Donini-Lenhoff & Brotherron, 2010) as well as potential
difficulties in future career opportunities (AAMC, 2010; Tekian, 1999). Thus, the
persistence of underrepresented minorities is an important topic to research.
Given the complexity of this issue and the dearth of research in the interaction of
motivational goals and learning strategies, this study illuminated how student adapt to the
4
challenges of the first year of medical school. Thus, this study will explore the complex
interaction between academic achievement goals, learning strategies, and academic
outcomes of different groups of students in the medical school context so that effective
supports and interventions can be implemented that benefit all students.
Statement of the Problem
The transition to the first year of medical school may be challenging for many
students and may cause shifts in motivation and learning strategies, which may have
implications for persistence and academic achievement. Thus, the interaction between
goal orientation and strategies for learning remains an important topic for exploration.
Additionally, different groups, such as underrepresented minority students, women, and
older students, may have additional challenges adapting to medical school, which may
impact academic achievement. This study explored the important interplay between
student’s achievement motivation, learning strategies, and academic achievement to
better characterize student transition to medical school. Additionally, this study explored
potential difference among different groups of students by gender, race, and age.
Purpose of the Study
The purpose of the study was to gain a better understanding of the interaction of
student motivation, study strategies, and academic achievement outcomes during the first
year transition to medical school. Additionally, subgroups of students were analyzed to
determine if differences exist amongst groups based on race, gender, or age in student
motivation, learning strategies, and academic outcomes.
5
Research Questions
The following questions guided this study:
1) Was there a difference between pre- and post-learning approaches for first
year medical students?
2) Which learning approach was the strongest predictor of academic
achievement outcomes for first year medical students?
3) Were there differences in learning approaches and academic achievement
outcomes between underrepresented and non-underrepresented
populations (in the categories of gender, ethnicity, and age) in first year
medical students?
4) Which motivational constructs were the strongest predictors of academic
achievement outcomes for first year medical students?
5) Were there differences in motivation between underrepresented and non-
underrepresented populations (in the categories of gender, ethnicity, age)
in first year medical students?
Significance of the Study
No prior studies have examined the relationship between motivation, approaches
to learning and academic achievement in a systematic way within a medical school
setting. Moreover, little research presently exists on early medical school experiences that
may present unique challenges to learners as they transition to the rigorous academic
demands of medical school. Therefore, this study was uniquely positioned to address how
these early experiences in the medical school setting shape motivation and learning
6
approaches and how these approaches correlate to academic performance. Moreover, this
study addresses potential issues for various groups of students, including
underrepresented minorities, women, and non-traditional aged students who may have
unique motivational and academic challenges that may explicate disparities that exist in
performance and persistence in medical school.
This study has the potential to inform practice significantly. For example,
admissions, student affairs, and curriculum offices might utilize this information for
addressing unique challenges that medical school entrants might face. Special programs
supports could be targeted to better prepare students the first year transition in
premedical, bridge, and orientation programs to address academic challenges. These
programs could target various motivational factors and specific learning strategies from
which first year medical students can benefit. In addition, programs and interventions
could be implemented for students struggling academically or at academic risk, and
interventions may be more effective through including a targeted combination of
motivational and study skills support. These programs could potentially be enacted
though support personnel and faculty mentors to ensure student success.
Methodology
This quantitative, correlational study utilized demographic data, survey responses,
and achievement data (test scores) for first-year students at the Keck School of Medicine.
The Approaches and Study Skills Inventory for Students (ASSIST; Tait, Entwistle,
McCune, 1998) was given twice, once at orientation prior to the start of classes and again
at the end of the first year to understand the extent to which students change learning
7
approaches and study skills during their early medical school experiences. Further,
correlational analysis was performed to determine the relationship between learning
approaches and academic outcomes, which includes composite scores for examinations in
each of the first year courses. In addition, demographic data such as race, gender, and age
was also included to determine differences amongst groups.
For the incoming cohort (Class of 2015) additional survey data was gathered. In
addition to the ASSIST instrument, students completed a survey of several motivational
constructs and goals for learning. The survey utilized the Motivated Strategies for
Learning Questionnaire (MSLQ; Pintrich & De Groot, 1990; Pintrich, Smith, Garcia &
McKeachie, 1991), which measured self-regulation strategies, self-efficacy, test anxiety,
task value, and test anxiety, and the Achievement Goal Questionnaire - Revised (AGQ-R;
Elliott & McGregor, 2001; Elliott & Murayama, 2008) to measure goals for learning,
which included mastery or performance achievement goal orientations. These instruments
were used to determine relationships between student motivation (as measured by self-
regulation, achievement goals, self-efficacy, test anxiety, and task value), approaches
learning, and academic achievement outcomes. These data were also used to explore
differences amongst demographic groups, by race, gender, and age.
Assumptions
For the purposes of this study, it was assumed that the subjects demonstrated
motivation in some form. It was assumed that the participants were not completely
lacking in motivation and the participants’ responses to the questionnaires were truthful
and reflect real behaviors. In addition, the researcher assumed that changes in the pre-
8
and post- learning approaches questionnaire are in response to the medical school
environment, curriculum, and assessment practices. While it is possible that personal
issues may influence a student’s approach to learning, this study attributed pre- to post-
changes to the academic demands of medical school.
Definition of Terms
For the purposes of this dissertation, the following terms were utilized:
Achievement Goal Orientation: A learner’s purposes or reasons for engaging in
behaviors of achievement in academic program or activity (Pintrich, 2000a). This is
further dissected in mastery orientation and performance orientation.
Mastery orientation: An orientation in which the individual focuses on learning,
mastering the task, self-development or self-improvement, and is often referred to as
“learning for learning’s sake” (Pintrich, 2000a). Mastery orientation is comprised of two
sub-orientations: Mastery-Approach and Mastery-Avoidance. Mastery-Approach: A goal
orientation in which the individual focuses on learning to approach success in mastering
the task or activity with the ultimate goal being learning and understanding (Elliott &
McGregor, 2001; Pintrich, 2000a). Mastery-Avoidance: A goal orientation in which the
individual focuses on learning with the purpose to avoid not learning or not mastering the
task (Elliott & McGregor, 2001).
Performance orientation: A goal orientation in which the individual focuses on
learning for the purpose demonstrating competence to others and may be driven by a
concern of being judged by others (Elliott & McGregor, 2001; Pintrich, 2000a).
Performance is further organized into two possible sub-orientations: Performance-
9
Approach and Performance-Avoidanace. Performance-Approach: A goal orientation in
which the individual is focused on appearing the best at the task or activity in comparison
to others; in this orientation, students may become very concerned with grades or being
the top of the class (Elliott & McGregor, 2001). Performance-Avoidance: is a
performance goal that focuses on not appearing incompetent in comparison to others, for
example, the goal of achieving a task with the intention of not being the lowest performer
in the class (Elliott & McGregor, 2001).
Approaches to Learning: The intention and process that are combined in student’s
learning, including the attitudes and aims that students have with their course of study,
and how actively they engage in behaviors to fulfill those aims (Entwistle & Peterson,
2004). These approaches are further described in a triadic model of deep, strategic, and
surface learning.
Deep learning: A learning orientation that is an active process, in which the
individual seeks to relate ideas, looks for patterns and principles to guide understanding,
uses evidence to support arguments, and shows a concern for logic (Entwistle, 2000).
Deep learning approaches have been linked to both holist (relating ideas) and serialist
(using evidence) learning and thus represents an adaptive and versatile approach to
studying (Entwistle, McCune & Tait, 2006). The deep learning orientation includes the
following dimensions: seeking connections amongst topics for better understanding
(relating ideas), questioning the material and examining what is presented (use of
evidence), a personal, intrinsic desire to learn the material (interest in ideas), and
10
reflecting on and seeking deeper understanding of the presented material (seeking
meaning).
Strategic Learning: An approach that is associated with the learner’s intention to
do well or achieve the highest grade possible through the use of study strategies,
organization, and effective time management and focused on metacognition and self-
regulation (Entwistle, 2000). The strategic orientation includes the following dimensions:
effective use of time and focused studying (time management), effective planning and
conditions for studying (organized studying), using feedback and cues from instructors to
guide studying (alertness to assessment), monitoring effectiveness of studying by
checking for understanding and reviewing as needed (monitoring effectiveness), and
intention to achieve the highest possible grades (achieving).
Surface learning: A learning orientation in which the learner is focused solely on
coping with the task with an emphasis on memorizing the material without understanding
(Entwistle, 2000). Surface orientation includes the following dimensions: a sense of
drowning in the material and negative thoughts (fear of failure), a focus on memorizing
and difficulty making sense of what is being learned (unrelated or rote memorization), a
syllabus bound focus on minimum requirements (syllabus boundness), and feeling unsure
of the reason for studying or being in the program of study (lack of purpose).
Self-Efficacy for Learning and Performance: The student’s expectancy for
success and the student’s confidence in and judgment of his/her abilities to complete an
action directed at a goal (Pintrich et al., 1991; Zimmerman, 2000).
Self-Regulation Strategies
11
The processes and actions that a student uses to control learning, such as
organizing the material, managing resources and time, planning and monitoring learning,
goal-setting, and self-adjusting their actions to increase understanding (Pintrich et al.,
1991).
Task Value: The extent to which the student views the task as interesting,
important, or useful (Pintrich et al., 1991).
Test Anxiety: The degree to which students worry or have emotional reaction to a
testing situation, which can include psychological anxiety, preoccupation, concern, or
negative thoughts about performance (Pintrich et al., 1991).
Organization of the study
Chapter one provides an overview of the study context and problem being
addressed. Chapter two supplies a review of literature pertinent to the problem. The
literature section includes discussion of four primary areas: 1) the medical school context
and challenges of first-year student transition; 2) motivation as an important component
of academic success in medical school; 3) learning approaches and study strategies as an
important component of academic success; and, 4) an overview of the sociocultural
context of medical school with a particular emphasis on describing the adjustment and
academic achievement of underrepresented minorities, women, and non-traditional aged
students.
Chapter three discusses the methodology that was used in the study with attention
given to research design, the context of the current study, the participants, the study
instruments, data collection methods, and an overview of the analysis procedures that will
12
be used. Chapter four presents the results of the collected data. Chapter five focuses on
discussion of the findings, including the significance and implications of the findings.
Chapter five also includes recommendations for intervention and practice based on the
findings and will suggest further areas of research related to findings of this study.
13
Chapter 2: Literature Review
The literature review is organized into three main sections. The first section
focuses on the students’ transition to the medical school environment. Next, approaches
to learning are explored, including what is known about the importance of these
approaches to academic outcomes. Then, motivational factors that may affect student
transition and persistence are also presented. Lastly, current research on the sociocultural
context of medical school will be presented, including demographic data and relevant
research of medical school performance of different groups of students, including
underrepresented minorities, women, and non-traditional aged students.
Transition to the Medical School Environment in the First Year
Medical students are often assumed to already have strong academic skills prior to
entry into medical school, and overall have much higher graduation rates than students in
other graduate-level or professional programs in the U.S. (Garrison, Mikesell & Matthew,
2007). However, the unique complexity of expectations for learning, clinical reasoning,
self-reflection, professionalism and responsibility can present challenges for many
students (Cooke, Irby & O’Brien, 2010; Epstein & Hundert, 2002; Hays, Lawson &
Gray, 2011; Hendricson & Kleffner, 2002; Hilton, 2008). In addition to mastering basic
science content, students are also evaluated on applying content to clinical vignettes
through complex decision making, reflecting and evaluating their decisions. Additional
demands of taking responsibility for patients and team members can add complexity to
academic and skill development, particularly in clinical settings in which even first year
students participate (Cooke, et al., 2010). In recent case study research, Kilminster,
14
Zukas, Quinton, and Roberts (2011) found evidence to support to understanding
transition to medical school as a critically intensive learning period vital not only to
student performance, but also important to the long-term professional development of the
learner. Thus, the transition process is an important stage to study, especially the first
year of medical school studies.
Few studies have addressed challenges in the transition to medical school during
the preclinical first year of medical school. Some research has shown that many students
do not begin their medical studies with sufficient academic skills to meet these demands
(McNamara, 2010; White, 2006). In the United Kingdom, Ferguson, James and Madeley
(2002) found that previous academic performance predicted 23% of performance in the
first two years of medical school and only 6% of performance in clinical environments.
In a qualitative interview study of Swedish medical students conducted by Bengtsson and
Ohlsson (2009), subjects reported many difficulties in their transition to medical school
and reported using strategies such as rote memorization in order to be successful. Most
notably the subjects who struggled reported a decline in curiosity and intrinsic motivation
after starting their studies, which the subjects attributed to a time constraints, keeping up
with studies, and struggling to make connections among various subjects taught in the
curriculum (Bengtsson & Ohlsson, 2009). Recent research has shown a need to explore
the transition of students into the first year of medical school as an important area of
research.
As academic rigor increases throughout medical school, students may have
difficulty adjusting their study skills and experience decreased motivation and lower
15
academic achievement. Similarly, students have been found to overestimate their self-
regulation skills by thinking that the same academic skills that worked in undergraduate
programs would work in medical school (White, 2006). In a study that consisted of in-
depth interviews with students at a Michigan medical school, White (2006) found that
students in traditional medical school environments had particular difficulty self-
assessing their knowledge and setting goals for learning. White also found that students
in a pass-fail curriculum, had difficulty understanding whether they were successful or
not. For these students, the difficulty with self-assessment, goal-setting, and interpreting
their success issues persisted throughout all four years of medical school (White, 2007).
In short, students reported feeling frustrated by not knowing what they did not know and
not understanding how to improve performance, which led to serious negative
consequences for motivation and feelings of autonomy, and led to surface learning
approaches focused on passing or getting good grades rather than learning (White, 2007).
This study has pointed to the need to investigate student adjustment to medical school to
ameliorate the consequences of academic struggles and maladjustment.
Academic self-efficacy and expectations for success have been found to be
important motivational indicators of adjustment, performance, and persistence (Chemers,
Hu & Garcia, 2001). However, many students may be uncertain of their abilities or what
is expected of them (Hurtado et al., 2007) and students can view the new environment as
challenging or threatening based on their experiences, which can affect the coping and
learning strategies students adopt (Chemers et al, 2001). Students who feel challenged by
the environment may adapt strategies or seek assistance in order to be successful;
16
however, students who view the environment as a threat might be disinclined to persist or
they may face academic difficulty (Dyrbye et al., 2010; O’Rourke, Hammond, O’Flynn
& Boylan, 2010; Sobral, 2004). Further, studies have indicated that students may need
additional support for study strategies and other academic skill development in the
medical school setting (Dyrbye et al., 2010). Teunissen and Westerman (2011) found in
their literature review that transitions to medical school, particularly to clinical situations,
students struggled with developing strong self-directed habits and strategies.
Additionally, students in Brennan et al.’s 2010 study noted in their interviews that stress
was significant as the students came to terms with dealing with the responsibilities and
expectations of medical school and reported an overwhelming feeling of not being
supported. Because it is generally assumed that students arrive with successful skills and
adequate knowledge in place prior to beginning studies, many faculty members do not
offer assistance or support to students (Tait & Entwistle, 1996), which is particularly
salient at the medical school level of studies (Dyrbye et al., 2010).
Testing in the medical school environment may present unique challenges to
learners as they adapt to the demands of medical school. The most common type of
examination is the multiple choice format, which can be problematic for some learners in
which many content areas are tested in a relatively short time and learners may be
following cues rather than demonstrating understanding of the material o rproblem-
solving skills (Epstein, 2007). In an interview study of medical students conducted by
Todres, Tsimtisiou, Sihu, and Jones (2012), low-achieving students defined as students
who failed one or more exams reported viewing tests as an end into themselves and
17
adopted surface, short-term approaches to their studies; whereas, high-achieving students
view the tests as a foundation to future practice and therefore adopted deeper learning
strategies to achieve their goals, thus indicating different attitudes and approaches toward
the testing process. In a 2012 comprehensive literature review of medical student
assessment, Al-Kadri, Al-Mamary, Roberts, and van der Vleuten found significant
differences in learning approaches based on the purpose of assessment, including
increased use of surface approaches in summative assessment and increased deep
approaches in formative assessment for all students, which may further indicate student
attitudes and approaches may vary based on the stakes and purpose of the exam. Timing
of exams may also play an important role, such that difficulties may arise when students
have insufficient time for formative experiences in which to expand their learning
through deep learning experiences and reflection (Al-Kadri et al, 2012; Todres et al,
2012).
Mental health issues, including stress and depression, have been associated with
lower academic performance and persistence (Eisenberg, Golberstein & Hunt, 2009); and
mental health issues have been found to be more prevalent in health care professionals
compared to the general population in the United States (Guthrie et al., 1995). This
appears to be particularly true for medical school students (Dyrbye, Thomas & Shanafelt,
2005; Rohe et al., 2006), especially during the first year of studies (Ball & Bax, 2002;
Parkerson, Broadhead & Tse, 1990). In a study conducted at Indiana University School of
Medicine, first year medical students showed significant decreases in socialization, sleep
and exercise, and also showed a significant increase in substance abuse and alcohol use
18
(Ball & Bax, 2002). The study found that stable sleep, exercise and social activities
predicted higher academic performance, and the use of alcohol significantly predicted
lower performance (Ball & Bax, 2002). In a study by Baker (2004), stress increased
psychological anxiety and decreased intrinsically motivated behaviors for students who
were less academically prepared compared to peers. Baker (2004) posited that motivation
works indirectly through perceived stress to influence student academic outcomes. In an
extensive 2002 literature review, Ferguson, James and Madeley found that anxiety related
to a specific event – examinations – had a significant negative relationship with
performance for students in the U.K. The study also found an inverted U-shaped
association between anxiety and performance for students during the first year of medical
school, and their study found that motivational and academic support were important
influences in student performance (Ferguson, James & Madeley, 2002). This indicated
the need to look at the relationship between test anxiety, motivation, and performance for
medical students in the U.S., especially in the first year of medical school when
examinations take a central role in students’ academic lives.
In summary, there is a clear need to look at motivational and cognitive skill
factors to better understand the transition into the medical school environment.
Exploration of the challenges students face in their first year of medical studies may lead
to effective strategies for student retention and higher levels of academic performance.
By giving attention to the motivation and academic skills of our learners, programs can
increase success and persistence. A focus on these aspects of medical education,
19
especially in the first year of medical school is critical to better understand how the
school supports or inhibits transition.
Approaches to Learning and Study Skills
Approaches to learning encompass both intentions and actions that a student takes
to a learning task (Byrne, Flood & Willis, 2004). Moreover, a student’s approach to
learning has an impact on the level of processing that a student will engage in as well as
the student’s level of achievement (Marton & Säljö, 2005). The student’s orientation can
change over time, for different learning tasks, or in different learning environments
(Entwistle & Peterson, 2004). In a series of in-depth interview studies with college
students in Sweden, Marton and Säjlö (1976) noted that students in their study took two
very different approaches to their studies: deep and strategic. This construct was later
expanded by Enwistle and his colleagues (Entwistle, 2005; Entwistle & McCune, 2004;
Tait, Entwistle & McCune, 1998) to a tripartite model of deep, strategic and surface
approaches.
In a deep approach, the learner is concerned with seeking meaning, relating ideas,
using evidence and an intrinsic interest in ideas, which leads to study patterns of looking
for patterns and underlying principles, checking evidence and relating it to conclusions,
examining arguments critically and monitoring understanding (Entwistle & Peterson,
2004) as well as thinking for oneself and with the intention to understand the whole
picture (McCune & Entwistle, 2000). This approach has also been called meaning-
directed in that the focus remains on understanding the meaning of the material,
including working to understand abstraction of meaning within the material and
20
understanding reality (Entwistle & McCune, 2004; Entwistle & Peterson, 2004;
Lindblom-Ylänne & Lonka, 1999, Marton & Säljö, 1976, 2005). This approach was
found to be related to intrinsic motivation for learning, deep processing, and self-
regulation (Entwistle & Peterson, 2004), as well as long-term recall of material (Ward &
Walker, 2008).
In contrast, a surface approach occurs in which the learner focuses on reproducing
content but lacks a purpose or a goal of understanding, and is often accompanied by a
fear of failure (McCune & Enwistle, 2000) and feelings of pressure or anxiety (Entwistle
& Peterson, 2004). The intention of the learner in a surface approach is to cope with the
course requirements, seeing the course as non-cohesive bits of unrelated material, and
having little value or meaning of the material or task (Entwistle & Peterson, 2004). The
student’s learning behaviors may focus solely on minimum course requirements, called
“syllabus boundness” in Entwistle’s terminology, and memorizing lists of facts and
details without purpose or the intention of understanding (Entwistle, 2004; Entwistle &
Peterson, 2004; Long, 2003). This approach has been found to be related to extrinsic
motivation, surface processing, and external regulation (Entwistle & Peterson, 2004).
Surface approach has also been linked with disorganized studying, anxiety, inability to
concentrate on studies, and course failure (Entwistle, Meyer & Tair, 1991; Tait &
Entwistle, 1996).
In a strategic approach, the learner focuses on organization, managing time
effectively, guiding studies based on assessment criteria and course requirements
(Entwistle & Peterson, 2004). The strategic orientation toward learning was found to be
21
related to both internal and external regulation and a vocational or practical interest of the
topic (Entwistle & Peterson, 2004). Entwistle and Peterson (2004) related strategic
orientation to concrete processing approaches.
Efficient and effective study skills are important for transition to a new school
program as well for overall academic success. Deep approaches to learning, such as
seeking meaning, checking evidence to reach conclusions, and looking for patterns and
underlying principles have been linked to higher grades and higher satisfaction in college
(Laird, Shoup, Kuh & Schwartz, 2008). The use of study skills such as effective time
management, using appropriate resources, organized studying, and monitoring
effectiveness of study time was also found to be important for academic success.
Research has revealed that students lacking effective study skills are more likely to adopt
surface learning approaches (Entwistle, Meyer & Tait, 1991). Although these students
may be expending time and energy into studies, they have difficulty learning new
information (Entwistle, 2005), see little value in their studies and may feel overwhelmed
by a fear of failure (Entwistle & Peterson, 2004).
Motivation and its Influence on Learning
Medical students may have various motives and goals that can influence how and
why they engage in their studies. These goals may also impact the extent to which a
student adapts to the medical school program. A student’s goals and reasons for engaging
in learning activities and tasks can influence effort, value of the learning activity,
strategies for learning, persistence, and academic outcomes, and has emerged as the most
influential approach to academic motivation (Elliott, 1999; Wilson, 2009). As the
22
research will show, the constructs of task value, self-efficacy, self-regulation, and
achievement goals have important influence on students’ motives and methods for
engaging in academic studies.
Task Value
Task value is the extent to which the student views the task as interesting,
important, or useful (Pintrich et al., 1991). Task value has been found to be predictive of
academic achievement and intention to persist in a study of undergraduates (Bong, 2001;
Schunk et al., 2008), but few studies have been found that explore task value of medical
students. In a qualitative study of medical students in New Zealand, Wilkinson, Wells,
and Bushnell (2007a) found that student reported “not feeling like a doctor” during the
first two years of medical school, which the respondents attributed to the norm-based
testing practices at the school and a feeling of competition with other students. When the
students shifted to a criterion-referenced, standards-based competency curriculum, the
students reported “feeling more like a doctor” and reported increased satisfaction, more
interest in their studies, and deeper study strategies. (Wilkinson et al., 2007a).
Self-Efficacy
Self-Efficacy is defined as a student’s judgment about his or her own abilities to
complete an action in pursuit of goals (Zimmerman, 2000). Self-efficacy grew out of the
work of Bandura who was interested in the strength and level of self-efficacy evident in
individuals as well the specificity or generality of that self-efficacy. In other words,
Bandura sought to measure self-efficacy in specific tasks and in general feelings of
capability (Bandura, 1997). Self-efficacy has been found to be quite different from self-
23
concept or self-esteem in that self-efficacy in specificity and relationship to tasks and
concept; whereas self-concept and self-esteem are more closely related to self-evaluation
and not related to performance tasks (Zimmerman, 2000). Self-efficacy has been found to
be a strong mediator for academic motivation, skill development, level of effort, and
persistence, and also has been found to influence emotions by decreasing stress, anxiety,
and depression (Chemers, Hu & Garcia, 2001; Zimmerman, 2000). Moreover, research
has demonstrated that a strong sense of self-efficacy engage in self-regulatory processes,
such as self-monitoring, goal setting, and study strategies use, and increased student
commitment to studies and achievement (Zimmerman, 2000).
However, some evidence suggests that medical student’s self-efficacy can
decrease in medical school, with the sharpest drop during the first year (Kaufman et al.,
2001; Pololi & Price, 2000). In a longitudinal study of medical students at the University
of North Carolina, Pololi and Price (2000) found that self-efficacy deteriorated
dramatically in students as they progressed through four years of medical school. Using a
survey created at the school annually with students in the program, Pololi and Price
(2002) also found that the students also perceived a deterioration of relationships with
their teachers and patients. The authors concluded that the diminished self-efficacy and
relationships were due, in large part, to a loss of control and trust for the students and
suggested that medical school strive for learner-centered environments (Pololi & Price,
2002). Thus, there is a need to look at how and why self-efficacy might decline,
especially during the first year of medical school studies. It is also important to look at
24
how self-efficacy is effected by early medical school experiences, particularly as students
adapt to the new environment and interact with peers and faculty.
Self-Regulation
Self-regulation refers to the processes and actions that a student uses to control
learning, such as organizing the material, managing resources and time, planning and
monitoring learning, goal-setting, and self-adjusting their actions to increase
understanding (Pintrich et al., 1991). Wolters, in his 1998 study of undergraduate
students, found that students with well-developed self-regulation strategies reported the
ability to focus more in their studies, fight distractions, improve their physical and
emotional readiness to learn, and gave the students motivation to complete the task. In a
survey of medical schools across the U.S., Paul, Hinman, Dottl, and Passon (2009) found
that the majority of students who struggled academically, sought help for skills related to
self-regulation, including time management, organizational skills, and goal-setting as
preparation for testing. West and Sadoski (2011) also found that strong self-regulatory
skills, particularly time management and self-testing skills, were strong predictors
academic performance during the first year of medical school. In summary, research has
demonstrated the important link between self-regulation and goals, which in turn, may
have a significant impact on academic performance.
Achievement Goals
Achievement Goal Theory seeks to understand an individual’s learning goals to
better understand motivation for engaging in an action or task (Nicholls, 1992). Within
academic contexts, a learner’s achievement goal orientation focuses on making sense of a
25
learner’s purposes or reasons for engaging in behaviors of achievement in academic
program or activity (Pintrich, 2000a; Schunk, Pintrich & Meece, 2008). The learner’s
goal orientation has been shown to have an impact on the use and quality of cognitive
strategies, including planning, utilizing resources, and monitoring progress toward
achieving the learner’s goal (Zimmerman, 1990), which, in turn, controls the quality of
achievement (Covington, 2000), reaction to failure or success (Dweck & Legget, 1988),
and persistence (Shim & Ryan, 2005).
Achievement goal theory includes two orientations: mastery and performance and
the approach or avoidance of these goals. Amotivation, or the lack of motivation, may
also be a potential orientation; however, this study will focus on mastery and
performance orientations because it is unlikely to find amotivated students with a total
lack of motivation in a medical school setting. Indeed, the high levels of academic
success required and the competitive nature of medical school admissions would indicate
some degree of motivation in the student rather than a total lack of it. The next section
with discuss in more detail mastery and performance goal orientations.
A mastery goal occurs when a learner is focused on mastering the material, self-
improvement or developing competence. In the literature, mastery orientation is also
called learning goal (Dweck & Legget, 1988) or task orientation (Ford, 1992; Midgely,
2002; Nicholls, 1984). A mastery orientation leads to actions to fulfill the goals, such as
using self-set standards to master tasks, developing competence and new skills,
attempting to gain knowledge and attempting to accomplish challenging tasks
(Covington, 2000; Schunk, Pintrich, & Meece, 2008). Mastery orientation has been
26
linked to deep learning strategies and were found to be integral to successful academic
outcomes (Entwistle, 2000; Entwistle,Tait & McCune, 2000; Long, 2003). Learners
adopting a mastery orientation have demonstrated a greater interest in the subject, greater
sense of control, a willingness to take risks in order to pursue more challenging tasks, and
greater self-regulation in approaching tasks (Covington, 2000).
However, some studies have shown that mastery orientation can lead to negative
academic achievement in some instances. Senko and Miles (2008) found that mastery
oriented undergraduate students in their study jeopardize their academic success due to
over focusing on topics of personal interest and neglecting topics that were considered
boring or less personally interesting, and these mastery-oriented students received lower
grades in the course, leading the researchers to conclude that a mastery orientation has
potential to jeopardize performance. In a 2008 study of high school and university
students in Germany, Stoeber, Stoll, Pescheck and Otto found that perfectionism was
positively related to a mastery-approach orientation and predicted increases in mastery-
avoidance goals later. Stoeber et al. (2008) suggested in their conclusion that
perfectionism and negative reaction to imperfection was a pattern that could lead to
maladaptive patterns.
These mixed results with respect to academic success of mastery-oriented learners
led Elliott and McGregor (2001) to propose approach and avoidance alignments within
mastery orientation. In a mastery approach alignment the learner is focused on gaining
competence, and in a mastery avoidance alignment in which the learner is focused on
avoiding feeling incompetent (Elliott & McGregor, 2001). Mastery-approach goals differ
27
from mastery-avoidance goals in that the level of competence to which the learner strives.
In an extensive study of undergraduate students, Elliott and McGregor (2001) found that
overall need for achievement, perceived academic engagement, and self determination
were antecedents positively related to a mastery-approach orientation, which was also
positively related to deep processing strategies and subsequent mastery-approach goals
and negatively related to health center visits and performance avoidance goals. In
contrast, Elliott and McGregor (2001) found that fear of failure was positively related as
an antecedent to a mastery avoidance orientation, which resulted in a significant positive
correlation to worry, disorganization, and test anxiety. Elliott and McGregor’s (2001)
study stresses the need to consider both approach and avoidance within the mastery
orientation framework, particularly in a high stakes environment such as medical school.
In contrast to mastery orientation, students with a performance orientation are
more concerned with demonstrating ability and avoiding failure as judged by others and
may be related to competition and rewards (Covington, 2000; Elliott & McGregor, 2001).
It has also been suggested that performance-orientation can be a self-protective
mechanism to not want to be seen as incompetent (Covington, 2000; Pintrich, 1999) or
may indicate perceiving others as a threat (Darnon, Butera & Harackiewicz, 2007). Lovitt
(2008) demonstrated that students with a performance-avoidance goal may fear failure
and have difficulty managing frustration or ambiguity as well as difficulty delaying
gratification. Other researchers suggest that a performance-approach orientation is
beneficial to students (Harackiewicz, et al., 2000; Harackiewicz, et al., 2002; Liem, Lau
& Nie, 2008). Liem et al. (2008) found that a performance-approach was a positive
28
predictor of deep learning strategies and positive peer relationships with classmates. In a
pre- and post-study of undergraduate students that spanned four semesters, Harackiewicz,
et al. (2000) found that performance goals positively predicted grades and academic
performance three semesters later. In a follow up article, Harackiewicz and her
colleagues (2002) endorsed a multiple goal theory in which mastery and performance
goals can combine for optimal achievement and motivation, and they also called for the
need to explore other variables that influence students’ goals, such as interest and the
impact of outcomes such as assessment and grades. Research has shown some potential
psychological challenges for performance-oriented learners. In a series of studies
reported in 2001, Elliott and McGregor found that performance approach was positively
correlated with a fear of failure.
The social environment may also influence a learner’s learning goal.
Environments and social networks that encourage mastery-orientation can influence a
learner’s adoption of mastery-oriented orientation and study approaches (Dompinier, et
al., 2009). Competition can increase performance goal orientation, but seems to only raise
achievement for the high achievers who would most likely seek challenge and persist
anyway. In contrast, competition may hurt the lower performing students who may avoid
the challenge and not persist because of a feeling of helplessness and despair. Ames
(1981) found that students who were in competitive situations had lower self-perceptions
than students who failed at a task and showed less persistence.
These conflicting studies demonstrate a gap in understanding of how goal-
orientation influences achievement and the need for studies in the medical school context,
29
which has a strong emphasis on high-stakes examinations, competiveness, and a high
volume of material. In a study of college students, Garcia and Pintrich (1994) found that
a mastery orientation was stronger in students who felt a sense of choice and in classes
that encouraged individual goal setting. However, the first two years of the medical
school curriculum is a lock-step program that does not allow for student choice in course
selection or course assignments; it is not until the third or fourth year of medical that
students are given choices of electives. In the majority of medical schools, including the
program under investigation in this study, the classroom structure is highly structured and
controlled by the instructor in a lecture setting, and assessment is based on multiple
choice examinations that stress recall of facts to the application of clinical vignettes
(Cooke, Irby & O’Brien, 2010).
In the context of the current study, the students participated in a pass-fail
curriculum that did not have grades. Only a small number of studies were found that
investigate the effect on student pass-fail curriculum. Several studies found that this type
of grading system lowered anxiety in medical students (Bloodgood, Short, Jackson &
Martindale, 2009; Rohe et al., 2006). In a study that compared groups before and after a
medical school curriculum was changed from grades to pass-fail, White and Fontone
(2009) found that students in the pass-fail program performed better overall, but had a
slight (but statistically significant) decrease in second-year performance. White and
Fontone (2009) suggested that pass-fail curriculum lowered competition among students,
increased collaboration, and allowed for additional time for extracurricular activities;
however more rigorous studies are needed in this area. Rohe, et al. (2006) found that
30
pass-fail curriculum enhanced group cohesion among the medical students in their study.
This enhancement of group cohesion and lowered anxiety could affect motivation and
learning orientation of students, but was not studied. In fact, no studies have been found
that explored the effect of a pass-fail curriculum on the motivation and learning
orientations of medical students.
In summary, task value, self-efficacy, self-regulation, and goal-orientation are
important, yet complex factors in medical student life. At this time very little is known
about the nature of these motivational constructs in this population and the role of
motivation on transition to medical school, adoption of study skills, and academic
achievement in the challenging medical school curriculum.
Interaction of Learning Orientation and Motivation
Students who are committed to a goal and have some amount of self-efficacy
choose to engage in activities that they think will lead to attaining their goal (Schunk,
Pintrich, & Meece, 2008). Zimmerman, Bandura and Martinez-Pons (1992) found that
for undergraduate students in their study academic achievement was predicted by the
goals students set for themselves and prior academic achievement. Wolters (1998) found
a positive relationship between both adoption of learning strategies and intrinsic
motivation with greater use of metacognitive and cognitive strategies. Phan (2009) found
positive correlations between mastery goals and self-efficacy, mastery goals and critical
thinking, and deep processing and critical thinking. Further, Shell and Husman (2008), in
a series of studies of advanced undergraduate students, found strong correlations between
mastery goal orientation, a sense of control, knowledge-building practices, and positive
31
affect, but did not find correlations between performance approach and positive affect.
Moreover, surface strategies to learning were linked to learned helplessness and low
feelings of control (Shell & Husman, 2008).
Wolters (1998) found evidence that extrinsic motivators can have a positive affect
on academic performance when engagement is controlled and personally selected by the
student, which may indicate that performance-approach goals may lead to academic
success. William Long (2003) is a study of 4,000 students in a variety of majors found
that academically successful students predominantly demonstrated deep, strategic, or a
combination of deep and strategic learning strategies that were highly integrated and
purposeful.
However, conflicting evidence has suggested that students who identified
themselves as having a performance orientation were more likely to report
procrastination and lower academic achievement (Wolters, 1998). Additionally, West,
Kahn and Kauta (2007) found that students reported procrastination when they view tasks
as difficult or they are uncertain about their ability to complete the task, which may
indicate that complexity of the task or uncertain goals may lead to procrastination.
Several studies have suggested that learning orientation and study skills are
influenced by teaching and assessment in medical school (Entwistle & Ramsden, 1983;
Linblom-Ylänne & Lonka, 2001; Linblom-Ylänne, Lonka & Leskinen, 1999;Ramsden,
1981, 2005). In a qualitative study of medical students in Finland, Linblom-Ylänne et al.
(1999) found that deep learning orientation was predictive of success for cumulative
exams and in clinical settings. Although they acknowledged that some students were
32
successful with strategic, memorization-based approaches, highly successful students
were able to adapt their approaches to the needs of the learning environment and moved
increasingly deep levels of learning as they progressed in their studies (Linblom-Ylänne
et al., 1999).
Evidence has suggested that learning orientations may be dynamic and change
with the environment and learning task (Eley, 1992; Eva, 2003; Gijbels, Van de
Watering, Dochy & Van den Bossche, 2005), and may impact student’s efficacy
(Papinczak, Young, Groves & Haynes, 2008) and long-term knowledge retention (Ward,
2011). In a follow up to their 1999 study, Linblom-Ylänne and Lonka (2001) conducted
extensive interviews with medical students that revealed that the students felt that
examination procedures of the program discouraged deep approaches to learning, and
called for surface learning strategies focused on memorizing instead of applying
knowledge. In a study of first year medical students in West Virginia, Ward (2011) found
that students who shifted to surface learning strategies in an anatomy course retained less
material and thus received lower scores at the end of the year. Papinczak et al. (2008)
found in a study of Australian first-year medical students found a significant link between
high self-efficacy and deep learning approaches, but also found that students made
substantial shifts to surface learning strategies and reported decreased self-efficacy during
the course of their first year. The study also revealed that with the shifts to surface
learning, the students demonstrated lower levels of confidence and decreased the
effectiveness of interventions aimed at supporting academic achievement (Papinczak et
33
al., 2008). These studies have demonstrated a need to explore the impact of transition to
new environment, such as entering medical school, on the learning strategies of learners.
In summary, there is a need to research the interaction of goals, study skills and
academic achievement. Specifically, little is known about how these factors interact in a
traditional medical school environment. This issue seems particularly important at the
time when students are adjusting to a new environment and fast-paced curriculum. In
light of the pace and examination structure of medical school, some evidence has
suggested that students might abandon deep strategies in favor of surface strategies which
may have implications for learning and achievement throughout medical school.
Medical School as a Socio-Cultural Context
Increasing diversity in the physician workforce has been a high priority in the
United States for the last three decades (Barr, Gonzalez & Wanat, 2008; Cohen, 1996;
Carrasquillo & Lee-Rey, 2008). However, recent data have indicated that race and gender
may have a significant impact on the decision to attend medical school, academic
achievement, persistence to graduation, and career choice after graduation (AAMC,
2010). The follow section presents data on the unique challenges of underrepresented
minorities in medical education, as well what is known about the effect of ethnicity,
gender, and age on student persistence in medicine.
Underrepresented Minority Students in Medical Education
For those underrepresented minority students (URMs) who enter medical school,
persistence and graduation of rates have remained low compared to their White and
Asian counterparts (Garrison, Mikesell & Matthew, 2007; Lumb & Vail, 2004). Recent
34
data have shown attrition rates for academic reasons for White and Asian students of less
than one percent. However attrition rates for URMs is much higher for Black/African-
Americans and Hispanic/Latino students, and these students seem to be particularly at
risk for delayed/late completion or non-completion of degree (Garrison et al.,
2007;Wiggs & Elam, 2000). Longitudinal studies have demonstrated that overall
completion rates for Black/African-American students lag far behind those of white
students, with many taking much longer to graduate with only 60% graduating in four
years (Garrison et al., 2007). These delays in graduation have resulted in significantly
increased educational costs for these students (Donini-Lenhoff & Brotherron, 2010) as
well as potential difficulties in securing a post-graduate residency or specialty program
(AAMC, 2010; Tekian, 1999). The data indicate that URM students are at higher risk for
academic difficulties in medical school, but little is currently known about the academic
readiness and adjustment of these students.
Under-represented minority students may face additional transitional and
motivational challenges during medical school studies. In a study of medical students at
five medical schools throughout the United States, Dytbye et al. (2007) found that a much
higher proportion of under-represented minority students had symptoms of burnout and
depression compared to their white counterparts. Moreover, many of the URM medical
students in the study reported feeling racial discrimination and prejudice, which in turn
led to feelings of isolation and a much lower overall quality of life (Dytbye, et al., 2007).
In a longitudinal study, Barr, Gonzalez, and Wanat (2008) found that poor advisement
experiences and negative school experiences led to a decline in interest and persistence in
35
medical studies for the African-American, Latino, and Native American students. These
emotional and motivational factors likely play a role in student achievement and
persistence; however, no studies have been found that attempt to explore the transition to
early medical school experiences or to measure learning and motivational factors for
URM students in a systematic way.
Gender in Medical Education
Women have made significant gains in medical school matriculation and
graduation in the past four decades. And while women have made significant gains in
entry and completion of medical school, differences have been found in the learning and
achievement of female students. Ramsbottom-Lucier, Johnson and Elam (1995) found
that men on average scored higher on academic measures compared to women in the
areas of biology, physics, chemistry and problem-solving, but women performed better
on clinical academic measures. Additionally, Haist, Wilson, Elam, Blue and Fosson,
2000 found that young men and older women were less likely to experience academic
difficulty (as measured by a medical school GPA of less than 2.5 or a failing score on
USMLE Step 1 or 2) compared to younger women and older men. However, no recent
studies have been found that explore the transition of female to students to medical
school, and how motivation and approaches to learning affect academic performance for
these students.
Student’s Age in Medical Education
Age has been shown to be a significant factor in academic achievement in
medical school. During the pre-clinical years of the medical school curriculum, which are
36
generally in the first two years and are largely lecture-based and focused on testing of
factual knowledge, younger students (younger than 27) have been shown to outperform
older students (older than 28) (Haist et al., 2000; Ramsbottom-Lucier et al., 1995).
However, in clinical settings, which generally occurs during the third and fourth years of
medical school, older students demonstrated higher performance (Haist et al., 2000).
Ramsbottom-Lucier et al. (1995) and Kleshinski et al (2009) found that age was a
significant predictor for performance on the USMLE Step 2 in which younger students
did much better than the older students. However, no studies have been found that look at
early medical school experiences for older students in the U.S..
Significant interactions of age and gender have been noted in several studies.
Haist et al., (2000) found interactions of age and gender, in which older women were the
least likely to experience academic difficulty, and older men were the most likely to
experience academic difficulty and have lower performance on academic measures. But
important factors, such as motivation and learning approaches, have not been explored.
Conclusion
In conclusion, studies of undergraduate students have demonstrated that many
students struggle as they transition to a new academic program, and it is likely that
students transitioning to medical school face similar challenges. Moreover, the transition
to an academically challenging program could threaten a medical student’s motivation by
affecting their self-efficacy, self-regulation, academic achievement goals and learning
strategies as they adapt to the challenging medical school curriculum. However, the
extent to which these challenges affect academic achievement for medical students is not
37
known. The medical school environment presents large amounts of material in a very
fast-paced, rigorous program in which students must utilize effective self-regulation
strategies and learning approaches to effectively cope with the demands. Additionally,
high-stakes examinations may lead some students to experience high levels of test
anxiety and maladaptive behaviors that may compromise their performance. The inability
to adapt to the challenges may result in decreased self-efficacy and poor academic
performance, including course failure or delayed graduation. It appears that race, gender,
and age may be significant factors that correlate with a variety of academic performance
measures, including motivational and academic performance, but the extent to which has
not been thoroughly studied.
Academic preparedness, including the use of study skills, and motivation may
play a key role in a student’s transition and academic performance in medical school.
Therefore, it is important to explore the extent to which students adapt their study skills
during their first year of medical school studies, as well as to understand their academic
achievement goals and value of the task of learning so that programs and interventions
can be implemented to ameliorate the challenges of the transition to medical school. A
literature review revealed no studies that address the complex issue of medical student
transition, learning goals and learning approaches during the first year of medical school
using established instruments and in a U.S. medical school context. The exploration of
the variables of goal orientation, task value, learning strategies, self-regulation, test
anxiety, and self-efficacy and their impact on academic achievement will make an
important contribution to the literature. In addition, exploring these variables through a
38
sociocultural lens also has the potential to inform practice and increase the academic
achievement of groups that demonstrate lower academic performance and higher attrition
rates, potentially leading to increased academic performance and successful adjustment.
39
Chapter 3: Research Methodology
The next chapter provides a detailed view of the methods that were used in this
study. The study was quantitative in nature, and explored the relationships between
learning approaches, motivational factors, and academic achievement data. This chapter
first gives an overview of the research questions under investigation and then describes
the details of the study, which includes discussion of the population of the study,
recruitment methods, data collection instruments and procedures, and provides an
overview of the analysis methods that will be used.
Research Questions
The following questions guided this study:
1) Was there a difference between pre- and post-learning approaches for first
year medical students?
2) Which learning approach was the strongest predictor of academic
achievement outcomes for first year medical students?
3) Were there differences in learning approaches and academic achievement
outcomes between underrepresented and non-underrepresented
populations (in the categories of gender, ethnicity, and age) in first year
medical students?
4) Which motivational constructs were the strongest predictors of academic
achievement outcomes for first year medical students?
40
5) Were there differences in motivation between underrepresented and non-
underrepresented populations (in the categories of gender, ethnicity, age)
in first year medical students?
Research Design
This quantitative, correlational study utilized data collected between 2008-2012.
The Approaches and Study Skills Inventory for Students (ASSIST; Tait, Entwistle,
McCune, 1998), was given at orientation prior to beginning classes and again at the end
of the first and second year and was analyzed to ascertain whether students change
learning approaches and study skills during their early medical school experiences. These
results were correlated with academic outcomes data (examination scores) to determine
what relationships exist. In addition, demographic data for race, gender, and age were
also included to determine differences amongst groups.
For the Class of 2015, additional survey data was gathered during the first
semester. In addition to the ASSIST instrument, students were recruited to complete the
Motivation Questionnaire, a 61-item questionnaire developed to reflect four possible goal
orientations, including mastery-approach, mastery-avoidance, performance-approach and
performance-avoidance goal orientations (Elliott & McGregor, 2001), self-regulation,
self-efficacy, and intrinsic/extrinsic goal orientation (Pintrich et al., 1991).
Population and sample
The study was conducted at the Keck School of Medicine at the University of
Southern California. All students enrolled in the first year of the medical school program
were recruited via an email invitation sent by the Information Technology Department
41
directing the student to the survey on the Qualtrics website. Participation was voluntary,
no enticements were offered, and students were assured of confidentiality. The numbers
of students who completed the survey at each administration can be found in Table 2,
below. Recruitment was a challenge for the cohort of 2015, which is discussed in the
limitations section.

Table 1

Numbers of Students who Completed Surveys

Number
of
Enrolled
Students
T1 ASSIST
completed at
orientation
(Late July/early
August)
T2 ASSIST
completed
(March)
Completed
both
ASSIST
surveys
Completed
Motivation
Survey
(October)
Class of 2012
(entering
Fall 2008)
178 106 102 53 N/A
Class of 2013
(entering
Fall 2009)
166 104 96 52 N/A
Class of 2014
(entering
Fall 2010)
172 154 73 60 N/A
Class of 2015
(entering
Fall 2011)
186 78 19 8 67
Total 702 442 290 173 67

The ASSIST surveys were distributed to all students entering medical school in
each year between 2008-2011, and the Motivation Survey was distributed to the class
42
entering in 2011. Because the study was given in multiple phases, demographic data have
been combined for the four years of the study.
The ethnicity composition of the study population was 27% Asian/Pacific
Islander, 5% Black/African American, 13% Hispanic, 0.3% Native American/Alaskan
Native, 43% White, and 11% unspecified/declined to state. The ethnic make-up of the
sample is comparable to percentages seen nationally (AAMC, 2011). The sample was
52% male and 48% female, reflecting a well-balanced sample.
Dichotomizing Age
Age was determined on August first of the year entering medical school. The
mean age of the sample was 23.9 (SD=2.8; range 20-45). Prior research has underscored
the importance of considering students’ age at admittance to medical school. Specifically
Haist et al. (2000) suggested treating age as a dichotomous variable separating students
aged 27 and below at time of admittance from students 28 and older. As little theoretical
rationale has been specified to explain the use of those specific years as a cut point, it was
decided to examine the students’ age of admittance in three ways before settling on a cut-
point. Specifically, in order to establish a cut-point the 5% most extreme age values were
found, age was broken into quartiles, and a histogram of age at admittance was examined,
5% most extreme values
Examination of the data in the present study revealed that students aged 28 and
above comprised the highest 5% of age scores in the sample, with ages ranging
from 28 to 45.

43
Quartiles
Examination of the highest quartile revealed that students age 25 and older
belonged in the top quartile of age.
Visual Inspection of Histogram
Visual inspection of a histogram helped to clarify the best location to establish a
cut-point. As shown in Figure 1, not only does the 27/28 cut-point agree with
previous literature and separate out the most extreme 5% of scores, but it also
serves to cleanly separate the tail from the body of this right-skewed distribution.
Figure 1
Histogram Comparing the Cut-Points Dichotomizing Student Ages Based on Upper
Quartile versus Upper 5% of scores.

44
On the basis of the above analyses, it was decided to dichotomize age at time of
admittance into a low category (age 27 and below) and a high category (age 28 and
above). Not only did this strategy replicate that conducted in past research, but visual
examination revealed it to effectively separate the right skewed tail of the distribution
from the more typical scores observed in the body of the bell curve.
Instrumentation
As previously discussed, two instruments were used in the course of this study.
The Approaches and Study Skills Inventory for Students (ASSIST) and a motivation
survey, which included the Achievement Goal Questionnaire (AGQ-R) and Motivated
Strategies for Learning Questionnaire (MSLQ), were administered. The following is a
detailed explanation of the two survey instruments.
ASSIST Questionnaire
The Approaches and Study Skills Inventory for Students (ASSIST) Questionnaire
(Tait, Entwistle & McCune, 1998) was selected to gain a better understanding of
students’ approaches to learning. Specifically, the triadic model proposed by Tait et al.
(1998) focused on different orientations to learning: Deep Approach (meaning
orientation), Surface Apathetic Approach (reproducing orientation), and Strategic
Approach (achieving orientation).
The instrument originated in the United Kingdom and was created for use with
undergraduate level students; therefore, some adaptations were made to instrument to
make it suitable for American respondents, such as replacing the term “tutor” to
“instructor” and adopting American word spellings. The original version of the survey
45
can be found at Tait, Entwistle, and McCune (1998). Permission was sought from and
granted by Noel Entwistle, one of the main authors of the instrument via email
correspondence. The instrument as it was administered in this study can be found in its
entirety in Appendix A.
The 59-item questionnaire lists statements regarding approaches to learning, such
as “I usually set out to understand for myself the meaning of what we have to learn.” And
the student rates the item on a Likert scale of 1 “disagree” to 5 “agree.” The coding
scheme with an item-by-item breakdown can be found in Appendix B.
The instrument was selected because studies indicated good reliability (α between
.6-.9) in the overall constructs of Deep, Strategic, and Surface learning (see Table 2). To
ascertain whether the items in the ASSIST instrument represented reliable scales,
Cronbach’s alpha values were calculated. The alpha values for the deep, strategic and
surface approaches were within acceptable guidelines (.84, .85, and .80, respectively);
however significant differences were found on subscore factor loadings. However, there
are several areas that have demonstrated inconsistent results. For the deep learning
orientation, use of evidence showed a much lower reliability (α = .59), which is much
lower than previous studies, which may indicate a different emphasis in medical
education or a need to re-evaluate or rephrase the statements related to this construct.
Within the surface/ apathetic approach, lack of purpose showed a very low reliability (α =
.42) indicating that this might also be a problematic construct for medical students.
Lastly, organized studying within the Strategic approach demonstrated a low Cronbach’s
alpha, which suggests these constructs may lack reliability. Sample means and instrument
46
reliability for ASSIST are given in table 2. These alpha values were consistent with prior
published study by the developers of the instrument, Tait, Entwistle, and McCune (1998).
Within the deep approach subscales, somewhat low alpha values were apparent
(seeking meaning =.64, relating ideas =.62, use of evidence =.59, and interest in
ideas =.62) indicating moderate reliability. Surface approach subscores revealed good
reliability for the fear of failure subscore ( =.81), but moderate reliability for unrelated
memorization ( =.65) and syllabus boundness ( =.67). In addition, the lack of purpose
subscale showed very low reliability ( =.42). For the strategic approach subscales, time
management showed high reliability ( =.80) and organized studying demonstrated low
reliability ( =.54), while the other subscores demonstrated moderate reliability
(monitoring effectiveness =.65, alertness to assessment =.68, and achievement
motivation =.65). Greater variation of reliability were found in the preferences for
teaching and learning scales which were included on the survey and have not been fully
validated in prior research (Entwistle, 2006) and were not central to this study. A single
question about self-assessment “Based on your work so far, how are you doing?” is
included in the table for informational purposes only to demonstrate change from time 1
and time 2.
47

48
Few studies exist that used the ASSIST instrument with medical students. In a
study of medical students at the University of Aberdeen in the United Kingdom, three
items proved problematic (Entwistle, personal correspondence; Long, 2003). First,
Long’s (2003) study indicated that some deep strategy items demonstrated lowered factor
ratings, which demonstrated that the wording on some of these items may have been
problematic for medical students. These deep items also were negatively associated with
a lack of purpose, suggesting a “purposive deep” approach (Entwistle, 2006). A second
difference was found in which monitoring effectiveness, alertness to demands, and
syllabus boundness, which was interpreted as a strategic approach in which the students
emphasized second-guessing the test creators and focusing effort toward instrumental
goals (Entwistle, 2006, personal correspondence). This difference seems to make sense
given the volume of material and the rapid pace of the curriculum. The third issue that
arose in the medical students of Long’s 2003 study was that the items on seeking
meaning and use of evidence did not correlate well and may need to be rephrased for the
medical student population. However, the population of students at the University of
Aberdeen is quite different from the medical students in this study in experience, age, and
structure of medical school program. These questions were included in the previous
administrations within this study; therefore, it is expected that some inconsistencies
would be found in these items on the analysis, which is discussed in the results section.
Motivation Questionnaire
The Motivation Questionnaire used in this study draws on previous validated
instruments, including the Achievement Goal Questionnaire (Elliott & McGregor, 1999)
49
and the Motivated Strategies for Learning Questionnaire (Pintrich et al., 1991). These
instruments were chosen because of high reliability and good fit for the study.
Achievement Goal Orientation (AGQ-R). To survey the students’ achievement
goal orientation, the Achievement Goal Questionnaire - Revised (AGQ-R; Elliott &
McGregor, 2001; Elliott & Murayama, 2008) was selected. This instrument utilizes the
2x2 model of achievement goals by Elliott and McGregor (2001), as described in the
literature review. Permission was sought and was granted by Andrew Elliott, the author
of the instrument. Reliability coefficients for the constructs can be found in Table 4,
below. The instrument has been widely used in prior studies and has demonstrated
reliability (α > .80) in these studies. Prior studies by Elliott and McGregor (2001)
demonstrated the reliability coefficients are as follows: Mastery-Approach (.87),
Mastery-Avoidance (.89), Performance-Approach (.92), and Performance-Avoidance
(.83). The AGQ contains 12 questions that are scored on a 5-point Likert scale (strongly
disagree to strongly agree. There are 3 statements for each of the subscales: mastery-
approach, mastery-avoidance, performance-approach, and performance-avoidance
orientations. The full text of the survey can be found in Appendix C.
Motivated Strategies for Learning Questionnaire (MSLQ). The MSLQ (Pintrich et
al., 1991) was selected as the instrument to measure additional motivational constructs
under examination in this study. Developed by Pintrich, Smith, Garcia, and McKeachie
(1991), the MSLQ has been widely used in numerous settings, including higher education
and medical school. However, the MSLQ was designed to measure course-level
motivational constructs (Pintrich et al., 1991), which did not provide a perfect fit for this
50
study. Rather than a focus on a specific course or domain, this study is directed at student
motivation in the medical school curriculum, which is composed of numerous subjects
covered within systems-based courses, such as Hematology, Skin, and Neuroscience.
Thus, the MSLQ was not used in its entirety, and scales were selected on best fit for the
setting and study. Four scales were selected which constituted a total of 43 items and are
rated by the students on a Likert scale of one ("not at all like me") to seven ("very much
like me"). Scales that were selected for this study include task value (α = .90), self-
efficacy for learning and performance (α = .93), test anxiety (α = .80), and metacognitive
self-regulation (α = .79). The complete text of the survey as it was presented to the
participants can be found in Appendix E. The subscales that were excluded because of a
poor fit included intrinsic goal orientation, extrinsic goal orientation, time and study
environment, and peer learning. Several scales of the MSLQ were not selected for
inclusion because of low reliability (α < .70); these subscales included control of learning
beliefs (α = .68), rehearsal (α = .69), organization (α = .64), effort regulation (α = .69),
and help seeking (α = .52).
To assess whether the motivational constructs formed reliable scales, Cronbach’s
alpha values were computed. The alpha values for all items except self-regulation were
between .70 and .89 showing good internal consistency reliability. The alpha for self-
regulation was .61 indicated much lower internal consistency. Correlations for the
Motivation questionnaire are shown in Table 3.

51
Table 3

Reliability Coefficients of Motivation Questionnaire (n=67)

Number of
items
Mean (SD)
α (current
study)
α (previous
studies)
Achievement Goal Questionnaire (AGQ-R)
Elliott &
McGregor,
2001
Mastery-Approach 3 6.19 (.73) .74 .87
Mastery-Avoidance 3 5.06 (1.34) .70 .89
Performance-Approach 3 5.45 (1.22) .86 .92
Performance-
Avoidance
3 5.37 (1.36) .89 .83
Motivated Strategies for Learning (MSLQ)
Pintrich et al
(1991)
Self-Efficacy 5 6.05 (.80) .81 .93
Self-Regulation 14 4.91 (.65) .61 .79
Task Value 6 6.24 (.70) .89 .90
Test Anxiety 5 3.11 (1.47) .86 .80

Academic Achievement Outcomes
The academic achievement outcomes consisted of scores for the following first
year courses: Core Principles, Skin, Hematology, Neurosciences, and the
Musculoskeletal System, as well as a Comprehensive Cumulative examination given at
the end of the year. There was no variation of the order of the courses or type of
assessments given to the various cohorts included in this study. Core Principles was
considered an introductory course that focused on a broad range of topics; whereas, all
other courses focused on more specific systems within medicine. Core Principles was the
first course and included an overview of a diverse of topics, including gross anatomy, cell
biology, pathology, biochemistry, microbiology, nutrition, behavioral sciences,
52
biostatistics, epidemiology, and a variety of other topics. The course was divided into two
sections, Core I and Core II; Core I was nine weeks concluding with an intensive final
exam week (five days of examinations which was divided into exams on gross anatomy,
molecular medicine, bio-structural medicine, disease and body’s response to disease,
behavioral and systems medicine) and Core II was ten weeks and included a midterm
exam and four full days of exams similar to Core I. However, a single grade was
aggregated from all exams taken over the course of the nineteen-week course and was the
source of data for the current study. The Skin system was the second course taken in
January, covered dermatological normal functioning and pathogenesis of disease, and
was a short one-week course concluding with a single exam. Hematology followed Skin
and was six weeks in length and ended with a single exam that constitutes the entirety of
the course grade. Hematology focused on blood and immunological functioning and
disease. The Neurosciences system followed Hematology immediately after spring break
and includes a midterm and a final exam over the course of the nine-week course. As the
name implies, Neurosciences focused on aspects of neurological functioning and disease,
and the course included a significant amount of neuroanatomy, microanatomy, and
pathology. The final course in the first year curriculum was the four-week
Musculoskeletal course, which focused on bone and muscle functioning and disease.
Approximately one week after the conclusion of the Musculoskeletal system, students
were required to pass a comprehensive cumulative exam that included material from all
the previous courses.
53
Thus, the academic achievement outcomes used in this study were comprised of
the final numerical score given in each of these courses and the comprehensive
cumulative exam. Means, standard deviations, and ranges for each exam are given below
in Table XX. Although course failure was not specifically used as a variable in this study,
a score of seventy or above was considered a passing grade in each course, with the
exception of the Comprehensive final exam which had a pass score of 65 or above.
Achievement measures (exam scores) of the sample are provided in Table 4,
below. The means and stand deviations for each course did not show any significant
differences amongst the four cohorts. Differences among groups by gender and race were
found and are elaborated in the discussion research question three in chapter 4.

Table 4

Student Performance Measures

System n Mean Range SD
Core 693 84.92 36-96 6.74
Skin 699 89.70 61-100 5.96
Hematology 518 82.03 47-99 8.34
Neurosciences 513 84.63 55-99 7.51
Musculoskeletal 510 85.12 30-99 8.49
Comprehensive
Exam
504 80.42 53-99 8.37

Data collection
All surveys were administered online via Qualtrics to students at the Keck School
of Medicine. The Information Technology Department generated an email for each
enrolled first year student inviting them to volunteer to take the survey. All survey
54
responses were coded so that all responses were not identifiable or attributable to
individual students, thus ensuring confidentiality of all participants. A timeline for data
collection can be found in Table 5 below.

Table 5

Timeline of Data Collection

ASSIST (T1)
Orientation
ASSIST (T2)
Spring of first
year
Motivational
Questionnaire
Class of 2012
Late July/Early
August 2008
March 2009 N/A
Class of 2013
Late July/Early
August 2009
March 2010 N/A
Class of 2014
Late July/Early
August 2010
March 2011 N/A
Class of 2015
Late July/Early
August 2011
March 2012 October 2011

Student demographic and academic achievement outcomes data were provided
by the University’s Curriculum Department. The Curriculum Department coded the data
so that individual student names were not identifiable in the study. The Curriculum
Department provided demographic information for age (as of August first of the year the
student began medical school), gender, and race. The site provided very detailed
demographic data for student race data. For ease of analysis, two broader categories were
created to reflect the literature. Chinese, Filipino, Indian, Japanese, Korean, Pacific
Islander, Pakistani, Vietnamese, and students who marked “other Asian” were combined
into the broader category of Asian. Cuban, Mexican-American, Puerto Rican, and
55
students who indicated “Other Hispanic” were combined into the broader category
“Hispanic” for enhancing the strength of analysis.
Institutional Review Board & Ethics Consideration
Institutional Review Board approval was obtained prior to beginning data analysis
(USC UPIRB # UP-11-00319). Because much of the data has already been collected as
part of normal educational practice and pose no threat of harm to the participants
expedited approval was granted. To protect the participants’ identities, all identifiable
information was removed and coded by a data analyst who not an investigator in this
study and all data were password protected on the Qualtrics site and on the computer
used for analysis. The motivation survey was an instrument not used previously;
therefore, a recruitment letter and fact sheet were created for participant reference and
sent with the survey link. Both documents can be found in Appendix F and G,
respectively.
All collected data were coded using numeric identifiers to ensure confidentiality
of all study subjects, and the identities of all participants were not known to the
investigator to ensure anonymity and unbiased analysis. All computers and files pertinent
to the study were password protected and not available to anyone other than the
investigator.
Data analysis
The data were analyzed utilizing the IDM Statistics Program for Social Sciences
(SPSS) version 20.0 for Mac. In order to explicate the data analysis that were used, each
56
research question is reiterated below and followed by a description of the statistical
method used.
Research Question 1: Was there a difference between pre- and post-learning
approaches for first year medical students? To answer this question, pre- and post- survey
data was compared for those students who completed the ASSIST survey at T1
(orientation) and T2 (during the spring semester of first year). To first check if there is a
difference in these students, a paired sample t-test for dependent variables was performed
for all paired surveys. This was found to be insufficient for the study because of risk of
error; therefore, a Bonferroni correlation analysis was performed.
Question 2: Which learning approach was the strongest predictor of academic
achievement outcomes for first year medical students? To answer this question, the
ASSIST data was compared to the exam scores for courses taken during the first year.
These courses include Core Principles, Skin, Hematology, Neurosciences, and
Musculoskeletal System. ASSIST learning orientation (IV) was used to determine predict
the exam score (DV) of the courses to ascertain whether a relationship existed using a
multiple linear regression statistical test. Due to the large number of learning approaches
in the study, a stepwise model-building procedure was used to decrease the risk of error.
Research Question 3: Were there differences in learning approaches and academic
achievement outcomes between underrepresented and non-underrepresented populations
(in the categories of gender, ethnicity, age) for first year medical students? To answer this
question, demographic data including race, gender, and age of the students (IV) was
analyzed to understand what relationships exist with the following dependent variables
57
learning approaches (ASSIST data) and exam scores. A multiple analysis of variance
(MANOVA) was used to better ascertain the relationships among these variables. In
order to
Research Question 4: Which motivational constructs were the strongest predictors
of student academic achievement outcomes for first year medical students? Analysis was
performed to include the constructs that are included in the motivational questionnaire,
including self-regulation strategies, self-efficacy for learning, test anxiety, task value, and
achievement goal orientation. Multiple linear regression analysis was used to test if these
motivational constructs (IV) predicted the dependent variables of learning approaches
(ASSIST) and exam scores.
Question 5: Were there differences in motivation between underrepresented and
non-underrepresented populations (in the categories of gender, ethnicity, age) in first year
medical students? To answer this question, the relationship between the nominal
demographic data, including race, gender, and age (IV) will be compared to the
motivational constructs (DV) using a t-test.
In summary, by analyzing survey data collected by the students, an understanding
of students’ learning strategies and motivation were linked to academic achievement
outcomes. Understanding the relationships among these variables has the potential to gain
important insights in successful strategies for student leaning in medical school, as well
as gleaning useful information that could be used to design programs and interventions to
improve student learning.
58
Chapter 4: Results
The current study was focused on the relationships amongst learning strategies,
motivation, which included student achievement goals, self-efficacy, task value, and test
anxiety, and student achievement as medical students transition in their first year of
studies. Specifically, the study first looked at the extent to which students change their
learning approaches during their first year of medical school and the relationship of
learning approaches to academic achievement as measured by student test scores. The
second part of the explored the motivation reported by the students, which included
achievement goals, self-efficacy, self-regulation, task value, and test anxiety, and the
extent to which these motivational correlate to academic achievement. The study also
included analysis of difference amongst groups of students based on gender, race, and
age (under 27 and over 28) to determine whether differences exist for learning
approaches and motivational constructs. The following section first presents the
relationships amongst the motivation and learning constructs and then the findings for
each of the five research questions is discussed.
Correlations Amongst Motivation and Learning Approaches
Although there was an insufficient response rate for students completing both the
learning approaches and motivation surveys to make direct comparisons, correlations
were computed to ascertain whether relationships exist amongst the constructs under
investigation. Because of the volume of constructs in this study, only those constructs
demonstrating moderate to high reliability were included in Table 6.
59

60
The correlational analysis revealed a strong and significant relationships between
deep seeking meaning and self-regulation (r=.66, p≤ .001), mastery approach and task
value (r=.67, p≤ .001), the strategic subscales of time management and achieving (r=.61,
p≤ .001), and the goal orientations of performance approach and performance avoidance
(r=.60, p≤ .001). Moderate effect correlations were evident including the following: the
surface orientation approaches of unrelated (rote) memorization and fear of failure
(r=.59, p≤ .001), deep seeking meaning and strategic monitoring effectiveness (r=.49, p≤
.001), deep seeking meaning and relating ideas (r=.57, p≤ .001), and deep relating ideas
and interest in ideas (r=.50, p≤ .001). Moderate relationships were also found between
self-regulation and deep relating ideas (r=.43, p≤ .01), self-regulation and strategic
alertness to assessment (r=.40, p≤ .01), task value and self-efficacy (r=.42, p≤ .01),
mastery approach and strategic monitoring effectiveness (r=.44, p≤ .01), and an inverse
correlation was found between surface syllabus boundness and test anxiety (r= -.44, p≤
.01) which means that the higher the test anxiety, the more bound to the syllabus the
learner is.
Although there was a low correlation, there was a significant negative relationship
between surface strategies and deep strategies. Surprisingly, a positive, but low
correlation was found in strategic orientation and both deep orientations and surface
orientation. This may indicate a more complex interaction of these factors rather than
clearly categorized strategies for the population of this study or, more broadly, medical
students.

61
Findings
This section describes the findings for each research question. Research Questions
one, two, three discuss research questions relate to learning approaches, academic
achievement, and whether differences exist among groups of students. Research
questions four and five discuss motivation and academic achievement, and whether
differences exist among groups of participants by race, gender and age.

Research Question 1: Were there any differences between pre- and post-learning
approaches for first year medical students?
In order to determine whether first year medical students change their learning
approaches from the beginning to the end of their first year of medical school, a series of
paired-samples t-tests were conducted to detect changes across 18 measures of learning
strategies (see Table 7). Due to the risk of an inflated familywise error rate associated
with multiple testing, a conservative Bonferroni correction was applied by dividing the
standard alpha value of .05 by the 18 tests to be run, resulting in statistical significance
being claimed only for tests that were significant at the .003 level of significance
(.05/18).
As shown in Table 7, first year medical school students made statistically
significant (p  .003) alterations to four of their learning approaches by the end of their
first year of medical school. Specifically, they were found to shift on surface learning
strategies, including decreased syllabus boundness (t(4.21), p< .001), but higher scores
on surface memorization strategies (t(-3.18), p≤ .002), and fear of failure (t(-2.95), p≤
62
.004), at the end of their first year. Deep orientation strategies, including relating ideas,
using evidence, and interest in ideas showed no significant change. Strategic orientation
for alertness to assessment, achieving, and self-monitoring showed significant decreases
during this year, which means that students reported increased attention to strategic
strategies in their studies. This means that students changed their study strategies in small
but significant ways. Rather than using strategies that led to deeper, more thorough
understanding, the students reported increased use of strategic approaches, including rote
memorization strategies. This was coupled with an increase in fear of failure and a
decrease in desire to achieve.

Table 7

Summary of Paired Samples t-tests Comparisons of Learning Approaches (n = 167)

Variable
Mean
Pre
Mean
Post
Test
Statistic

df

sig
Deep Orientation 3.83 3.83 0.19 159 .85
Relating Ideas 3.64 3.66 -0.41 159 .68
Use of Evidence 3.74 3.72 0.41 159 .68
Interest in Ideas 4.25 4.25 -0.03 159 .98
Strategic Orientation 3.79 3.68 3.18 159 .002
Time Management 3.80 3.83 -0.59 159 .56
Alertness to Assessment 3.48 3.35 2.41 159 .02
Achieving 4.19 4.03 2.84 159 .005
Self-Monitoring 3.82 3.72 1.98 159 .05
Surface Orientation 2.75 2.77 -3.64 159 .72
Syllabus Boundness 3.05 2.68 4.21 159 <.001
Memorizing 2.49 2.68 -3.18 159 .002
Fear of Failure 2.74 2.98 -2.95 159 .004
Learning Preference
Instrumental 4.37 4.37 -0.17 166 .87
Developmental 4.30 4.29 0.31 166 .76

63
Research Question 2: Which learning approach was the strongest predictor of academic
achievement outcomes for first year medical students?

In order to determine which of the 18 baseline learning approaches investigated
would most strongly predict students’ performance across 6 exams (Core, Skin,
Hematology, Neurology, Musculoskeletal System, and Comprehensive), multiple
regression modeling was conducted. Due to the volume of learning strategies investigated
and the absence of a guiding theory as to the order of entry of the 18 predictors, the items
were entered using a stepwise model-building procedure to minimize and balance the risk
of type I and II errors. Differences were found amongst the courses, so each will be
addressed separately to explicate these relationships.
The stepwise analysis found that the strategic strategy of time management was
the most significant predictor (p ≤ 0.001) of performance on Core, the first course in the
medical school curriculum. The surface strategy of rote memorization was inversely
related to performance. A developmental preference for learning, signified by items such
as defining learning as developing as a person and understanding material for yourself,
was also inversely related to performance (p ≤ 0.05). Although the correlation was weak-
to-moderate, time management and rote memorization were the most significant factors
(see Table 8). Thus, as students began adjusting to medical school studies during their
first course, time management was a very important factor for success in the Core course.

64
Table 8

Summary of Stepwise Regression Modeling of the Learning Approaches on Performance
on the Core Exam (n = 373)

Variable

B

SE B


Model 1
Strategic Time Management 1.51 .35 .22***
Model 2
Strategic Time Management 1.67 .35 .24***
Learning Preference: Developmental -1.82 .61 -.15**
Model 3
Strategic Time Management 1.63 .35 .24***
Learning Preference: Developmental -1.90 .61 -.16**
Surface Unrelated Memorizing -.98 .43 -.11*
Note. R
2
= .05 for Step 1; R
2
for step 2 = .02; R
2
for step 3 = .01 (ps < 0.01).
* p  0.05. ** p  0.01. *** p  0.001

In the second course of the curriculum, Skin, the strategic achieving strategy
characterized by a determination to do well, was a small but statistically significant
predictor (  = .21) of achievement (see table 9). Thus, each point increase on the strategic
achieving scale, model 2 predicts a 2.06 point increase in subjects’ Skin Exam score. The
cluster of deep orientation, which includes the combination of seeking meaning, relating
ideas, use of evidence, and an interest in ideas none of which were found to be significant
independently, also demonstrated a small but significant inverse prediction (  = -.13) of
Skin system test score. Thus for every point of increase the deep orientation scale, model
2 predicts a 1.51 drop in the student’s exam score. The Skin course is very short with
only one week of instruction and one exam that is composed of multiple-choice
questions. It is possible that because of the briefness of the course, a desire to achieve
was very important for success.
65
Table 9

Summary of Stepwise Regression Modeling of the Learning Approaches on Performance
on the Skin Exam (n = 377)

Variable

B

SE B


Model 1
Strategic Achieving 1.74 .50 .18***
Model 2
Strategic Achieving 2.06 .51 .21***
Deep Orientation -1.51 .60 -.13*
Note. R
2
= .03 for Step 1 (p  .001) ; R
2
for step 2 = .02 (p  .05)
* p  0.05. *** p  0.001

The Hematology course shows a small but highly significant prediction for the
strategic strategy of time management on the exam score ( = .24) in exam performance
(see table 10). Similar to the case for the Core course, time management was correlated
with success in the Hematology course. As a six-week course, hematology test score was
significantly correlated with the ability to keep up with complex material over a longer
time frame.

Table 10

Summary of Stepwise Regression Modeling of the Learning Approaches on Performance
on the Hematology Exam (n = 312)

Variable B SE B 
Model 1
Strategic Time Management 2.01 .50 .22***
Model 2
Strategic Time Management 2.13 .50 .24***
Learning Preference: Developmental -1.73 .87 -.11*
Note. R
2
= .05 for Step 1 (p  .001) ; R
2
for step 2 = .01 (p  .05)
* p  0.05. *** p  0.001

66
The Neurosciences course also demonstrated prediction based on the strategic
strategy of time management, which was found to be small (β=.25) in model but highly
significant (p ≤ .001) in all models (see table 11). Neurosciences was a long course
lasting 9 weeks and involved significant hours of lab work, which may be connected to
the increased emphasis on time management as the most important learning strategy for
success on the examinations. The testing schedule may have also influenced the students’
study approaches in which four examinations were given during the course and these
included a gross anatomy midterm and final, as well as multiple-choice midterm and
cumulative final that covered the material presented in the course. This means that time
management had a small but statistically significant importance in the Neurosciences
course.

Table 11

Summary of Stepwise Regression Modeling of the Learning Approaches on Performance
on the Neuroscience Exam (n = 312)

Variable B SE B 
Model 1
Strategic Time Management 3.31 .72 .25***
Model 2
Strategic Time Management 3.53 .72 .27***
Learning Preference: Developmental -3.02 1.24 -.13*
Model 3
Strategic Time Management 2.49 .89 .19**
Learning Preference: Developmental -3.35 1.25 -.15*
Strategic Achieving 2.60 1.31 .14*
Note. R
2
= .06 for Step 1 (p  .001) ; R
2
for step 2 = .02 (p  .05); R
2
for step 3 = .01 (p  .05)
* p  0.05. ** p  0..01. *** p  0.001

67
Musculoskeletal system course demonstrated differences compared to other
courses (see Table 12). Strategic orientation, which includes organized studying, time
management, alertness to assessment, and achieving, was shown to be the strongest
predictor in all the attempted models ( =.38, p ≤ .001). Interestingly, self-monitoring
which is considered a strategic strategy had a small but significant inverse relationship
( =-.20, p ≤ .01) to the musculoskeletal exam scores. The self-monitoring orientation
includes items about planning and checking for understanding, which may be challenging
within the time constraints of a four-week course. The timing of the musculoskeletal
course may also play a role in which is the final course of the first year curriculum at
which time students have presumably developed study habits and a understanding of
expectations.

Table 12

Summary of Stepwise Regression Modeling of the Learning Approaches on Performance
on the Musculoskeletal Exam (n = 312)

Variable B SE B 
Model 1
Strategic Orientation 6.20 1.42 .24***
Model 2
Strategic Orientation 9.77 1.92 .38***
Strategic Self-Monitoring -4.40 1.61 -.20**
Model 3
Strategic Orientation 9.58 1.91 .38***
Strategic Self-Monitoring -4.30 1.60 -.20**
Definition of Learning: Remembering things well -1.55 .72 -.12*
Note. R
2
= .06 for Step 1 (p  .001) ; R
2
for step 2 = .02 (p  .01); R
2
for step 3 = .01 (p  .05)
* p  0.05. ** p  0.01. *** p  0.001

68
The Comprehensive exam, which is given at the end of the year and covers all the
material covered throughout the year, also demonstrated the relationship between exam
score and strategic achieving orientation in the three models tested (see Table 13).
Although there was a small relationship between these variable (  = .18; p  0.01), the
relationship was significant. Surface strategies, which includes lack of purpose, rote
memorization, syllabus boundness, and fear of failure, was inversely correlated to
achievement on the comprehensive exam in a small but significant relationship ( =-.12, p
≤ .05). Covering a broader range of material, namely the entire first year curriculum on a
single test, a desire to achieve seemed to play a dominant role.

Table 13

Summary of Stepwise Regression Modeling of the Learning Approaches on Performance
on the Comprehensive Exam (n = 302)

Variable B SE B 
Model 1
Strategic Achieving 2.03 .74 .16**
Model 2
Strategic Achieving 2.37 .74 .18**
Learning Preference: Developmental -2.25 .86 -.15**
Model 3
Strategic Achieving 2.28 .74 .18**
Learning Preference: Developmental -2.42 .86 -.16**
Surface Orientation -1.65 .75 -.12*
Note. R
2
= .03 for Step 1 (p  .01) ; R
2
for step 2 = .02 (p  .05); R
2
for step 3 = .02 (p  .05)
* p  0.05. ** p  0..01.

In summary, the data indicated strategic strategies were most correlated with
exam performance in all courses. However, some unique features were found. For
69
example, time management was the strongest predictor for the core, hematology and
neuroscience courses, and self-monitoring and achieving were the strongest predictors in
the musculoskeletal and skin exams, respectively. These differences may be related to the
length of the courses. Skin is one week and musculoskeletal is four weeks; whereas the
others range between eight to nineteen weeks.

Research Question 3: Were there differences in learning approaches and academic
achievement outcomes between underrepresented and non-underrepresented populations
(in the categories of gender, ethnicity, age) in first year medical students?

To investigate the impact of gender, ethnicity, and age on students’ learning
approaches and academic achievement outcomes, testing with a MANOVA was first
proposed. As test scores and learning techniques were considered substantively different,
it was decided to examine these outcomes in two separate MANOVAs, seen as Research
Question 3a and 3b, below.

Research Question 3a: Were there differences in academic achievement outcomes
between underrepresented and non-underrepresented populations (in the categories of
gender, ethnicity, age) in first year medical students?
Before examining the test results, the assumption of homogeneity of covariances
bears discussion. While calculation of Box’s test suggest that there may be a violation
(Box’s M = 380.47, F
(168, 6715.76)
= 1.92, p  0.001), it has been suggested that Box’s test
70
may be disregarded when sample sizes are equal, as the test is unstable, robust test
statistics are available to address possible violations, and impact of violations has not yet
be clearly quantified (Field, 2009). Examination of Pillai’s Trace revealed no statistically
significant differences in students’ test scores across age groups (V = .02, F
(6, 425)
= 1.62,
p = .14) or gender (V = .01, F
(6, 425)
= 1.00, p = .43). However, statistically significant
differences were found amongst the ethnic groups examined (V = .20, F
(18, 1281)
= 5.02, p
< .001, partial ε
2
= .07). Follow-up investigation of between-subjects effects further
clarified the nature of these differences, with ethnic differences being detected across
every test score (see Table 14). The partial eta squared value tests demonstrated stronger
variability for the Core exam and the Comprehensive exam, and accounted for 11% and
13% of the variance in those courses. This means that race was a significant factor in
student achievement.

Table 14
Summary of Between-Subjects Effects for Follow-Up ANOVA Analyses of Ethnicity on
Exam Scores.

Exam Score F df sig partial ε
2

Core 17.34 3, 511.85 <.001 .11
Skin 3.26 3, 89.90 .02 .02
Hematology 8.43 3, 471.46 <.001 .06
Neuroscience 12/31 3, 577.33 <.001 .08
Musculoskeletal 9.16 3, 629.20 <.001 .06
Year 1 Comprehensive 21.52 3, 1634.37 <.001 .13

71
Table 15

Summary of Mean Differences (Row Mean – Column Mean) by Ethnicity and Exam.

Core Exam 1. White
2. Asian/Pacific
Islander
3. Black
1. White -
2. Asian / Pacific Islander -0.30 -
3. Black -4.95*** -4.65*** -
4. Hispanic -4.76*** -4.46*** 0.19

Skin Exam
1. White -
2. Asian / Pacific Islander -.66 -
3. Black -1.31 -0.66 -
4. Hispanic -2.34** -1.69 -1.02

Hematology Exam
1. White -
2. Asian / Pacific Islander -1.72 -
3. Black -3.68 1.96 -
4. Hispanic -5.01*** 3.30* -1.34

Neuroscience Exam
1. White -
2. Asian / Pacific Islander -0.84 -
3. Black -5.45*** -4.61* -
4. Hispanic -5.20*** -4.36*** -0.26

Musculoskeletal Exam
1. White -
2. Asian / Pacific Islander -0.17 -
3. Black -3.73 -3.56 -
4. Hispanic -5.80*** -5.63*** -2.07

Comprehensive Exam
1. White -
2. Asian / Pacific Islander -3.17** -
3. Black -6.65** -3.48 -
4. Hispanic -9.34*** -6.17*** -2.69
* p  0.05. ** p  0..01. *** p  0.001

72
To better elucidate the differences amongst ethnic groups, further tests were
performed to compare mean differences (see Table 15). Little difference was found
between White and Asian students in all examination scores, except on the
Comprehensive final exam in which White students outperformed Asian students by an
average of 3.17 points. White students also outperformed Hispanic students across all
exams, and Asian and Black/African American students performed at approximately the
same levels, with the Asian students outperforming Black/African American students on
the Hematology exam.

Research Question 3b: Were there differences in learning approaches between
underrepresented and non-underrepresented populations (in the categories of gender,
ethnicity, age) in first year medical students?

In order to answer this research question, a multivariate analysis of variance was
selected as an appropriate test. In this analysis, examination of Pillai’s Trace revealed
statistically significant differences in students’ learning approaches across age groups (V
= .10, F
(15, 316)
= 2.40, p < .01 partial ε
2
= .10), ethnic groups (V = .21, F
(45, 954)
= 1.60, p <
.01, partial ε
2
= .07), and gender (V = .16, F
(15, 316)
= 3.95, p < .001, partial ε
2
= .16).
Follow-up investigation of between-subjects effects clarified the nature of these
differences, as shown in Table 16. Due to volume of tests examined, only statistically
significant findings are presented for detailed examination. Differences were found in
Deep and Surface learning strategies for each analyzed group.

73
Table 16

Summary of Between-Subjects Effects for Follow-Up ANOVA Analyses of Age, Ethnicity,
and Gender on Learning Approaches.

Age
F df sig
partial
ε
2

Deep Orientation 6.45 1, 330 .01 .02
Deep Seeking Meaning 7.28 1, 330 .01 .02
Deep Use of Evidence 7.64 1, 330 .01 .02
Surface Orientation 5.14 1, 330 .02 .02
Surface Lack of Purpose 8.43 1, 330 .004 .03
Surface Syllabus Boundness 6.90 1, 330 .01 .02
Ethnicity
Learning Preference: Developmental 4.08 3, 330 .01 .04
Surface Orientation 4.30 3, 330 .01 .04
Surface Lack of Purpose 4.33 3, 330 .01 .04
Surface Unrelated Memorizing 3.49 3, 330 .02 .03
Gender
Deep Orientation 4.75 1, 330 .03 .01
Deep Relating Ideas 10.62 1, 330 .001 .03
Deep Use of Evidence 6.01 1, 330 .02 .02
Surface Lack of Purpose 4.46 1, 330 .04 .01
Definition of Learning: Remembering Things Well 8.88 1, 330 .003 .03
Learning Preference: Developmental 7.83 1, 330 .01 .02

Further, in contrasting younger (≤ 27) and older (≥28) students significant
differences were found (see table 17). Older students reported higher usage of deep
learning strategies, particularly in seeking meaning and using evidence to support
learning, compared to their younger counterparts. Younger students reported increased
use of surface strategies compared to older students, and older students reported
increased usage of deep learning strategies, including seeking meaning and using
evidence, compared to their younger colleagues. This means that older students might be
using prior knowledge to gain deeper understanding of the material; whereas, younger
students may not have the experience and background to apply deeper strategies.
74
Table 17
Summary of Mean Differences by Age and Gender by Learning Approaches.

Mean
Age Younger Older sig
Deep Orientation 3.81 4.11 .01
Deep Seeking Meaning 3.63 4.05 .01
Deep Use of Evidence 3.72 4.13 .01
Surface Orientation 2.84 2.55 .02
Surface Lack of Purpose 2.86 2.45 .004
Surface Syllabus Boundness 3.12 2.60 .01
Gender Male Female
Learning Preference: Developmental 4.33 4.48 .01
Deep Orientation 4.02 3.90 .03
Deep Relating Ideas 3.84 3.60 .001
Deep Use of Evidence 4.01 3.84 .02
Surface Lack of Purpose 2.73 2.58 .04
Definition of Learning: Remembering Things Well 2.61 2.94 .003

Research Question 4: Which motivational constructs were the strongest predictors of
academic achievement outcomes for first year medical students?

In order to determine which of the eight motivational constructs investigated
would most strongly predict students’ performance across three exams (Core, Skin, &
Hematology), multiple regression modeling was conducted. Due to the volume of
motivation constructs investigated, the items were entered using a stepwise model-
building procedure to minimize and balance the risk of type I and II errors.
In the core course, performance approach goal orientation was a small, but
significant prediction of examination score ( =.29; p≤ .05). Mastery avoidance goal, on
75
the other hand, had an inverse relationship with Core scores in one of the models ( = -
.29; p≤ .05), indicating that students who strive to avoid incomplete understanding of the
material may have difficulty in achievement in the core course. The final regression
model for the Core course is presented in Table 18, below.

Table 18

Summary of Stepwise Regression Modeling of Motivational Constructs on Performance
on the Core Exam (n = 50)

Variable B SE B 
Model 1
Performance Approach 1.33 .64 .29*
Model 2
Performance Approach 1.35 .62 .29*
Mastery Avoidance -1.21 .56 -.29*
Note. R
2
= .08 for Step 1 (p < 0.05); R
2
for step 2 = .08 (p < 0.05).
* p  0.05.

For the skin course, the mastery avoidance goal orientation emerged as the only
statistically significant predictor of exam score (see Table 19). Mastery avoidance was
inversely related to exam score (( = -.30; p≤ .05) indicating students may have difficulty
in the mindset of avoiding less than what is possible to know. Given that Skin was such a
brief course lasting only one week, it would seem that there was insufficient time to adopt
mastery-oriented strategies. In contrast to the Core Course, which contained numerous
examinations, Skin consisted of one single exam, which would preclude students having
practice time or sufficient opportunities to monitor their learning.

76
Table 19

Summary of Stepwise Regression Modeling of Motivational Constructs on Performance
on the Skin Exam (n = 50)

Variable

B

SE B


Model 1
Mastery Avoidance -1.29 .59 -.30*
Note. R
2
= .09 (p < 0.05)
* p  0.05.

None of the motivational constructs examined met the 0.05 threshold for inclusion
when attempting to predict students’ Hematology exam scores, therefore, no regression
model was produced.
As noted previously, many challenges existed in the administration of the
motivation survey to the class of 2015. Several students did not complete the full survey,
but rather skipped some questions that lowered the total number of usable surveys for
some of the achievement goal questionnaire responses to 50. Therefore, these constructs
need further investigation to further ascertain the relationships between motivation and
learning approaches. However, the literature suggests a strong relationship between
mastery-approach and deep learning strategies (Newble & Entwistle, 1986) which leads
to greater feelings of competence and higher levels of performance (Entwistle &
Peterson, 2004; Senko, Hulleman & Harackiewicz, 2011). For some students, however,
mastery-approach orientation can jeopardize performance as students might only focus on
personally interesting material (Senki & Miles, 2008), which can increase perfectionism
and mastery-avoidance goals later (Stoeber, Stoll, Pescheck & Otto, 2008). Because this
77
survey was administered at one point in time, future study might plan repeated measures
to ascertain change over time.
Performance-approach was the strongest predictor in the Core course. It is
possible that competition to get into medical school might potentially predispose this
population to performance-approach orientation. This finding was intriguing in light of
the pass/fail curriculum in which grades are deemphasized. It was noteworthy that Core
was the longest course in the curriculum and contained the most examination dates;
however, at the time of the survey, the students had not completed any examinations
aside from a non-stakes practice exam that did not have a recorded score. As an early
medical school experience, there may have been increased pressure to perform well or
feelings of competition.
Another possible explanation might be that students at this stage have difficulty
self-assessing the effectiveness of their studies and thus place a higher emphasis on
performance-approach. The ASSIST survey contained a single question that prompted,
“How well do you think you are doing in your assessed work so far?” A paired-samples
t-test revealed that the mean dropped considerably from T1 (M=7.18, SD=1.52) and T2
(M=6.40, SD=1.68), which was statistically significant (t(164)=5.75, p<.001). Further,
Peason’s correlation revealed that the students’ self-assessment at the beginning of the
school year (T1) demonstrated a low correlation to actual performance (see Table 20).
The students’ self-assessment became slightly more accurate as the year progressed.
However, this may give some evidence to the difficulty that learners in medical school
may have in self-assessing their performance.
78
Table 20

Correlations between Self-Assessment and Academic Achievement

T1 T2 Core Skin Heme Neuro MSK Comp
T1 Self-
Assessment
.409** .370** .221** .361** .419** .320** .316**
T2 Self-
Assessment
.609** .401** .540** .526** .437** .540**
Core .513** .743** .818** .675** .771**
Skin .407** .432** .370** .428**
Heme .660** .544** .703**
Neuro .761** .760**
MSK .683**
** p<.01

Avoidance goals, on the other hand, were inversely related to achievement. This
makes sense as avoidance goals are associated with higher use of surface strategies,
disorganized studies, and stress (Senko, Hulleman & Harackiewicz, 2011), and may
indicate perceptions of an overwhelming workload and task complexity or a lack of
information (Kyndt, Dochy, Struyven & Cascallar, 2011). As the curriculum in medical
school contained lectures that lasted 4-8 hours five days per week, it would seem
apparent that students may feel overwhelmed by the volume and complexity of the
material and the workload. This may be further complicated by difficulty with self-
assessing their own learning, which may affect motivation and approaches to learning to
help them successfully manage the curriculum.
Students overwhelmingly reported studying alone in the ASSIST survey.
Seventy-eight percent of students reported studying entirely alone and 20% reported
studying in a group. This isolated studying approach may have implications for
motivation as students may not have models or feedback from which to compare their
79
learning. Although this was not a focus of this study, previous research has shown that
collaborative learning may have a postive impact on student motivation and learning
approaches (Blumenfeld, Kempler & Krajcik, 2006; Järvelä, Volet & Järvenoja, 2010). A
follow up study that explores motivational effects of group versus independent study may
be valuable in a medical education setting.

Research Question 5: Are there differences in motivation between underrepresented and
non-underrepresented populations (gender, ethnicity, age) in first year medical students?

In order to answer this research question, a multivariate analysis of variance was
performed. In performing this analysis, a Pillai’s trace was performed, which is
appropriate in instances in which there is a violation of homogeneity of covariance
matrices assumption (Leech, Barrett & Morgan, 2008). Examination of Pillai’s Trace
revealed no statistically significant differences in students’ motivation scores across
ethnicity (V = .25, F
(8, 39)
= 1.66, p = .14), even when categories’ of White/non-White
were used. No analysis with age was possible due to the small sample size.
However, statistically significant differences were found between male and female
students (V = .34, F
(8, 39)
= 2.54, p = .03, partial ε
2
= .34). Follow-up investigation of
between-subjects effects further clarified the nature of these differences, with gender
differences being detected across three of the eight measures of motivation (see Table
21).

80
Table 21

Summary of Between-Subjects Effects for Follow-Up ANOVA Analyses of Gender on
Motivation

Exam Score F df sig partial ε
2

Mastery Approach 7.92 1, 46 .007 .15
Mastery Avoidance 0.83 1, 46 .37 .02
Performance Approach 3.72 1, 46 .06 .08
Performance Avoidance 1.03 1, 46 .32 .02
Task Value 8.04 1, 46 .01 .15
Self-Regulation 0.02 1, 46 .90 <.001
Self-Efficacy 7.72 1, 46 .01 .14
Test Anxiety 0.47 1, 46 .50 .01

Means comparison data (see Table 22) revealed that women had lower scores for
self-efficacy, task value, performance approach, and mastery approach compared to their
male counterparts. However, women demonstrated higher levels of mastery avoidance
orientation, which may be problematic for learners who are focused on avoiding feeling
incompetent (Elliott & McGregor, 2001). Learners who adopt a mastery avoidance
orientation are at higher risk for worry, disorganization, and test anxiety (Elliott &
McGregor, 2001), thus this could be a potential area of concern for female students. As
the ASSIST data found, women also demonstrated lower scores for deep learning
orientation and the deep learning strategies of relating ideas and use of evidence, which
may be related to increased levels of mastery avoidance. A 2008 study by Blanch, Hall,
Roter, and Frankel found that women reported lower confidence and anxiety about their
own perceived competence compared to men, which may partly explain the mastery
avoidance orientation found in the women of the present study.

81
Table 22

Summary of Between-Subjects Differences by Gender and Motivation

Mean Scores
Male Female
Mastery Approach 6.56 6.08
Mastery Avoidance 4.99 5.35
Performance Approach 5.81 5.20
Performance Avoidance 5.57 5.13
Task Value 6.59 6.10
Self-Regulation 4.90 4.92
Self-Efficacy 6.33 5.77
Test Anxiety 3.25 2.98

Summary

In summary, students in the study changed their approaches to learning to
decreased levels of strategic strategies including being alert to assessment demands, self-
monitoring their studies, and concern for achieving at their highest level. The data
demonstrated that students also experienced higher levels of fear of failure, became less
bound to the syllabus, and engaged in more rote memorization activities throughout the
course of their first year of medical school. Time management, strategic orientation, and
an achieving orientation were the strongest predictors of academic success, and a deep
orientation, rote memorization, self-monitoring, and surface learning orientation were
negative predictors of success. However, these predictors varied by course in what seem
to be related to the length of the course or the number of exams in the course. Table 23
summarizes these findings.
82
Differences in motivation were also found. A performance-approach was the
strongest predictor of academic performance for the subjects in this study. Performance-
approach has been associated with higher levels of competition and may be affected by
the high-stakes environment of medical school. However, the program under
investigation had a pass/fail curriculum which decreased emphasis on grades, so the
relationship between the program’s grading system interaction and the students’
performance-approach orientation is uncertain. Mastery avoidance had an inverse
relationship with academic performance. Research has shown that students who adopt
mastery avoidance strategies are at-risk.

Table 23

Summary of Findings, by Course

Length
of
course
(in
weeks)
Number
of
Exams
Learning Approaches Motivation
Predictors
Negative
Predictors
Predictors
Negative
Predictors
Core 19 10
Time
management
Memorization
Performance-
approach
Mastery
avoidance
Skin 1 1 Achieving
Deep
orientation

Mastery
avoidance
Heme 6 1
Time
management

Neuro 9 4
Time
management
Achieving

MSK 4 2
Strategic
orientation
Self-
monitoring

Comp 1 1 Achieving
Surface
orientation

Core= Core Principles; Heme=Hematology; Neuro=Neurosciences; MSK=Musculoskeletal;
Comp=Comprehensive Cumulative Exam
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Differences in learning approaches and motivation were found for
underrepresented minority students in the categories of ethnicity, gender, and age.
African-American and Hispanic students demonstrated significantly lower academic
achievement compared to their White and Asian student counterparts, and these students
showed higher levels of surface learning strategies, particularly unrelated (rote)
memorization and feeling a lack of purpose. No significant differences were found in
academic achievement by gender or age, but differences were found in learning
approaches. Female students reported a lower use of deep orientation learning strategies,
including relating idea together and using evidence to support learning, and lower self-
efficacy. Female students also reported lower levels of a lack of purpose and task value
compared to the male students. Older students (over 28) reported higher reliance on deep
learning strategies for learning, including seeking meaning and using evidence in their
studies. These differences may have important implications for successful adjustment
and performance in medical school for these students.

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Chapter 5: Discussion
The purpose of this study was to examine student learning approaches,
motivation, and achievement to gain understanding of student transition during the first
year of medical school. This chapter provides a discussion of the findings followed by a
discussion of implications and recommendations in the key areas of admissions, student
services, underrepresented minority student, and teaching and curriculum.
Discussion of Findings
Prior research has revealed that students may be academically unprepared when
they begin their medical school studies. Students may not have developed their
approaches to learning, which can impact their level of processing and achievement
(Byrne, Flood & Lewis, 2004; Marton & Säljö, 2005). This study found that students
changed their approaches during their first year of studies in which use of surface
approaches increased, strategic approaches decreased, and deep approaches were
unchanged.. Specifically, the students reported decreased alertness to assessment, desire
for achieving, and self-monitoring, and showed significant increases in unrelated (rote)
memorizing and fear of failure and a decrease in syllabus boundness.
These changes are consistent with prior research of students who have difficulty
coping with academic demands and workload (Entwistle, Mayer & Tait, 1991). The
increased fear of failure and lowered achieving desire point to students having difficulty
coping with the demands and possibly decreasing expectations for success. This is
consistent with the literature that indicates high levels of stress, depression, and
frustration experienced by medical students, especially in the first year of studies (Vall &
85
Bax, 2002; Dyrbye, Thomas & Shanafelt, 2005). The current study is unique in that there
are indications that students in the study may be less sure of how they are being assessed
as shown by the decrease in alertness to assessment demands and syllabus boundness.
Further, it is possible that this further increases fear of failure and increased use of rote
memorization strategies. The lack of change in deep learning strategies also indicates that
students may not feel the need or the ability to understand the material more deeply or
relating ideas to support their learning. The increase in use of memorization strategies
and the unchanged deep strategy of using evidence to support arguments may have
implications for how the students view the purpose of learning and may be driven by the
types of assessments or experiencing a lack of connection amongst the material. This is
consistent with Bengstson and Ohlsson’s 2010 study that showed student decline in
curiosity due to struggling with time constraints, rigorous testing, and making connection
among subjects in the curriculum. Moreover, it is possible that the complexity of the
material and the workload are overwhelming, causing a shift away from deep strategies to
surface strategies (Kyndt et al., 2010). Further study is needed to understand this
relationship, but this study points to a significant early change in these learning
approaches.
The second research question explores which learning approaches are most
predictive of academic achievement. Research has found that deep approaches to learning
are linked to higher grades and student satisfaction (Laird, Shoup, Kuh & Schwartz,
2008) and strategic approaches were important for academic success (Entwistle, Meyer &
Tait, 1991). The findings in this study, however, partially contradict these prior findings.
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Strategic study strategies, specifically time management, organized approaches to
studying, and striving for achievement, were most significantly correlated with exam
scores overall, but the correlation was rather low. Interesting differences were found
within different courses. In general, for longer courses, time management was the most
significant predictor. Core (a 19-week course), Hematology (a 6-week course), and
Neurosciences (a 9-week course) all showed strategic time management as the most
predictive approach. The length of time of the course and the multiple subjects within
each of these courses may have presented particular challenges for student, resulting in
the importance of strategic time management strategies. The musculoskeletal system
course demonstrated a different pattern. In this four-week course, the overall strategic
orientation, which includes time management, organized studying, achievement
orientation, alertness to assessment, were together most predictive of academic
achievement. The time frame of this course may have lent itself well to using additional
strategies toward success. The Skin system, the shortest course lasting only one week,
showed a different pattern in which achievement orientation (the desire to do well) was
the most predictive approach. Within such a short time frame, the desire to do well seems
particularly important and the timing (immediately after winter break) may potentially
influence a student’s approach in which the student is well rested and enthusiastic about
his/her studies. Similarly, the comprehensive exam is taken one week after the
completion of the musculoskeletal course also showed achieving as the most statistically
significant predictor of academic achievement. This short amount of time to study for this
exam may also lead to the desire to perform well (achieving); whereas, deep approaches
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to learning may seem unfeasible and unrealistic. In sum, in courses that were shorter in
length, an achieving learning orientation was the most significant predictor of test scores;
whereas, in longer courses, time management was the strongest predictor. These findings
demonstrate the challenges learners experience in courses of different lengths, and
illustrates how the curriculum and assessments drive students’ learning approaches.
An inverse relationship was found in some courses for deep and surface learning
strategies. Unrelated memorizing (also referred to as rote memorization) was inversely
related to exam scores for the core course. The combination of surface strategies (which
includes unrelated memorizing, lack of purpose, fear of failure, and syllabus boundness)
was also inversely related to test achievement in the core course. Both of these
examinations cover multiple topics, and therefore, rote memorization and other surface
approaches may be insufficient for the breadth and depth of material. Deep orientation
was inversely correlated to performance for the skin exam. Given the short duration of
the skin system, deep learning strategies might possibly mean that the student is spending
too much time on smaller bits of material and not having time to cover the necessary
breadth and depth of the material in a short amount of time.
The final focus of this study was whether students of different groups (by age,
gender and, ethnicity) demonstrated differences in their learning approaches and
achievement. No significant differences were found in achievement in the groups by age
and gender. However, significant differences were found in groups by ethnicity. White
and Asian students outperformed other student groups in all examinations, except the
comprehensive exam in which Asian student performed significantly lower than their
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White classmates. Black students scored significantly lower than their White counterparts
on all exams. Hispanic student also scored significantly lower compared to their White
counterparts on all exams, with the biggest difference on the comprehensive final on
which the Hispanic students scored on average 9.34 points lower than White students.
The most substantial performance disparity was evident in the Comprehensive exam for
Asian students scored an average of 3.17 points, Black/African American students scored
on average 6.65 points, and Hispanic students scored an average of 9.34 points lower
than their White classmates. These findings suggest that Hispanic and Black/African
American students did not adapt to the medical school environment compared to White
and Asian students.
In the investigation of students grouped by race, differences in learning
approaches were found. A between subjects examination indicated a significant increased
use of surface strategies – specifically lack of purpose and rote memorizing – in non-
White students. This is consistent with research that has shown that surface strategies are
more likely adopted by students lacking effective study skills (Entwistle & Tait, 1991).
These differences seem to increase as the year progresses, indicating that students’ study
skills might not be enhanced or supported by interactions with peers, faculty, or
administration in this school. The score disparity on the comprehensive final exam also
might indicate a lack of long-term learning retention or possible other effects, such as
stress, anxiety, or other states that could hinder achievement.
Significant differences in learning approaches were also found by age. As a
whole, older (28 or older) students reported higher use of deeper strategies than their
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younger counterparts. Although deep strategies seemed to have led to decreased
performance for many students, older students did not experience significantly different
levels of achievement, which may indicate that older students were more successful at
deeper learning strategies, including meaning-seeking and using evidence to support
learning because of increased prior knowledge and experience. Older students also
reported increased use of syllabus boundness strategies, generally considered a surface
strategy, and may have helped guide studies rather than detract from learning or signify
an adaptation that is unique to this population. Older students also indicated an increased
sense of lack of purpose, which could potentially stem from decisions to change careers
or other personal factors. The use of deep strategies is consistent with previous research
which has found that older students demonstrated more maturity and perseverance in
their studies and less academic difficulty compared to younger students (Haist et al.,
2000; Kusurkar et al., 2010).
Differences were found between genders as well. Men and women performed
comparably on academic achievement measures; however, differences in approaches to
learning were found. Male students reported significantly higher use of deep strategies
and slightly higher level of lack of purpose compared to female students in the study.
These seemingly contradictory orientations could indicate potential difficulty that these
students may have with the curriculum and assessment in the program; id est, having
strategies targeted at deeper learning, but may not have a sufficient understanding of the
importance of the material, organization of the curricular material, or assessment.
Women in the study demonstrated lower levels of self-efficacy, which may indicate
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women may feel less confident in their abilities and skills, which is consistent with
Blanch et al. (2008) study that sound that female medical students were significantly less
confident compared to male medical students. Prior research has shown that self-efficacy
declines during medical school (Kaufman et al., 2001; Pololi & Price, 2000), which
might place females at additional risk for lowered motivation, increased stress,
depression, and anxiety (Chemers et al., 2001).
These gender and racial differences may be a result of stereotype threat, a
situation in which the person associates negative stereotype with his/her own status and
can trigger psychological and physiological responses that can affect performance,
increase anxiety and negative emotions (Spencer, Steele & Quinn, 1999; Steele &
Aronson, 1995). In their study, Burgess, Warren, Phelan, Dovido and van Ryn (2010)
found evidence of stereotype threat for medical trainees that affected performance and
also affected interactions with patients. In an interview-based study by Rao and Flores
(2007), African-American pre-med students reported a lack of role models in medicine,
little support or encouragement, and a perception of racism in medicine, which is also
consistent with stereotype threat. Further study on this population is needed to explore
differences amongst these groups of students.
Motivation plays a vital role in a student’s goals, strategies for learning, and
persistence in learning tasks (Elliott, 1999). A goal of the present study was to explore
the interaction amongst motivation, learning approaches, and academic achievement.
Unfortunately this study was unable to accomplish the exploration of the relationship
between motivation and learning approaches because of unforeseen difficulties in
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sampling. However, data was collected that gives some insight into the relationship
between motivation and achievement.
Achievement goals were found to be the most significant factors for student
performance. For the participants in this study, a performance approach goal was found
to be the most significant motivational construct for exam performance and mastery
avoidance goal orientation was inversely related to exam performance. In performance
approach, students indicated a goal of performing better relative to others; whereas
mastery avoidance is concerned with an internal process of avoiding learning less than
possible. This may indicate that the curriculum and assessments are not conducive to
mastery learning such that performance-approach learning is most related to academic
success. This data is counter to the stated goals that a pass/fail medical school curriculum
increases self-directed, life-long learning, and intrinsic motivation, reduces competition,
and fosters collaboration amongst students (Rohe et al, 2006;White & Fantone, 2010).
However, this study indicates that despite a pass/fail curriculum, performance-approach
was a significant factor in academic success.
No statistically significant differences were found for the motivational constructs
based on ethnicity, even when grouped into broad White/non-White categories, and age.
This finding was surprising as differences in self-efficacy and other measures were
expected. This may indicate that the sample size was insufficient to meet statistical
significance.
In contrast, significant differences were found between the men and women in the
study. Women demonstrated lower levels of self-efficacy, performance approach goal
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orientation, and test anxiety compared to the men in the study. It is possible that women
may experience stereotype threat or place less importance on the competition that can
arise in performance approach goals.
Implications and Recommendations
Student Learning and Motivation
The findings of this study have important implications for practice. Students in
the current study shifted study strategies during the first year of medical school. Although
the program stresses deep and lifelong learning for its students, this study shows that this
is not occurring at the present time. If deep learning and mastery are a goal of the
program, this study indicates a need to revisit the curriculum to ascertain how the
curriculum and assessments meet this goal. In short, building learning and assessment
activities and tests that build and encourage deep learning strategies could be
implemented to meet this objective.
The findings indicate that students utilize different strategies for different courses.
The length of time and intensity of the course seems to affect student-learning behaviors.
It may be advantageous for programs to consider ways to construct the school year in
ways that smooth out the transition. For example, the 19-week Core course was most
correlated to time management which may indicate students were overwhelmed or having
difficulty keeping up, rather than a desire to achieve or experiencing deeper learning
strategies. Breaking down the course into smaller, more focused components and
restructuring the frequency of examinations might help to foster learning and success
among students and provide opportunities to develop learning skills. In addition, students
93
may benefit from small quizzes and exercises through which they can monitor their
progress and learn skills for self-assessment.
The assessments may also drive the students’ shift to surface strategies. Most
examinations were multiple-choice question format, which may foster surface learning
strategies in students and emphasize performance-approach orientation. Research
suggests that other types of assessments, such as problem-based learning, case-based
learning, or other forms of assessment would foster deeper learning and mastery-
approach learning in students and may help foster self-directed learning skills (Cooke,
Irby & O’Brien, 2010). When multiple-choice questions are utilized, attention to the
context of the questions, including providing a rich context and avoiding nuanced cultural
ambiguities, could also help foster deeper learning and develop problem-making and
diagnostic reasoning skills by minimizing word recognition or cueing during assessment
(Epstein, 2007). Although most medical students tend to be successful in traditional and
other types of curriculum and assessment, problem-based or case-based learning and
assessment has the potential to help learners see connections between learning and
clinical practice in authentic ways, and may also facilitate higher levels of academic
achievement in struggling students (Cooke, Irby & O’Brien, 2010; Lindblom-Ylänne &
Lonka, 2001).
Despite the presence of a pass/fail curriculum, the results of this study indicated
that the students were motivated by performance-approach goals that may indicate
competition. Often there is an expectation that medical students are high-achieving,
mastery-oriented learners; however, this is not always the case. Some students may be
94
performance-oriented because medical school is very competitive for entry. Evidence
from this study suggests that students who struggle in medical school may have difficulty
in avoidance-oriented orientations in which the learner strives to avoid failure
(performance-avoidance) or fear of not mastering the task (mastery-avoidance). The
avoidance orientations in this study were problematic and negatively affected
performance. Providing opportunities for mastery experiences, support for skill building,
and building self-efficacy would likely have an impact on shifting student focus from
avoidance to approach orientations, which in turn, may positively impact performance.
This type of focus on skill development has been recommended in the literature as of
particular importance in medical education to cope effectively with the varied
environments and roles that medical students need to perform, such as in different
academic and clinical situations, as well as in patient interactions (Murdoch-Eaton &
Whittle, 2012).
The social environment may also have an impact on student learning. Most of the
students (78%) in the study reported studying alone most of the time. Fostering non-
competitive group learning opportunities may have a positive impact on student
motivation and learning approaches, and may potentially lead to increased use of deep
learning strategies and self-efficacy (Baeten, Kyndt, Struyven & Dochy, 2010; White,
2007). Opportunities for quality peer engagement and learning may also help in adapting
to the medical school environment by providing supportive environments that emphasize
deep and mastery learning as well as emotional support from peers to prevent anxiety and
stress. However, group or team-based approaches may be problematic for learners who
95
have difficulty interacting and learning with others, therefore scaffolding, clear guidelines
for participation, and faculty development are necessary to facilitate these approaches.
Evidence suggests that team-based approaches may improve clinical reasoning skills and
interpersonal skills, as well as provide feedback that may facilitate mastery and deep
learning (Cooke, Irby & O’Brien, 2010). Cooke et al. (2010) emphasize that the key to
these approaches is strong advising, guidance, mentoring, and feedback, and they
emphasize the need for strong teacher development and practices that progressively shift
from teacher-directed feedback to learner-directed self-motivated feedback for
improvement.
First-Year Student Transition
Awareness of the challenges of successful adjustment to medical school is an
important consideration in admissions and student services practice. It may be
advantageous for admissions offices to look beyond performance measures to other
factors such as achievement goals, approaches to learning, anxiety, and adaptation skills
when considering applicants for medical school. Having pre-matriculation academic
enrichment (bridge) programs or orientation activities that help support healthy
adaptation to medical school would likely have a significant impact on the matriculants’
ability to effectively transition to the first year of medical school. Interventions like this
may be particularly salient for underrepresented minorities, women, or older students,
who may have difficulty with transitioning.
Ongoing support is needed to develop and maintain learning strategies with an
emphasis on motivation, including most importantly, building self-efficacy. As this study
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found, differences amongst various courses were found, demonstrating that students need
to learn to adapt their studies for each course. This points to the need for students to
develop strong self-assessment skills in order to monitor their own learning and make
adjustments as needed. However, evidence from this study demonstrated that students
had difficulty self-assessing their knowledge and academic performance. Self-reflection
and self-assessment may help students understand their own goals for learning and
assessing the effectiveness of their study strategies, which would likely raise feelings of
autonomy and control in their studies leading to increased self-efficacy and performance,
and decrease maladaptive strategies. Literature suggests that medical students have
particular difficulty self-assessing their own skills; low achieving students have been
shown to be overgenerous in self-assessments while higher achieving students tend to
underestimate and be harsher than faculty in self-assessment of their own knowledge and
skills (Langendyk, 2006). A study by White (2007) found that traditional-aged students
(e.g. younger students) had the most difficulty with self-assessment and had increased
difficulty in clinical settings that require high levels of self-directed learning; further
White recommends interventions beginning in the first and second year of medical school
to foster self-assessment skills and facilitate transition to clinical practice. Cooke, Irby,
and O’Brien (2010) also point to the need to develop self-awareness and self-reflection
skills as integral to self-assessment in professional development. Thus, providing students
with opportunities for self-reflection, guided self-assessment, and feedback to guide
continued student self-awareness would likely have a positive impact for learners’
learning, achievement, and successful adaptation to the demands of medical school.
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Underrepresented Minorities in the Medical School Context
Differences were found within the various groups of this study, the most
significant differences were found amongst ethnic groups. Hispanic and Black/African
American students showed the lowest examination scores for all the groups, and also
showed a decline through the year, indicating that students in these groups have the most
difficult adjustment to medical school and significantly lower achievement compared to
their peers. These students also demonstrated a decreased desire to achieve, lowered
alertness to assessments, and less self-monitoring of their own learning. This could
indicate a potential issue in which these students may be unsure how to monitor their
learning and prepare for examinations. These findings indicate the need for programs and
support networks for students of color to encourage healthy transition into medical
school. Pre-health programs to promote effective learning strategies prior to medical
school or programs focused on study skills at the start of medical school may help to
foster achievement for students of color. In addition, focusing on strategic approaches to
learning and self-assessment skills may help students adjust their strategies to the
demands in the program and lower fear of failure. These types of programs may help to
alleviate the discrepancies in achievement that have been well documented in the
literature (Dytbye et al., 2007; Garrison et al., 2007) by providing opportunities to
develop academic skills that are effective for helping minority students successfully adapt
to academic environments and thus provide opportunities for all students to be successful
(Aronson, Fried & Good, 2002).
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To offset the potential for stereotype threat, it is important to build supports for
students. Increasing faculty and administration sensitivity and publicly discussing
stereotypes and stereotype threats would be helpful as a first step. In addition, building
self-efficacy and confidence of students of color and female students should include
working with students to build effective learning strategies and implementing programs
to lower stress and anxiety. An additional activity might include practice exercises to
train students on showing confidence might be impactful for interactions with faculty and
for patient care and may give valuable feedback to learners on how to handle potential
issues related to gender or race, which the literature suggests to be effective for women
(Blanch et al., 2008) and Black learners (Biernat & Danaher, 2012). Providing a healthy
support network, engaging with gender-based or racially congruent role models and
mentors, developing effective skills to increase confidence, and having programs to lower
stress and anxiety, may help to deter the deleterious effects of stereotype threat, aid in
adjustment to the medical school environment, and enhance performance. Therefore,
programs that give students opportunities to develop the academic skills necessary for
success in medical school as well as strong mentoring and role models would have a
significant impact on the transition to medical school and academic outcomes.
In summary, building in supports for students, restructuring the curriculum, and
enhancing learning through activities and assessments that support effective learning
strategies would likely have a positive impact on student learning approaches and
achievement. These recommendations would most likely be impactful for
underrepresented minority students, but would probably benefit all students.
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Limitations
The main limitation was a low survey response rate for the cohort entering in
2012. Numerous issues confronted this cohort, including the tragic death of a classmate,
leading to a noticeable decrease in all surveys and evaluations presented to the class
during the entirety of the year. This limitation is crucial to this study because the
motivation survey was only presented to this cohort. The low number of respondents of
the motivation survey (n=60) and ASSIST survey (n=67), led to a smaller number (n=8)
completing both. Therefore, relationships amongst learning approaches and motivational
constructs were not possible to ascertain. As such, research questions about learning
approaches (research questions one, two, and three) were analyzed separately from the
research questions about motivation (questions four and five) in the analysis and
discussion sections.
Another limitation of this study was having only one study site. This limited pool
of students from a single site limited the generalizability of the findings. Although four
cohorts participated in the study, the single location may present potential bias.
An additional limitation is that the central instrument has only had limited usage
in the United States. Although the Approaches and Study Skills Inventory (ASSIST) has
an extensive history of use in higher education, most studies have been conducted outside
the United States. Therefore, differences in demographics and schooling in these
countries are evident. For example, in most of these countries students begin medical
school immediately after high school so the average age of medical students is much
lower than what is found in the United States where an undergraduate degree is required
100
for entry to medical school. Significant differences exist in medical education practices,
assessment, and expectations amongst schools in countries of prior studies. Thus,
significant differences were expected between data from published studies using the
ASSIST instrument and the current study.
Delimitations
This study was limited to one specific medical school population. To help
ameliorate the biased sample selection, comparisons were made to published national
data of other medical schools in the United States to ascertain whether the participants
were demographically representative of the national population of medical students.
When compared to national data, the school sample and the sample were found to be
comparable to the population of medical students at large.
Additionally, the sample in this study represent a self-selected, volunteer group of
participants that may not be reprehensive of the medical school population. This was
particularly evident on the administration of the motivation survey and the second
administration of the ASSIST instrument, which yielded low response rates. This may
indicate challenges of timing the administration of the surveys during the busy school
year and the online administration of surveys.
There were additional issues that arose with the administration of the ASSIST
survey. The instrument was quite lengthy, which may have negatively affected the
response rate. The ASSIST survey demonstrated low Cronbach alphas on some measures,
indicating low reliability. This indicates a need to explore alternative surveys that might
potentially be more appropriate for this population.
101
Recommendations for Future Research
Medical student motivation and goals are not well understood. Gaining a better
understanding of skills and motivation has the potential to impact admissions into
medical school, educational practices of the curriculum, and supports available to
medical students. A first step would be to replicate this study with a larger number of
students to better understand the impact of the motivational constructs of achievement
goals, self-efficacy, self-regulation, and others included in this study. It is also
recommended to repeat these measures to see if differences exist in other contexts and
points of time in the medical career, for example, in the clinical setting, during residency
years, or in professional practice. Important potential research might be aimed at
monitoring interventions targeted at enhancing learning strategy and motivation to
understand the impact of these efforts.
It is also unclear what specific challenges exist for underrepresented minority
students and what supports are most effective in supporting them. Qualitative interview
or focus group studies may help answer these questions to understand unique challenges
to African-American and Hispanic and women students in their transitions to medical
school, as well as what supports are most important to help in this transition. For
example, a clearer understanding of the effect of mentors and role models would be
useful to help in the development and implementation of programs. Similarly, input from
these students might give rise to other avenues of support, such as programs for families
and communities that may be helpful in minimizing stereotype threat and lack of
confidence that may affect many of these men and women.
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Need exists to explore other important transitions experienced during medical
school training and beyond. For example, the clinical transition during the third year of
medical school is an important transition that may draw on different learning approaches
as motivation as students encounter new contexts, increase patient contact, and other
experiences related to early clinical experiences. The transition to residency can also
present unique challenges to the learning and motivation as the learner adapts to new
contexts and responsibilities, as well as teaching others and independent learning. Lastly,
the important transition to professional practice can also present unique transitional
experiences. A better understanding of how learners make successful transitions and
adaptations would give better insight into medical education and training.
Conclusion
In conclusion, many students struggle in their transition to the first year of
medical school. Evidence from this study suggests that many shift to more surface
strategies and decreased self-monitoring and concern for achievement. Many students
also increased their fear of failure and engaged in more rote memorization during the
course of the first year of medical school. The strongest predictors for academic success
were time management, strategic learning approaches and desire to achieve, but deep
learning strategies, rote memorization, self-monitoring, and surface learning approaches
were inversely related to achievement, but were dependant on which course the student
was taking. Success in longer courses was more highly related to time management;
whereas shorter courses were more highly correlated with strategic orientation and desire
to achieve. Performance-approach was the motivation construct most high correlated with
103
academic success and mastery-avoidance negatively predicted success. The program
under investigation had a pass/fail curriculum which decreased emphasis on grades, so
the relationship between the program’s grading system interaction and the students’
performance-approach orientation is uncertain.
Underrepresented minority students who identified as African-American or
Hispanic experienced lower test scores compared to Asian and White counterparts. These
students tended to utilize more surface strategies, particularly unrelated (rote)
memorization and higher levels of a fear of failure. It is possible that stereotype threat or
other variables may play a role in these differences in learning approaches and
achievement. Further studies are needed to better understand these factors and to develop
effective interventions and supports for these students.
Other differences were also found amongst groups of students. Older students
(>28 years old) utilized deeper learning strategies, including relating ideas together and
seeking meaning, more than their younger student counterparts who relied more heavily
on surface strategies and syllabus boundness. Women also utilized deep learning
strategies more often than men, but also demonstrated lower levels of self efficacy and
higher levels of mastery-avoidance compared to men, which might indicate lower levels
of confidence or stereotype threat that can affect their approaches. Further studies are
needed to understand the potential causes and solutions for these differences.
The findings of this study indicate a need for medical schools to develop
programs, interventions, and supports for students who are transitioning during the
critically important first year of medical school. Supports for students that enhance self-
104
efficacy and explore challenges to medical students are needed and should include
programs for underrepresented minorities. In sum, careful attention must be given to the
learning approaches and motivation of incoming students, especially underrepresented
minority students, to ensure successful transition.

105
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136
Appendix A: ASSIST Questionnaire

ASSIST Approaches and Study Skills Inventory for Students
(Adapted from Tait, Entwistle, McCune, 1998)

This questionnaire has been designed to allow you to describe, in a systematic way, how
you go about learning and studying. The technique involves asking you a substantial
number of questions which overlap to some extent to provide good overall coverage of
different ways of studying. Most of the items are based n comments made by other
students. Please respond truthfully, so that your answers will accurately describe your
actual ways of studying, and work your way through the questionnaire quite quickly.

1. Name (first last) Age (in years)
2. Sex:
1. Male
2. Female
3. Considering your study time as a medical student: Please write the % of total
study time that you:
1. study alone?
2. study in a group?
4. In a typical week, how many hours are spent on outside non-academic activities?
5. What outside activities are you involved in?

A. What is learning?
When you think about the term ‘LEARNING ‘, what does it mean to you?
Consider each of these statements carefully, and rate them in terms of how close they are
to your own way of thinking about it.
Very
close
Quite
Close
Not so
close
Rather
different
Very
different
a. Making sure you remember things
well.
5 4 3 2 1
b. Developing as a person. 5 4 3 2 1
c. Building up knowledge by acquiring
facts and information.
5 4 3 2 1
d Being able to use the information
you’ve acquired.
5 4 3 2 1
e. Understanding new material for
yourself.
5 4 3 2 1
f. Seeing things in a different and more
meaningful way.
5 4 3 2 1

137
B. Approaches to studying
The next part of this questionnaire asks you to indicate your relative agreement or
disagreement with comments about studying again made by other students. Please work
through the comments, giving your immediate response. In deciding your answers, think
in terms of this particular lecture course. It is also very important that you answer all the
questions: check you have. 5 means agree 4 = agree somewhat 2 = disagree somewhat
1 = disagree.
Try not to use 3 = unsure, unless you really have to, or if it cannot apply to you or your
course.
1. I manage to find conditions for
studying which allow me to get on with
my work easily.
5 4 3 2 1
2. When working on an assignment, I’m
keeping in mind how best to impress the
instructor.
5 4 3 2 1
3. Often I find myself wondering whether
the work I am doing here is really
worthwhile.
5 4 3 2 1
4. I usually set out to understand for
myself the meaning of what we have to
learn.
5 4 3 2 1
5. I organize my study time carefully to
make the best use of it.
5 4 3 2 1
6. I find I have to concentrate on just
memorizing a good deal of what I have to
learn.
5 4 3 2 1
7. I go over the work I’ve done carefully
to check the reasoning and that it makes
sense.
5 4 3 2 1
8. Often I feel I’m drowning in the sheer
amount of material we’re having to cope
with.
5 4 3 2 1
9. I look at the evidence carefully and try
to reach my own conclusion about what
I’m studying.
5 4 3 2 1
10. It’s important for me to feel that I’m
doing as well as I really can on the
coursework.
5 4 3 2 1
11. I try to relate ideas I come across to
those in other topics or other courses
whenever possible.
5 4 3 2 1
12. I tend to read very little beyond what
is actually required to pass.
5 4 3 2 1
138
13. Regularly I find myself thinking
about ideas from lectures when I’m doing
other things.
5 4 3 2 1
14. I think I’m quite systematic and
organized when it comes to reviewing
for exams.
5 4 3 2 1
15. I look carefully at evaluations and
feedback to see how to get higher marks
next time.
5 4 3 2 1
16. There’s not much of the work here
that I find interesting or relevant.
5 4 3 2 1
17. When I read an article or book, I try
to find out for myself exactly what the
author means.
5 4 3 2 1
18. I’m pretty good at getting down to
work whenever I need to.
5 4 3 2 1
19. Much of what I’m studying makes
little sense: it’s like unrelated bits and
pieces.
5 4 3 2 1
20. I think about what I want to get out of
this curriculum to keep my studying well
focused.
5 4 3 2 1
21. When I’m working on a new topic, I
try to see in my own mind how all the
ideas fit together.
5 4 3 2 1
22 I often worry about whether I’ll ever
be able to cope with the work properly.
5 4 3 2 1
23. Often I find myself questioning
things I hear in lectures or read in books.
5 4 3 2 1
24. I feel that I’m getting on well, and
this helps me put more effort into the
work.
5 4 3 2 1
25. I concentrate on learning just those
bits of information I have to know to
pass.
5 4 3 2 1
26. I find that studying specific topics or
systems can be quite exciting at times.
5 4 3 2 1
27. I’m good at following up some of the
reading suggested by lecturers or tutors.
5 4 3 2 1
28a. I keep in mind who is going to
evaluate an assignment and what they’re
likely to be looking for.
5 4 3 2 1

139
28b. I keep in mind who created the exam
questions and what they are likely to be
looking for.
5 4 3 2 1
29. When I look back, I sometimes
wonder why I ever decided to come here.
5 4 3 2 1
30. When I am reading, I stop from time
to time to reflect on what I am trying to
learn from it.
5 4 3 2 1
31. I work steadily through the term or
semester, rather than leave it all until the
last minute.
5 4 3 2 1
32. I’m not really sure what’s important
in lectures so I try to get down all I can.
5 4 3 2 1
33. Ideas in books or articles often set me
off on long chains of thought of my own.
5 4 3 2 1
34. Before starting work on an
assignment or exam question, I think first
how best to tackle it.
5 4 3 2 1
35. I often seem to panic if I get behind
with my work.
5 4 3 2 1
36. When I read, I examine the details
carefully to see how they fit in with
what’s being said.
5 4 3 2 1
37. I put a lot of effort into studying
because I’m determined to do well.
5 4 3 2 1
38. I gear my studying closely to just
what seems to be required for
assignments and exams.
5 4 3 2 1
39. Some of the concepts in the
curriculum I find really engaging.
5 4 3 2 1
40. I usually plan out my week’s work in
advance, either on paper or in my head.
5 4 3 2 1
41. I keep an eye open for what lecturers
seem to think is important and
concentrate on that.
5 4 3 2 1
42. I’m not really interested in some
subjects, but I try my best anyway.
5 4 3 2 1
43. Before tackling a problem or
assignment, I first try to work out what
lies behind it.
5 4 3 2 1
44. I generally make good use of my time
during the day.
5 4 3 2 1

140
45. I often have trouble in making sense
of the things I have to remember.
5 4 3 2 1
46. I like to play around with ideas of my
own even if they don’t get me very far.
5 4 3 2 1
47. When I finish a piece of work, I
check it through to see if it really meets
the requirements.
5 4 3 2 1
48 Often I lie awake worrying about
work I think I won’t be able to do.
5 4 3 2 1
49 It’s important for me to be able to
follow the argument, or to see the reason
behind things.
5 4 3 2 1
50. I don’t find it at all difficult to
motivate myself.
5 4 3 2 1
51. I like to be told precisely what to do
in essays or other assignments.
5 4 3 2 1
52. I sometimes get ‘hooked’ on
academic topics and feel I would like to
keep on studying them.
5 4 3 2 1
C. Preferences for different types of course and teaching
5 means definitely like 4 = like to some extent 2 = dislike to some extent 1 = definitely
dislike. Try not to use 3 = unsure, unless you really have to, or if it cannot apply to you
or your course.
a. lecturers who provide detailed lecture notes. 5 4 3 2 1
b. lecturers who encourage us to think for ourselves and show us how
they themselves think
5 4 3 2 1
c. exams which allow me to show that I’ve thought about the course
material for myself.
5 4 3 2 1
d. exams which test only the material provided in our lecture notes. 5 4 3 2 1
e. courses in which it’s made very clear what the learning objectives are. 5 4 3 2 1
f. courses where we’re encouraged to read beyond the subject a lot for
ourselves.
5 4 3 2 1
g. materials which challenge you and provide explanations which go
beyond the lectures.
5 4 3 2 1
h. materials which give you definite facts and information which can
easily be learned.
5 4 3 2 1
Finally, how well do you think you have been doing in your assessed work overall, so
far?
Please rate yourself objectively, based on the grades you have been obtaining
Very well Quite Well About average Not so well Rather badly
9 8 7 6 5 4 3 2 1
Thank you very much for spending time completing this questionnaire: it is much
appreciated
141
Appendix B: Coding scheme for ASSIST questionnaire
Deep Approach

Seeking meaning

4. I usually set out to understand for myself the meaning of what we have to learn.
17. When I’m reading an article or book, I try to find out for myself exactly what the
author means.
30. When I am reading I stop from time to time to reflect on what I am trying to learn
from it.
43. Before tackling a problem or assignment, I first try to work out what lies behind
it.

Relating ideas

11. I try to relate ideas I come across to those in other topics or other courses
whenever possible.
21. When I’m working on a new topic, I try to see in my own mind how all the ideas
fit together.
33. Ideas in course books or articles often set me off on long chains of thought of my
own.
46. I like to play around with ideas of my own even if they don’t get me very far.

Use of evidence

9. I look at the evidence carefully and try to reach my own conclusion about what
I’m studying.
23. Often I find myself questioning things I hear in lectures or read in books.
36. When I read, I examine the details carefully to see how they fit in with what’s
being said.
49. It’s important for me to be able to follow the argument, or to see the reason
behind things.

Interest in ideas (Related sub-scale)

13. Regularly I find myself thinking about ideas from lectures when I’m doing other
things.
26. I find that studying academic topics can be quite exciting at times.
39. Some of the ideas I come across on the course I find really gripping.
52. I sometimes get ‘hooked’ on academic topics and feel I would like to keep on
studying them.

142
Strategic approach

Organized studying

1. I manage to find conditions for studying which allow me to get on with my work
easily.
14. I think I’m quite systematic and organized when it comes to revising for exams.
27. I’m good at following up some of the reading suggested by lecturers or tutors.
40. I usually plan out my week’s work in advance, either on paper or in my head.

Time management

5. I organize my study time carefully to make the best use of it.
18. I’m pretty good at getting down to work whenever I need to.
31. I work steadily through the term or semester, rather than leave it all until the last
minute.
44. I generally make good use of my time during the day.

Alertness to assessment demands

2. When working on an assignment, I’m keeping in mind how best to impress the
marker.
15. I look carefully at tutors’ comments on course work to see how to get higher
marks next time.
28. I keep in mind who is going to mark an assignment and what they’re likely to be
looking for.
41. I keep an eye open for what lecturers seem to think is important and concentrate
on that.

Achieving (Related sub-scale)

10. It’s important to me to feel that I’m doing as well as I really can on the courses
here.
24. I feel that I’m getting on well, and this helps me put more effort into the work.
37. I put a lot of effort into studying because I’m determined to do well.
50. I don’t find it at all difficult to motivate myself.

Monitoring effectiveness (Related sub-scale)

7. I go over the work I’ve done carefully to check the reasoning and that it makes
sense.
20. I think about what I want to get out of this course to keep my studying well
focused.
143
34. Before starting work on an assignment or exam question, I think first how best to
tackle it.
47. When I have finished a piece of work, I check it through to see if it really meets
the requirements.

Surface Apathetic Approach

Lack of purpose
3. Often I find myself wondering whether the work I am doing here is really
worthwhile.
16. There’s not much of the work here that I find interesting or relevant.
29. When I look back, I sometimes wonder why I ever decided to come here.
42. I’m not really interested in this course, but I have to take it for other reasons.

Unrelated memorizing

6. I find I have to concentrate on just memorising a good deal of what I have to
learn.
19. Much of what I’m studying makes little sense: it’s like unrelated bits and pieces.
32. I’m not really sure what’s important in lectures, so I try to get down all I can.
45. I often have trouble in making sense of the things I have to remember.

Syllabus-boundness

12. I tend to read very little beyond what is actually required to pass.
25. I concentrate on learning just those bits of information I have to know to pass.
38. I gear my studying closely to just what seems to be required for assignments and
exams.
51. I like to be told precisely what to do in essays or other assignments.

Fear of failure (Related sub-scale)

8. Often I feel I’m drowning in the sheer amount of material we’re having to cope
with.
22. I often worry about whether I’ll ever be able to cope with the work properly.
35. I often seem to panic if I get behind with my work.
48. Often I lie awake worrying about work I think I won’t be able to do.

Preferences for different types of course and teaching Scored as the sum of the four
items.

Supporting understanding (related to a deep approach)
b. - lecturers who encourage us to think for ourselves and show us how they themselves
think.
144
c. - exams which allow me to show that I’ve thought about the course material for myself.
f. - courses where we’re encouraged to read around the subject a lot for ourselves.
g. - books which challenge you and provide explanations which go beyond the lectures.

Transmitting information (related to a surface approach)
a. - lecturers who tell us exactly what to put down in our notes.
d. - exams or tests which need only the material provided in our lecture notes.
e. - courses in which it’s made very clear just which books we have to read.
h. - books which give you definite facts and information which can easily be learned.

145
Appendix C: Motivation Questionnaire
(Adapted from Pintrich et al, 1991 & Elliott & McGregor, 1999)

Part A
Directions: The following questions ask about your experiences and motivation in the
program. There are no right or wrong answers to any of the questions, just answer as
honestly and accurately as possible. Use the scale to answer the questions. If you think
the statement is very true of you, answer 7; if the statement is not true of you, click on 1.
If the statement seems somewhat true of you, select the number between 1 and 7 that best
describes you.

PART A: Motivation & Goals

Not at
all
true
of me
very
true
of
me
1 I like what I am learning in class. 1 2 3 4 5 6 7
2
Understanding the material is important to
me.
1 2 3 4 5 6 7
3
My aim is to completely master the
material presented in this class.
1 2 3 4 5 6 7
4
I am striving to do well compared to other
students.
1 2 3 4 5 6 7
5
My goal is to avoid performing poorly
compared to others.
1 2 3 4 5 6 7
6
It is important for me to learn what is
being taught in this course.
1 2 3 4 5 6 7
7
My aim is to perform well relative to
other students.
1 2 3 4 5 6 7
8
I am striving to understand the content as
thoroughly as possible.
1 2 3 4 5 6 7
9
I am striving to avoid performing worse
than others.
1 2 3 4 5 6 7
10
My aim is to avoid doing worse than other
students.
1 2 3 4 5 6 7
11
My goal is to avoid learning less than it is
possible to learn.
1 2 3 4 5 6 7
12
My aim is to avoid learning less than I
possibly could.
1 2 3 4 5 6 7
146
13
I think that what we are learning in this
class is interesting.
1 2 3 4 5 6 7
14 My goal is to learn as much as possible. 1 2 3 4 5 6 7
15
I am striving to avoid an incomplete
understanding of the course material.
1 2 3 4 5 6 7
16
I think I will be able to use what I learn in
this class in the future.
1 2 3 4 5 6 7
17
My goal is to perform better than the other
students.
1 2 3 4 5 6 7
18
I think that what I am learning in this class
is useful for me to know.
1 2 3 4 5 6 7

Part B
Directions: The next section asks questions about your study skills and strategies for
learning in the program. Again, there are no right or wrong answers to any of the
questions, just answer as honestly and accurately as possible. Use the scale to answer the
questions. If you think the statement is very true of you, answer 7; if the statement is not
true of you, click on 1. If the statement seems somewhat true of you, select the number
between 1 and 7 that best describes you.

PART B: Learning Strategies & Study Skills

Not at
all
true
of me
very
true
of
me
19
If course materials are difficult to
understand, I change the way I read the
material.
1 2 3 4 5 6 7
20
I’m certain I can understand the ideas
taught in this course.
1 2 3 4 5 6 7
21
When I take a test I think about items on
other parts of the test I can't answer.
1 2 3 4 5 6 7
22
When studying for this course, I try to
determine which topics I don't
understand well.
1 2 3 4 5 6 7
23
I am so nervous during a test that I
cannot remember facts I have learned.
1 2 3 4 5 6 7
24
Before I study new course material
thoroughly, I often skim it to see how it
is organized.
1 2 3 4 5 6 7
147
25
Before I begin studying I think about the
things I will need to do to learn.
1 2 3 4 5 6 7
26
When reading for this course, I make up
questions to help focus my reading.
1 2 3 4 5 6 7
27
When I become confused about
something I'm reading for this course, I
go back and try to figure it out.
1 2 3 4 5 6 7
28
I try to change the way I study in order to
fit the course requirements and the
instructor's teaching style.
1 2 3 4 5 6 7
29
I have an uneasy, upset feeling when I
take a test.
1 2 3 4 5 6 7
30
I am sure I can do an excellent job on the
problems and tasks assigned for this
class.
1 2 3 4 5 6 7
31
When I take a test I think about how
poorly I am doing.
1 2 3 4 5 6 7
32 I expect to do very well in this class. 1 2 3 4 5 6 7
33
When I study for this class, I set goals for
myself in order to direct my activities in
each study period.
1 2 3 4 5 6 7
34
I know that I will be able to learn the
material for this class.
1 2 3 4 5 6 7
35
I ask myself questions to make sure I
know the material I have been studying.
1 2 3 4 5 6 7
36
During class time, I often miss important
points because I'm thinking of other
things.
1 2 3 4 5 6 7
37
If I get confused taking notes in class, I
make sure I sort it out afterwards.
1 2 3 4 5 6 7
38
I am certain I can master the skills being
taught in this course.
1 2 3 4 5 6 7
39
When I take tests I think of the
consequences of failing
1 2 3 4 5 6 7
40
I often find that I have been reading for
class but don’t know what it is all about.
1 2 3 4 5 6 7
41
I try to think through a topic and decide
what I am supposed to learn from it
rather than just reading it over when
studying.
1 2 3 4 5 6 7
148
Appendix D: Coding Scheme for Motivation Questionnaire
Part A: Motivation & Goals
Mastery-Approach

3. My aim is to completely master the material presented in this class.
8. I am striving to understand the content as thoroughly as possible.
14. My goal is to learn as much as possible.

Mastery-Avoidance
11. My goal is to avoid learning less than it is possible to learn.
12. My aim is to avoid learning less than I possibly could.
15. I am striving to avoid an incomplete understanding of the course material.

Performance-Approach
4. I am striving to do well compared to other students.
7. My aim is to perform well relative to other students.
17. My goal is to perform better than the other students.

Performance-Avoidance
5. My goal is to avoid performing poorly compared to others.
9. I am striving to avoid performing worse than others.
10. My aim is to avoid doing worse than other students.

Task Value

1. I like what I am learning in class.
2. Understanding the material is important to me.
6. It is important for me to learn what is being taught in this course.
13. I think that what we are learning in this class is interesting.
16. I think I will be able to use what I learn in this class in the future.
18. I think that what I am learning in this class is useful for me to know.

149
Part B: Study Skills and Learning Strategies

Metacognitive Self-Regulation

19. If course materials are difficult to understand, I change the way I read the
material.
22. When studying for this course, I try to determine which topics I don't understand
well.
24. Before I study new course material thoroughly, I often skim it to see how it is
organized.
25. Before I begin studying I think about the things I will need to do to learn.
26. When reading for this course, I make up questions to help focus my reading.
27. When I become confused about something I'm reading for this course, I go back
and try to figure it out.
28. I try to change the way I study in order to fit the course requirements and the
instructor's teaching style.
33. When I study for this class, I set goals for myself in order to direct my activities in
each study period.
35. I ask myself questions to make sure I know the material I have been studying.
36. During class time, I often miss important points because I'm thinking of other
things.
37. If I get confused taking notes in class, I make sure I sort it out afterwards.
40. I often find that I have been reading for class but don’t know what it is all about.
41. I try to think through a topic and decide what I am supposed to learn from it rather
than just reading it over when studying.

Self-Efficacy for Learning

20. I’m certain I can understand the ideas taught in this course.
30. I am sure I can do an excellent job on the problems and tasks assigned for this
class.
32. I expect to do very well in this class.
34. I know that I will be able to learn the material for this class.
38. I am certain I can master the skills being taught in this course.

Test Anxiety

21. When I take a test I think about items on other parts of the test I can't answer.
23. I am so nervous during a test that I cannot remember facts I have learned.
29. I have an uneasy, upset feeling when I take a test.
31. When I take a test I think about how poorly I am doing.
39. When I take tests I think of the consequences of failing.

150
Appendix E: Participant Recruitment Letter

Page 1 of 1

Date of Preparation: August 31, 2011
UPIRB#: UP-11-00319
Dear [student],
My name is Jane Rosenthal, and I am a doctoral candidate in the Rossier School of Education at
University of Southern California. I am conducting a research study as part of my dissertation, focusing
on the motivation and use of study strategies of first year medical students. You are invited to
participate in the study. If you agree, you are invited to participate in an online survey. The survey is
anticipated to take no more than 15 minutes to complete.
Participation in this study is voluntary. Your identity as a participant will remain anonymous at all times
during and after the study. Your relationship with USC will not be affected whether or not you
participate in this study.
If you have questions or would like to participate, please contact me at janerose@usc.edu or (323) 442-
2380.
Thank you for your participation,
Jane Rosenthal
University of Southern California
151
Appendix F: Information Sheet

Page 1 of 1
Version: 9-24-2010
Date of Preparation: 8-31-2011
UPIRB#: UP-11-00319
Keck School of Medicine
University of Southern California
1975 Zonal Avenue,
Los Angeles, CA 90089

INFORMATION/FACTS SHEET FOR NON-MEDICAL
RESEARCH

MOTIVES AND METHODS: MOTIVATION, LEARNING APPROACHES, AND
ACADEMIC ACHIEVEMENT OF STUDENTS DURING FIRST YEAR TRANSITION
TO MEDICAL SCHOOL

PURPOSE OF THE STUDY
Currently, little is known about the motivation and study strategies of medical students during
their first year in medical school. This study in aimed at a better understanding the interaction
between motivation, study approaches, and academic performance in order to create future
programs that facilitate the transition to medical school, inform teaching and curricular practices,
and improve student support services for all medical students. You must be aged 18 or older to
participate.

PARTICIPANT INVOLVEMENT
You are being asked to complete a brief, 43-question survey via the Qualtrics website. The
survey takes, on average 10-15 minutes to complete. Participation is voluntary. Your grades will
not be affected whether or not you participate.

CONFIDENTIALITY
Your name, student ID number or any identifiable information will not be linked to your
responses. All data will be reported in aggregate form so that no individual person can be
identified.

The members of the research team and the University of Southern California’s Human Subjects
Protection Program (HSPP) may access the data The HSPP reviews and monitors research
studies to protect the rights and welfare of research subjects.

INVESTIGATOR CONTACT INFORMATION
Jane Rosenthal
Keck School of Medicine at the University of Southern California
1975 Zonal Ave, KAM216, Los Angeles, CA 90089
janerose@usc.edu or (323) 442-2380

IRB CONTACT INFORMATION
University Park IRB, Office of the Vice Provost for Research Advancement, Stonier Hall, Room
224a, Los Angeles, CA 90089-1146, (213) 821-5272 or upirb@usc.edu

Abstract (if available)

Abstract The transition into the first year of medical school can be challenging for many students. Students may have difficulty adjusting their learning strategies in the fast-paced, high stakes medical school environment. Medical students may also experience changes in their expectations and motivation for learning in the medical school learning environment. The purpose of this study is to identify approaches to learning and motivational factors reported by students and how these relate to academic achievement outcomes as the students transition through their first year of medical school. ❧ This study had three overarching goals. First, this study explored the learning approaches reported by the students, how these strategies change over the course of the first year, the correlation between these approaches and academic achievement outcomes, and whether there are differences among underrepresented groups determined by age, gender, and ethnicity. The second part of the study focused on the motivation of medical students in the areas of achievement goals, self-efficacy, task value, test anxiety, and self-regulation strategies, how these variables related to overall academic performance, and whether differences exist among groups of underrepresented students. ❧ The findings indicated that students shifted to decreased alertness to assessment, desire to achieve, self-monitoring, but increased in rote memorization strategies and fear of failure. Effective time management, organizational strategies, and desire to achieve were most significantly related to achievement and to deep and surface learning approaches were inversely related to achievement. Underrepresented minority students, particularly Hispanic and African-American students were most at risk for adopting rote memorization strategies and experiencing higher levels of fear of failure, which appeared to impact exam performance. Recommendations for admissions, curricular innovations, and student support are described.

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Asset Metadata

Creator Rosenthal, Jane Lynn (author)

Core Title Motives and methods: motivation, learning approaches, and academic achievement of students during first year transition to medical school

School Rossier School of Education

Degree Doctor of Education

Degree Program Education

Publication Date 07/25/2012

Defense Date 06/21/2012

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag learning approaches,Medical education,Motivation,OAI-PMH Harvest,student achievement

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Hirabayashi, Kimberly (committee chair), Seli, Helena (committee member), Sullivan, Maura (committee member)

Creator Email janerose@usc.edu,Quidnunc@aol.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-66075

Unique identifier UC11290001

Identifier usctheses-c3-66075 (legacy record id)

Legacy Identifier etd-RosenthalJ-996.pdf

Dmrecord 66075

Document Type Dissertation

Rights Rosenthal, Jane Lynn

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA