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The sport of learning: the effect of college athletes' perception of identity on approach to learning

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The sport of learning: the effect of college athletes' perception of identity on approach to learning

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Content THE SPORT OF LEARNING 1

THE SPORT OF LEARNING: THE EFFECT OF COLLEGE ATHLETES’ PERCEPTION OF
IDENTITY ON APPROACH TO LEARNING

by

Katrin R. Wilson

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

May 2016

Copyright 2016 Katrin R. Wilson

THE SPORT OF LEARNING 2

Acknowledgements
To whom much is given, much is required. I acknowledge the Lord who created me and
the family He blessed me with. It was a dream of mine to attend the University of Southern
California and with faith, dedication, and an awesome support system, my dream came true. I
have learned through this journey of higher education, that while I may be the one acquiring the
education, I too must share the knowledge.
My family Great Grandparents, Levi Slack, Sr. and Lollie Wilson; my grandparents
Andrew and Bernice Collins, Louis and Irene Clark; my parents Robert L. Wilson, Sr. and Pearl
Clark Wilson; my aunt Brenda A. Clark; my siblings, Jacqueline I. Wilson, Robert L. Wilson, Jr.,
Ronald E. Wilson; my Cousin David L. Boseman; my nieces and nephews Robert L. Wilson, III,
Erique M. Wilson, Roneshia S. Wilson, Princess R. Wilson and Ashley I. Bell, my great nieces
and nephews MyKala, Cyden, Robert IV, and Kyleigh as well as the others that carry the Slack,
Wilson, Collins, Clark name. You are my constant inspiration.
Dr. Omar Cook, Dr. Nadira Charaniya, Dr. Dyrell Foster, Dr. Kidoko Kennedy, Dr.
Barbara Vercher, Dr. Ruth Johnson, Dr. Lessie Caballero, Dr. Gwen Lisboa, Dr. Catherine Leach,
Dr. Jennifer Castro and those I failed to mention, thank you for your words of encouragement and
unwavering belief that I will be “ok”.
My “Dream Team”, Dr. Patricia Tobey, Dr. Patrick Crispen and Dr. Helena Seli, Dr. Bob
Keim, thank you for supporting my vision and encouraging me during the process. Your presence
during this journey will never be forgotten.
To the student athletes that participated, the counselors, coaches and former student
athletes that served as additional support and editors, I am forever grateful for the position you
played in getting this dissertation to the critical next play…the game continues!
THE SPORT OF LEARNING 3

Table of Contents
Acknowledgements 2
List of Tables 6
List of Figures 8
Abstract 10
Chapter One: Overview Of The Study 11
Background of the Problem 12
Historical Overview of the Role Athletics in American Higher Education Institutions 13
Multiple Roles of the College Athlete 16
Structural Terminology of Student Athlete 18
The NCAA’s Academic Reform Policies 20
Statement of the Problem 22
Purpose of the Study 24
Research Questions 25
Importance of the study 25
Limitations and Delimitations 27
Definition of Terms 27
Organization of the Study 29
Chapter Two: Literature Review 31
Identity 31
Formation 32
Athletic Identity Measurement 33
Social Identity Theory 34
Motivation 38
Pluralistic Ignorance 39
Athlete Motivation Measurements 41
Expectancy-Value Theory 42
Learning Strategies 43
Identity and Approach to Learning 44
Measuring Approach to Learning 46
Student Approach to Learning Theory 47
Summary 49
Chapter Three: Methodology 50
Method of Study 51
Sample and Population 53
Location 54
Participants 55
Survey Participants 57
Interview Participants 58
Instruments 58
Athletic Identity Measurement Scale 59
Learning Approach Inventory 60
College Athlete Questionnaire 61
Interview Protocol 62
Data Collection 62
THE SPORT OF LEARNING 4

Data Analysis 63
Quantitative Analysis 63
Qualitative Analysis 64
Chapter Four: Results/Analysis Of Data 65
Procedures 66
Part I: Quantitative Findings 66
Data Screening 66
Descriptive Characteristics of College Athlete Sample 67
Summary of College Athlete Sample 72
Athlete Identity Measurement Scale 73
Learning Approach Inventory 75
Primary Analysis 77
Results for Research Question One: How does a college athletes’ perception of
identity influence their approach to learning? 77
Results for Research Question Two: To what extent does a college athletes’ perception
of identity influence their approach to learning, controlling for their year in school? 78
Results for Research Question Three: Are there differences in college athletes’
perception of identity based on gender, ethnicity, sport of participation played and
scholarship status? 79
Results for Research Question Four: Are there differences in college athletes’
approach to learning based on gender, ethnicity, sport of participation played and
scholarship status? 92
Summary of Quantitative Finds 104
Research Question One: How does a college athletes’ perception of identity influence
their approach to learning? 104
Research Question Two: To what extent does a college athletes’ perception of identity
influence their approach to learning, controlling for their year in school? 105
Research Question Three: Are there differences in college athletes’ perception of
identity based on gender, ethnicity, sport played, and scholarship status? 105
Research Question Four: Are there differences in the college athletes’ approach to
learning based on gender, ethnicity, sport played, and scholarship status? 106
Part II: Qualitative Findings 107
Design and Analysis 107
Findings and Discussion 108
Qualitative Analysis 109
Identity Themed Interview Questions: 109
Motivation Themed Interview Questions 110
Learning Strategies Themed Questions 113
Summary 115
Chapter Five: Discussion 117
Summary of Findings 119
Relation to Literature 120
Implications for Practice 121
Innovative Implications for Practice 123
Summary of the Study 125
Suggestions for Further Research 125
THE SPORT OF LEARNING 5

References 127
Appendix A: Advisor Assistance Letter 142
Appendix B: Invitation Letter 143
Appendix C: Consent Form 144
Appendix D: Surveys 145
Appendix E: Interview Protocol 148
Appendix F: Distribution Charts 150

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List of Tables
Table 1: College Athlete Identity and Approach to Learning 32
Table 2: Population Profile 2012-2013 (OPE, 2012-2013) 56
Table 3: Supplemental Academic Support Programs 57
Table 4: AIMS Items, Factors 60
Table 5: LA-i Items, Factors 61
Table 6: College Athlete Questionnaire and Associated Factors 62
Table 7: Distribution of Gender 67
Table 8: Distribution of Race/Ethnicity 68
Table 9: Distribution of Year in School 68
Table 10: Distribution of Female Sport of Participation 69
Table 11: Distribution of Male Sport of Participation 70
Table 12: Distribution of Athletic Scholarship Status 71
Table 13: Distribution of Depth Chart Position 71
Table 14: Descriptive Statistics AIMS Items 74
Table 15: Means and Standard Deviations for AIMS 74
Table 16: Descriptive Statistics Learning Approaches Inventory (LA-i) 76
Table 17: Means and Standard Deviations for LA-i 76
Table 18: Pearson Correlation for AIMS and LA-I by Year in School 78
Table 19: Independent Sample Test for AIMS by Gender 80
Table 20: One-Way ANOVA AIMS Ethnicity 81
Table 21: One-Way ANOVA AIMS Sport of Participation 85
Table 22: One-Way ANOVA AIMS Scholarship Status 89
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Table 23: Independent Sample Test for LA-i by Gender 93
Table 24: One-Way ANOVA LA-i Ethnicity 94
Table 25: One-Way ANOVA LA-i Sport of Participation 98
Table 26: One-Way ANOVA LA-i Scholarship Status 101
Table 27: Interviewed College Athlete Demographics 108

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List of Figures
Figure 1: College Athlete Identity and Approach to Learning Conceptual Model 13

Figure 2: Simple Conceptual Model 26

Figure 3: Data Collection Process 53

Figure 4: Probability and Stratified Sampling 54

Figure 5: Athlete Identity Measurement Scale 75

Figure 6: Learning Approaches Inventory 77

Figure 7: SPSS ANOVA Output for Social Identity Factor by Ethnicity (AIMS) 82

Figure 8: SPSS ANOVA Output for Exclusivity Factor by Ethnicity (AIMS) 83

Figure 9: SPSS ANOVA Output for Negative Affectivity Factor by Ethnicity (AIMS) 84

Figure 10: SPSS ANOVA Output for Social Identity Factor by Sport (AIMS) 86

Figure 11: SPSS ANOVA Output for Exclusivity Factor by Sport (AIMS) 87

Figure 12: SPSS ANOVA Output for Negative Affectivity Factor by Sport (AIMS) 88

Figure 13: SPSS ANOVA Output for Social Identity Factor by Scholarship Status (AIMS) 90

Figure 14: SPSS ANOVA Output for Exclusivity Factor by Scholarship Status (AIMS) 91

Figure 15: SPSS ANOVA Output for Exclusivity Factor by Scholarship Status (AIMS) 92

Figure 16: SPSS ANOVA Output for Surface Approach to Learning by Ethnicity (LA-i) 95

Figure 17: SPSS ANOVA Output for Strategic Approach to Learning by Ethnicity (LA-i) 96

Figure 18: SPSS ANOVA Output for Deep Approach to Learning by Ethnicity (LA-i) 97

Figure 19: SPSS ANOVA Output for Surface Approach to Learning by
Sport of Participation (LA-i) 99

Figure 20: SPSS ANOVA Output for Strategic Approach to Learning by
Sport of Participation (LA-i) 100

Figure 21: SPSS ANOVA Output for Surface Approach to Learning by
Scholarship Status (LA-i) 102
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Figure 22: SPSS ANOVA Output for Strategic Approach to Learning by
Scholarship Status (LA-i) 103

Figure 23: SPSS ANOVA Output for Deep Approach to Learning by
Scholarship Status (LA-i) 104

THE SPORT OF LEARNING 10

Abstract
The purpose of this research was to determine how, and to what extent, college athletes’
perception of identity influenced their approach to learning. This study’s rationale was to add to
existing literature a new perspective on athlete academic performance that includes viewing the
correlation between identity perception and approach to learning.
The study used a sequential explanatory mixed-method research design that consisted of
two phases. The first phase, the quantitative research design was used for college athletes from
one private university on the west coast. They were asked to complete the Athlete Identity
Measurement Scale (AIMS) and the Learning Approaches Inventory (LA-i), along with the
College Athlete Questionnaire. The second phase used the qualitative research design to collect
data from semi-structured interviews with college athletes that scored in the criteria of Social
Identity on the AIMS.
The data collected in both phases of the study provided an inclusive explanation of
college athletes’ perception of identity as an influential factor to their approach to learning. The
mixed method analysis revealed that in addition to identity, motivation, expectation and value of
the academic experience, the learning environment and prior experiences were variables that
influenced a college athletes’ approach to learning.
Overall, the study adjoins the literature pertaining to college athlete identity and
academic performance. The findings from this study imply that with further longitudinal studies
with an intentionally targeted population, strong perceptions of identity could serve to predict a
college athletes’ approach to learning.

THE SPORT OF LEARNING 11

CHAPTER ONE: OVERVIEW OF THE STUDY
There are thirty seconds remaining on the game clock. The home team has battled from
behind against their cross-town rivals for the entire game. Now, they have an opportunity to pull
ahead and win with this final play. The stadium is going wild with anticipation; fans from both
teams are selfishly praying for the best. As the quarterback takes his position, he observes the
defensive scheme and acknowledges his offensive personnel, and then signals the running back
who then goes in motion. In what seems like one solid move; the center snaps the ball high, the
quarterback leaps to catch it. Once he lands, he makes a spin move to avoid a defender, then sets
his feet and throws a rocket pass to the tight end that was wide open in the middle of the end
zone. Touchdown home team!
Exciting scenarios like this play out every Saturday across the nation during the college
football season. There are highlights and lowlights, winners and losers, and most of all, there are
college athletes. For this moment of glory, college athletes put in an enormous amount of mental
and physical preparation, not only in the environment of competition, but also in the classroom
(Despres, Brady, & McGowan, 2008). For decades, there has been ongoing deliberation of how
college athletes experience college and the effects of seemingly placing athletics in a higher
priority status than academics (Adler & Adler, 1991; Beamon & Bell, 2006; Cowley, 1930;
Khan, Jamil, Khan, & Kareem, 2012). Female college athletes and those from diverse economic
backgrounds have also been the subject of the academics and athletics discussion (Jolly, 2008;
Pascarella, 1999; Potuto, 2007; Suggs, 2001). Although this is a year-round concern, most
national attention is given to this matter during the time of high stake games such as the national
collegiate football championship game or the end of the year national collegiate basketball
tournaments (Chong & Sommers, 2011; Reid, Whisenant, Martin, & Dees, 2013). Regardless of
THE SPORT OF LEARNING 12

the timing of the attention drawn to the academics and athletics discussion, opportunities are
being afforded to research the college athletes’ academic experiences.
This chapter presents the foundation for the academics and athletics discussion by
providing a historical overview of the role of athletics in American higher education, the
multiple roles of the college athlete, the rationale for the development of the term “student
athlete”, the National Collegiate Athletic Association (NCAA) academic reform policies and
measurements that influence the academic experiences of college athletes. These sections will
serve as a preface for examining the role college athletes’ perception of identity has on their
approach to learning.
Background of the Problem
The focus on academics and college athletes prompted scholars to question the role of
athletics in higher education (ASHE, 2010; Blackman, 2008; Comeaux & Harrison, 2011).
Research on college athletes in higher education illustrated factors influencing academic
performance such as: demographic variables (Reynolds, Fisher, & Cavil, 2012); the improper
balance of intercollegiate athletics and the goal of higher education (Gayles & Hu, 2009);
academic interest, motivation and preparation (Comeaux & Harrison, 2011; Gaston-Gayles,
2004 ; Parsons, 2013), stereotype threat and identity foreclosure (Beamon, 2012; Dee, 2014),
effects of identity development and academic performance (Cohen & Garcia, 2008; Finnan &
Kombe, 2011; Walton & Cohen, 2007), college experiences (Anderson, 2010; Comeaux, 2007;
Ferrante & Etzel, 1991; Pascarella & Terenzini, 2005), and the National Collegiate Athletic
Association (NCAA) academic requirements (Adler & Adler, 1991; Comeaux & Harrison, 2011;
Ferrante & Etzel, 1991; National Collegiate Athletic Association, n.d.; Watt, 2001). As a result
of these studies, colleges and university athletic departments began to provide support to college
THE SPORT OF LEARNING 13

athletes in an attempt to improve their academic performances. However, none of these studies
specifically acknowledged college athletes’ perception of identity and its influence on their
approach to learning as a factor influencing their academic performance. There is a gap in the
conceptual understanding of identity and learning as it relates to college athletes. The following
sections will provide a lens in which to organize a conceptual framework of the relationship
between college athletes’ identity and their learning experiences with a specific focus on their
approach to learning.

Figure 1. College Athlete Identity and Approach to Learning Conceptual Model
Historical Overview of the Role Athletics in American Higher Education Institutions
Today, athletics are closely associated with American institutions of higher education
although that was not always the case. Many early institutions were limited in extracurricular
activities as the main focus of education was on teaching classical studies and preparing students
for religious, law or professorial occupations (Flowers, 2009; Siegel, 2004). In an act of
innovation, students began to create, administer, and finance activities they found interesting
such as college newspapers, debate societies, Greek fraternal organizations, music groups and
athletics (Casinger, n.d.; Massoni, 2011). The early presence of athletics on American higher
education campuses were viewed only as an extracurricular activity with no significant
Social
Exclusive
Athletic
Identty
Negative
Affect
Surface
Strategic
Approach
to
Learning
Deep

THE SPORT OF LEARNING 14

contributions to the overall experience of the college student, but by the early 1900’s athletics
had become the most important social function in American higher education (Miller, 2003).
Intercollegiate athletics generated the spirit of community that demonstrated institutional
commitment that attracted new students and increased institutional awareness (Flowers, 2009;
Rosandich, 2002). The number of colleges participating in athletics increased, as did concern for
this student group on campus (Crowley, 2006; Zimbalist, 1999). An increase in the athlete
presence was met with both enthusiasm and disdain as some institutions welcomed the
opportunity athletic competition provided in creating good will and connecting with the greater
community (Rosandich, 2002); likewise, there were institutions that advanced the notion of
athletic participation as a demoralizing influence on the personal and academic lives of collegiate
athletes (Cowley, 1930).
There was a growing sentiment that athletic participation would not accentuate the
academic mission of higher education. A competition in philosophies was becoming more
apparent (Flowers, 2009). These competing philosophies resulted in intercollegiate athletics
being referred to as American higher education’s ‘peculiar institution’ given that their presence
was unavoidable, yet their balance with academics remained perplexed (Thelin, 1994). Few
college administrators believed character was constructed and intellect developed because of
participation in athletics despite their administrative colleagues championing the cause of
muscular Christianity, which suggested that athletics developed Christian character (Flowers,
2009). The attempt to integrate academic values and athletic accomplishments is a unique
American conception and is still a challenge for American higher education institutions today
(ASHE, 2010; Comeaux, 2007; Gayles & Hu, 2009).
THE SPORT OF LEARNING 15

One of the initial attempts to integrate academic values and athletic accomplishments was
the existence of rules committees. These rules committees created regulatory standards for
competiton and at the same time intertwined the Greek philosophy of properly developing the
mind and the body (Crowley, 2006). Athletics went from being the “peculiar institution” to the
dangerous institution due to the number of violent occurrances as a result of participation. Rules
were in place, although in a chaotic state, yet the deaths of students participating in athletics
required a profound shift in operations. The charge was to either abolish or reform athletics,
particularly football (Crowley, 2006).
In 1905, the President of the United States of America, Theodore Roosevelt, invited
administrators from Harvard, Princeton, and Yale to the White House to discuss the need for
change in football (Crowley, 2006). This meeting and subsequent meetings between
administrators, faculty, and coaches resulted in the formation of The Intercollegiate Athletic
Association of the United States, which later became the National Collegiate Athletic
Association, or commonly referred to as the NCAA (Crowley, 2006).
The original purpose of the NCAA was to serve as the governing body to establish and
enforce safety rules in football . In the Association’s founding documents the stated intention
was to maintain ethical standards similar to the dignity and high purpose of education (Brand,
2006; Siegel, 2004). Even with these high standards set forth, the existence of prohibited
practices in recruitment activities, player incentives, and student status was widespread across
campuses (Crowley, 2006).
Recruiting violations, improper pay-offs and ineligible athletes is not exclusive to the
most recent headlines. These issues were present as early as the late-1800’s when universities,
land grant colleges, and technical institutions were competing for students, visability and public
THE SPORT OF LEARNING 16

support (Flowers, 2009). Athletics then became the equalizing force as victories in competiton
garnered positive instutional recognition. The enhancement of institutional recognition because
of athletic succeses begin the unfortunate trend of unsavory practices that was the catalyst of
eventual concern and reform.
One of the earliest examples of the detrimental systemmic practices was the recruitment
of graduates and those enrolled in professional or technical institutions to be athletes at another
college (Flowers, 2009). Often, these athletes were provided financial consideration for their
participation which included but was not limited to room, board and employment. Colleges even
established non degree programs to enhance their athletic programs and to increase enrollment.
This practice caused concern which lead to an ultimate reform to ensure that athletes were also
students at the institution.
Multiple Roles of the College Athlete
American higher education is habitually in a stage of transition, be it in faculty and
student composition, pedagogy, funding allocations or other dimensions that are the consequence
of an ever-changing society (Ehrenberg, 2012). Sport has often been viewed as a microcosm of
society that advances the notion of social union, replication of a social order; for example, men’s
and women’s athletics, revenue and non-revenue generating sports, ethnic minority and non
minority athletes, scholarship and non-scholarship athletes (Davis, 2007; Wang, Chia, & Chang,
2013). Regardless of gender, ethnicity, socio-economic status, or chosen sport of participation,
college athletes must at some point cope with the task of balancing the roles of student and
athlete (Cheville, 2001; Pope & Miller, 1996;Watt, 2001).
The interplay between the elements that comprise the role of student and athlete is
somewhat reminiscent of Plato’s theory of forms (Plato, 360 B.C.); W.E.B. DuBois’ double
THE SPORT OF LEARNING 17

consciousness (DuBois, 2006) and Gordon Allport’s nature of prejudice (Allport, 1979). Plato’s
(360 B.C.) theory of forms suggests that two distinctive levels of reality exist, conceptual and
rational. When the athlete first achieved prominence on college campuses, it came long after
their presence on campus as students (Massoni, 2011). They existed on a conceptual level, their
athlete roles because during the late-1800’s the college athlete was a new phenomenon with
limited framework of role definitions. This all changed in the early-to-mid -1900’s when athletic
competition became a major social function. The athlete acquired another role, which is in the
rational level of Plato’s theory of forms. College athletes had become the good will ambassadors
and community builders because of their participation in sport (Adler & Adler, 1991; Miller,
2003). From that point forward, college athletes had a duality of role distinctions, student and
athlete (Clark, 2013).
W.E.B. DuBois wrote about a duality role distinction that was present in society in 1903,
three years before the official organization of the National Collegiate Athletic Association
(NCAA). It was the psychological concept of double consciousness from the perspective of the
African American in American society (DuBois, 2006). DuBois (2006) described double
consciousness as the event of assessing one’s worth and purpose by the standards of those who
were amused and disappointed in their dual role existence, in this instance, an American and a
African. The college athlete’s “twoness” (DuBois, 2006, pg.9) is that of the student and the
athlete. To some, college athletes are a source of entertainment and pride in the construct of
athletic competition, but in the classroom, they are perceived as less than motivated students
(Beamon & Bell, 2006; Parsons, 2013). This dual role existence not only was an example of a
divided self, it was also evidence of being divided from other students on campus, thereby
creating a group with shared experiences.
THE SPORT OF LEARNING 18

Literature is replete with examples of how college athletes are compared to their athlete
peers as well as other student peer groups (Anderson, 2010; Cohen & Garcia, 2008; Gayles,
2009). Allport (1978) asserts that membership in a group is paramount in formulating a loyality
for that group. It also stands true that common experiences create group loyality, but also an
acknowledgement of the perceived characteristics of the group. An example of that can also be
observe in Allport’s Nature of Prejedice (1978) when he accounts for an experience of a minority
child that is seldom rewarded for their ethnicity, yet they grow up with a loyality to this racial
group. This occurrence is further discussed in reference to the college athlete and stereotype
threat. Stereotype threat has been operationally defined as a perceived risk of confirming
negative characterizations held about one’s social identity through behavior or performance
(Feltz, Schneider, Hwang, & Skogsberg, 2013). The college athletes identity is not only
contextually distinctive, it can also be negatively stereotyped.
Structural Terminology of Student Athlete
The term student-athlete denotes both an identity and a role with social and cultural
implications. The term student athlete illustrates that an individual is not exclusively an athlete,
which connotes a social identity; the role of the student athlete involves an individuals ability to
successfully navigate the cultural paramaters of academics and athletics (Yopyk & Prentice,
2005). Literature has discovered the definition of the term student athlete as being based on the
literal representation of the hierarchical modeling system that describes the intercollegiate
athletic participant as an individual who is perceived to be student first and athlete second
(Staurowsky & Sack, 2005; The Big Ten Conference, Inc. v. The Department of Revenue, 2000).
Student-athlete first appeared in 1953 when Walter Byers, NCAA president, mandated
the term’s usage as a substitute for the words players and athletes when the possibility arose that
THE SPORT OF LEARNING 19

college football players could be determined to be institutional employees (Crowley, 2006;
Miller, 2003). Some argue that the term was introduced into the American lexicon as a tactic to
counteract the negative publicity and political pressure being created since the first scholarships
were awared in the 1930’s (Miller, 2003; Staurowsky & Sack, 2005).
There are some educators, administrators, and researchers who have criticized the use of
the term student athlete because of the continuous reminder of the supposed inconsistant
relationship between being an athlete and a college student, consequently intensifying concerns
about stereotypes (Stone, 2012). The “one-and-done” phenomenon occuring in men’s basketball
was the result of the 2006 National Basketball Association (NBA) collective-bargaining
agreement that required college basketball players, in part, to be at least one year removed from
high school before declaring their interest to play professionally(National Basketball
Association, 2005). This resulted in the “one-and-done” college athlete who enrolled for one
year which illustrated an athlete entering college with no academic interest, yet they are still
identified as student-athletes. Staurowsky and Sack (2005) were impelled to suggest a
reconsideration of using the term in academic research because of the ideological bias as a means
of identifying and categorizing a subpopulation of students. This study will use the term college
athlete when referencing this student population.
The positive and negative connotations connected with the role and the term student-
athlete is entrenched in the history of college sports (Comeaux, 2007; Crowley, 2006). Research
has shown that finding a proper balance between academics and athletics in an effort to promote
learning and personal development has been a challenge to institutions of higher education
(Comeaux, 2007;Gayles & Hu, 2009). There are factors and influences associated with
academic success and learning among college atheletes (Gayles & Hu, 2009; Parsons, 2013;
THE SPORT OF LEARNING 20

Reynolds, Fisher, & Cavil, 2012; Was, Al-Harthy, Stack-Oden, & Isaacson, 2009). Identity
formation and college athletes have been studied many times over the years (Beamon, 2012;
Comeaux, 2007; Feltz, Schneider, Hwang, & Skogsberg, 2013; Godfrey, 2013; Harper, 2009;
Pascarella, 1999), with limited studies addressing identity and approach to learning, specific to
the college athlete student population. It is the intent of this study to examine how and to what
extent college athletes’ perception of identity influences their approach to learning.
A college athlete’s identity is inclusive of gender, ethnicity, socio-economic status,
athletic status (scholarship or non-scholarship), prior academic experiences, and chosen sport of
participation (Beamon, 2012; Billings, 2012; Flowers, 2009;Harrison, et al., 2009;Staurowsky &
Sack, 2005). In addition to balancing their identities, they must also at some point in their
intercollegiate athletic experiences cope with the undertaking of balancing the roles of student
and athlete (Chen, Mason, Middleton, & Salazar, 2012; Cheville, 2001; Godfrey, 2013; Parsons,
2013; Pope & Miller, 1996;Watt, 2001). Research has shown that this act of equilibrium is
compouned by personal and environmental factors that include self and social perceptions of
identity (Beamon, 2012; Cohen & Garcia, 2008) and approaches to learning (Bliuc, Ellis,
Goodyear, & Hendres, 2011; Laird, Shoup, Kuh, & Schwarz, 2008). According to Godfrey
(2013), college athletes’ academic performance is also influenced by the expectations and
demands placed on them by parents, teammates, coaches and the athletic governing body, such
as the NCAA.
The NCAA’s Academic Reform Policies
The academic and athletic discussion often centers around two major observations: the
prioritization of athletics over academics (Denhart, Villwock, & Vedder, 2009; Gatmen, 2011)
and the academic performance of college athletes, with special attention being paid to those who
THE SPORT OF LEARNING 21

participate in high profile, revenue generating sports such as football and men’s basketball
(Levine, Etchison, & Oppenheimer, 2014; McArdle, Paskus, & Boker, 2013). Myles Brand
(2006), a past president of the National Collegiate Athletic Association (NCAA), argued that
higher education has failed to meet the personal and intellectual growth of its athletes, thus
emphasizing the importance of predicting the academic success of college athletes through
institutionalized benchmarks and measurement scales.
Benchmarks and measurement scales have been implemented into educational policies as
a technique to assess the proficiency of learning and skill acquisition of students in elementary
and secondary educational settings (Burke & Marshall, 2010). According to the Student-Right-
to-Know Act passed in 1990 (P.L. 101-542), universities receiving federal financial assistance
were required to report graduation rates of college athletes. Congress found that there was
increasing concern about the academic perfomance and graduation rates of college athletes. They
affirmed that prospective college athletes should be made aware of the educational commitments
of institutions in order to make an informed decision of attendance (Student-Right-to-Know Act,
1990). The NCAA created the Graduation Success Rate (GSR) and the Academic Progress Rate
(APR) to gather academic outcome data for college athletes with the GSR providing graduation
rates and APR to measuring academic success as a predictor to graduation (Petr & Paskus,
2009).
Evaulative methods are used to improve goals, develop stategies and determine the
effeciveness of a practice and are categorized as either formative or summative (Ambrose,
Bridges, DiPietro, Lovett, & Norman, 2010). Based on the definition of summative assessments
by Ambrose et al (2010) the academic progress rate (APR), academic success rate (ASR) and
graduation success rate (GSR) can be classified as such since these reports provide an evaulation
THE SPORT OF LEARNING 22

of an individuals proficiency based on final assessments, i.e., grades. The progress toward degree
measurement used by the NCAA is a formative assessment that communicates the students
peformance relative to the specific target criteria, in this case, progress toward degree (Ambrose,
et. al., 2010; NCAA, Academics-What We Do). On the basis of the evidence currently available,
it seems fair to suggest that there is a need to understand and explain the cumulative processes
that influence college athletes academic success (Comeaux & Harrison, 2011). These reports
expound on outcomes, which is an important measurement, but just as important, is the process
in which a college athlete approaches learning.
Statement of the Problem
The National Collegiate Athletic Association (NCAA) requires member institutions to
report the Graduation Success Rate (GSR) and Academic Progress Rate (APR) as well as affirm
progress towards degrees for their college athletes (NCAA Academics, n.d.). These reports are a
part of a series of policies implemented to strengthen the academic preparedness of Division I
college athletes and is used as an institutional accountability benchmark to measure college
athletes academic performance (NCAA Academics, n.d.; NCAA Division I Academic
Philosophy, n.d.). Unfortunately,these reports alone do not provide a full understanding or
evaulation of the college athletes’ academic experience. The shortfall of these measurements is
the lack of connectivity to the learning strategies implemented to encourage academic
achievement. Lacking from previous studies is an explanation of how and to what extent college
athletes perception of self and social identity influence their approach to learning which directly
effects present and future academic outcomes.
Assessments and measurements are being viewed as the missing proponent in American
education reform (Burke & Marshall, 2010) and this ideolgy has permeated intercollegiate
THE SPORT OF LEARNING 23

athletic departments nationwide with policies requiring reports to assess college athletes’
academic performance: Academic Progress Rate (APR), Academic Success Rate (ASR) and
Graduation Success Rate (GSR) (LaForge & Hodge, 2011). The educational issue this study
addressed is the need to consider the college athletes approach to learning as an intregal
component to the measurement of their academic performance and how their perception of
identity influences that approach.
There is still a great deal unknown about the approach to learning individuals employ
within and across learning contexts (The British Psychological Society, 2013). In an effort to
promote learning and personal development among college athletes, colleges and universities
offer many support services and programs, yet finding the proper balance between academics
and athletics continues to be unresolved (Comeaux, 2007;Gayles & Hu, 2009; Kamusoko &
Pemberton, 2012). However, Dr. David Graham, an assistant provost and associate athletic
director for student success at The Ohio State University, states that the tension between
academics and athletics is a necessity because it results in new academic standards and college
athlete well-being measurements (Graham, 2012). While standards, measurements and reports
assist in acquiring the educational outcomes and performance rates of college athletes, there
remains an analytical gap to explain and describe the approach to learning applied by college
athletes.
Some studies have identified how prior academic experiences, inclusive of high school
grades and standardized test scores, are the best predictors of academic performances and
graduation rates of college athletes (Adler & Adler, 1991; Beamon & Bell, 2006; Despres,
Brady, & McGowan, 2008; Khan, Jamil, Khan, & Kareem, 2012; McArdle, Paskus, & Boker,
2013). Although these studies centered on the initial and final stages of academic performance,
THE SPORT OF LEARNING 24

Le Crom, Warren, Clark, Marolla and Gerber (2009), concluded that these reports did not
accurately depict the issues of retention as it related to academic performance.
In the academic performance measurement era of intercollegiate athletics, colleges and
universities must fix the power and incentive structure of assessment by shifting the focus to
student’s approach to learning instead of merely student learning outcomes. The NCAA has a
history of academic reform policies that address the concerns of initial eligibility, progress
toward degree and graduation, but does not account for the college athlete’s approach to leaning,
the catalyst of the measurments. This study will focus on college athletes’ perception of identity
as an influential factor to their approach to learning which could be used as a predictor of
successful academic performance. Included in this study will be a review of literature analyzing
identity, motivation and students’ appraoch to learning.
Purpose of the Study
The purpose of this sequential explanatory mixed-method research was to examine how,
and to what extent, college athletes’ perception of identity influences their approach to learning.
A sample population of university college athletes participating in academic support program
were used in this study. There are two divergent interactive phases of the sequential explanatory
mixed-method. The first phase of this process was to gather quantitative data of college athlete
identity formation and perception. Two assessments were used in this phase, Brewer, Van
Raalte and Linder’s Athletic Identity Measurement Scale (1993) and Yusoff’s Learning
Approach-Inventory (2011) were the assessment scales used to measure identity and approaches
to learning respectively. The quantitative data gathered from the first phase allowed criteria to be
established based on the ratings of the participants. The criterion used to determine which
college athletes would participated in the second phase of the study which consisted of a semi-
THE SPORT OF LEARNING 25

structured interview. Interviews with the selected college athletes was conducted in the second
phase of the study and gathered qualitative data on college athletes’ perceptions of identity as an
influential factor of their approach to learning. The purpose for the second phase was to provide
a more in-depth explanation of identity perceptions of college athletes and their approach to
learning. Data was collected to document college athletes’ academic performances were often
outcome based with a wide range of influential factors predicting that outcome.
Research Questions
To better understand how and to what extent a college athletes’ identity perception
influences their approach to learning, there first needs to be a greater understanding of the
relationship between identity and learning in general then particularly in regards to this specific
population. The educational problem this study addressed is how the college athlete perceives
their identity in terms of attitudes and behaviors and how those factors influence their approach
to learning. The research questions guiding this study were:
1. How does a college athletes’ perception of identity influence their approach to learning?
2. To what extent does a college athletes’ perception of identity influence their approach to
learning, controlling for their year in school?
3. Are there differences in college athletes’ perception of identity based on gender,
ethnicity, sport played, and scholarship status?
4. Are there differences in college athletes’ approach to learning based on gender, ethnicity,
sport played, and scholarship status?
Importance of the study
Institutionalized benchmarks for college athletes’ academic outcomes were often
measured by grade point averages (GPA), academic progress rates (APR), progress toward
THE SPORT OF LEARNING 26

degree, academic success rates (ASR) and graduation success rates (GSR) (Chong & Sommers,
2011; Graham, 2012; LaForge & Hodge, 2011; McArdle, Paskus, & Boker, 2013). However,
research is limited in addressing the college athletes approach to learning and the mediating
factors for that approach. This study intended to examine how and to what extent a college
athletes’ perception of identity influences their approach to learning.
This study used the research of Bliuc, Ellis, Goodyear, and Hendres (2011) as a lens to
understand the relationship between a student’s social identity and approach to learning as it
related specifically to the college athlete population. Bliuc et al (2011) proposed the idea that
strong student identity is often associated with a deep approach to learning that is also linked to
higher academic performance; this research focused on determining if this would hold true with
the American college athlete, a very different population than the original study. The findings of
this study provided additional insight to the college athlete academic experience. These
experiences not only served to expand the body of literature in the field of academics and
athletics, but also allowed their academic experiences to not be viewed through a lens of
deficiency, but rather a lens of self-regulation and identity. Collegiate athletic departments that
provide academic support services for college athletes will be able to infuse the findings of this
study into their existing practices.

Figure 2. Simple Conceptual Model

College
Athlete
Identity
Approach to
Learning
THE SPORT OF LEARNING 27

Limitations and Delimitations
In 2014, the NCAA verified that there are over 400,000 athletes participating in 23 sports
at over 1,000 colleges (Statistic Brain, 2014). This study was limited in the amount of college
athletes to be used in this study. This study possessed those limitations that accompanied a
single-institution study. External validity, therefore, is limited to the extent that an institution
pertained to this study. To combat this, population parameters of the institution were examined
and found to resemble those of both NCAA Division I institutions and those in higher
education overall.
The researcher was employed part-time with this particular institution in the athletic
department as an academic tutor and learning specialist. In order to ensure ethical compliance,
all college athletes who had previous experience with the researcher was not invited to
participate in the study. The director of tutorial services and academic advisors for athletes also
made sure all college athlete participants had not had prior contact with the researcher.
Definition of Terms
Academic Progress Rate (APR): a metric indending to provide real-time feedback on the
progress toward graduation of a student athlete (LaForge & Hodge, 2011)
Athlete Identity Measurement Scale (AIMS): a 10-item questionnaire used to measure athletic
identity, which is the degree to which an individual identifies with the athlete role (Brewer,
Cornelius, Stephan, & Van Raalte, 2010)
Approach to Learning: the ways in which students apply methods towards learning (Biggs, 1987)
College Athlete: an individual that participates in athletics at an institution of learning in higher
education (Yopyk & Prentice, 2005)
THE SPORT OF LEARNING 28

Deep Approach to Learning: characteristics of a learner that is intrinsically motivated,
academically engaged in appropriate learning activities, such as integrating subject matter with
prior knowledge (Yusoff, 2011)
Eligibility: minimun requirements to participate in athletic competition (NCAA Academics, n.d.)
Exclusivity: A factor in the Athletic Identity Measurement Scale that characterized the extent to
which an individual's self is determined only by performing as an athlete in an athlete role
(Brewer, Van Raalte, & Linder, 1993)
Graduation Success Rate (GSR): graduation rate methodology developed by the NCAA that
credits institutions for incoming transfer and midear enrollees who graduate (LaForge & Hodge,
2011)
Learning Approach Inventory (LA-i): a 9-item measurement tool developed to assess students
aproaches to learning (Yusoff, 2011)
National Collegiate Athletic Association (NCAA): Intercollegiate athletic governing body
(NCAA Academics, n.d.)
Negative Affectivity: A factor in the Athlete Identity Measurement Scale that describes the
extent in which an individual experiences negative affect response from undesirable outcomes in
athletic domains (Brewer, Van Raalte, & Linder, 1993)
One-and Done: 2006 National Basketball Association (NBA) collective-bargaining agreement
that required college basketball players in part to be at least one year removed from high school
before declaring their interest to play professionally (National Basketball Association, 2005)
Pure Athlete: athlete who devoted all of their time and energy to their athletic activity (Snyder,
1985)
THE SPORT OF LEARNING 29

Pure Scholar: athlete who is highly committed to the academic role with little or no commitment
to the athletic role (Snyder, 1985)
Scholar Athlete: athlete who is highly committed to both academics and athletics; example All-
American team member (Snyder, 1985)
Social Identity: factor in the Athlete Identity Measurement Scale in which the extent an
individual views himself or herself as occupying a socially recognized role as an athlete (Brewer,
Van Raalte, & Linder, 1993)
Stereotype Threat: the perceived risk of confirming, through one’s behavior or outcomes,
negative stereotypes that are held about one’s social identity (Dee, 2014)
Strategic Appoach to Learning: a learner applies a systematic manner of learning that is specific
to the task in order to attain the highest grades possible; motivation for these learners are most
likely to be extrinsic because of the competitive nature with other learners in the common
learning environment (Entwistle & Ramsden, 1982; Yusoff, 2011)
Student-Athlete: an individual that participates in athletics at an institution of learning from
primary to higher education (Yopyk & Prentice, 2005)
Surface Approach to Learning: individual applies rote memorization learning with no connection
to prior knowledge and other experiences; selective in their methods of information processing;
extrinsically motivated only focusing on the task at hand and subscribing to the “means-to-an-
end” mindset (Duff & McKinstry, 2007; Ferla, Valcke, & Schuyten, 2010; Haggis, 2003; Platow,
Mavor, & Grace, 2013; Smyth, et. al, 2013)
Organization of the Study
This study was designed to reseach how the perceptions of identity of college athletes
influenced their approach to learning. There is a continual concern among reseachers of the
THE SPORT OF LEARNING 30

analytical gap that exisist regarding academic performance among college athletes. Chapter 1
presents an overview of the study and the National Collegiate Athletic Association (NCAA)
academic measurements. Also included is a statement of the problem and the research questions
to be answered in this study.
Chapter 2 presents an overview of the impact of NCAA assessments on research
regarding college athlete academic success. Literature concerning identity, motivation and
students’ approach to learning will be reviewed. The methodology for conducting this research
will appear in Chapter 3; Chapter 4 will present results and Chapter 5 will present the
conclusions of the study.

THE SPORT OF LEARNING 31

CHAPTER TWO: LITERATURE REVIEW
This research study focused on college athletes’ perception of identity and the influence it
has on their approach to learning. The educational issue this study addressed was the need to
consider the college athletes’ approach to learning as an integral component to the measurement
and prediction of academic performance. The review of literature focused on three factors
influencing college athletes’ academic experiences: identity, motivation, and learning strategies.
Included in this chapter is a review of popular measurement scales and inventories intending to
assess identity (AIMS; Brewer, Van Raalte, & Linder, 1993), motivation (SAMSAQ; Gaston-
Gayles, 2005), and students approaches to learning (LA-i; Yusoff, 2011). The purpose of
reviewing these measurements is to add legitimacy to the discussion of how and to what extent a
college athletes’ perception of identity influences their approach to learning. Also included in
this chapter is a review of the tenets of social identity theory (Tajfel & Turner, 1979),
expectancy-value theory (Eccles, Adler, Futterman, Goff, & Kaczala, 1983), and the student
approach to learning theory (Marton & Säljö, 1976) as foundational components to the rationale
for exploring the association between a college athletes’ perception of identity and their
approach to learning.
Identity
The construction of self-concept by way of role participation and behavior are commonly
used categorizations of identity (Hogg, Terry, & White, 1995; Korte, 2007; Stets & Burke,
2000), likewise, social identity solidifies the relationship between an individual and group
membership that maintains and supports their personal identity (Stets & Burke, 2000; Tajfel &
Turner, 1979). In 1985, Eldon Snyder presented a theoretical analysis of the academic and
athletic roles classification based on levels of commitment. This study moved the level of
THE SPORT OF LEARNING 32

analysis beyond the descriptive cross-tabulation of data (Snyder, 1985). The table below is an
adaptation of the classification regarding the level of commitment as explained by Snyder
(1985), with an additional column for approach to learning to connect with the scope of the
study.
Table 1
College Athlete Identity and Approach to Learning
Identity Academic
Role
Athletic
Role
Characteristic Approach to
Learning
Scholar Athlete High High Academic All
American
Unknown
Pure Scholar High Low Rhodes Scholar Unknown
Pure Athlete Low High Dumb Jock Unknown

Formation
Identity is a result of an individual’s understanding of their connection to a social group
and from the principles and emotional merit attached to that membership (Turjeman, Mesch, &
Fishman, 2008). Athletes, particularly college athletes, are limited in time and energy necessary
to explore identities and often attach to the identity in which they receive the most reward and
encouragement, the athlete identity (Beamon, 2012). In 1991, Adler and Adler’s participant-
observer study of a Division I basketball team assessed the salience of identity in order to
examine the academic and athletic roles. The research concluded many athletes come to college
with a salient academic identity, however, at some point in their college career, the salient
identity changes to that of an athlete.
The changing of identity formation is not exclusive to the athlete population; the title
itself has changed over time, beginning in 1953. Walter Byers, NCAA president, mandated the
use of the term “student-athlete” as a substitute for the words “players” and “athletes” because
the possibility arose that college football players could be determined to be institutional
THE SPORT OF LEARNING 33

employees (Crowley, 2006; Miller, 2003). Crowley (2006) documented the information in the
Centennial edition of the NCAAA’s written history, while Miller (2003) also wrote an historical
perspective of athletics and academics and the function awarding scholarships had on the
meaning of “amateur status” and the “student-athlete” term. Staurowsky and Sack (2005) argued
that the term was introduced as a ploy to counteract negative publicity and political pressure.
Additional findings in the Adler and Adler (1991) study suggested that identity
foreclosure is caused by the perceived treatment of others as only acknowledging their “athletic
selves.” Since then, many studies have examined the importance of understanding athlete
identity and its influence on their experiences in higher education (Comeaux, 2007; Dee, 2014;
Ferrante & Etzel, 1991; Harrison, et al., 2009; Kamusoko & Pemberton, 2011). Beamon and Bell
(2006) conducted research utilizing the quantitative design with a predominantly White Division
I institution’s football team. The study explored the aspects of socializaion process of sport
participation and examined the degree in which emphasis is placed on athletics compared to the
emphasis placed on academics (Beamon & Bell, 2006). Common themes emerged from the
results of these research inquires on the manner in which athletes evaluated their identity through
measurement scales and inventories.
Athletic Identity Measurement
A common practice of assessing athlete identity has resulted in many measurement
instruments. Yukhymenko-Lescroart (2014) created the Academic and Athletic Identity Scale
(AAIS) to measure the identification of students participating in sports and to gain an
understanding for which being academically and athletically engaged is central to their sense of
self. The Academic and Athletic Identity Scale (AAIS) was created through a mixed method
research design with Division I athletes. The first study was the scale development and then
THE SPORT OF LEARNING 34

content validation; while the second study was the use of the revised scale with the 596 athletes
at the Division I University in various sports. Another measurement scale developed in response
to the academic/athletics discussion was the Baller Identity Measurement Scale (BIMS;
Harrison, Tranyowicz, Bukstein, McPherson-Botts, & Lawrence, 2014), also measures the
athletic and academic identities, however, this specific instrument addresses cultural relevancy.
Of all the measurement tools and instruments available, the most often used is the
Athletic Identity Measurement Scale (AIMS; Brewer, Van Raalte, & Linder, 1993). AIMS was
developed after an initial evaluation study of 243 undergraduate athletes and two follow-up
studies with 449 undergraduates and 90 college football players. The AIMS is documented as a
reliable and valid measure of athletic identity. This scale measures the degree to which an
individual identifies with the athlete role and discovered three factors that influence their
identity. The first factor is the social identity in which the extent an individual views himself or
herself as occupying a socially recognized role as an athlete. The second factor, exclusivity,
characterized the extent to which an individual's self is determined only by performing as an
athlete in an athlete role and the third factor, negative affectivity is the extent that an individual
experiences negative affect response from undesirable outcomes in athletic domains (Brewer,
Boin, & Petitas, 1993; Brewer & Cornelius, 2001).
Social Identity Theory
Social identity theory (SIT) uses a social psychological framework that constitutes the
concepts of personality, environment and behaviors, to explain the consequences and actions of
an intergroup (Tajfel, 1974; Tajfel & Turner, 1979). The initial expertmental designed used both
children and adults as subjects and randomly categorized as membes of two nonoverlapping
groups. The findings of this study revealed that intergroup categorizations lead to ingroup
THE SPORT OF LEARNING 35

favoritism and discrimination against the out group. Based on the understanding of social
groups, Tajfel (1974) outlined four principles of SIT: social categorization; social comparison;
social identity; and psychological distinctiveness, which later was acknowledged as self-esteem
(Treote, 2014). The four principles of SIT will be fundamental for gaining knowledge of college
athletes as a social identity and exploring their consequences and behavior as it relates to their
approach to learning.
Information processing theorists confirm the application of schemas and categories as a
method of understanding and simplyfing data (Alexander, Schallert, & Reynolds, 2009;
Ambrose, Bridges, DiPietro, Lovett, & Norman, 2010). Likewise, the first prinicple of social
identity theory, social categorization, provides the cognitive tools to divide, sort and order the
social environment that defines an individuals place in sociological environment (Treote, 2014).
Tejfel (1974) proposes the social categorization as the recognition of identity in socially defined
terms. It was documented in the previous section outling the structural termonology of the term
‘student-athlete’ as a social identity because they were members of the student group as well as
the athlete group solidifying their identity as having social distinction (Yopyk & Prentice, 2005).
Yopyk and Prentice (2005) conducted a quantitative study to examine task performance
as a precursor and consequence of identity salience using male athletes at a highly selective
university as the participant population. Athletes were chosen because of the competing identites
relevant to the academic domain; student identity and athletic identity.
The concept of group membership and social distiction can be expressed in its most
simplest form as the “we” and “them” dicodomies (Stets & Burke, 2000). The second SIT
principle of social comparison is evident in research as occuring with the college athlete
population (Hawley, Hosch, & Bovaird, 2014). Stone, Harrison and Mottley (2012) make a clear
THE SPORT OF LEARNING 36

delineation, that not only are athletes compared to non-athletes, they are also compared to their
athlete peers. This quantitative research study had 151 college athlete participants from a large
state university in the southern United States using an adaption of the Intellectual Disengagement
subscale of the Intellectual Orientation Inventory (Stone et al, 2012). The conclusion of this
study provided insight to the stereotype threat process and the effect of identity priming.
The non-athlete peer comparison is often the result of faculty, administrators and other
non-athlete students using the term “dumb jock” as a perception of their deficiency in
intelligence, motivation and academic preparation (Dee, 2014; Parsons, 2013; Stone, Harrison, &
Mottley, 2012). The athlete peer comparison appears in the naming of a social group, because it
mandates meanings in the form of individual and group expectations (Stets & Burke, 2000).
Beamon (2012) and Stone (2012) rationalize the student identification as having expectations of
academic motivation and focus, while the athlete identification expectation is to be competetive
and aggressive in the athletic arena. Group memberships, especially the student-athlete group,
can be seen as both an enhancement and a threat to an identity because it describes and
prescribes how the individual should think, feel and behave (Tajfel & Turner, 1979).
Social identities have been illustrated in research as cognitive constructs of self that
impacts on an individuals’ perceptions, emotions and behaviors (Elemers, Spears, & Doosje,
2002; Korte, 2007). Literature concerning social identity continues to broaden the overall
understanding of the psychological mechanisms that emphasizes the behaviors of identified
group members (Ashforth & Mael, 1989; Hawley, Hosch, & Bovaird, 2014; Huy, 2011).
Individuals often identity with groups to ascertain a sense of pride, involvement, stability and
meaning (Korte, 2007; Treote, 2014).This identification can lead to characterizations that express
stereotype labels from those not in the group membership (Brown & Pinel, 2003). Brown and
THE SPORT OF LEARNING 37

Pinel’s (2003) study focused on sterotype theat among women in regards to math. Forty-nine
female undergraduates from the University of Oklahoma participated in survey to measure their
identification with mathematics and gender identification scales. It was also noted in the study
that stigmatized individuals may experience sterotype threat under certain conditions, however,
individuals who are more aware of stigmas may experience streotype threat more frequently
(Brown & Prinel, 2003).
The naming of a social group often direct connotations in the form of expectations of
individual and group behavior. Identity foreclosure and stereotype threat are often associated
with college athletes in literature. A basic Google Scholar search for college athlete identity and
acadmic performance for one year, 2013, resulted in over 11,000 matches which could be used as
evidence for the purpose of this study to determine how and to what extent does a college
athletes’ perception of identity influence their approach to learning.
Psychological distinctiveness, self-esteem, is the final principle of the social identity
theory proposed by Tajfel and Turner (Tajfel, 1974; Tajfel & Turner, 1979). It has been
suggested that this final principle is fundamental to motivation. Motivation to persevere is
paramount in social identity, it is essential for self image and self improvement (Trepte, 2014).
In the early work of Tajfel (1974) it was made obvious a distinction between secure and insecure
social identity. Secure social identity was defined by Tajfel (1974) as the relationship between
social goups in which a difference between them was implausible. While in contrast, the insecure
social identity implied the existance of consent to the nature and future of their social identity. In
literature regarding athletes, this social identity insecurity is known as identity forclosure in
which an individuals identity is excluselively athletic and most of their self characterization and
self significance is supported by their participation and success in athletics (Beamon, 2012).
THE SPORT OF LEARNING 38

Literature has established college athletes as belonging to a group of students with similar
experiences for which opportunities for entrenching loyalty is afforded (Anderson, 2010;
Clopton & Bourke, 2012; Comeaux, 2007; Gayles, 2009; Watt & Moore, 2001). When loyalty is
instituted, a tendency for identity formation occurs. This formation of identity based on shared
experiences and loyalty is the primary definition of social identity (Treote, 2014). Social identity
theory has a relevence to athlete identity in college and their academic experiences.
Instead of trying to decide if the athlete or student identity is most important, it is
imperative for educators to correlate the identity to the approach to learning. By indicating the
identity in which the individual discovers and determine how and to what extent that identity
influences their approach to learning will assist academic support practicitioners in providing the
most efficient servcies for students.
Motivation
Identity has been proven to be a factor influencing the academic experiences of college
athletes (Cohen & Garcia, 2008; Comeaux, 2007), so too has motivation toward academic
achievement (Gayles & Hu, 2009; Pascarella, 1999). Motivation occurs in two forms,
intrinsically and extrinsically. Intrinsic motivation refers to engaging in a task or activity because
of self interest or enjoyment, while extrinsic motivation is engaging in a task or activity for a
reward or other desirable outcomes (Ryan & Deci, 2000; Schunk, Pintrich, & Meece, 2008).
Schunk, Pintrich, and Meece (2008) explained how intrinsic and extrinsic motivation are reliant
on the task or activity and the moment in which the task or activity must be performed; meaning
one activity can be intrinsically or extrinsically motivating for different individuals.
Studies about academic motivation frequently return to the focus of identity (Comeaux &
Harrison, 2011; Gatmen, 2011; Was, Al-Harthy, Stack-Oden, & Isaacson, 2009). Comeaux and
THE SPORT OF LEARNING 39

Harrison (2011) proposed a theroretical model that connected individual and environmental
characteristics to educational outcomes. The purpose of the model was to explain the
longitudinal process of varying forms of interaction that leads to an athlete’s academic success.
This sudy concluded the more validation received, the more committed, or motivated, they were
to continue the action that prompted the response from others (Comeaux & Harison, 2011). The
documented academic underperformance of college athletes is attributed to their identity as an
athlete. Researchers have affirmed that identity commitment serves as a mediating factor for
processing styles and outcome behaviors (Berzonsky, Cieciuch, Duriez, & Soenens, 2011; Was,
et. al., 2009). Berzonsky, et al (2011) advances this thought by establishing identity styles, a
method of how individuals behave and the attribution of value orientations to a belief of how an
individual should behave. How an individual behaves and how they should behave are generally
two different concerns, but when viewing it in the scope of this study of how college athletes’
perception of identity influences their approach to learning, they are correlating concepts. How a
college athlete should approach learning and how they actually approach learning can be
clarified when it is known if they are intrinsically or extrinsically academically motivated. It can
be presumed that a college athlete’s academic motivation is intrinsic if they engage positively in
their academic persuits, i.e., honor roll, deans list etc., and extrinsic if they are passively engaing
in academic pursits to remain eligible for athletic participation.
Pluralistic Ignorance
It has been well-established in the history of college sports that positive and negative
connotations are associated with the role and the term “student-athlete” (Comeaux, 2007;
Crowley, 2006); these connotations impact the academic experiences (Gatmen, 2011; Was, et.
Al., 2009) and motivation of college athletes (Parsons, 2013; Potter, 2013). A salient identity that
THE SPORT OF LEARNING 40

has negative stereotypes can decrease ones ability to perform (Benson, 2000; Martin, Harrison,
Stone, & Lawrence, 2010). Prior inquiry has provided evidence that effort and performance are
influenced by identity (Dee, 2014). Levine, Etchison and Oppenheimer (2014) presented a
study that aimed to explore the possibility of pluralistic ignorance within the populations of
midle school, high school, and collegate athletes. The quantitatve research study used forty-nine
6
th
-12
th
grade athletes and 98 male athletes that participated in a Division III football team at a
university in the midwest United States. The findings suggests that pluristic ignorance was found
among all populations of students surveyed (Levine, et al, 2014).
Their observation is that in order to fit in, some college athletes conform to the behavioral
ideology of publically discrediting their academic performance while privately having a positive
attitude (Levine, Etchison, & Oppenheimer, 2014). This behavior is called pluralistic ignorance
it occurs when an individual changes their behavior to assimilate into the misperecieved attitudes
and behaviors of their peers (Miller & McFarland, 1991). Benson (2000) found in a study that
athletes prefered not to be in class with other athletes because of the negative effects they had on
each other. Whether or not this was an act of plurastic ignorance was not fully explored in this
study. Conversely, evidence from other studies regarding athlete’s academic motivation argue
that athletes who perceive their peers as underperforming and major clustering are more likely
to underperfom themselves (Bowen & Levin, 2003). Levine et. al (2014) concluded in their
study that athletes generally agree with the student first athlete second adage, but at the same
time misperceive their athlete peers as reversing the precedence. The phenomenon of pluralistic
ignorance has been used to guage perception of group norms in society from the war in Iraq to
college drinking, yet it has been mininally considered an academic motivation factor for college
THE SPORT OF LEARNING 41

athletes. As this study intends to examine how and to what extent a college athletes’ perception
of identity influences their approach to learning, assessing factors toward motivation is essential.
Athlete Motivation Measurements
Assessing motivation provides an opportunity to understand intrinsic and extrinsic
orientation by observation, rating by others and self-reports (Schunk, et. al, 2008). Most of the
inventories used for college athletes are only measuring motivation in sport, specific to sports
mental toughness (PPI-A; Golby, Sheard, & van Wersch, 2007), sport specific coping skills
(ASCI-28; Smith, Schutz, Smoll, & Ptacek, 1995), with only one assesing athletes motivation
toward academics (SAMSAQ; Gaston-Gayles, 2005). Gaston-Gayles (2005) used the
expectancy-value famework to develop the Student Athletes’ Moivation toward Sports and
Academics Questionnaire (SAMSAQ). Two hundred thirty-six athletes participating in eight
varsity sports at a Division I university in the midwest of the United States was the population
for this study. The SAMSAQ consisted of 30 items that examined the extent in which athletes
related to the presented statements. The results of this study supports the use of expectancy-
value as a framework for measuring academic and athletic motivation of college students
(Gaston-Gayles, 2005).
The Student Athletes’ Motivation toward Sports and Academics Questionnaire is the only
tool that measures both motivation in sports and toward academics (Gaston-Gayles, 2005).
Bowen and Levin (2003) claimed that academic performance measurement tools do not assess
late-developing qualities in which the SAMSAQ sought to assess. Comeaux and Harrison (2011)
agreed with Bowen and Levin (2003) on the need to advance knowledge and illuminate the
complexity associated with the academic experiences of college athletes. In an effort to gain an
THE SPORT OF LEARNING 42

understanding of how college athletes approach to learning, gathering information on their
expectations and values of their academic experience is imperative.
Expectancy-Value Theory
Literature has addressed the various roles and identity characterization of college athletes
in higher education with the intention of understanding their academic experiences (Godfrey,
2013). Research has also shown that identity priming can have adverse effects on individuals if
negative inferences are associated with the identity (Harrison, et al., 2009). It has also been
proven by experts that stereotype threat is detrimental to the outcome of performed tasks (Dee,
2014; Feltz, Schneider, Hwang, & Skogsberg, 2013). The question in literature remains how
then does a college athlete’s perception of identity influence their approach to learning.
There are four foundational components of the expectancy-value theory’s role in
acknowledging how and to what extent a college athletes’ identity influences their approach to
learning. These four components are: (1) awareness of the historical nature of the athlete’s
presence in higher education (Crowley, 2006); (2) the academic, athletic and social expectations
placed upon them (Bowen & Levin, 2003); (3) the formation of “student-athlete” as a term of
identification (Staurowsky & Sack, 2005), and (4) in-group, athlete peers, and out-group, non-
athlete peers, comparison (Brickson, 2013). This section of literature utilized the concepts of the
expectancy-value theory as a framework to recognize the stimuli for a college athletes’ approach
to learning.
The expectancy-value theory (EVT) was proposed by Jacqueline Eccles as a
comprehensive framework to understand the social and academic experiences, values and
beliefs, task specific expectancy and achievement behavior of adolescents (Eccles, n.d.) This
theoretical model linked the achievement related choices to the social factors influencing short
THE SPORT OF LEARNING 43

term and long term academic related goals and behaviors (Neuville, Freny, & Bourgeois, 2007).
Two common concepts guide the expectancy-value theory, the first is the expectation of success,
and the second is the value attached to the task (Neuville, et. al., 2007). From the two concepts,
came two questions: “why should I perform this task” and “am I able to perform the task”? The
question “why should I perform task” specifically addresses the value of the task. The second
question: “am I able to perform the task” refers to the expectancy of successful performance in
the task (Eccles, Adler, Futterman, Goff, & Kaczala, 1983). The answer to this question relied
on the learner’s belief that they possessed the ability to complete the task with some level of
success (Wigfield & Eccles, 2000). The “am I able to perform the task” question can also be
associated with negative stereotype that permeates the mindset of the student to not have
confidence to successfully complete a task. Marginalized students often have to contend with
communicated low expectations placed on them.
Learning Strategies
Student learning strategies, especially in higher education, has been a source of inquiry
that has resulted in a substantial accumulation of literature that provides evidence of the ways in
which students approach learning (Bliuc, Ellis, Gooodyear, & Hendres, 2011; Platow, Mavor, &
Grace, 2013;Smyth, Mavor, Platow, Grace, & Reynolds, 2013). What has emerged over time in
this field of inquiry is the association between identity and approach to learning as predictors of
academic performance (Bliuc, Ellis, Gooodyear, & Hendres, 2011; Haggis, 2003).
Early student learning strategy inquiries centered on cognitive processing strategies and
motivation; as the research genre progressed, attention then went to cognitive strategies and
approach to learning (Marton & Säljö, 1976). Marton and Säljö (1976) attempted to identify
various levels of information processing among groups of Swedish university students. The
THE SPORT OF LEARNING 44

experiment resulted in evidence of learnng differences and student’s ability to adapt their way of
learning (Marton & Säljö, 1976).
Inquires also considered metacognition as a strategy for learning. Unfortunately, many of
the strategies: cognitive, metacognitive, and motivation, were rarely examined in the same study.
Recent trends have studied identity and approach to learning, but rarely, if ever, is the American
college athlete the target population of such studies.
Identity and Approach to Learning
When the NCAA proposed grand academic eligibility legislation to ensure that athletes
were actively pursuing academic achievement in addition to athletic accolades (Crowley, 2006)
studies on student learning in higher education flurished with many researchers using prior
knowledge of the foundational studies on secondary school children and their learning strategies
(Vermunt & Vermetten, 2004). As explained by Ligorio (2012), school is perceived as a critical
juncture in development that may cause cultural disorientation if the role of sustaining identity
among learners is disregarded. Ligorio (2012) also acknowledge that school attendance not only
provides opportunities for cognitive development, but also for an expansion of self-perception.
Athletic participation and academic achievement have been studied for decades with most
studies focusing on the ethnic and social identity of African American males and those
participating in revenue generating sports such as basketball and football (Beamon & Bell, 2006;
Beamon, 2012; Dee, 2014; Emmert, 2014; Martin, Harrison, Stone, & Lawrence, 2010). This
present study does not intend to focus on any specific gender, ethnicity or sport participation in
order to assemble a breadth of experiences from college athletes regarding their identity and their
approach to learning.
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A study by Wortham (2004) described an account in which he evidenced in an empirical
analyses of one student in a a ninth-grade English and history classroom over one academic year,
the interrelatedness of social identification and learning. The account demonstrated the notion of
students bringing their social idenitity into the learning environment. In this case, the subject
was a 14 year-old African American male who had assessed in a percentile lower than what his
perceived intellect would have measured. The student, Maurice, was a personable, academically
engaged athlete. The study required the teacher and female students to systematically disregard
any male academic contributions and repeatedly attributed their “stupid” (p.721) comments to
their male identity. This practice can also be seen among college athletes as stereotype threat
when they are disproportionately labled as at risk and academic failures when studies have
shown there are athletes who achieve a high level of success in the classroom (Bimper, Harrison,
& Clark, 2012). Like many students, Maurice struggled with having a negative perception of his
identity being both salient and uncomfortable (Adler & Adler, 1987; Benson, 2000; Comeaux,
2007; Wortham, 2004).
The methods students use to engage in academic endeavors is habitually directed by their
salient idenity (Smyth, Mavor, Platow, Grace, & Reynolds, 2015). The study by Smyth et. al.
(2013) examined the effects of social identification and educational norms on learning
approaches through an online survey with students at an Australian university. Results from the
study concluded a significant role of social identification in predicting approaches to learning
even when controlling for personal factors (Smyth, Mavor, Platow, Grace, & Reynolds, 2013).
Salient identity is linked to group norms and behaviors in which their identity is
constructed. A number of researchers have suggested that a definite link exists between identity,
motivation and learning (Cohen & Garcia, 2008; Nolen & Ward, 2012). Moreover, researchers
THE SPORT OF LEARNING 46

report that an individuals identity is being developed at the same time that knowledge is being
acquired (Berzonsky, Cieciuch, Duriez, & Soenens, 2011; Cohen & Garcia, 2008). The
conclusion of these studies in which the reserachers presented the topic of identity and learning
adds credence to the argument that there is an association between the two, to what extent does
identity influence learning is still undisclosed.
Measuring Approach to Learning
Assessments are used to verify what an individual has learned and the methods used to
acquire that knowledge including the learning processes applied (Meyer, 2011). Measuring
learning processes have resulted in a number of instruments intending to measure an individuals’
preferences for instructional strategies (LSI; Renzulli & Smith, 2002), learning and study
strategies (LASSI; Weinstein, Schulte, & Palmer, 1987), and learning habits which is the extent
to which individuals exhibit productive learning practices (PLHS; Thompson, 2013).The
learning approaches inventory (LA-i; Yusoff, 2011) assesses students approach to learning using
the learning approach concepts developed by Marton and Säljö (1976). The qualitative research
resulted in the 9-item Learning Approach Inventory after an extensive literature review and
discussion with experts in medical education (Yusoff, 2011).
Measuring approaches to learning made known characteristics associated with the chosen
approach which Biggs (1987) concluded was not an unchangeable behavior manipulated by
individual characteristics. When from a cultural standpoint, Gutierrez and Rogoff (2003)
addressed the cultural historical practices of engagement that may infleunce a students’
approach to learning. Specifically being mindful during the assessment process of individuals
who have been historically underserved in U.S. school systems and revising the philosophy of
learning differences rather than deficits (Gutierrez & Rogoff, 2003). The college athlete has
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often been documented as academically unmotived and research has shown that a primed
identity can negitively impact performance, including assessing for a student’s approach to
learning (Adler & Adler, 1987; Harrison, Stone, Shapiro, Yee, Boyd, & Rullan, 2009; Yopyk &
Prentice, 2005).
Student Approach to Learning Theory
The students’ approach to learning (SAL) was developed by Marton and Säljö (1976)
with fundamental contributions from earlier researchers studying the levels of processing and
memory (Duff & McKinstry, 2007). Duff and McKinstry (2007) wrote an extensive overview of
the students’ approaches to learning that included new models, theories, and instruments. The
students’ approach to learning (SAL) was developed and largely understood in academia in the
United Kingdom and Australasia, yet limited research using these ideas have been done in North
America. Other researchers acknowledge the concepts of the students’ approach to learning is
inspired by the need to understand learning by describing the process and intentions of the
activity related to the learning task (Bliuc, Ellis, Gooodyear, & Hendres, 2011; Ferla,Valcke, &
Schuyten, 2010). There are three different approaches to learning deep, surface, and strategic
(Entwistle & Ramsden, 1982; Marton & Saljo, 1976) that comprise the concept of the student
learning approach.
A deep approach consists of seeking to find a meaning in the learning task or activity and
to relating it to other experiences. The characteristics of a learner that employs a deep approach
to learning is intrinsically motivated (Yusoff, 2011), academically engaged in appropriate
learning activities, such as integrating subject matter with prior knowledge (Smyth, Mavor,
Platow, Grace, & Reynolds, 2015). Bliuc, Ellis, Gooodyear, and Hendres (2011) states that the
learner who engages in a deep approach to learning operates at a high cognitive level and
THE SPORT OF LEARNING 48

experiences higher quality learning outcomes than those who engage in the surface approach to
learning.
The surface approach suggests that the individual applies rote learning, which is the
practice of rote memorization with no connection to prior knowledge and other experiences
(Duff & McKinstry, 2007; Ferla, Valcke, & Schuyten, 2010; Haggis, 2003). Learners who
engage in the surface approach to learning are selective in their methods of information
processing (Smyth, et. al, 2013). Most are extrinsically motivated only focusing on the task at
hand and subscribing to the “means-to-an-end” mindset (Platow, Mavor, & Grace, 2013).
According to Platow, et. al., (2013), surface approach learners are most likely to yield poor
academic outcomes.
The third approach to learning is the strategic approach, in which a learner applies a
systematic manner of learning that is specific to the task in order to attain the highest grades
possible (Entwistle & Ramsden, 1982; Yusoff, 2011). Motivation for these learners are most
likely to be extrinsic because of the competitive nature with other learners in the common
learning environment. Research specific to the strategic approach to learning is limited as most
studies have idenitifed this approach to be linked to the surface approach to learning (Entwistle
& Ramsden, 1982).
The link between approaches to learning and academic performance is an important
factor to fostering academic achievement (Bliuc, et. al., 2011), yet Haggis (2003), reports that the
students’ approach to leaning does not account for the majority of students in school, rather the
attention is placed on the academic elite and their goals and values toward learning. Equally
important is cultural awareness; it is needed in assessing what is surface and what is a deep
approach to learning. An illustration of the need for cultural awareness is the example set forth
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by Haggis (2003) in which Chinese students use memorization (surface approach to learning) in
a manner that leads to understanding (deep approach to learning). Redefining the notion of
memorization and understanding and becoming more culturally aware of learning differences
instead of perceiving them as learning deficits is essential (Gutierrez & Rogoff, 2003; Marton,
Dall'Alba, & Kun, 1996).
Summary
In summary, Bliuc, et. al. (2011) observed that social identity is pertinent to the manner
in which students approach learning and gain a perspective of themselves,which affords
opporunities to discover innovative behaviors toward learning that could positively influence
their academic outcomes. Building upon previous work on the topic of identity and approach to
learning while understanding the motivating factors, it is evident that students’ approach to
learning is a vital component to a conceptual framework in which to examine how and to what
extent a college athletes’ perception of identity influences their approach to learning.

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CHAPTER THREE: METHODOLOGY
This chapter offered an overview of the methodology used to collect and analyze data for
this study. The focus and intent for the inquiry conducted was to determine how and to what
extent a college athletes’ perception of identity influences their approach to learning. Reports
conducted by National Collegiate Athletic Association (NCAA) member institutions gauge
predictive behavior towards academic progress and graduation (LaForge & Hodge, 2011).
Uunfortunately, these reports do not account for the learning processes involved in the academic
experiences of college athletes. The aim of this study was to gather data that would better inform
the work of athletic academic support programs with the intent to better serve the college athlete.
The primary focus of this study was to determine the manner in which college athletes’
perception of identity influenced their beliefs concerning learning and to understand the degree
in which these beliefs have an effect on their behavior toward learning. The goal of this research
was to recognize college athletes’ attitudes and beliefs about identity as a principal factor that
prompts their approach to learning.The research questions guiding this study were:
1. How does a college athletes’ perception of identity influence their approach to learning?
2. To what extent does a college athletes’ perception of identity influence their approach to
learning, controlling for their year in school?
3. Are there differences in college athletes’ perception of identity based on gender,
ethnicity, sport played, and scholarship status?
4. Are there differences in college athletes’ approach to learning based on gender, ethnicity,
sport played, and scholarship status?
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The next section discussed the method of study followed by a description of the sample
and population, including selection criteria. Subsequent sections in this chapter detailed the
instruments used for the study and concluded with the process of data collection and analysis.
Method of Study
Studies examining the college athlete academic experiences utilized qualitative methods
(Beamon, 2012; Bimper, Harrison, & Clark, 2012; Cosh & Tully, 2014; Lawrence, Harrison, &
Stone, 2009; Levine, Etchison, & Oppenheimer, 2014) quantitative methods (Butterworth &
Rich, 2013; Chen, Mason, Middleton, & Salazar, 2012; Dee, 2014; Feltz, Schneider, Seunghun,
& Skogsberg, 2013) and mixed method research designs (Georgakis, Wilson, & Ferguson, 2014;
Kamusoko & Pemberton, 2011). Although qualitative and quantitative methods are efficient
empirical research methods, the choice to conduct a mixed-method research design for this
particular study was to accurately address the complexity of the problem that is often limited
when viewed from a singular scope of either quantitative or qualitative (Creswell, 2009).
The utility of the sequential explanatory research design created an opportunity to explain
and interpret the relationship of variables (Creswell, 2009), it did not intend to replace data from
one area of research to another, but rather complement the two with a breadth of collected data to
increase understanding (Johnson, Onwuegbuzie, & Turner, 2007) . Since the ultimate goal of this
study was to inform the practices of athletic academic support programs, explaining the
relationship between the variables of identity perception and approach to learning is ideal. The
sequential explanatory design is comprised of two phases of inquiry implemented in succession
with the quantitaive data collection and analysis occuring first, followed by the collection and
analysis of the qualitative data (Creswell & Clark, 2011). The first phase gathered quantitative
and demographic information from the college athlete through two surveys and a questionnarie,
THE SPORT OF LEARNING 52

the Athlete Identity Measurement Scale (Brewer, Van Raalte, & Linder, 1993), and the Learning
Approach Inventory (Yusoff, 2011). The second phase was the use of semi-structured interviews.
The quantitative data yielded results on the identity of college athletes and their approach to
learning. The qualitative data extends the understanding of the quantitaive data to determine if a
college athlete’s identity influences their approach to learning.
Creswell and Clark (2011) emphasize the importance of determining the level of
interaction between the quantitave and qualitative phases of research as being either independent
or interactive. An interactive level transpires when a direct relation between the quanitative and
qualitative research componets exists (Creswell, 2009). For example, in this study, the selection
of the participants and the design and process of conducting the semi-structured interview
depended on the results from the quantitative data. The two methods had an equal priority in that
both are uniformly important in the role of addressing the college athletes’ perception of identity
and its influence on their approach to learning (Creswell, 2009). According to Creswell (2009),
where and how to mix the quantitative and qualitative data components is a vital process. For
this study, the mixing occured during the first phase of data collection because the results of the
first phase shaped the data collection in the second phase by providing the purposeful selection
of participants (Creswell, 2009).

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Figure 3. Data Collection Process
Sample and Population
There are two basic forms of sampling, probability and nonprobability (Merriam, 2009),
the most appropriate sampling strategy for this study, was the probability sampling. Särndal,
Swensson and Wretman (2003) define probability sampling as the opportunity for every member
in a given population to be selected. In this study, the college athlete participant has an equal
chance to be selected. The second phase of research uses stratified sampling as a method of
diving the members of the population into subgroups based on the results of the quantitave data
results in Phase 1.
The target population for this study were college athletes who utilized athletic academic
support programs at their particular institution of participation. The sample university has over
600 athletes participating in competitive sports (Office of Postsecodary Education, 2012-2013);
to study the entire population will go beyond the scope of this study. The sample population was
comprised of athletes participating in a supplemental academic support program.
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Creswell (2009) asserts that the target population is comprised of individuals who have
common characteristics and are easily identifiable by the researcher. The researcher was able to
easily identify the athlete paricipants because the location in which the study took place was a
location securely limited to college athletes and authorized personnel.

Figure 4. Probability and Stratified Sampling
Location
The University chosen for this study was a private, prodominately white institution
(PWI), designated by the NCAA as Division I, and located in an urban area of a state found near
the southwestern region of the Pacific coast. The 2012 total enrollment was 39, 956, 39% White;
23% Asian; 14% Hispanic; 12% Non-Resident Alien; 4% Black; 4% two or more races and 3%
unknown (OPE, 2012-2013). The athletic department is a member of the Pacific-12 athletic
conference.
Many institutions provide academic support for athletes, this study did not address the
variety of services rendered, nor evaluated the efficiency of those services; instead this study
focused on the college athletes’ perception of identity and its influence on their approach to
learning to better inform the work of athletic academic support programs. This institution was
chosen because it represents universities that are known for their academic and athletic legacy.
Probability Sampling Stratified Sampling
College Athletes at
selected institution

Sample
population

Sample population

Purposeful
Sample
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This location is one of the most appropriate locations to lean about college athlete, identity and
their approaches to learning.
Participants
Institutional Review Board (IRB) approval was submitted followed by contact of athletes
through their advisors and was informed of the purposes of the study, and encouraged to
participate. Data was collected while some teams were either participating in pre-season training
or at the beginning of their respective competitive seasons. To regulate data collection across
each of the research participants, a scripted explanation of the cover letter and instructions for
completing the instruments was provided. Questionnaire packets were distributed in a
distraction-free environment convenient to the team, and participants were encouraged to provide
honest responses.
Athletes participating in a supplemental academic support program from various teams
with no preference given to race, ethnicity, or gender were invited to participate in the study.
Table 2 provides a summary of each sport offered, with the number men and women athlete
participants documented. To sample a sufficient number of participants for this study based on
the number of college athletes attending this institution would far exceed the scope and tme
constraints of this study.
The goal of this study was to gather an acceptable number of participants for the survey
(Phase 1 Quantitative) and the interview (Phase 2- Qualitative). Keeping in mind the time
constaints for the study, an exact number of participants has not confirmed during the prilimiary
plannng stages. Based on institutional data, it is documented that there are over 600 athletes on
the 15 competitive teams at this one institution. There is no data available that anticipates how
THE SPORT OF LEARNING 56

many of these athletes participate in any of the supplemental academic support programs. That
information was gathered on the College Athlete Questionnaire (Appendix E).
Table 2
Population Profile 2012-2013 (OPE, 2012-2013)
University Sponsored Sports Teams Men’s
Teams
Women’s
Team
Baseball 35
Basketball 17 16
Beach Volleyball 18
Football 110
Golf 8 5
Lacrosse 27
Rowing 75
Soccer 35
Swmming and Diving 34 31
Tennis 10 11
Track and Field (Indoor) 21
Track and Field (Outdoor) 57 54
Track and Field (Cross Country) 18
Volleyball 22 13
Water Polo 38 27
Total Participants 331 351
Unduplicated Number of Participants 327 312

Based on information acquired from the institution’s website, there are four supplemental
academic support programs offered . The institution asserts that the programs are empirically
based using the constructivist learning theory principles as a foundation. Each program intends to
develop goal setting and planning skills and evaluating progress toward goal achievement.

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Table 3
Supplemental Academic Support Programs
Program Purpose
Learning Assistant Program Develop time management, study skills and enhance
learning strategies
Tutorial Program Subject area specific, objective based structured to meet
the individual academic needs based on student perfered
learning style
Self-Regulated Learner
Program
Provides task-oriented support to meet the athletes
individual needs
Directed Studies Tutor Program Provides one-on-one subject area support and time
management, study skills and learning strategy development
of the most at risk college athletes

Survey Participants
The initial contact was made with the Director of Athletic Academic Support Services of
the institution for the permission to conduct the study with this student population, but also to
contact the academic advisors to assist with the initial communication with the college athlete
(Appendix A). Additional contacts for college athlete participation were: the Assistant Director
and Tutorial Coordinator, Academic Adviors, and peer tutors. The selection criteria for the
survey participants were college athletes at one selected institution who used any of the four
supplemental academic support services.
Athletes were invited to participate in the study through an electronic mail message sent
to them by their academic advisor and written invitation by their peer tutor (Appendix B ). This
message detailed the purpose of the study and provided information on the next steps in the
process if they elected to participate (Apprendix C). Upon completion of the required approval
for this study, including the Institutional Review Board (IRB) at the research site, and the signed
consent form by the college athlete (Appendix D), Phase 1 of the research took place.
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Interview Participants
During Phase 1, data was collected, analyzed and intepreted to create criterion for the
purposefully selected participants for the semi structered interviews which took place in Phase 2.
The criterion was based on the results from the two survey instruments in which the participants
with high ratings on the social identity factor of the Athlete Identity Measurement Scale (AIMS)
were invited to particpate in the semi-structured interviews to examine the degree in which
identity influenced their approach to learning.
Instruments
Data was collected in two phases. During the first phrase, participants who have elected
to participate in the study completed the self-report surveys to collect responses about athletic
identity and approaches to learning and a questionnaire to amass demographic data. Each
administered survey consisted of the Athlete Identity Measurement Scale (Brewer, Van Raalte,
& Linder, 1993), the Learning Approach Inventory (Yusoff, 2011), and a College Athlete
Questionnaire (see Appendix E). The completion of the surveys took place in a secured location
limited to athletes and authorized personnel. Permission was granted for the use of the Athletic
Identity Measurement Scale (AIMS; Brewer, Van Raalte, & Linder, 1993) and the Learning
Approach Inventory (LA-I; Yusoff, 2011) by the statement from the PsycTESTS database of the
American Psychological Association: “Test contents may be reproduced and used for non-
commercial research and educational purposes without seeking written permission. Distribution
must be controlled, meaning only to the participants engaged in the esearch or enrolled in the
educational activity”.
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Athletic Identity Measurement Scale
The Athletic Identity Measurement Scale (AIMS; Brewer, Van Raalte, & Linder, 1993) is
a 10-item questionnaire used to measure athletic identity, which is the degree to which an
individual identifies with the athlete role. The 10 items of the AIMS are rated from 1(strongly
disagree) to 7 (strongly agree) on Likert-type scales. In the primary validation study for the
AIMS (Brewer, et al, 1993), the measure confirmed high test-retest reliability (r=.89 over a 2-
week period) and internal consistency (alpha coefficients ranging from .81 to .93). In this study,
the Cron Bauch’s Alpha was .82 which is strong reliability. Analysis revealed three factors of
athletic identity: social identity, the extent to which individuals view themselves as occupying
socially recognized role as an athlete, exclusivity which is characterized by the extent to which
an individual's “self” is determined only by performing as an athlete in an athlete role. Negative
affectivity is characterized by the extent in which an individual experiences negative affect
response from undesirable outcomes in athletic domains (Brewer, Boin, & Petitas, 1993; Brewer
& Cornelius, 2001).
Research studies using an abbreviated 7-item AIMS (Brewer & Cornelius, 2001) has
shown the AIMS to be a valid and reliable means of assessing athletic identity in American and
English-speaking cultures in China (Li & Andersen, 2008; Visek, Hurst, Maxwell, & Watson,
2008), Greece (Proios, 2012) as well as with athletes with disabilities and rehabilitating from
injury (Brewer, Cornelius, Stephan, & Van Raalte, 2010; Martin, Eklund, & Adams Mushett,
1997). For this study, all 10 items were used and analyzed.

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Table 4
AIMS Items, Factors
Item Factor
1 I consider myself an athlete. Social Identity
2 I have many goals related to sport. Social Identity
3 Most of my friends are athletes. Social Identity
4 Sport is the most important part of my life. Exclusivity
5 I spend more time thinking about sports than anything else. Exclusivity
6 I need to participate in sport to feel good about myself. Exclusivity
7 Other people see me mainly as an athlete. Social Identity
8 I feel bad about myself when I do poorly in sport. Negative Affectivity
9 Sport is the only important thing in my life. Exclusivity
10 I would be very depressed if I were injured and could not
compete in sport.
Negative Affectivity

Responses were averaged across items such that larger scores indicate a stronger athletic identity
than smaller scores.
Learning Approach Inventory
The second survey administered to the college athletes during Phase 1 was the Learning
Approach Inventory (LA-I; Yusoff, 2011), a 9-item measurement tool developed to assess
students aproaches to learning. The inventory was developed based on the learning approach
dimensions proposed by Marton and Säljö (2005). The items were rated using a 5-point Likert-
scale ranging from 1(least like you) to 5 (most like you). Factor analysis revealed a 3-factor
solution: surface approach, strategic approach, and deep approach. Reliability analysis shows the
alpha coefficient is .867 which indicates a high level of internal consistency, for this study it was
.75 which was considered high moderate reliability.

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Table 5
LA-i Items, Factors
Item Factor
1 I’m motivated to learn by a concern to complete the course. Surface Approach
2 I’m motivated to learn by fear of failure. Surface Approach
3 Most of the time, I’m learning through acquiring
information, mechanical memorization without
understanding it, and reproducing it on demand in a test.
Surface Approach
4 I’m motivated to learn by a need to achieve high marks. Strategic Approach
5 My learning focus is depending on what is required by the
course.
Strategic Approach
6 Most of the time, I’m learning through understanding and
memorizing of the subject matter based on assessment
requirement.
Strategic Approach
7 I’m motivated to learn by an interest in the subject matter. Deep Approach
8 I’m motivated to learn by a need to make sense of things and
to interpret knowledge.
Deep Approach
9 My learning intention is to reach an understanding of the
subject or material.
Deep Approach

College Athlete Questionnaire
The College Athlete Questionnaire asked questions in three categories: demographic
information; academic information; and athletic information (Appendix E). The demographic
information requested was gender, age, and ethnicity. Under the academic information portion
of the questionnaire, the participant was asked to provide information regarding their year in
school, declared major and minor and the supplemental academic support program they used.
Athletic information was the final section purposed and gathered data on the chosen sport of
participation, specific position or event, scholarship status, position on depth chart and year in
the sport.

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Table 6
College Athlete Questionnaire and Associated Factors
Item Factor
1 Gender Demographic
2 Age Demographic
3 Ethnicity Demographic
4 Year in school Academic
5 Major/Minor Academic
7 Supplemental academic support services used Academic
8 Sport Athletic
9 Position/Event Athletic
10 Status Athletic
11 Rank Athletic
12 Year Athletic

Interview Protocol
The interview protocol used for this study was adopted from one used by the RAND
Corporation, a nonprofit research organization (Harrell & Bradley, 2009). Included in the
protocol were introduction and closing sections with questions separated by topic with an
estimated time allotted that ensured timely progression through the entire interview (Appendix
E). The researcher introduced themselves and the purpose of the study to the participants. An
overview of the study, data usage, assurances of confidentially and authority to discontinue their
participation was explained during the introductory portion of the interview. The participants
had the opportunity to ask questions about the study and any other questions that were
appropriate.
Data Collection
This study employed a sequential explanatory mixed methods design approach that
occurred in two distinct phases (Creswell, 2009). Phase 1 included the quantitative data
collection from two surveys administered in person with a hardcopy format in a private tutorial
setting in the athletic academic facilities. The first survey assessed athletic identity (AIMS;
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Brewer, Van Raalte, & Linder, 1993) and the second survey assesses students approach to
learning (LA-I; Yusoff, 2011). Also within this first phase was the collection of the college
athlete’s information in the areas of demographic, academic and athletic information.
Qualitaive data was collected in Phase 2 of the research design through semi-structured
interviews. The one-on-one interviews were conducted in a private conference room setting in
the athlete academic facilities. Data was collected through audiotape and notes taken during the
interview. The semi structured format of the questions afforded the opportunity for probing to
encourage expansion of athlete responses. The same protocol was used for each interview (see
Appendix E).
Data Analysis
Are college athletes’ identities, as measured by the AIMS (Brewer, Van Raalte, &
Linder, 1993) correlated to their approach to learning as measured by LA-i (Yusoff, 2011)?
Quantitative and qualitative data analysis was used as an attempt to answer this question. In this
study, the dependent variable, approach to learning, was defined as the response to or results of
the influence of the independent variable (Creswell, 2014). Athlete identity was the independent
variable for this study based on the results of the AIMS (Brewer, Van Raalte, & Linder, 1993)
and the College Athlete Questionnaire.
Quantitative Analysis
The goal of the study was to determine how and to what extent a college athletes’
perception of identity influenced their approach to learning. Phase 1 of the sequential
explanatory research method assessed the identity and approaches to learning of each participant.
Upon completion of the surveys and questionnaire, data was analyzed to categorize the
participants that rated high in the AIMS as Social Identity factor. The highly rated social identity
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signifies the identity salience of the participant; these participants were in a pool to be selected to
continue to Phase 2.
Qualitative Analysis
Phase 2, the semi-structured interviews, were conducted with participants that rated high
in the AIMS Social Identity factor and in no particular factor in the LA-i to discover if their
identity was a factor in how they approached learning. The semi-structured interview questions
provided an opportunity for the college athlete participant to reflect on their learning experiences
and communicate the degree in which their perceptions of identity were influential.
Transcriptions and notes from the interview provided evidence of emerging themes and
experiences among the population of college athlete participants in this study.

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CHAPTER FOUR: RESULTS/ANALYSIS OF DATA
The overarching aim of this study was to explore aspects of college athletes identity
perception and its influence on approach to learning. A mixed method study design was
employed to gain a greater perspective of this concern and the nuances that exists in the
academic and personal identity development experiences of college athletes. This chapter
presents the results of the data analysis from the Athletic Identity Measrement Scale (AIMS),
Learning Approach Inventory (LA-i) and the qualitative interview. The results discussed were
based on the four research questions:
1. How does a college athletes’ perception of identity influence their approach to learning?
2. To what extent does a college athletes’ perception of identity influence their approach to
learning, controlling for their year in school?
3. Are there differences in college athletes’ perception of identity based on gender,
ethnicity, sport played, and scholarship status?
4. Are there differences in college athletes’ approach to learning based on gender, ethnicity,
sport played, and scholarship status?
This chapter presents the findings of this research in two main parts. Part I of chapter four
reports results from the quantitative research inquiry in the following sections: a) data screening,
b) descriptive characteritics and statistics, and c) primary analysis. Part II of chapter four
presents qualitative data that addressed the themes of identity, motivation and aproaches to
learning. The results of the qualitative inquiry are presented according to the emergent themes
gained through semi structured interviews.

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Procedures
Twenty-five participation forms were distributed to the eight Academic Counselors at the
selected university, which was a total of 200 forms. Once a week, the researcher would collect
the forms that provided the email address for the participant. The researcher then emailed the
survey link to the participants. Email reminders were sent to the Academic Counselors on a
weekly basis. After one month, only 11% (22) of the forms had been returned which resulted in
only 54% (12) completed surveys. An impromptu conversation with the Assistant Director of
Tutorial Services provided an opportunity to solicit the assistance of student tutors to provide
paper copies of the survey to complete during the first or last ten minutes of the session. After
four weeks of this practice, 107 surveys were completed, bringing the total number of completed
surveys to 119. The qualitative portion of data collection was a random selection of the
participants that scored at least 20 on the Social Identity factor of the Athletic Identity
Measurement Scale (AIMS).
Part I: Quantitative Findings
Data Screening
Quantified data was screen for data entry accuracy and missing values. The data set
included responses from N = 119 partcipants. Most of the values representing the responses were
within their defined range with feasible means and standard deviations. There were 29
participants with missing data throughout survey responses. Among those missing data were
demographic items identifying age (n = 22), race/ethnicity (n = 2), description of major area of
study (n =23), supplemental academic support services used (n =7), athletic standing (n =1),
position played (n=29). On the Athlete Identity Measurement Scale (AIMS) Item 5 (n = 1), Item
7 (n =1), Item 9 (n =1), and Item 10 (n = 2). The Learning Approach Inventory (LA-i) had
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missing information on Item 9 (n = 1). There were no other discrepancies in the data in
comparison to the participants that did not display any missing data items. All of the participants
with missing data were maintained for further analysis.
Descriptive Characteristics of College Athlete Sample
Table 7 displays the distribution of the college athlete by gender. Gender includes male
and female. Of the overall population participating in this study N=119, 35 (29%) reported as
female and 84 (71%) reported as male.
Table 7
Distribution of Gender
Frequency Percent
Female 35 29.0
Male 84 71.0
Total 119 100.0

The distribution of college athlete by age ranged from 17-24 years old with 1 reporting
17 years old; 16 reporting 18 years old; 35 reporting 19 years old; 16 reporting 20 years old; 14
reporting 21 years old; 13 reporting 22 years old; 1 reporting 23 years old; 1 reporting 24 years
old, and 22 missing or non-applicable responses (Appendix F).
Table 8 displays the distibution of college athlete by race/ethnicity. Race/ethnicity
includes Afican American/Black, Asian, Caucasion/White, Hispanic/Latino(a), Pacific Islander,
and other. The participants reported 54 African American/Black, 2 Asian, 37 Caucasion/White,
7 Hispanic/Latino(a), 8 Pacific Islander and 11 other. Responses entered for other 4 Bi-Racial, 1
Serbian, 1 Persian, 2 declined to state and 1only indicated other with no further descriptive data
recorded.

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Table 8
Distribution of Race/Ethnicity
Frequency Percent
African American/Black 54 45.0
Asian 2 2.0
Caucasian/White 37 31.0
Hispanic/Latino(a) 7 6.0
Pacific Islander 8 7.0
Other 11 9.0
Total 119 100.0

Table 9 displays the distribution of college athlete by year in school, which included
freshman, sophomore, junior, senior and graduate student. 49 freshmen, 34 sophomores, 20
juniors, 15 seniors and 1 graduate student.
Table 9
Distribution of Year in School
Frequency Percent
Freshman 49 41.0
Sophomore 34 29.0
Junior 20 17.0
Senior 15 12.0
Graduate Student 1 1.0
Total 119 100.0

College athletes by major is shown in Appendix F. The reported responses were: (2)
African American Studies, (1) Anthropology; (1) Bio Chemistry; (3) Business Administration,;
(1) Civil Engineering; (1) Classics; (12) Communication; (1) Computer Science; (4) Economics;
(7) Human Biology; (1) Human Performance; (2) International Business; (1) Music Industry;
(1) Non-Governmental Organization and Social Change; (4) Public Policy, Planning and
Development; (2) Psychology; (1) Art and Design; (1) Critical Studies; (10) Sociology; (1)
Theatre; (39) Undecided and 23 did not respond.
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The distribution of college athlete by supplemental academic services used is shown in
Appendix F. The recorded responses were (10) Learning Assistance Program, (100) Tutorial
Program, there were no participants that participated in the Self-Regulated Learner Program, (30)
Directed Studies, (2) Unknown, (2) other and 7 did not register a response.
Table 10 displays the distribution of female college athlete by sport of participation.
There were 35 female college athlete participants. The distribution per sport was as follows: (6)
basketball, (1) sand volleyball, (1) golf, (1) lacrosse, (12) crew, (3) soccer, (2) swimming and
diving, none indicated tennis and volleyball, (5) track and field, and (4) water polo.
Table 10
Distribution of Female Sport of Participation
Frequency Percent
Basketball 6 17.0
Beach/Sand Volleyball 1 3.0
Golf 1 3.0
Lacrosse 1 3.0
Rowing/Crew 12 34.0
Soccer 3 9.0
Swimming & Diving 2 6.0
Tennis 0 0.0
Track & Field 5 14.0
Volleyball 0 0.0
Water Polo 4 11.0
Total 35 100.0

Table 11 displays the distribution of male college athlete by sport of participation. Of the
overall college athletes participating in the study, 86 identified as male and reported sports
participation as follows: (6) baseball, (7) basketball, (50) football, (2) golf, (5) swimming and
diving, (2) tennis, (9) track and field, (2) volleyball and (3) water polo.

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Table 11
Distribution of Male Sport of Participation
Frequency Percent
Baseball 6 7.0
Basketball 7 8.0
Football 50 58.0
Golf 2 2.0
Swimming & Diving 5 6.0
Tennis 2 2.0
Track & Field 9 10.0
Volleyball 2 2.0
Water Polo 3 3.0
Total 86 100.0

The recorded responses for the distribution of positions played by the college athlete are
documented in Appendix F. The responses were: All of them (1), Center (2), Corner/DB/Safety
(6), Coxswain (4), Defensive Line (7), Defender (1), Driver (3), FB/RB/TB (5), Field and Guard
(1), Forward (6), Goalie (2), Guard (8), Holding Center/Midfield (2), Infield (3), Left
Side/Outside Hitter (2), Left Tackle (1), Linebacker (8), Offensive Line (9), Pitcher (3),
Port/Rower (4), Quarterback (3), Starboard (3), Tight End (2), Wide Receiver (5) and 28
recorded No Response.
Sporting event results from the college athletes participating in the study were N=119,
(1) 100m, (1) 200m, (2) 400m, (1) 800m, (1) 1500m, (1) 110mh, (6) 2k, (1) 400mh, (1) 4x400,
(1) 5k, (1) 100y Breaststroke, (1) 200y Breaststroke, (1) Long Jump, (2) Distance, (1) Doubles,
(1) Butterfly, (4) Freestyle, (1) Hammer, Discus, Javelin, (1) Hurdles, (1) Negattas, (1) Singles,
(5) Sprinter and 82 participants recorded No Response for the question asking to document the
sporting event in which they participated. These answers were generally from track and field,
swimming, tennis, and rowing (Appendix F).
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Table 12 displays the distribution of the college athlete by athletic scholarship status. Of
the overall college athlete population in this study (N=119), 101 Full Scholarship, (4) Half
Scholarship, (3) Partial/Less than Half Scholarship, (10) Non-Scholarship/Walk-on and (1)
Other.
Table 12
Distribution of Athletic Scholarship Status
Frequency Percent
Full Scholarship 101 85.0
Half Scholarship 4 3.0
Partial/Less than Half Scholarship 3 3.0
Non-Scholarship/Walk-On 10 8.0
Other 1 1.0
Total 119 100.0

Table 13 displays the distribution of the college athlete by depth chart position. Athlete
depth chart rank levels include starter/first team, back-up/moderate playing time, minimal or less
playing time and other. The responses indicate 45 starter/first team, 66 back-up/moderate
playing time, 7 minimal or less playing time and 1 other.
Table 13
Distribution of Depth Chart Position
Frequency Percent
Starter/First Team 45 38.0
Back-up/Moderate Playing Time 66 55.0
Minimal or Less Playing Time 7 6.0
Other 1 1.0
Total 119 100.0

Appendix F displays the distribution of the college athletes by athletic standing. 44
Freshman, 10 Red-Shirt Freshman, 23 Sophomore, 9 Red-Shirt Sophomore, 11 Junior, 5 Red-
Shirt Junior, 8 Senior, 7 Red-Shirt Senior, no recorded 6
th
Year Senior, 1 Other and 1 No
Response.
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Summary of College Athlete Sample
The college athlete sample for this study provided a lens in which to analyze additional
data from the college athlete perspective. A summary of the 119 participants discovered the
largest percentages of responses revealed the potential criteria for the sample population in future
studies. 71% of the college athlete participants were male, 36% were 19 years old, 41% were
academic freshmen. Academic freshmen are those who had not completed one full academic
year of study at the time of participation in this research study. Athletic freshmen are athletes
who are participating in athletics during their first academic year of study. In this population,
37% were athletic freshmen, which is not a major significance between academic freshmen, thus
supporting the confirmation of over 50% of the participants in a back up to moderate active
playing role in their sport.
Responses for the declaration of major confirmed 33% of the college athletes had not
declared a major and 19% did not respond to the prompt. In total, over 50% of the participants
were either undecided on a major or did not respond. This could potentially have an impact on
approach to learning, as the course of study is primarily general education courses. The level of
engagement and interest may influence the approach to learning in concert with goals of
maintaining athletic and academic eligibility. The tutorial program is only available to athletic
scholarship awarded college athletes, which was recorded as 85% were full scholarship
recipients. The tutorial program had a 66% participation response that could influence approach
to learning which is dependent on the self-regulation strategies of the individual college athlete.
Thirty-four percent of the female college athlete population participated in rowing while
58% of the male college athlete population participated in football. Both of these sports have a
high number of available roster spots and can account for the high percentage of participation.
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Research on the commonalities between the two sports could also reveal commonalities between
the identity perceptions of its participants.
The college athletes were also asked to record the sports position they primarily played as
well as the events in which they participated. The data for these questions were low in responses
and not enough information was available for any substantial analysis.
Athlete Identity Measurement Scale
The Athlete Identity Measurement Scale (AIMS) and the Learning Approach Inventory
(LA-i) were questionnaires disseminated among the participants. The surveys were designed to
measure athletic identity and approaches to learning. The AIMS consisted of 10 items intending
to categorize identity salience as either social, exclusive or having negative affectivity. Each
item was scored on a Likert scale with ratings from 1 (Strongly Disagree) to 7 (Strongly Agree).
Table 14 shows the descriptive statistics (means and standard deviation) for all 10 items
on the Athlete Identity Measurement Scale (AIMS). Participants had response options of 1 –
Strongly Disagree to 7 – Strongly Agree therefore, the calculations are based on scores ranging
from 1 to 7.

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Table 14
Descriptive Statistics AIMS Items
Item Statement Mean SD
1 I consider myself an athlete 6.60 .85
2 I have many goals related to sport 5.89 .96
3 Most of my friends are athletes 5.71 .96
4 Sport is the most important part of my life 3.92 1.34
5 I spend more time thinking about sports than anything else 4.05 1.27
6 I need to participate in sport to feel good about myself 3.97 1.43
7 Other people see me mainly as an athlete 5.90 1.19
8 I feel bad about myself when I do poorly in sport 4.35 1.61
9 Sport is the only important thing in my life 2.73 1.31
10 I would be depressed if I were injured and could not participate
in sport
4.91 1.41

The Athlete Identity Measurement Scale (AIMS) has three factors in which athletes are
categorized based on scores on the scale: Social Identity, Exclusivity, and Negative Affectivity.
Social identity describes how an individual views himself or herself as occupying a socially
recognized role as an athlete. Exclusivity characterizes the extent to which an individual's “self”
is determined only by performing as an athlete in an athlete role and negative affectivity
describes the scope in which an individual experiences negative affect response from undesirable
outcomes in athletic domains (Brewer, Van Raalte, & Linder, 1993). Table 15 shows the means
and standard deviations for the Athlete Identity Measurement Scale.
Table 15
Means and Standard Deviations for AIMS
AIMS Mean SD N
Social Identity 20.90 2.74 119
Exclusivity 14.61 4.28 119
Negative Affectivity 9.18 2.62 119

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The overall responses of college athlete participants in this study on the Athletic Identity
Measurement Scale resulted in 47% Social Identity, 33% Exclusivity and 20% Negative
Affectivity.

Figure 5. Athlete Identity Measurement Scale
Learning Approach Inventory
The Learning Approach Inventory (LA-i) was the second survey administrated to the
college athlete participants to assess their approach to learning. The LA-i consisted of 9 items
intending to categorize approaches to learning as surface, strategic or deep. Each item was
scored on a Likert scale with ratings from 1(Least Like You) to 5 (Most Like You).
Table 16 shows the descriptive statistics for the 9-item Learning Approaches Inventory
(LA-i).

47%
33%
20%
Athlete Identity Measurement Scale
Social Identity Exclusivity Negative Affectivity
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Table 16
Descriptive Statistics Learning Approaches Inventory (LA-i)
Item Statement Mean SD
1 I am motivated to learn by a concern to complete the course 3.86 .86
2 I’m motivated to learn by fear of failure 3.69 .99
3 Most of the time, I’m learning through acquiring
information, mechanical memorization without
understanding it, and reproducing it on demand in a test
3.49 .81
4 I’m motivated to learn by a need to achieve high marks 3.74 0.75
5 My learning focus is depending on what is required by the
course
3.80 0.72
6 Most of the time, I am learning through understanding and
memorizing of the subject matter based on assessment
requirement
3.74 0.71
7 I’m motivated to learn by an interest in the subject matter 3.81 0.69
8 I’m motivated to learn by a need to make sense of things
and to interpret knowledge
3.64 0.71
9 My learning is to reach an understanding of the subject or
material
3.80 0.67

Table 17
Means and Standard Deviations for LA-i
LA-i Mean SD N
Surface 11.9 2.69 119
Strategic 14.87 3.72 119
Deep 13.37 4.91 119

The overall responses of college athlete participants in this study on the Learning
Approaches Inventory resulted in 33% Surface, 34% Strategic and 33% Deep.
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Figure 6. Learning Approaches Inventory
Primary Analysis
Results for Research Question One: How does a college athletes’ perception of identity
influence their approach to learning?
The primary analysis for the first phase of the research was quantitative. The college athlete
participants (N=119) were associated with the Athlete Identity Measurement Scale (AIMS) in the Social
Identity factor M=20.9 (SD = 2.74), Exclusivity factor M= 14.61 (SD =4.28) and Negative Affectivity
factor M = 9.18 (SD =2.62). By comparison the college athlete participants (N=119) were also associated
with the Learning Approaches Inventory (LA-i) the Surface factor M =11.9 (SD =2.69), Strategic factor
M= 14.87 (SD=3.72) and Deep factor M= 13.37 (SD=4.91). In order to answer the first research
question: how does a college athletes’ perception of identity influence their approach to learning,
a Pearson Correlation was used. While both the Athlete Identity Measurement Scale (AIMS)
and the Learning Approaches Inventory (LA-i) were significant measurement scales, there was
no significant relation between the two scales. This result suggested a college athlete’s
perception of identity does not influence their approach to learning based on the measurements
used.
33%
34%
33%
Learning Approaches Inventory
Surface Strategic Deep
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Results for Research Question Two: To what extent does a college athletes’ perception of
identity influence their approach to learning, controlling for their year in school?
In order to test the correlation between college athletes’ perception of identity and their
approach to learning while controlling for year in school, a Pearson Correlation was performed.
Table 18 shows the results from the test that concluded in year four, there is a noticeable
difference in results based on year in school. Although, there is no relationship between the
Athlete Identity Measurement Scale (AIMS) and Learning Approach Inventory (LA-i) scales, the
results of year in school was viewed respective to the scales.
Table 18
Pearson Correlation for AIMS and LA-I by Year in School
Year in School Measurement
Tool
AIMS LA-i
1 AIMS Pearson Correlation 1 -.138
Sig. (2-tailed) .349
N 48 48
LA-i Pearson Correlation -.138 1
Sig. (2-tailed) .349
N 48 49
2 AIMS Pearson Correlation 1 .038
Sig. (2-tailed) .839
N 32 31
LA-i Pearson Correlation .038 1
Sig. (2-tailed) .839
N 31 33
3 AIMS Pearson Correlation 1 .239
Sig. (2-tailed) .324
N 19 19
LA-i Pearson Correlation .239 1
Sig. (2-tailed) .324
N 19 20

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Table 18, continued
4 AIMS Pearson Correlation 1 .509
Sig. (2-tailed) .053
N 15 15
LA-i Pearson Correlation .509 1
Sig. (2-tailed) .053
N 15 15
5 AIMS Pearson Correlation + +
Sig. (2-tailed)
N 0 0
LA-i Pearson Correlation + +
Sig. (2-tailed)
N 0 1
* Significant at the p<0.05 level
+Cannot be computed because at least one of the variables were constant

Results for Research Question Three: Are there differences in college athletes’ perception
of identity based on gender, ethnicity, sport of participation played and scholarship status?
Gender. There was another research question that intended to obtain additional
information on the athlete identity by asking if there were differences in college athletes’
perception of identity based on gender, ethnicity, sport of participation and scholarship status. In
order to answer this question, focusing on gender, an independent samples t-test was conducted.
As can be seen in Table 19, there was statistical significance in the overall scores recorded on the
Athlete Identity Measurement Scale (AIMS) for male college athletes (M=46.62, SD=7.57) and
female college athletes (M=50.97, SD=7.44), conditions t (112) =-2.81, p=.006.

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Table 19
Independent Sample Test for AIMS by Gender
Levene’s Test for
Equality of Variances
t-test for Equality of Means
F Sig T df Sig
(2-tailed)
Social
Identity
Equal
variances
assumed
.100 .752 .106 116 .916
Equal
variances
no assumed
.116 80.277 .908
Exclusivity Equal
variances
assumed
1.226 .271 -2.657 115 .009*
Equal
variances
no assumed
-2.512 57.027 .015*
Negative
Affectivity
Equal
variances
assumed
.950 .332 -4.182 115 .000*
Equal
variances
no assumed
-4.557 74.887 .000*
AIMS
Total
Equal
variances
assumed
.026 .873 -2.816 112 .006*
Equal
variances
no assumed
-2.836 63.320 .006*
* Significant at the p<0.05 level

Ethnicity. In this same research question, it also asked if there were differences in
college athletes’ perception of identity based on ethnicity. The one-way ANOVA analysis was
used to determine if there are any significant differences between college athletes’ ethnicity as it
related to the Athlete Identity Measurement Scale (AIMS). As seen in Table 20 the groups’
variances showed no statistical significance.
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Table 20
One-Way ANOVA AIMS Ethnicity
AIMS Factor (Ethnicity) Sum of
Squares
df Mean
Square
F Sig.
Social Identity Between Groups 9.096 5 1.819 .232 .948
Within Groups 879.997 112 7.857
Total 889.093 117
Exclusivity Between Groups 125.958 5 25.192 1.406 .228
Within Groups 1988.965 111 17.919
Total 2114.923 116
Negative
Affectivity
Between Groups 47.020 5 9.404 1.440 .215
Within Groups 724.792 11 6.530
Total 771.812 116
AIMS Total Between Groups 314.161 5 62.832 1.044 .396
Within Groups 6500.128 108 60.186
Total 113
* Significant at the p<0.05 level

The mean plots show the mean and standard deviation among the college athlete
participants and was used to graphically display the means for ethnicities as it related to the
various factors of the Athlete Identity Measurement Scale (AIMS). The X-axis represents the
ethnic identities of the college athlete and the Y-axis represents the mean for the factor of the
Athlete Identity Measurement Scale (AIMS). The higher the number, the more likely a college
athlete identified with the factor; the lower the number, the least likely the college athlete
identified with the factor.
Figure 7 shows that Asian college athletes are more likely to identify with the Social
Identity factor of the Athlete Identity Measurement Scale (AIMS) while the college athletes who
ethically identified as other were least likely to identify with this factor.
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Figure 7. SPSS ANOVA Output for Social Identity Factor by Ethnicity (AIMS)
Figure 8 shows that Caucasian/White college athletes are more likely to identify with the
Exclusivity factor of the Athlete Identity Measurement Scale (AIMS) while the college athletes
who ethically identified as other were least likely to identify with this factor.

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Figure 8. SPSS ANOVA Output for Exclusivity Factor by Ethnicity (AIMS)
Graph 9 shows that Hispanic/Latino(a) college athletes are more likely to identify with
the Negative Affectivity factor of the Athlete Identity Measurement Scale (AIMS) while Pacific
Islander and college athletes who ethically identified as other were least likely to identify with
this factor.

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Figure 9. SPSS ANOVA Output for Negative Affectivity Factor by Ethnicity (AIMS)
Sport of participation. Another tenant to the research question addressed the
differences in college athletes’ perception of identity based on sport of participation. A one-way
ANOVA analysis was used to determine if there were any significant differences between
college athletes’ chosen sport of participation as it related to the Athlete Identity Measurement
Scale (AIMS). As seen in Table 21 the groups’ overall variances were not statistically
significant. However, there is evidence of statistical significance in the Exclusivity factor of the
Athlete Identity Measurement Scale (AIMS).

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Table 21
One-Way ANOVA AIMS Sport of Participation
AIMS Factor (Sport of Participation) Sum of
Squares
df Mean
Square
F Sig.
Social Identity Between Groups 116.593 17 6.858 .888 .589
Within Groups 772.500 100 7.725
Total 889.093 117
Exclusivity Between Groups 487.789 17 28.693 1.746 .047*
Within Groups 1627134 99 16.436
Total 2114.923 116
Negative
Affectivity
Between Groups 174.961 17 10.292 1.707 .054
Within Groups 596.851 99 6.029
Total 771.812 116
AIMS Total Between Groups 1527.597 17 89.859 1.632.071 .071
Within Groups 5286.693 96 55.070
Total 6814.289 113
* Significant at the p<0.05 level

The following mean plots graphically display the mean and standard deviation for college
athlete sport of participation with the various factors of the Athlete Identity Measurement Scale
(AIMS). The X-axis represents the sport of participation of the college athlete and the Y-axis
represents the mean for the factor of the Athlete Identity Measurement Scale (AIMS). The
higher the number, the more likely a college athlete identified with the factor; the lower the
number, the least likely the college athlete identified with the factor.
Figure 10 shows that Women’s Beach/Sand Volleyball, Women’s Golf, and Men’s
Volleyball college athletes are more likely to identify with the Social Identity factor of the
Athlete Identity Measurement Scale (AIMS) than the Women’s Lacrosse college athletes who
were least likely to identify with this factor.

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Figure 10. SPSS ANOVA Output for Social Identity Factor by Sport (AIMS)
Figure K shows that Women’s Soccer college athletes are more likely to identify with the
Exclusivity factor of the Athlete Identity Measurement Scale (AIMS) than the Men’s Water Polo
college athletes who were least likely to identify with this factor.
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Figure 11. SPSS ANOVA Output for Exclusivity Factor by Sport (AIMS)
Figure 12 shows that Women’s Soccer college athletes are more likely to identify with
the Negative Affectivity factor of the Athlete Identity Measurement Scale (AIMS) than the
Men’s Water Polo college athletes who were least likely to identify with this factor.

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Figure 12. SPSS ANOVA Output for Negative Affectivity Factor by Sport (AIMS)
Scholarship status. The last tenet in research question three was to determine the
differences in college athletes’ perception of identity based on scholarship status. In order to
determine these differences, a one way ANOVA was conducted for the scholarship status of
college athletes as it related to their perception of identity within the Athlete Identity
Measurement Scale (AIMS). Table 22 shows the results to be statistically non-significant.

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Table 22
One-Way ANOVA AIMS Scholarship Status
AIMS Factor (Scholarship Status) Sum of
Squares
df Mean
Square
F Sig.
Social Identity Between Groups 42.833 4 10.708 1.430 .229
Within Groups 846.260 113 7.489
Total 889.093 117
Exclusivity Between Groups 58.320 4 14.580 .794 .531
Within Groups 2056.604 112 18.363
Total 2114.923 116
Negative
Affectivity
Between Groups 10.085 4 2.521 .371 .829
Within Groups 761.727 112 6.801
Total 771.812 116
AIMS Total Between Groups 126.133 4 16.577 1.031 .394
Within Groups 6688.156 113 16.078
Total 6814.289 117
* Significant at the p<0.05 level

The graphical display of the means and standard deviations for the various factors of the
Athlete Identity Measurement Scale (AIMS) by scholarship status is noted in the following
graphs. It should be noted the X-axis represents the scholarship status of the college athlete and
the Y-axis represents the mean for the factor of the Athlete Identity Measurement Scale (AIMS).
The higher the number, the more likely a college athlete identified with the factor; the lower the
number, the least likely the college athlete identified with the factor.
Figure 13 shows college athletes who documented their scholarship status as other were
more likely to identify with the Social Identity factor of the Athlete Identity Measurement Scale
(AIMS) than those who documented as being partial/less than half scholarship status athletes
who were least likely to identify with this factor.
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Figure 13. SPSS ANOVA Output for Social Identity Factor by Scholarship Status (AIMS)
Figure 14 shows college athletes who documented their scholarship status as other were
more likely to identify with the Exclusivity factor of the Athlete Identity Measurement Scale
(AIMS) than those who documented as half scholarship status athletes who were least likely to
identify with this factor.
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Figure 14. SPSS ANOVA Output for Exclusivity Factor by Scholarship Status (AIMS)
Figure 15 shows college athletes who documented their scholarship status as partial/less
than half and non-scholarship/walk on status were more likely to identify with the Negative
Affectivity factor of the Athlete Identity Measurement Scale (AIMS) than those who
documented as other scholarship status athletes who were least likely to identify with this factor.
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Figure 15. SPSS ANOVA Output for Exclusivity Factor by Scholarship Status (AIMS)
Results for Research Question Four: Are there differences in college athletes’ approach to
learning based on gender, ethnicity, sport of participation played and scholarship status?
Gender. Research question four sought to determine the differences in college athletes
approach to learning based on four tenets; the first tenet to measure was gender. In order to
answer this question, focusing on gender, an independent samples t-test was conducted. As can
be seen in Table 23, There were statistical significance in the overall scores recorded in the
Learning Approaches Inventory (LA-i) for male college athletes (M=32.81, SD=3.87) and
female college athletes (M=35.34, SD=3.83), conditions t(116)=-3.26, p=.001.

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Table 23
Independent Sample Test for LA-i by Gender
Levene’s Test for
Equality of Variances
t-test for Equality of Means
F Sig t df Sig
(2-tailed)
Surface Equal
variances
assumed
1.609 .207 -1.786 117 .077
Equal
variances
no assumed
-1.696 57.221 .095
Strategic Equal
variances
assumed
.010 .922 -4.679 117 .000*
Equal
variances
no assumed
-4.664 63.267 .000*
Deep Equal
variances
assumed
.024 .878 -1.243 116 .216
Equal
variances
no assumed
-1.183 57.570 .242
LA-i Total Equal
variances
assumed
.005 .941 -3.263 116 .001*
Equal
variances
no assumed
-3.277 64.635 .002*
* Significant at the p<0.05 level

Ethnicity. The second tenet of research question four asked the question: are there
differences in college athletes’ approach to learning based on ethnicity? A one-way ANOVA
analysis was used to determine if there are any significant differences between college athletes’
ethnicity as it related to the Learning Approaches Inventory (LA-i). As seen in Table 24 the
groups’ variances were statistically significantly different from each other. There was no
statistical significance on Surface Approach to Learning, [f (5,113) =1.4, p=.248] and Strategic
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Approach to Learning [f (5,113) = 1.5, p=.188]. However, there was a significant effect on Deep
Approach to Learning [f (5,112) = 2.4, p=.043].
Table 24
One-Way ANOVA LA-i Ethnicity
LA-i Factor (Ethnicity) Sum of
Squares
df Mean
Square
F Sig.
Surface Between Groups 24.376 5 4.875 1.352 .248
Within Groups 407.489 113 3.606
Total 431.866 118
Strategic Between Groups 20.191 5 4.038 1.523 .188
Within Groups 299.658 113 2.652
Total 319.849 118
Deep Between Groups 34.008 5 6.802 2.385 .043*
Within Groups 319.348 112 2.851
Total 353.356 117
LA-i Total Between Groups 193.720 5 38.744 2.569 .031*
Within Groups 1689.365 112 15.084
Total 1883.085 117
*Significant at the p<0.05 level

The following mean plots graphically illustrated the mean and standard deviation results
for their college athlete ethnicity with the various factors of the Learning Approaches Inventory
(LA-i). The X-axis represented the ethnicity of the college athlete and the Y-axis represents the
mean for the factor of the Learning Approaches Inventory (LA-i). The higher the number, the
more likely a college athlete identified with the factor; the lower the number, the least likely the
college athlete identified with the factor.
Figure 16 shows that Asian college athletes are more likely to identity with the Surface
approach to learning on the Learning Approaches Inventory (LA-i) than Caucasian/White
college athletes who were least likely to identify with this factor.
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Figure 16. SPSS ANOVA Output for Surface Approach to Learning by Ethnicity (LA-i)
Figure 17 shows college athletes who ethnically identified as other to identify with the
Strategic approach to learning on the Learning Approaches Inventory (LA-i) than African
American/Black college athletes who were least likely to identify with this factor.
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Figure 17. SPSS ANOVA Output for Strategic Approach to Learning by Ethnicity (LA-i)
Figure 18 shows college athletes who ethnically identified as other primarily scored in
the Deep approach to learning factor on the Learning Approaches Inventory (LA-i) than Asian
college athletes who were least likely to identify with this factor.

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Figure 18. SPSS ANOVA Output for Deep Approach to Learning by Ethnicity (LA-i)
Sport of participation. Sport of participation was the third tenet for the fourth research
question seeking to determine if there were differences in college athletes’ approach to learning
based on sport of participation. The one way ANOVA test revealed there were no statistically
significant differences between college athletes’ chosen sport of participation as it related to the
Learning Approaches Inventory (LA-i) as seen in Table 25.

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Table 25
One-Way ANOVA LA-i Sport of Participation
LA-i Factor (Sport of Participation) Sum of
Squares
df Mean
Square
F Sig.
Surface Between Groups 69.334 17 4.078 1.136 .331
Within Groups 362.532 101 3.589
Total 431.866 118
Strategic Between Groups 68.299 17 4.018 1.613 .074
Within Groups 251.550 101 2.491
Total 319.849 118
Deep Between Groups 28.390 17 1.670 .514 .941
Within Groups 324.966 100 3.250
Total 353.356 117
LA-i Total Between Groups 210.836 17 12.402 .742 .753
Within Groups 1672.249 100 16.722
Total 1883.085 117
*Significant at the p<0.05 level

The mean plots below graphically illustrated the mean and standard deviations of college
athletes’ ethnicity and factors from the Learning Approaches Inventory (LA-i). The X-axis
represented the ethnicity of the college athlete and the Y-axis represented the mean for the factor
of the Learning Approaches Inventory (LA-i). The higher the number, the more likely a college
athlete identified with the factor; the lower the number, the least likely the college athlete
identified with the factor.
Figure 19 shows Men’s Tennis college athletes are more likely to identify with the
Surface approach to learning on the Learning Approaches Inventory (LA-i) than Men’s
Swimming college athletes who were least likely to identify with this factor.

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Figure 19. SPSS ANOVA Output for Surface Approach to Learning by Sport of Participation
(LA-i)

Figure 20 shows Women’s Swimming and Diving college athletes are more likely to
identify with the Strategic approach to learning on the Learning Approaches Inventory (LA-i)
than Women’s Beach/Sand Volleyball college athletes who were least likely to identify with this
factor.
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Figure 20. SPSS ANOVA Output for Strategic Approach to Learning by Sport of Participation
(LA-i)
Scholarship status. The fourth and final tenet from research question four specifically
set out to substantiate any differences in college athletes’ approach to learning based on
scholarship status. Table 26 documented no statistical significant results from the one way
ANOVA analysis used to determine differences between college athletes’ chosen sport of
participation as it related to their approach to learning using the Learning Approach Inventory
(LA-i).

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Table 26
One-Way ANOVA LA-i Scholarship Status
LA-i Factor (Scholarship Status) Sum of
Squares
df Mean
Square
F Sig.
Surface (LA-i) Between Groups 22.141 4 5.535 1.540 .195
Within Groups 409.724 114 3.594
Total 431.866 118
Strategic (LA-i) Between Groups 12.374 4 3.094 1.147 .338
Within Groups 307.475 114 2.697
Total 319.849 118
Deep (LA-i) Between Groups 9.979 4 2.495 .821 .514
Within Groups 343.377 113 3.039
Total 353.356 117
LA-i Total Between Groups 66.308 4 16.577 1.031 .394
Within Groups 1816.777 113 16.078
Total 1883.085 117
*Significant at the p<0.05 level

Mean plots were used to show the mean and standard deviation and graphically displays
the results for sport of participation for college athletes using the various factors of the Learning
Approaches Inventory (LA-i). The scholarship status is represented with the X-axis of the
college athlete and the mean for the factor of the LA-i was the Y-axis. The higher the number,
the more likely a college athlete identified with the factor; the lower the number, the least likely
the college athlete identified with the factor.
Figure 21 shows college athletes who identified as non-scholarship/walk on scholarship
status or are more likely to identify with the Surface approach to learning factor of the Learning
Approaches Inventory (LA-i) than the partial/less than half scholarship status college athletes
who were least likely to identify with this factor.

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Figure 21. SPSS ANOVA Output for Surface Approach to Learning by Scholarship Status
(LA-i)
Figure 22 shows college athletes who identified as non-scholarship/walk on scholarship
status are more likely to identify with the Strategic approach to learning factor of the Learning
Approaches Inventory (LA-i) than the partial/less than half scholarship status college athletes
who were least likely to identify with this factor.
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Figure 22. SPSS ANOVA Output for Strategic Approach to Learning by Scholarship Status
(LA-i)
Figure 23 shows college athletes who identified as partial/less than half scholarship status
are more likely to identify with the Deep approach to learning factor of the Learning Approaches
Inventory (LA-i) than half scholarship status college athletes who were least likely to identify
with this factor.
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Figure 23. SPSS ANOVA Output for Deep Approach to Learning by Scholarship Status (LA-i).
Summary of Quantitative Finds
For this mixed methods research, various tests were used to obtain results that would
assist in either answering or gaining additional insight on understanding college athlete identity
and its relation to how they approach learning.
Research Question One: How does a college athletes’ perception of identity influence their
approach to learning?
While both the Athlete Identity Measurement Scale (AIMS) and the Learning Approach
Inventory (LA-i) were reliable tools respectively, the results from the Pearson Correlation
revealed no relation between the scales. It was not definitively answered if a college athletes’
perception of identity influenced their approach to learning based on the instruments used in this
study (AIMS and LA-i). Although there was no correlation between the scales, there was
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conclusive evidence regarding the overall results from the scales. For all college athletes who
participated in the study according to the results from the Athlete Identity Measurement Scale
(AIMS) 47% scored in the Social Identity factor, 33% ranked in the Exclusivity factor and 20%
rated in the Negative Affectivity factor. According to the results from the Learning Approach
Inventory (LA-i), for all participating college athletes in this study, 33% were most likely to use
the Surface approach to learning, 34% would use the Strategic approach to learning, and 33%
were likely to use the Deep approach to learning.
Research Question Two: To what extent does a college athletes’ perception of identity
influence their approach to learning, controlling for their year in school?
In order to test the correlation between college athletes’ perception of identity and their
approach to learning while controlling for year in school, a Pearson Correlation was performed.
The results from the test concluded that in year four there is significance, even though there is no
correlation between the Athlete Identity Measurement Scale (AIMS) and Learning Approach
Inventory (LA-i) scales.
Research Question Three: Are there differences in college athletes’ perception of identity
based on gender, ethnicity, sport played, and scholarship status?
Research question three aimed to discover differences in college athletes’ perception of
identity based on four areas: gender, ethnicity, sport of participation and scholarship status. The
independent samples test concluded there were statistical significant differences in how college
athletes’ perceived their identity when controlled for gender. When looking at the factors
individually, there was no significant difference in the Social Identity factor and significant
differences within the Exclusivity and Negative Affectivity factors when controlling for gender.
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In contrast, when controlling for ethnicity, the independent samples test yielded no
significant statistical differences in how college athletes’ perceived identity. Even when
reviewing the factors separately, there were no significant differences within the Social Identity,
Exclusivity, and Negative Affectivity factors when using ethnicity as a lens.
The third area in research question four was to determine the differences in college
athletes’ perception of identity based on sport of participation. The test resulted in the overall
differences not being significant, but having looked at the factors, the results were not significant
with Social Identity and Negative Affectivity, yet, the Exclusivity factor based on sport of
participation concluded statistically significant differences. Because of the large number of sport
participation options and the varying rate of participation, the data was not disaggregated.
College athletes’ perception of identity, with scholarship status as the variable resulted in
non-significant statistical differences overall and for each factor, Social Identity, Exclusivity and
Negative Affectivity.
Research Question Four: Are there differences in the college athletes’ approach to learning
based on gender, ethnicity, sport played, and scholarship status?
Similar to research question three, research question four attempted to discover
differences in the college athletes’ approach to learning based on the same four areas: gender,
ethnicity, sport of participation and scholarship status. The independent samples test used to
determine differences among college athletes’ and their approach to learning when controlled for
gender revealed significant statistical differences. When the individual factors were examined, it
resulted in no significant statistical differences for the Surface and Deep approaches to learning,
although there were significant statistical differences in the Strategic approach to learning when
controlled for gender.
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When ethnicity was controlled, the test resulted in overall significant statistical
differences. The Surface and Strategic approaches to learning concluded no significant statistical
differences while the Deep approach to learning resulted in significant statistical differences in
college athletes’ approach to learning.
There were no significant statistical differences in college athletes’ approach to learning
when controlled for sport of participation and scholarship status. During the process of further
examining the factors of the Learning Approach Inventory (LA-i), Surface, Strategic, and Deep
approaches learning, there were no significant statistical differences found.
Part II: Qualitative Findings
The semi-structured interviews were conducted with participants that rated high in the
AIMS Social Identity factor and in no particular factor in the LA-i to discover if their identity
was a factor in how they approached learning. The semi-structured interview questions provided
an opportunity for the college athlete research study participants to reflect on their learning
experiences and communicate the extent in which their perceptions of identity were influential.
Design and Analysis
The methodology adopted for this study was the sequential explanatory research design
which combined quantitative survey (AIMS and LA-i) and qualitative interview data queries.
Using the quantitative and qualitative inquiry in conert allowed for an in-depth understanding of
college athletes perception of identity and its influence on approach to learning. The qualitative
data obtained from the interview were analyzed with emergent themes coded and placed into
categories. During the process of qualitative data collection, the researcher was the instrument
and evaluated the data for new emering themes from transcipts.
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The one-on-one interviews took place in a private conference room in the acadmic center
specifically designed for the university’s athletes and authorized personnel during a time that
was mutually agreed upon by the researcher and the college athlete. Two interviews took place
on two early Friday mornings and the remaining three took place from the late afternoon to late
evening on a Sunday. These scheduled days worked well with the college athlete as the facilities
were open, but did not have the usual high traffic of students and staff which provided a
comfortable and private environment in order to conduct the interview.
Findings and Discussion
The findings were presented and discussed in terms of the college athletes’ demographics
and their perception of identity as an influential factor to their approach to learning. Summary
findings from the Athlete Identity Measurement Scale and the Learning Approaches Inventory
were preseted to lend a context to the College Athlete Questionnaire focus and findings. Table
27 displays the college athlete demographics which includes gender, ethnicity, athletic status
(Year in Sport), sport participation, athletic scholarship status and depth chart position.
Table 27
Interviewed College Athlete Demographics
College
Athlete
Gender Ethnicity Athletic
Status
Sport of
Participation
Athletic
Scholarship
Status
Depth
Chart
Position
Dewayne Male Pacific Islander Red-Shirt
Sophomore
Football Full
Scholarship
Reserve
Casey Female White Junior Beach/Sand
Volleyball
Full
Scholarship
1
st

Team
Mike Male Other Sophomore Baseball Full
Scholarship
1
st

Team
Nadia Female Other Freshman Water Polo Full
Scholarship
1
st

Team
Jesse Male African
American/Black
Freshman Track and
Field
Full
Scholarship
1
st

Team

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Table 27 identifies the five college athletes that were interviewed including two females and
three males. Two of the college athletes ethnically identified as Other, one African
American/Black, one Pacific Islander, and one White. Two of the athletes were Freshman, one
Red-Shirt Sophomore, one Sophomore, and one Junior.
Each college athlete played a different sport Baseball, Beach/Sand Volleyball, Football,
Track and Field and Water Polo. All of the college athletes interviewed were Full Scholarship
athletes. Four of the interviewees were first team athletes and only one was identified as a
reserve.
Qualitative Analysis
Identity Themed Interview Questions:
College athletes described their introduction in sport as occurring early in their lives and
the importance of having an athletic identity. This sense of identity development was expressed
by Dewayne:
You could say I was introduced to sport at a very early age. I can look at pictures of
myself as a kid and I had some kind of ball or bat in my hand. Then there are the pictures
of me in Tee Ball or even with the family on holiday. I have seen pictures with me and
my uncles at family outings playing basketball, baseball and cricket. My family was
really involved in sports. Not just my uncles, but my parents, sisters and brothers,
everybody. It was almost like I had no choice because everything around me was related
to sport.
Although some of the college athletes were exposed to sport early in life, others expressed being
encouraged to try sport by people other than family.
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By the time I was in middle school, I was already 5’7”. I never looked at my height for
athletics. I was always interested in the modeling and fashion industry. But you know
how in middle school you had P.E. classes and had to participate in everything. I had a
teacher who told me I was athletic and picked up on sports easily and should try
volleyball, I would like it. Well, I tried it and I really liked it! (Casey).
Athletic identity was expressed by college athletes as an important part of their lives and created
opportunities to travel as well as learn interpersonal communication skills. Nadia said, “because
sports did not exist in schools where I come from. Club Sports was an after school activity that
allowed me and my twin to travel and play sport.” Nadia also mentioned, “I moved a lot as a kid
and was always changing schools. I used sport as a way to ‘fit in’ as the new kid and that kinda
became my identity even if they didn’t know my name. I was ‘the new kid who could run fast’
or whatever.
Motivation Themed Interview Questions
The second category created by the researcher was motivation. The purpose of this
category was to gain insight to the college athletes’ academic motivation using the expectancy-
value theory as a platform (Eccles, n.d.). Some of the college athletes believed their academic
transition to college was challenging. Dewayne stated, “at the beginning it was really hard. I
didn’t know what to expect, so I had to adjust.” Another response from Mike revealed, “I knew
before I got here that things would be different. I just accepted it as a challenge and really made
sure that I got all the assistance I needed. But I always took school seriously so it's like I get it,
it's not easy, but I have to put in the work to get it done ya know.”
Placing value in the classroom experience came with varying responses. One response
noted:
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Many of my classes are with other athletes and sometimes it is hard to really concentrate
when you have your teammates in the same class with you. I know that’s bad to say, but
it’s true. I try to stay connected to my teammates, but I also try to not let the professor
see me as a “dumb jock” or that I am not taking my education seriously. It’s really hard
to do both at the same time. (Dewayne)
Mike believed that:
Class is an extension of baseball to me. You have to prepare to perform and I get that. I
sit at the front of the class, no matter what I feel about the class and the professor. It's
like facing a pitcher…I meet the challenge and swing hard for the fences (laughs). But
for real, I value the classroom experience because there is no other way for me to get
what I need from the source and that’s the professor.
On the contrary, Jesse saw more value in the tutoring sessions than in the classroom:
For one class, I got more out of the tutoring sessions than I did the class because I was
able to ask questions and look at the internet to better understand the material. I wish I
could value class experiences, but it seems like so much going on in the class that I can’t
get what I need. I do well on tests and assignments, but that in-class experience is a
different story. (Pause) I guess in a way, I do value the classroom experience because
without it I would not know what I wanted to work with my tutor on. That’s another way
to look at it (laughs).
In observance with the expectancy-value platform, college athletes were asked if they are
academically performing as they had expected, many discovered that this question was multi-
layered. The Freshman college athletes interviewed collectively affirmed during their first
semester, they did not perform academically as they had expected. The Red-Shirt Sophomore,
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Sophomore and Junior college athletes also confirmed that at the beginning of their college
careers, their expectations did not draw a parallel to the reality of their final course grades.
Dewayne said he is currently performing academically as he expected because:
Now, with the help of learning specialists and tutors, I know what I need to do in the
classroom. When I first got here, I was so lost, man! (Researcher: Explain how you were
"lost”) I had no idea how to manage my time between classes, practice, study hall, rehab,
man it was something else. But like I said, I got it together now, but the beginning was
rough... (Laughs)
In addition to time management, the concept of focus was also paramount. Casey said:
I had to learn to be a student when I was injured. I had to go to therapy, but when the
girls travelled, I didn't go, so I HAD to focus on my studies even more. That was a rough
time for me. (Researcher: Explain what was so rough.) It was the first time I was ever
injured to the point where I couldn’t compete. I was never in a position like that before.
I wanted to quit, but then I would've felt like a failure. My teammates tried to encourage
me and include me, but I felt like an outsider. I didn’t know what to do, but I didn’t want
to be here. (Researcher: Did you get past that feeling?) I went to see a sports therapist
and talked through those things. Best thing to happen to me actually!
Academic expectations are also aligned with athletic eligibility as Nadia stated, “I know I need to
make good marks in order to remain athletically eligible so I had to put forth a greater effort to
get past my anxiety of being separated from my twin sister for the first time.”
Jesse admitted that “learning how to become a student and an athlete at the same time required
assistance.”
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Learning Strategies Themed Questions
The final category created by the researcher involved learning strategies.
Comprehending how college athletes approach learning and the reason for using certain
strategies was captured with these inquires. Note taking was the primary strategy used by the
interviewees.
Dewayne stated, “Note taking is big for me. I have learned how to take notes that I
understand to go with either what the professor is saying, or what I read in a textbook.” While
Mike comments, “Note taking, and YouTube are my primary learning strategies. Sometimes I
can't get a full understanding from the notes, or classmates or tutoring, so I look it up on
YouTube and I get a quick breakdown and I get it. I use all available resources I can.
Nadia reveals, “Note taking is all I do. I don’t often use anything else. I need to write things
down so I can understand.” Casey uses her interest in fashion design as an innovation approach
to note taking, “I have to make sense of the material based on what I already know. I had to
make connections and actually draw things out. It allows me to see the relationship of concepts
in a way that I fully comprehend. It does not work for all classes, but I at least try.”
College athletes have chosen to utilize certain learning strategies; however, the reason for
operating in those strategies, according to the interviewees, is based on two rationales one,
course requirements and secondly, personal preferences. The question was: What does your
choice of learning strategies depend on? Why? How?
Dewayne acknowledged, “How I'm doing in the class. If I am struggling, I know I need
to do something to get it in my head what I need. I usually do flash cards or something to help
me out.” Jesse had similar sentiments, “The class. Some classes require more from me because
I do not get the subject so those require more or different strategies. Not just the one I use all the
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time. I know that I can't just rely on one way to study for all classes.” These two college
athletes suggest that the course is an important factor to choosing their learning strategies.
Three college athletes agreed their personal preferences were the deciding factor in
choosing their learning strategies. Casey admits that “I approach all classes the same way; I
have work to do so get to it. Because anything else would be making excuses, to me, and I did
that all through high school so, it was time to grow up in a way. I draw. I wanted to be a fashion
designer when I was younger so I always have a visual for stuff so I draw it out.” Nadia also
asserts, “I use the same strategy for all of my classes. Note taking works for me and I like to stay
with what works. I understand my own writing and can make sense of what I need to know.”
Jesse combines both the course requirements and personal preferences as vital to choosing his
learning strategy. He admitted that, “The goals I set for myself is how I choose my learning
strategy. If I set an extremely high goal in a challenging course, I know I have to work hard and
work smart to get it done. I do not like to fail and I don’t like not trying even more. I have to
put in the most effort possible, I do not know how to do anything less. YouTube has helped me a
lot, but the usual note taking and tutoring usually helps.”
When asked if they could increase academic performance by changing the learning
strategy, the responses were diverse. Dewayne responded, “I wish I would have learned earlier
in school how to organize myself and my time cause being a student athlete on the D1 level is no
joke.” (Researcher: Has organization made you a better student?) Most definitely. It makes me
a better person, so yeah organization is key for me.” Casey replied, “I wish I took academics and
athletics seriously together at a younger age. I felt like I learned strategies too late…well not too
late, because I'm still here… (Laughs), but I may have been less stressed once I was on this
level.” Mike answered, “I think I have a solid strategy to my academic performance. I have
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learned to use additional resources to help me. I used to only depend on what was given in class.
I tell you, YouTube helped me to understand the Classics more than any notes I have read…
(Laughs).” Nadia asserted, “If there was a strategy that I can use besides note taking that will not
take a long time to learn, I would do it.” Finally, Jesse claimed, “I would learn different styles so
that I have more to choose from depending on my understanding of the subject.”
Summary
The mixed method analysis answered the research questions: How, and to what extent, do
college athletes’ perception of identity influence approach to learning. The factors of the Athlete
Identity Measurement Scale (AIMS) were grouped into three categories; Social Identity,
Exclusivity, and Negative Affectivity. There were also three factors of the Learning Approaches
Inventory (LA-i); Surface, Strategic and Deep. Although this study only interviewed college
athletes that scored high on the Social Identity factor of the Athlete Identity Measurement Scale
(AIMS), it is possible additional information would have been discovered if the selection
criterion were more inclusive. Many themes emerged that give credence to the effect of identity
on approach to learning.
There were five areas used for identification, gender, ethnicity, sport of participation and
scholarship status. Quantitative data in this research has shown there is no correlation between a
college athletes’ perception of identity and its influence on their approach to learning. The
qualitative data analysis however, provided additional information to answer the question, “To
what extent does a college athletes’ perception of identity influence approach to learning.”
The qualitative data analysis revealed the diverse backgrounds in which the college
athlete has emerged. While it is often perceived that all athletes formulate identity an acquire
skill and knowledge in the same manner, these research participants clearly articulated the
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contrary. Each was able to remember when and how their athletic identity was formed in
addition to acknowledging the processes in which they approached learning.
The next chapter discusses the purpose and significance of the study, summary of
findings and its relation to literature as well as implications for practice. This discussion will be
followed by concluding remarks, suggestions, and recommendations for future research.

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CHAPTER FIVE: DISCUSSION
Previous studies examining theoretical predictors to college athletes academic success
excluded the importance of identity and approach to learning ( Reynolds, Fisher, & Cavil, 2012;
LaForge & Hodge, 2011; Vermunt & Vermetten, 2004). There is agreement in literatue that
identity and learning are related and identity influences learning by mediating behavior (Hand &
Gresalfi, 2015; Murayma & Elliot, 2009). As college athletes navigate the challenges associated
with their academic and athletic roles, the perception of these roles have a direct effect on their
approach to learning. Dewayne stated, “It is hard to really get into class since many of my
classes are with other athletes and somethimes its hard to really concentrate when you have your
teammates in class with you. I know its bad to say, but its true. At least for me, I know I don’t
give my best like I should when I am in class with my teammates.” These college athletes have
to balance academic tasks within the timeframes of their sports schedules which includes
practice hours, injury rehabilitation and travel to away competition.
As I moved forward in my major, I did not mention to people that I was an athlete. I
don’t like the sterotypes associated with female student athletes. When I was a freshman,
it was hard to escape because we were all taking general ed classes, but when we started
branching out in our majors, there was more separation between me and many other
athletes in class and I was able to really focus. (Casey)
There are college athletes who approach learning differently than other college athletes,
notwithstanding the challenges of balancing the pluralistic identities and responsibilities of being
both a student and an athlete. Research in student learning has shown that students tend to adopt
different approaches to learning (Bliuc, Ellis, Goodyear, & Hendres, 2011). Bliuc, et al (2011)
also concluded there is substantial evidence advancing the knowledge that approaches to learning
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are closely related to other relevant student variables. Limited research expound on the
relationships between which learning approaches are utilized and how they may change over
time.
The purpose of this sequential explanatory mixed-method research was to examine how,
and to what extent, college athletes’ perception of identity influenced their approach to learning.
Four independent variables were tested with identity and approach to learning to discover
possible correlation and statistical significance. These variables were gender, ethnicity, sport of
participation, and scholarship status. The college athlete’s who particiated in this study assisted
in providing valuable data in the context of identity perception and approach to learning.
Institutionalized benchmarks for college athletes’ academic outcomes were often
measured by grade point averages (GPA), academic progress rates (APR), progress toward
degree, academic success rates (ASR) and graduation success rates (GSR) (Chong & Sommers,
2011; Graham, 2012; LaForge & Hodge, 2011; McArdle, Paskus, & Boker, 2013). However,
research is limited in addressing the college athletes approach to learning and the mediating
factors for that approach.
This study used the research of Bliuc, Ellis, Goodyear, and Hendres (2011) as a
foundational context to understand the relationship between a student’s social identity and
approach to learning as it related specifically to the college athlete student population. Bliuc et
al (2011) proposed the idea that strong student identity is often associated with a deep approach
to learning that is also linked to higher academic performance; this research focused on
determining if this would hold true with the American college athlete, a very different population
than the original study.
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Summary of Findings
The findings of this research will serve as another source for innovative support for
athletes at various academic levels. 119 participants comprised of 35 female and 84 males; 35
reported being 19 years old. There were more African American/Black college athletes research
participants than any other race/ethnicity. Freshmen were the largest class of students with 39 of
the overall participants not having declared a major. The largest number of college athletes
utilized the tutorial services offered by the university.
A majority of female athletes were on the rowing/crew team and football dominated the
male sport category. In regards to scholarship status, over 100 of the 119 college athletes were
full scholarship recipients. There were more back-up/moderate playing time athletes than
starters. Freshmen also out ranked all others in the athletic standing category much like the
academic standing category.
Forty-seven percent of all college athlete participants scored in the Social Identity factor
of the Athlete Identity Measurement Scale (AIMS) and thirty-four percent on the Strategic factor
of the Learning Approaches Inventory (LA-i).
The qualitative findings provided a personal perspective to what was revealed in the
quantitative findings. Most participants identified as an athlete while still in elementary school.
This early athletic identity formation proved to serve well for some in one capacity or another. It
allowed for travel and created an interpersonal gateway in which to create friendships based on
sport and athletic interest.
Learning approaches and strategies varied as each athlete became aware of their
information processing in the classroom at different times; one had to experience an injury in
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order to focus on academic endeavors. It was also revealed that note taking was the most
commonly used learning strategy.
Relation to Literature
In 1985, Snyder wrote in detail about the athletic and academic commitment of student
athletes. He classified them as scholar athlete, the pure scholar and the pure athlete based on the
level of commitment placed on the task or activity. This study advanced the notion of
commitment to be comparable to the approach to learning. The scholar athlete would be the
student who would most often employ the strategic approach to learning, while the pure scholar
and pure athlete would respectively use the deep and surface approach to learning.
With the demanding schedules of college athletes, the time allotted to discover their
identities is limited and often attached to that which they receive the most reward and
encouragement (Beamon, 2012). One college athlete in this study alluded to the fact that had it
not been for an injury, they would not have fully utilized the academic and mental health support
services available to them. The time away from the normal activities of her sport afforded her
the opportunities to not only explore available resources, but also to come to a greater
understanding of her identity as a learner.
Benson (2000) found in a study that athletes prefered not to be in class with other
athletes because of the negative effects they had on each other. As noted in the interview portion
of this study, college athletes prescribed to this behavioral ideology of pubilically discrediting
their academic performance to fit in, this is called pluristic ignorance (Levin, 2014).
Acknowleding this behavior could prove as an attempt to remedy classroom experiences as well
as apply a different approach to learning .
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Recent literature has created discourse regarding the relevance of the situated perspective
in educational psychology. This raised an inquiry of how to view college athletes’ perception of
identity influences approach to learning from a situative perspective. Turner and Nolen (2015)
defined the situative perspective as seeking to understand the relation of achievement or
motivation to the situation “including what participants did, how learning was constucted in the
class, the role of student identity in learning” (p. 167). As research in the area of learning and
identity progresses, how college athlete perceive their identy and how it influences their
approach to learning will continue to be areas of interest.
Hand and Gresalfi (2015) advances the belief that identity is likely to impact learning by
mediating behavior. This belief is supported by the assumption that identity is a “joint
accomplishment” developed through participation of an activity or set of activities which is the
centerpiece of situated perspective of identity. Joint Accomplishment, as defined by Greeno and
MMAP (1998) is the idea that what someone does in an activity is always done in relation to
what one has the opportunity to do. If one were to use this definition of joint accomplishment
with college athletes, one would be able to assert with certainty that academic responsibilities
affords the opportunity for athletics as well as athletics participation provides the opportunity for
education.
Implications for Practice
The educational issue this study addressed was the need to consider the college athletes’
approach to learning as an integral component to the measurement and prediction of athlete
academic performance. The review of literature focused on three factors influencing college
athlete’s academic experiences: identity, motivation, and learning strategies. Although the
measurement scales used in this study did not have a correlated relationship, athletes’ perception
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of identity and their approach to learning is an important implication for practice in the field of
student athlete development based on the results of the semi-structured interviews conducted in
phase two, the qualitative section of this explanatory mixed method designed study.
This study focused on college athletes, however, from the semi-structured interview
questions, the participants revealed their athletic participation and identity formation occurred
well before becoming college students, some as early as five years old. Santrock (2009) asserted
in “Life-span Development” that during the middle to late childhood stages of development,
between the ages of 5-7 years old, they are experiencing advanced gross and motor skills and
cognitive development.
According to Erikson and Erikson (1988), youths at this age are also in the
developmental stage of initiative versus guilt where they begin to ask questions akin to “am I a
good or bad”. Peer relationships and play, specifically sports and athletics, had a special place in
the young child’s life and is an important context for both cognitive and socio-emotional
development. From the ages of 8-11 years old, perceptions of identity based on psychological
characteristics (i.e. mean, nice, smart, and dumb) are formed. This confirms that in adolescence,
children begin to spend more time thinking about their identity. The relative correlation between
identity and cognitive development expressed by Erickson and Piaget is substantiated with the
concept of creating an educational environment where students are provided with more
opportunities to become competent in strategic learning (Santrock, 2009).
Recent research indicates that psychological and academic advantages are present in
college athletes who identify as student-athletes. Killeya-Jones (2005) surmised that football
players in elite programs who equally place a high value on both academics and athletics are
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more likely to experience higher levels of academic as well as life satisfaction This is further
evidence that identity indeed has a relative correlation with learning and its various approaches.
An implication of practice is to begin the efforts and conversations regarding “Student
Athlete Development” long before the individual attends college. Research has shown there is
some semblance of congruency within the concepts of athletic identity and academic
performance (Adler & Adler, 1991; Beamon & Bell, 2006; Bliuc, Ellis, Goodyear, & Hendres,
201;Comeaux & Harrison, 2011; Killeya-Jones, 2005; Snyder, 2009). The creation of a
restructured approach in youth sports would benefit the participating athletes and allow coaches,
parents, teachers, counselors, and administrators, who are responsible for the holistic
development of youths to foster a positive athletic and academic identity.
Innovative Implications for Practice
One could make the assumption, the lack of correlation between the two measurement
scales (AIMS and LA-i) would result in an “L”. An “L” in sports is a loss; however, an “L” in
education is a lesson. The lesson learned in this research leads to an innovative implication for
practice or, a game plan for success. As research has shown, there is an historical relationship
between the industries of education and athletics. A paramount concept in learning is to assist
students with making sense of new information. Most learners make sense of new information
by comparing it to their existing knowledge.
Any good game plan starts with a fundamental concept. This fundamental concept can
be taught to the youth athlete to begin the process of parallels between academic and athletic
domains. The academic components are teacher, classroom/lecture, test, and grade; all common
terms in academics, which then can be compared to athletic components. These athletic
components are coach, practice, game, and score. Creating these parallels at a young age will
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remove the stigma have having to be student before athlete instead, they are developing as a
student and athlete concurrently. These concepts can be introduced at any stage in a person’s
identity formation process.
Stereotype threat and identity foreclosure are terms often associated with athletes. These
terms not only stigmatize, but also marginalize the individual, yet most scales and inventories
require self-reporting. It is possible that athletes are not completing the apparatus in complete
honesty in fear of how they would be perceived. To combat this, the game plan for success
suggests a questionnaire asking the participant to describe their sport of participation and
position played. Upon gathering information on both the sport and the position, the professional,
preferably an academic counselor, would be able to comprise the basic rules and schema of the
sport along with the characteristics or responsibilities of the position. Having this information
can prove advantageous when advising an athlete on an academic matter. For example, if an
athlete is finding it difficult to takes notes in class, they can be advised to draw conceptual notes
much like a play in a sport. Another example is if an athlete is not comfortable visiting the
teacher or professor during a conference or office hours. The professional can confirm this
interaction is comparable to a coach giving them instruction and feedback on how to perform
better.
There are dozens of examples of how to utilize parallel practices of academics and
athletics to assist an athlete in accomplishing joint achievement. It requires a commitment from
the professional to learn about the athlete and their sporting interests and create strategies for
their success using their interest as a platform instead of creating a platform in which they should
fit.
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Summary of the Study
The results of this study cannot be generalized beyond the sample of College Athletes
used in this study; however, the results aligned and reinforced previous findings about college
athletes’ identity (Beamon, 2012; Comeaux, 2007; Crowley, 2006; Snyder, 1985; Wortham ,
2004), as well as furthers the concept Bliuc, Ellis, Goodyear, & Hendres (2011) conceived
regarding approach to learning being influenced by social identity. The study also revealed that
in addition to identity, motivation, expectation and value of the academic environment there
were also additional variables, such as year in school, that influenced a college athletes’
approach to learning (Comeaux & Harrison, 2011; Gaston-Gayles, 2004 ; Parsons, 2013).
Suggestions for Further Research
College athletics and the subject of academic success predictors will continue to be topics
of discussion for institutions of higher learning. As was discovered during the second phase of
this study, college athletes articulated a desire to be presented with the “student-athlete”
concept earlier in their lives while they were yet formulating and cementing their athletic and
academic identites. This early introduction to balancing sports and school activities and
responsbilites could influence not only their identity formation, but also their approach to
learning. One suggestion for futher research is to exaime how youth sports particpants navigate
the student athlete role from ages 7-9.
The histoy of athletics in American higher education was a purposeful, intentional,
systemmatic formative act. In that same manner, a system to assist athletes in the foundational
awareness of self linked with an academic responsiveness ought to exist as early as they begin
youth sport activities. This could have a profound affact on college athletes not just in the
competitive area, but in society as a whole.
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Another recommendation for futher study is to conduct a longitudinal inquiry to explore
the possiblilities of identity changes based on life changes. For example, in this study when the
Athlete Identity Measurement Scale (AIMS) was compared to the Learning Approaches
Inventory (LA-i) when controlling for year in school, there was a statistical significant difference
by year four. An examiniation of why those differences occurred would yield results that will
add to the general body of knowledge regarding athletics and academics.
Using correlated measurement scales when examining results from two different areas
will assist in the legitimacy of the results of the tests. For this study, the Athlete Identity
Measurement Scale (AIMS) and the Learning Approach Inventory (LA-i) were not correlated
and therefore, was unable to substantiate many of the assumtions that could have been revealed
in the testing.
The final recommendation for further study would be to focus on a specific subgroup of
athletes. For example, in this study there was no limit to the sport of participation, which lead to
not being able to disagregate data because of the varying number of participants. A future study
would focus on a specific sport and authenticate rich data.

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THE SPORT OF LEARNING 142

Appendix A
Advisor Assistance Letter

September, 2014

Dear [insert name of academic counselor],

I enjoyed speaking with you a few weeks ago. I am writing as a follow up to our conversation.
The Institutional Review Board and my dissertation committee have approved my research
design. Therefore, I am ready to begin the data collection. As I mentioned earlier, the focus of
my research is on college athletes and their approach to learning. The purpose of this study is to
determine how and to what extent college athletes’ perception of identity influences their
approach to learning. Data will be collected at two points in time: beginning and end of fall term
2014.

As we discussed earlier, I need your help and would like you to email the attached letter to your
advising caseload that introduces the study and informs them that they being asked to participate
in a study of college athletes. The letter will explain that the purpose of the study is to learn
about their approach to learning in order to provide better support for student-athletes in the
future. In the attached letter, it assures them that if they elect to participate, their responses will
remain confidential and that their participation is strictly voluntary.

I have attached a letter for you to send to solicit potential participants for the study.

If you have any questions please don’t hesitate to call or e-mail (323) xxx-xxxx or
katrinwi@usc.edu.

I can’t thank you enough for your time and effort.

Best regards,

Katrin Wilson
Assistant Learning Specialist and Doctoral Student

THE SPORT OF LEARNING 143

Appendix B
Invitation Letter

Dear Student-Athlete,

My name is Katrin Wilson, and I am a graduate student at the University of
Southern California, Rossier School of Education doctoral program. As a counselor and
educator, I have considerable interest in the learning experiences of collegiate athletes.
Therefore, my dissertation research is designed to study the ways in which a college athletes’
perception of identity influences their approach to learning.

You have been recommended to me by your academic counselor to be a part of my dissertation
research, and my hope is that you will accept this accept the invitation to be a part of this
opportunity to share in your own athletic and academic experiences. My research is designed in
two phases, the first of which is a survey that should take no more than 30 minutes to complete.
The second phase of my research involves a semi-structured one-on-one interview with me to
discuss some of the results regarding how college athletes’ perception of identity influences their
approach to learning. You will have the opportunity to email me directly at, katrinwi@usc.edu.

I certainly hope that you will choose to participate in my research, and should you have any
questions or comments, please feel free to email me at any time. Thank you in advance for your
time!

Katrin R. Wilson
USC Doctoral Student

THE SPORT OF LEARNING 144

Appendix C
Consent Form

Dear Student,

Thank you for agreeing to participate in this study. You will be asked to respond to an important
survey questions about your experience as a student athlete. The purpose of these surveys is to
examine the extent identity influences approaches to learning. The knowledge gained from this
study will help to increase the understanding of the academic services that are most important to
student athletes. Your cooperation in conducting this study is very much appreciated. The
questionnaires are brief and will only take about twenty or thirty minutes to complete. Your
participation is voluntary, and there is no penalty for choosing not to participate. You may be
assured that your identity and responses will remain confidential. Your name will not appear on
any form. Nobody will know anything about the responses you provide.

The purpose of asking for your name on the first page of the survey is for statistical purposes.
The only time the researcher will match your name to any information is if you are contacted in
December for a follow up interview. The interview will take approximately 60 minutes. Your
signature on this consent form will allow the researcher to obtain the academic information from
the surveys.

If you decide to participate in this study, you will be asked to complete a questionnaire. Again,
this survey will only take you about twenty or thirty minutes to complete. In the first section of
the questionnaire, you will be asked to provide some general information about yourself (e.g.
gender, sport, university enrolled in). In the second section of the questionnaire, you will be
asked to read a number of questions about your approach to learning and indicate the degree to
which you agree or disagree with each statement. A few of you will be contacted in December
for a
interview.

Potential risks to you for participating in this study are minimal. It is intended that you not suffer
harm, embarrassment, stress, or any other negative effects. Should you feel uncomfortable at
any time, you may discontinue your participation in the study without penalty.

If you agree to participate in this study, please provide the information requested below.
I, ___________________________________, willingly agree to participate in a study of student
athletes and their perceptions of identity and their approach to learning.

I understand that there is no penalty for not participating in this study and that I may obtain a
copy of the survey results by providing my address in the space below.

_________________________________________ Date: ____________________

Thank you very much for your help!
Please return this consent form with your questionnaire.
THE SPORT OF LEARNING 145

Appendix D
Surveys

Part A. Instructions:

Strongly Strongly
Disagree Agree
I consider myself an athlete. 1 2 3 4 5 6 7
I have many goals related to sport. 1 2 3 4 5 6 7
Most of my friends are athletes. 1 2 3 4 5 6 7
Sport is the most important part of my life. 1 2 3 4 5 6 7
I spend more time thinking about sports than anything else. 1 2 3 4 5 6 7
I need to participate in sort to feel good about myself. 1 2 3 4 5 6 7
Other people see me mainly as an athlete. 1 2 3 4 5 6 7
I feel bad about myself when I do poorly in sport. 1 2 3 4 5 6 7
Sport is the only important thing in my life. 1 2 3 4 5 6 7
I would be very depressed if I were injured and could not compete in sport. 1 2 3 4 5 6 7

Part B. Instructions:

Least Like Most Like
You You
I’m motivated to learn by a concern to complete the course. 1 2 3 4 5
I’m motivated to learn by fear of failure. 1 2 3 4 5
Most of the time, I’m learning through acquiring information, mechanical
memorization without understanding it, and reproducing it on demand in a
test.
1 2 3 4 5
I’m motivated to learn by a need to achieve high marks. 1 2 3 4 5
My learning focus is depending on what is required by the course. 1 2 3 4 5
Most of the time, I’m learning through understanding and memorizing of
the subject matter based on assessment requirement.
1 2 3 4 5
I’m motivated to learn by an interest in the subject matter. 1 2 3 4 5
I’m motivated to learn by a need to make sense of things and to interpret
knowledge.
1 2 3 4 5
My learning intention is to reach an understanding of the subject or
material.
1 2 3 4 5

Part C: College Athlete Questionnaire
Instructions: Please provide answered in the spaces provided or check the most appropriate box.

Demographic Information

Gender: Male Female

Age:_______

Ethnicity: African American/Black Asian Caucasian/White
Hispanic/Latino (a) Pacific Islander Other _________
THE SPORT OF LEARNING 146

Academic Information

Year in school:

Freshman Sophomore Junior Senior Graduate Student

Major:________________________________ Minor:_____________________________

What Supplemental Academic Support Services do you use?

Learning Assistant Program
Tutorial Program
Self-Regulated Learner Program
Directed Studies Program
Unknown

Athletic Information

What sport do you participate in?

Women’s Sports

Basketball Beach Volleyball
Golf Lacrosse
Rowing Soccer
Swimming & Diving Tennis
Track & Field Volleyball
Water Polo

Men’s Sports
Baseball Basketball
Football Golf
Swimming & Diving Tennis
Track & Field Volleyball
Water Polo

What position do you play or event you participate in?______________________________

What is your current athletic status?
Full-
Scholarship
Half-
Scholarship
Partial/Less than half-
Scholarship
Non-
Scholarship/Walk-On

What is your current rank on the team depth chart?
THE SPORT OF LEARNING 147

Starter (First Team)
Back-up or Moderate playing time (Second Team)
Minimal or less playing time (Reserve)

Athletically, are you a
Freshman Sophomore Junior Senior
Freshman (RS) Sophomore (RS) Junior (RS) Senior (RS)
Senior (6
th
year)

THE SPORT OF LEARNING 148

Appendix E
Interview Protocol

Interviewer: Participant Pseudonym:
Date: Time:
Start Time: End Time:

Introduction

(5 minutes)
Thank you for agreeing to meet with me. I’m Katrin Wilson, a Doctoral
student at the University of Southern California. I am conducting interviews as
partial requirements for my dissertation on college athletes’ perception of
identity and their approach to learning. As a counselor and educator, I would
like to talk with you about your experiences as a student-athlete. The purpose
of this interview is to learn more about your identity, motivation, and learning
strategies. What I learn from today’s discussion will help improve academic
support given to student athletes. I estimate the interview will last
approximately 60 minutes.

I will treat your answers with confidentiality. I will not use your name or any
other information that could identify you in this study. I will destroy the notes
and audiotapes after they are transcribed.

Do you have any questions about the study?
Topic 1

Identity

(10 minutes)

Topic #1: Identity

1. To begin, please tell me how long you have been participating in sports?
a. PROBE: What sports you have participated in?
2. How were you introduced to sports?
a. PROBE: How has that formed your athletic identity?
3. Is being an athlete an important factor in your life?
a. PROBE: If so, why? If so, how?
b. PROBE: If not, why not? If not, how not?
Topic 2

Motivation

(20 minutes)
Topic #2: Motivation

Now, I would like to discuss your academic motivation.

4. Describe your academic experiences in college?
5. Do you value your classroom experiences?
a. PROBE: If so, why? If so, how?
b. PROBE: If not, why not? If not, how not?
6. Are you academically performing as you expected?
c. PROBE: If so, why? If so, how?
d. PROBE: If not, why not? If not, how not?

Topic 3 Topic #3: Learning Strategies
THE SPORT OF LEARNING 149

Learning
Strategies

(20 minutes)

The last thing I’d like to discuss with you is your learning strategies,
particularly, your approach to learning.

7. Describe the strategies you use to learn?
8. What does your choice of learning strategies depend on?
a. PROBE: Why?
b. PROBE: How?
9. If you could increase your academic performance by changing your
learning strategy, what would you do?
Conclusion

(5 minutes)
Those were all the question I wanted to ask.

10. Do you have any final thoughts about athletic identity and approach to
learning that you would like to share?

Thank you for your time.

THE SPORT OF LEARNING 150

Appendix F
Distribution Charts

Age Distribution

Major Distribution

Frequency Percent
African American Studies 2 2.0
Anthropology 1 1.0
Bio Chemistry 1 1.0
Business Administration 3 3.0
Civil Engineering 1 1.0
Classics 1 1.0
Communication 12 10.0
Computer Science 1 1.0
Economics 4 3.0
Human Biology 7 6.0
Human Performance 1 1.0
International Business 2 2.0
Music Industry 1 1.0
Non-Governmental
Organization
1 1.0
Public Policy, Planning and Dev 4 3.0
Psychology 2 2.0
ROSKI Art and Design 1 1.0
SCA Critical Studies 1 1.0
Sociology 10 8.0
Theatre 1 1.0
Undecided 39 33.0
No Response 23 19.0
Total 119 100.0
Frequency Percent
17 years old 1 1.0
18 years old 16 16.4
19 years old 35 36.0
20 years old 16 16.4
21 years old 14 14.4
22 years old 13 13.4
23 years old 1 1.0
24 years old 1 1.0
Total 97 100.0
THE SPORT OF LEARNING 151

Distribution of Supplemental Academic Support Services

Frequency Percent
Learning Assistance Program 10 7.0
Tutorial Program 100 66.0
Self-Regulated Learner
Program
0 0.0
Directed Studies Program 30 20.0
Unknown 2 1.0
Other 2 1.0
No Response 7 5.0
Total 151 100.0

Distributions of Positions Played

Frequency Percent
All of them 1 1.0
Center 2 2.0
Corner/DB/Safety 6 5.0
Coxswain 4 3.0
Defensive Line 7 6.0
Defender 1 1.0
Driver 3 3.0
FB/RB/TB 5 4.0
Field and Guard 1 1.0
Forward 6 5.0
Goalie 2 2.0
Guard 8 7.0
Holding Center
Midfield/Midfield
2 2.0
Infield 3 3.0
Left Side/ Outside Hitter 2 2.0
Left Tackle 1 1.0
Linebacker 8 7.0
Offensive Line 9 3.0
Pitcher 3 3.0
Port/Rower 4 4.0
Quarterback 3 3.0
Starboard 3 3.0
Tight End 2 2.0
Wide Receiver 5 4.0
No Response 28 24.0
Total 119 100.0

THE SPORT OF LEARNING 152

Distribution of Event Participation

Frequency Percent
100 1 1%
200 1 1%
400 2 2%
800 1 1%
1500 1 1%
110mh 1 1%
2k 6 5%
400mh 1 1%
4x400 1 1%
5k 1 1%
Breaststroke (100y) 1 1%
Breaststroke (200y) 1 1%
LJ 1 1%
Distance 2 2%
Diver 1 1%
Doubles 1 1%
Fly 1 1%
Freestyle 4 3%
Hammer, Discus, Javelin 1 1%
Hurdles 1 1%
Negattas 1 1%
Singles 1 1%
Sprinter 5 4%
Total 39 100.0

Distribution of Athletic Standing

Frequency Percent
Freshman 44 37.0
Red- Shirt Freshman 10 8.0
Sophomore 23 19.0
Red-Shirt Sophomore 9 8.0
Junior 11 9.0
Red-Shirt Junior 5 4.0
Senior 8 7.0
Red-Shirt Senior 7 6.0
6
th
Year Senior 0 0.0
Other 1 1.0
No Response 1 1.0
Total 119 100.0

Abstract (if available)

Abstract The purpose of this research was to determine how, and to what extent, college athletes’ perception of identity influenced their approach to learning. This study’s rationale was to add to existing literature a new perspective on athlete academic performance that includes viewing the correlation between identity perception and approach to learning. ❧ The study used a sequential explanatory mixed-method research design that consisted of two phases. The first phase, the quantitative research design was used for college athletes from one private university on the west coast. They were asked to complete the Athlete Identity Measurement Scale (AIMS) and the Learning Approaches Inventory (LA-i), along with the College Athlete Questionnaire. The second phase used the qualitative research design to collect data from semi-structured interviews with college athletes that scored in the criteria of Social Identity on the AIMS. ❧ The data collected in both phases of the study provided an inclusive explanation of college athletes’ perception of identity as an influential factor to their approach to learning. The mixed method analysis revealed that in addition to identity, motivation, expectation and value of the academic experience, the learning environment and prior experiences were variables that influenced a college athletes’ approach to learning. ❧ Overall, the study adjoins the literature pertaining to college athlete identity and academic performance. The findings from this study imply that with further longitudinal studies with an intentionally targeted population, strong perceptions of identity could serve to predict a college athletes’ approach to learning.

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Asset Metadata

Creator Wilson, Katrin R. (author)

Core Title The sport of learning: the effect of college athletes' perception of identity on approach to learning

School Rossier School of Education

Degree Doctor of Education

Degree Program Education (Leadership)

Publication Date 02/08/2016

Defense Date 10/30/2015

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

Tag approach to learning,athlete,identity,Learning and Instruction,OAI-PMH Harvest

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Tobey, Patricia (committee chair), Crispen, Patrick (committee member), Seli, Helena (committee member)

Creator Email katrin.r.wilson@gmail.com,katrinwi@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-206675

Unique identifier UC11278492

Identifier etd-WilsonKatr-4085.pdf (filename),usctheses-c40-206675 (legacy record id)

Legacy Identifier etd-WilsonKatr-4085.pdf

Dmrecord 206675

Document Type Dissertation

Format application/pdf (imt)

Rights Wilson, Katrin R.

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