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Academic coaching for Pell-eligible, academically at-risk freshmen: an evaluation study

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Academic coaching for Pell-eligible, academically at-risk freshmen: an evaluation study

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Academic Coaching for Pell-eligible, Academically At-Risk Freshmen: An Evaluation Study

by

Jonathan David Yancey

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 2021

Copyright 2021 Jonathan David Yancey

ii
Acknowledgements
It would not have been possible to write this doctoral dissertation without the help and support of
the wonderful people around me, some of whom I would like to give particular mention to here.
I would like to thank Dr. Alison Muraszewski for chairing my dissertation committee,
and for her continual support, encouragement, and the occasional prodding. Additionally, I
would like to thank Drs. Kathy Stowe and Patricia Tobey for graciously serving on my
dissertation committee, their deep expertise and guidance was critical to the development of the
final product. I would also like thank Dr. Marc Pritchard for all his assistance with data analysis
and presentation.
I would also like to thank my institution for affording me the opportunity to study a topic
so personally meaningful to me, and my Provost for encouraging me to pursue a doctorate.
Additionally, I would like to thank and acknowledge the program staff at the heart of this
research study, without whose help and guidance this study would not have been possible.
Finally, and most importantly, I would like to express my deepest thanks to my family,
without whose support this endeavor would not have been possible. To my wife, for her
unwavering belief in my ability to succeed, her shouldering of the family load, and for her
refusal to allow me to give up on my dissertation. To my three daughters, for their constant
support, their acceptance of time not spent with them, and for their toleration of my papers
strewn about the house.
Without these individuals, and many others, this goal could not have been achieved. As
with all difficult things, it takes a team of people to ultimately realize success. I am deeply
grateful for all that contributed to this endeavor.

iii
Table of Contents
Acknowledgements ii
List of Tables v
List of Figures vii
Abstract viii
CHAPTER ONE: INTRODUCTION 1
Introduction of the Problem of Practice 1
Organizational Context and Mission 3
Organizational Goal 5
Academic Coaching as a Practice 6
Academic Coaching at Axios University 6
Related Literature 9
Pre-college Identification 9
Performance Identification 14
Related Literature Summary 16
Description of Stakeholder Groups 17
Stakeholder Group for the Study 18
Purpose of the Project and Questions 20
Methodological Framework 22
Definitions 24
Organization of the Project 25

CHAPTER TWO: REVIEW OF THE LITERATURE 26
Influences on the Problem of Practice 26
Evolution of Student Success Theory 26
Models of Student Success Theory 27
History of Academic Underachievement 34
Challenges Specific to Socioeconomic Status 35
Knowledge, Motivation and Organizational Influences Framework 38
Stakeholder Knowledge, Motivation, and Organizational Influences 40
Knowledge and Skills 40
Knowledge Influences 42
Motivation 46
Organization 53
Conceptual Framework: Knowledge, Motivation, and Organizational Influences 56
Conclusion 60

CHAPTER THREE: METHODS 61
Participating Stakeholders 61
Survey Sampling Criteria and Rationale 62
Survey Sampling (Recruitment) Strategy and Rationale 62

iv
Data Collection and Instrumentation 63
Surveys 64
Data Analysis 66
Validity and Reliability 67
Ethics 68

CHAPTER FOUR: FINDINGS 71
Introduction 71
Participating Stakeholders 72
Results and Findings 73
Quantitative Analysis Overview 73
Research Question 1: Academic Coaching Participant Retention 75
Research Question 2: Knowledge and Motivation 78
Research Question 3: Organizational Influences 93
Summary 99

CHAPTER FIVE: RECOMMENDATIONS 101
Discussion of Findings 101
Recommendations for Practice to Address KMO Influences 101
Knowledge Recommendations 101
Motivation Recommendations 108
Organization Recommendations 115
Kirkpatrick and Kirkpatrick Training Evaluation 120
Level 4: Results and Leading Indicators 121
Level 3: Behavior 123
Level 2: Learning 126
Level 1: Reaction 132
Evaluation Tools 133
Data Analysis and Reporting 134
Limitations and Delimitations 136
Recommendations for Future Research 139
Summary 141

References 143

Appendices 163
APPENDIX A: Knowledge and Motivation Assessments 163
APPENDIX B: Psychological Sense of School Membership Scale-Adapted 167
APPENDIX C: College Student Stress Scale 168
APPENDIX D: Sample Observation/Feedback Assessment Instrument 169
APPENDIX E: Sample Summative Assessment Instrument 170

v
List of Tables
Table 1: Knowledge Influences, Types, and Assessments for Knowledge Gap Analysis 45
Table 2: Motivation Influences, Types, and Assessments for Knowledge Gap Analysis 52
Table 3: Organizational Influences, Types, and Assessments for Knowledge Gap Analysis 55
Table 4: KMO Influencer Cronbach’s Alpha 75
Table 5: Academically At-Risk Freshmen Retention Data 76
Table 6: Academic Coaching Enrollment and Retention Data 76
Table 7: Academic Coaching Enrollment and Retention Data 77
Table 8: Contingency Table – Continued Enrollment and Gender 78
Table 9: Pearson’s Correlation Coefficients – Total Sample 79
Table 10: Pearson’s Correlation Coefficients – Returning Students Fall 2020 (Pell) 80
Table 11: Pearson’s Correlation Coefficients – Returning Students Fall 2020 (Non-Pell) 81
Table 12: Descriptive and Means Comparisons – Navigating Institutional Systems 83
Table 13: Pearson’s Correlation Coefficients – Study Participants Fall 2020 83
Table 14: Descriptive Statistics and Means Comparisons – Creating Systems of Support 84
Table 15: Pearson’s Correlation Coefficients – Returning Students Fall 2020 85
Table 16: Pearson’s Correlation Coefficients – Returning Students Fall 2020 86
Table 17: Descriptive Statistics and Means Comparisons – Understanding Financial
Circumstances 87
Table 18: Descriptive Statistics and Means Comparisons – Expectancy-Value 88
Table 19: Pearson’s Correlation Coefficients – Study Participants Fall 2020 89
Table 20: Descriptive Statistics and Means Comparisons – Attribution 90
Table 21: Pearson’s Correlation Coefficients – Study Participants Fall 2020 91

vi
Table 22: Descriptive Statistics and Means Comparisons – Self-Efficacy 92
Table 23: Pearson’s Correlation Coefficients – Study Participants Fall 2020 93
Table 24: Pearson’s Correlation Coefficients – Survey Respondents Fall 2020 94
Table 25: Pearson’s Correlation Coefficients – Survey Respondents Fall 2020 95
Table 26: Descriptive Statistics and Means Comparisons – Sense of Belonging 96
Table 27: Descriptive Statistics and Means Comparisons – Student Life Stressors 98
Table 28: Summary of Knowledge Influences and Recommendations 103
Table 29: Summary of Motivation Influences and Recommendations 109
Table 30: Summary of Organization Influences and Recommendations 116
Table 31: Outcomes, Metrics, and Methods for External and Internal Outcomes 122
Table 32: Critical Behaviors, Metrics, Methods, and Timing for Evaluation 123
Table 33: Required Drivers to Support Critical Behaviors 125
Table 34: Evaluation of the Components of Learning for the Program 131
Table 35: Components to Measure Reactions to the Program 132

vii
List of Figures
Figure 1: Freshman Retention by Cohort 5
Figure 2: Vincent Tinto’s explanatory model of the dropout process 29
Figure 3: Conceptual Framework 59

viii
Abstract
Public institutions of higher education have a dual mandate to provide the best possible
education while also maintaining accessibility to students within its state. This study explores
the knowledge, motivation, and organizational influences associated with the retention of Pell-
eligible, academically at-risk freshmen, and examines an academic coaching intervention
addressing this population. As a population defined by a specific performance outcome,
academic probation, the causes and factors contributing to that outcome are many and varied. In
an effort to provide a framework for understanding those causes and factors, this study reviews
the current literature on the subject and lays out two main premises. The first being that a
number of pre-existing social factors, largely stemming from structural economic inequality,
increase the likelihood of becoming academically at-risk. The second being that certain active
behavioral decisions on students’ parts, based on pre-college levels of cultural (academic)
capital, lead to a decreased likelihood of persisting to sophomore year. Taken together, the
research provides a foundation for institutions to begin examining their Pell-eligible student
populations and tailoring interventions in an effort to improve persistence among Pell-eligible,
at-risk freshmen populations.

Keywords: academic coaching, academic development, at-risk students, cultural capital,
expectancy-value, Pell, self-efficacy, vulnerable populations

CHAPTER ONE: INTRODUCTION
Introduction of the Problem of Practice
In 2016 state and local governments spent over $288 billion on higher education, nearly
10% of total general spending by those entities (US Census, 2016). This level of public spending
on higher education indicates that higher education is a top public policy priority for legislatures
around the country, and as such, they are keenly interested in the success of these systems.
Additionally, the federal government supports student attendance through direct federal loan
programs, giving both the federal government and the students that attend these institutions
significant interest in student success. While many constituents are invested in student success,
recent data from the National Center for Education Statistics indicate that only 6 of 10 students
entering college will graduate within six years (NCES, 2019). Due to the complexity of issues
surrounding college completion, it is helpful to focus on the largest areas of dropout in an effort
to improve completion rates. For public institutions, the greatest student loss comes in students’
first year at the institution. Nationally, public institutions lose nearly 20% of their incoming
freshmen prior to the students’ sophomore year (NCES, 2019). This large retention loss in the
first year is a large factor in the ultimate completion rate for the institution, which makes this
problem a significant one for institutions of higher education.
This study addresses the problem of low retention rates for academically at-risk, Pell-
eligible freshmen college students at Axios University, a pseudonym for a public comprehensive
teaching institution located in the southeast United States of America. For the purpose of this
study, academically at-risk students are defined first-time freshmen completing their second
semester at university with a cumulative GPA below 2.0, the minimum GPA to remain in good
academic standing at the institution. This study will focus on an academic coaching intervention

2
aimed at improving the retention of academically at-risk students. The study will further focus
on the Pell-eligible students within the academically at-risk student population as the main
stakeholder group, and examine the knowledge, motivation, and organizational influences
affecting the persistence of these Pell-eligible, academically at-risk students. Additional,
unexamined stakeholders, including the institution’s administration, faculty, parents, and
governmental organizations may also impact the problem as well.
Axios University has historically suffered from comparatively low freshman retention,
with an institutional low of less than 60% in 2011, at which time the retention of academically
at-risk students was less than 40% (Legislative Oversight, 2018). Since 2011, overall retention
has improved to nearly 70%, however, retention of academically at-risk, Pell-eligible freshmen
during the same period has fallen to below 30%, well below the institutional average and counter
to larger institutional retention trends (AU, 2018). In order for the institution to achieve its
mission (AU, 2018), it must address key areas of improvement such as the increased retention of
Pell-eligible, academically at-risk freshmen. The current retention rate of this population,
relative to the overall institutional retention rate, demonstrates the urgency of this problem.
Current literature on student success highlights that students from diverse abilities and
backgrounds are increasingly being admitted to institutions of higher learning (Grawe, 2017;
Magloire, 2019), and these diverse populations face unique challenges to succeeding in
university studies. Among these populations, research indicates that socioeconomic background
is a significant predictor of academic success (Michalski, Cunningham, & Henry, 2017; Reason,
2009). Differences in socioeconomic circumstances and familial support have also been shown
to affect students’ sense of belonging and negative psychosocial effects, precollege levels of
cultural capital, and frequency and intensity of stressor events (Karimshah, et al., 2013;

3
Mattson, 2014; Stuber 2011; Tough, 2014; Walpole, 2003). Taken in its totality, there is a
broad array of institutional, student, and societal factors affecting student persistence, and
ultimately, graduation. These national trends support data observed at Axios University, which
indicate that Pell-eligible students are overrepresented among academically at-risk freshmen
(AU, 2019).
This critical problem is important to address because it strikes at the core of the
institution’s mission. Public institutions of higher learning serve twin missions of providing the
best possible education, while also maintaining access for the residents of the state (Heller,
2001). If the university is unable to build an environment that can increase access to higher
education while maintaining a pathway to graduation, it will not be able to fulfill its key societal
role as a public institution of higher learning. Currently, only slightly more than a quarter of the
adult residents in the institution’s state possesses a bachelor's degree or higher (American
Community Survey, 2017). Without addressing key areas of retention loss, such as academically
at-risk, Pell-eligible freshmen, the institution will struggle to achieve its institutional and societal
mission as a public university. If these retention trends continue, more states’ legislatures are
likely to follow California in instituting performance funding models that tie state appropriations
to student success measures (Fain, 2018), which could dramatically change the landscape of
higher education. Such measures could negatively affect the access goals of the state’s public
universities by discouraging them from enrolling populations with historically low retention
rates.
Organizational Context and Mission
The focus of the study will be a new academic coaching intervention related to a
retention initiative at Axios University, a public comprehensive teaching institution in the

4
southeast region of the United States. Axios University is a medium sized institution within its
state’s public higher education system. The university enrolls approximately 10,000
undergraduates, with more than 90% of degree-seeking undergraduates attending full-time
(IPEDS, 2018). During his inauguration speech, the university president stated that his vision of
the fulfillment of the institution’s mission would make the school the best comprehensive
university in the state (Axios President, 2007). In an effort to fulfill this vision, the institution
has recently implemented a new academic coaching intervention as a part of a retention initiative
designed to increase retention rates in academically at-risk populations (AU, 2018). As a whole,
the undergraduate population is approximately two-thirds Caucasian and one-third people of
color. Additionally, the University is majority female and about 85% of the incoming
undergraduate class entering with a 3.0 high school GPA or higher (AU, 2018).
The institution has experienced a university-low freshman retention rate of below 60% in
2011 and increased that rate to above 70% by 2017, following a concerted effort focused on
retention (AU, 2014). While an improvement, these retention rates still trail peer and aspirant
institutions, as shown in Figure 1 (NCES, 2020). This study will examine one initiative designed
to improve freshman retention rates, an academic coaching center targeting academically at-risk
freshman. This initiative’s goals are designed to align with institutional mission and values, as
the success of this vulnerable population is critical in order for the university to fulfill its
president’s vision to become the best comprehensive university in the state.

5

Figure 1. Freshman retention by cohort. NCES, 2020 [dataset].

Organizational Goal
This study will focus on the academic coaching center, an intervention established in
support of a new retention initiative for academically at-risk freshman students at Axios
University. The focus on academically at-risk freshmen is institutionally important due to the
size of the population and its impact on the institutional mission. In order to achieve that
mission, the institution must address student populations that fail to show adequate retention and
completion rates. Academically at-risk freshmen represent over 15% of all enrolled freshmen in
the most recently reported year and exhibited a retention rate over forty points lower than the
freshman population as a whole (AU, 2018). In order to fulfill its mission, the institution must
address populations such as this, and significantly improve their outcomes. This necessity is
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Fall 2017 Fall 2016 Fall 2015 Fall 2014 Fall 2013 Fall 2012 Fall 2011
Axios University Peer - Texas University
Peer - Georgia University Aspirant - North Carolina University
Aspirant - Virginia University

6
evident to the institution and is observed through the university’s strategic plan, which specifies
a key objective as requiring the institution to develop a process to facilitate timely degree
completion (AU, 2018).
Academic Coaching as a Practice
Academic coaching is generally defined within the broader context of student success
programs that often include advising and tutoring (Minglin, 2019). In contrast to other student
success programs, however, academic coaching is not intended to be a transactional relationship
based around task accomplishment, but rather an ongoing relationship oriented towards students’
overall academic goals (Robinson, 2010). An academic coaching program is based on
psychological research related to coaching, mentoring, and learning whereby global goals are
articulated and mentors facilitate repeated interactions in an effort to promote growth by the
mentee (Law, 2013).
Academic coaches serve as guides to their mentees, helping students find, practice, and
master success behaviors shown to enhance the likelihood that a student reaches their academic
goals (Barkley, 2011; Ben-Yehuda, 2015). These behaviors include help-seeking, study habits,
behavioral habits such as class attendance, and institutional navigation support. Through
repeated interactions with students, academic coaches are able to build and sustain behavioral
change in ways that are not possible through limited interaction. It is the sustained relationship
with the student that forms the core of academic coaching, and it is what separates it from other
programs designed to promote student success.
Academic Coaching at Axios University
This study is designed to evaluate the efficacy of the academic coaching center as a
whole, which includes both course and non-course interactions with students. The academic

7
coaching center was created to specifically target academically at-risk freshmen with the goal of
correcting their academic course and ensuring timely degree completion. This new unit was
tasked with improving the success rate of the academically at-risk freshman population, which
currently consists of more than 275 students at less than a 30% retention rate (IRO, 2018). The
goal of this unit is to build academic support structures with the goal of steadily improving the
retention rate of this population to the point at which it meets or exceeds the institutional
freshman retention rate. In order to achieve this goal, the academic coaching center was tasked
by the Provost to reach a successive series of progressive benchmarks in an effort to provide
comprehensive assessment and multiple opportunities for course correction. These benchmark
milestones begin with the 2018-2019 freshman cohort and set a performance goal of 35%
retention among that population. These benchmarks continue to rise each successive cohort,
culminating in a 2021-2022 benchmark of 50% retention for academically at-risk freshmen
(Center Director, 2018). This final benchmark coincides with the university’s completion of its
current strategic plan, and the assessment results from this effort and others will inform
subsequent strategic planning processes. In conjunction with these annual benchmarks, a series
of program assessment instruments have been developed in an effort to provide both formative
and summative assessment of program outcomes at various point in its development. These
assessments include a student intake questionnaire, academic coach reports, and a summative
assessment of student retention at the beginning of each cohort’s sophomore year (Center
Director, 2018).
Potential candidates for academic coaching are identified following the conclusion of
their first semester of their freshman year. All freshman students that finished their first semester
on academic probation, which means they earned a GPA of under 2.0, are asked to participate in

8
the academic coaching initiative for the Spring semester. Each potential student, and their
academic adviser, receives communication informing them of required participation in academic
coaching for the upcoming Spring semester. The students in instructed to register for a specific
academic coaching course, and to schedule their first meeting with the academic coach.
The academic coaching course is designed as a semester-long, pass/fail course that meets
three hours per week during the semester and is taught by a member of the academic coaching
staff. The course is designed around academic strategy development, with a focus on helping
students develop a better understanding of their individualized academic and personal areas of
strength. Additionally, the course seeks to challenge and develop students’ skills and strategies
necessary to improve their academic and personal performance. Some critical skill development
areas include course assessment, reflection, and self-regulation. The semester-long course is
intended to provide sustained and repetitive opportunities for students and academic coaches to
interact, with significant opportunities for goal setting, feedback, and reflection.
In addition to the semester-long course, students are expected to meet with an academic
coach at least four times during the semester. These required meetings are intentionally
scheduled with academic coaches that are not the instructor in their academic coaching course,
which provides the academic coaching staff with triangulation opportunities as well as providing
students a range of individuals with which to connect. Additionally, the meetings provide a
check-in point for student progress and opportunities for students to engage their coach regarding
pressing issues they are experiencing. Academic coaches are able to provide proven learning
strategies, model academic success behaviors, and act as an accountability partner for students
struggling to self-regulate. The regular meetings also provide coaches opportunities to support

9
student motivation through encouragement, emotional support, illuminating the value of success
behaviors and reassuring students of their own self-efficacy.
Related Literature
This section will review the literature surrounding the retention of academically at-risk
freshmen, focusing on the methods of identification for such students. The first section will
center on pre-college identification, focusing on demographic, psychosocial, and institutional
characteristics that have been shown through research to be predictive of future academically at-
risk status. The second section will center on performance identification and will focus on
academic and social behaviors that have been shown to be predictive of academically at-risk
status. Many of the characteristics discussed in this section are not mutually exclusive, in that
multiple predictors have been shown to produce a cumulative effect on academically at-risk
outcomes (Judge, 2013). As many of these characteristics, especially those associated with pre-
college predictors, are related to the environment from which the student is emerging, it is
important for the institution to be cognizant of these predictors and have a plan for addressing the
needs of these students. While public higher education institutions have very little control over
the environment from which students come, the institution has significant control over the
environment into which the student is immersed. This ability to control the freshman
environment may prove pivotal in an institution’s ability to combat the deleterious outcomes
predicted by pre-college indicators.
Pre-college Identification
Axios University’s effort to increase retention of academically at-risk students begins
even before the student sets foot on campus for the first time. Throughout the admissions and
onboarding process, the institution gathers a number of demographic and psychosocial points in

10
an effort begin directing institutional resources to where they are needed (University Provost,
2018).
Academic and demographic predictors. The first data points typically gathered are
those related to demographic information that is generally included with the student’s application
and academic transcripts. These include age, race/ethnicity, gender, high school, high school
GPA, and more recently, first-generation status. There has been significant research into
demographic characteristics as predictive measures of academic success, and some
characteristics have proven more predictive than others (Heisserer & Parette, 2002). One
demographic factor that has been the focus of much research is that of race/ethnicity. A number
of studies have found that, while acceptance rates for African-American and Latinx students
have increased, completion rates for those same populations have lagged against their Caucasian
and Asian counterparts (Baker, Klasik, & Reardon, 2018). Baker et al. (2018) postulate that
these persistent achievement gaps are the result of long-term structural economic inequalities
present in those communities. This theory is supported by the work of numerous other
researchers. Issues of structural economic inequality would likely lead these communities to
have lower college completion rates, which would naturally lead to more first-generation
students emanating from this community (Reynolds & Johnson, 2011).
Research into first-generation college students has shown that parental educational
attainment is an important factor in student success as college-educated parents are able to help
their children navigate the collegiate experience (Tough, 2014). Additionally, structural
economic inequality would naturally lead to other risk indicators in first-generation students,
including academic preparation and socioeconomic status. Communities experiencing structural
economic inequality are more likely to have underfunded K12 educational institutions (Darby &

11
Saatcioglu, 2015) and significantly higher concentrations of low socioeconomic status (Ishitani,
2006). The issues surrounding race, economic inequality and school quality all contribute
toward another predictor of academic success, namely, academic preparation.
Bound, Lovenheim, and Turner (2010) cite academic preparedness as a strong pre-
college predictor of college academic success. Many colleges, including Axios University, use
high school GPA as a benchmark data point to assess a candidate’s prior academic record.
While not the sole determinant of academic success, high school GPA has proven to be
correlated with collegiate academic success (DeBerard, Spielmans, & Julka, 2012; Tucker &
McKnight, 2017). This measure of academic preparedness has proven to be more reliable a
predictor than the often-used standardized test scores, so much so that universities are beginning
to no longer require such scores in an effort to attract more low-income and first-generation
students (Jaschik, 2018).
Taken as individual pieces to a larger mosaic, demographic information such as a
student’s race/ethnicity, their status as a first-generation student, and indicators of their academic
preparedness can begin to give universities some insight into which of their incoming freshmen
may need additional supports to be successful. Moreover, institutions’ ability to know these data
points ahead of a student’s arrival increase the likelihood that a university can marshal the
necessary resources in a timely manner to support academic success from incoming freshmen.
Psychosocial predictors. While demographic data points have been collected by
institutions’ admissions processes for years, a growing number of institutions are following the
academic research in acknowledging the potential of psychosocial predictors to academic
success (Laskey, 2004). There have been numerous studies linking psychosocial predictors to

12
academic success, including personality research (Dollinger & Huber, 2008), coping skills
(DeBerard, Spielmans, & Julka, 2012), and resilience (Stuber, 2011).
While these studies examined different aspects of students’ psychosocial profile,
common themes across the research include students’ ability to see a challenge through to
completion, persist through difficulties, and rebound from failure. In their work using the Big
Five Personality Trait Assessment, Laskey and Hetzel (2011) found that the only personality trait
predictive of academic success was that of “Conscientiousness”. Conscientiousness, as defined
in the Big Five personality assessment, measures the level of thoughtfulness, impulse control,
and goal-directed behaviors exhibited by the subject. Their research found that a student’s desire
to stick to a task was highly predictive of future academic success. There has also been
significant research into harder-to-measure traits such as grit and resilience (Duckworth, 2014;
Stuber, 2011). Research into grit and resilience seek to identify and quantify students’ ability to
persist through challenges.
Collegiate life is a wave of new experiences for freshmen, therefore a student’s ability to
persist through difficulty is highly predictive of their ability to be successful in a university
environment (Saunders-Scott, Braley, & Stennes-Spidahl, 2018). Collegiate life will challenge
many new freshmen in ways they have not experienced before, and therefore persistence alone
will not be sufficient for academic success. Additionally, students must be able to cope with
failure as they will likely face their most significant academic challenges as they transition from
high school to college (DeBerard, Spielmans, & Julka, 2012). Taken in total, psychosocial
predictors attempt to elucidate a student’s potential outcomes when they encounter the academic
rigors of university studies. These indicators seek to predict students’ reactions to decreased

13
supervision, adversity, and failure. The degree to which students can manage and overcome
these challenges is highly predictive of their overall academic success.
Institutional predictors. In addition to demographic and psychosocial predictors of
academic success, both of which center on the student, there are also a number of institutional
predictors that have been shown in the research to be indicative of academic success. As
discussed in the previous sections on student predictors, these indicators provide institutions the
information needed to direct resources needed to support freshmen that are academically at-risk.
However, these indicators are only useful to the institution insofar as the institution’s ability to
marshal resources in response to those indicators. Previous research by Casey (2004) showed
that institutional selectivity was inversely related to the academic success of at-risk students,
meaning that at-risk students were more likely to be successful the more selective the institution
they were attending. This is potentially due to the fact that more selective institutions are more
likely to have a lower concentration of academically at-risk students and thus could potentially
concentrate available resources on those students who are present in the student population
(NCES, 2019). Less selective institutions are likely to have higher concentrations of
academically at-risk students, as well as being less resourced than their more selective
counterparts due to tuition variances between institutions related to their varying enrollment
demand (Tinto, 1993). Additionally, the availability of institutional resources, such as
remediation, have been found to be predictive of academic success in at-risk populations
(Bastedo & Gumport, 2013). These institutional resources provide the scaffolding necessary to
bring academically at-risk populations the stability needed to be successful in their university
studies. While a number of institutional resources have been identified as being impactful to
academically at-risk students, the research indicates that appropriate resource allocation is

14
necessary to create an environment of success and institutions should be mindful of their
capacity to deliver those resources.
Performance Identification
While pre-enrollment academic success efforts focus on data points predictive of
academic success, a second area of focus is on the performance indicators of academic success.
While predictive indicators seek to anticipate performance, performance indicators measure the
actual outcomes achieved as students interact with their collegiate experience. In the realm of
performance indicators, research generally categorizes these into those that focus on academic
behaviors and those that focus on social behaviors. If the institution’s admission policy is
working as intended, the student possesses enough predictors of cognitive ability such that the
institution believes the student can be successful at the university. These pre-enrollment
predictors, however, can only anticipate the likelihood of academic and social behaviors that lead
to academic success, they cannot guarantee them. As such, it is incumbent upon institutions to
assess students’ behaviors and use those behaviors as learning opportunities to move towards
academic success.
Academic behaviors. While there have been numerous studies on academic behaviors
related to student success, Heisserer and Parette (2002) produced a comprehensive work that
synthesized the work of prior researchers. In their study, they found a number of academic
behaviors that were correlated to academic success. These behaviors included help-seeking
activities, such as going to a tutoring center or writing center. Additionally, faculty interactions,
and the quality of those interactions, were found to be highly correlated with freshmen success
(Heisserer & Parette, 2002). Beyond help-seeking behaviors, Heisserer and Parette (2002) found
that the academic behavior most predictive of freshman success was regular attendance at class

15
meetings. While this may seem obvious, freshmen are especially susceptible to the decreased
oversight associated with their transition from high school to college, and this decreased
oversight often translates to a decrease in class attendance (Dollinger & Huber, 2008). This
decrease in attendance is highly correlated with freshman academic success, and as such, should
be central in an institution’s assessment of academic behavior.
Social behaviors. Outside of academic behavior, students’ social behaviors have been
found to be highly correlated with academic success as well. Tough (2014) chronicles one
student’s time as a freshman at the University of Texas, and details the many social situations
facing new college students. Many students are presented choices that may negatively affect
their academic career, including the opportunity to drink, smoke, and party (DeBerard,
Spielmans, & Julka, 2012). These social behaviors are of particular interest to Axios University
administrators as many student affairs personnel are faced with the challenge of guiding
freshmen in their social choices yet allowing them the opportunity to make mistakes and grow
from the experience. To that end, Axios implemented both the Alcohol.edu and Haven
programs, each designed to educate incoming freshmen to the dangers of alcohol and sexual
violence, respectively. These educational programs are designed to assess students’ likelihood to
engage in dangerous behaviors and generate corrective action before the damage is too great to
overcome (Provost, 2018). In addition to increased exposure to vices, students’ ability to
integrate into the campus community and form a social support network is highly predictive of
future success (BeBerard, Speilmans & Julka, 2012).
These networks have been shown to provide a bulwark against the challenges many
freshmen face as they transition to collegiate life, as these social networks provide a support
structure for the student to lean upon. Taken together, student academic and social behavior is

16
one of the most predictive indicators of academic success, as all other indicators seek only to
predict subsequent behavior.
Related Literature Summary
Overall, the literature on the success of Pell-eligible, academically at-risk freshmen
focuses on the intersection between preparation and execution. While many pre-college
indicators focus on data points that are indicative of the resources and preparation students bring
to their college experience, performance indicators speak more directly towards a student’s
ability to execute the tasks needed to be successful in college. While economic inequality has
been shown to be the source of numerous challenges to Pell-eligible, academically at-risk
students, research also shows that these challenges can be overcome with specific institutional
programs and resources. Additionally, if institutions can educate students to the deleterious
effects of certain social decisions, then the institution can empower the student to control their
destiny and ultimately their collegiate success. While resources and preparation might be out of
a student’s immediate control, their behavior and decision-making capabilities are completely
within their control.
The research on student success tells us is that, regardless of starting point, student
success is possible. In study after study, researchers found that certain institutions outperformed
their peers in a given predictor group, which indicates that institutional academic excellence is
possible regardless of the institution’s composition (Carey, 2004; Heisserer & Parette, 2002;
Tinto, 1993). These studies illustrate that focused programming and assessment can maximize
institutional resources in furtherance of success for academically at-risk freshmen.

17
Description of Stakeholder Groups
This study focuses on a retention improvement initiative within a public comprehensive
higher education institution and therefore encompasses a broad array of stakeholder groups.
These groups include the subjects of the initiative, academically at-risk freshman students, as
well as directly participating groups including faculty, academic coaching center staff, and
university administration. Other interested stakeholders include students’ parents, financial aid
counselors, and state legislators.
These three levels of stakeholders form a bullseye, with the organizational goal as the
center target. The stakeholder group that is likely to be the most impactful, with respect to
achieving the organizational goal, is the group of academically at-risk students themselves. This
group is the target of the initiative, therefore any advances in this stakeholder group directly
correlate to advances in the organizational goal.
Surrounding this center stakeholder group is an array of associated stakeholders with
direct interest in the success of this initiative. This group includes the faculty of the courses in
which the students are enrolled. These faculty are directly involved in the attainment of the
organizational goal through their referral of at-risk students to the academic coaching center, in
addition to their continued collaboration with the academic coaching center in addressing their
students’ academic success barriers. Working directly with students’ faculty, the academic
coaching center’s staff constitutes another significant stakeholder. This stakeholder group, more
than any other, is explicitly responsible for the attainment of the organizational goal through
their explicit charging of the goal in the creation and establishment of the center. The center’s
director is directly responsible for the success of the center, as defined by the attainment of
annual benchmarks that result in the ultimate attainment of the organizational goal. Through the

18
design and implementation of the center’s structure and processes, the center director is the
institution’s most explicit stakeholder in the attainment of the institution’s organizational goal.
Supporting the center director in this effort is an array of university administrators who are
equally invested in the university attaining its organizational goal. These administrators include
the university President, the Provost, the Assistant Provost for Academic Operations, the chair of
Faculty Senate, the Dean of Students, and the Director of the Tutoring Centers. These
administrators form the network from which the academic coaching center draws its support, and
they represent the administrators most directly invested in the attainment of this organizational
goal.
The outer ring of stakeholders constitutes other interested parties, those not directly
invested in the attainment of the organizational goal, but those for whom its attainment furthers
their own goals. This group of stakeholders include students’ parents, financial aid counselors,
and state legislators. While retention in and of itself is not a goal of the students’ parents, it is a
necessary condition for the attainment of their own goal, namely the completion of a college
degree. Similarly, financial aid counselors are not directly concerned with students’ academic
success, however, that success enables the qualification for continued aid distribution. State
legislators rarely delve into the operational details of individual universities; however, they have
shown keen interest, generally, in ensuring additional supports are available to enable the success
of academically at-risk populations. As such, legislators would likely have interest in programs
that interact with these populations.
Stakeholder Group for the Study
Although a complete evaluation of the academic coaching center would involve all
relevant stakeholder groups, for practical purposes a single stakeholder group has been selected

19
as the focus of this study. In examining each stakeholder group, a logical choice for the primary
stakeholder group for this study should be the Pell-eligible, academically at-risk students
themselves. This is the only stakeholder group that can take independent action to further the
organizational goal. While specific actions will vary from group to group, all other stakeholder
groups must act relative to the students themselves. No other stakeholder group can directly
affect the organizational goal without first acting upon the student group, who in turn affect the
organizational goal. This referential nature of all other stakeholders positions them as
subordinate stakeholders relative to the academically at-risk students themselves.
As the primary stakeholder group for this study, the organizational goal is written as a
goal for this stakeholder group. Due to its recent inception as an entity, the academic coaching
center does not have a single, rigid goal for this stakeholder group, but rather incremental
benchmarks that scaffold over time in an effort to reach the ultimate organizational goal of a
50% retention rate for academically at-risk freshmen. This scaffolding begins in the 2018-2019
academic year with a benchmark rate of 35% retention for this student population, which is 6
percentage points higher than its current rate. These benchmark rates continue to rise five
percentage points each academic year, culminating in the benchmark rate of 50% in the 2021-
2022 academic year. These annual targets were derived through twin constraints. The first of
these constraints is an organizational goal of a 50% graduation rate, as such, a 50% first year
retention rate moves the overall student population towards the institution’s graduation rate goal.
The second constraint is fiscal in nature. Based on the size of the target population and the cost
of the interventions proposed, the 50% retention rate is necessary to increase revenue sufficiently
to warrant the expenditure of the resources. While it would be ideal for student success concerns

20
to be independent of financial considerations, such considerations are necessary at an institution
funded primarily through tuition revenue.
Academically at-risk freshmen represent an important population to the university, and
improvements in student success within this population translates directly into institutional
mission success more broadly. Improvements in this population forward the institution’s twin
mission of access and academic excellence by providing additional academic supports for a
population that may not otherwise be able to continue their studies. Without improvement in this
significant population, it would prove difficult for the university to achieve its mission.
Purpose of the Project and Questions
The purpose of this study is to evaluate the efficacy of academic coaching, a probation
initiative, at a public comprehensive institution located in the southeast United States and to
describe the mechanics leading to such efficacy. This academic coaching probation initiative is
designed to target academically at-risk freshmen, as defined by students who are placed on
academic probation following the conclusion of their first semester as freshmen. The purpose of
this probation initiative is to intervene with academic supports with a goal of retaining these
academically at-risk freshmen into their sophomore year. This study will specifically examine
the Pell-eligible, academically at-risk freshmen, a sub-population of the larger academically at-
risk population.
The study seeks to evaluate two primary questions articulated in McEwan and McEwan’s
work (2003), namely the evaluative question of whether or not the intervention is working for the
students, and the process question of how it is working. An additional goal of the study is to
produce research results that can be adopted by practitioners in the field with the intent to
improve the efficacy of the program. Malloy (2011) notes that educational research is too often

21
retained within the confines of academia without permeating out into the field and improving the
practice of the practitioners actually implementing such interventions. This study seeks to
ameliorate this deficiency of educational research by partnering with the institution to produce
results that can then be integrated into future iterations of the program. In this sense, this study
can be considered both research and evaluation (Alkin, 2011), as the research will lead to
conclusions about the current state of the intervention, but it will also serve an evaluative
function as it will also lead to future decisions about the program components.
In an effort to answer both the evaluative and process questions, a collection of research
questions have been established that seek to encompass both goals. These research questions
include:
1. To what extent are participants in the academic coaching initiative retained into their
sophomore year?
2. What is the knowledge and motivation of Pell-eligible, academically at-risk freshmen
related to their ability to persist to sophomore year?
3. What is the interaction between academic and social campus culture and context and
the knowledge and motivation of academically at-risk freshmen in relation to their
ability to persist to sophomore year?
By answering these three research questions, this study seeks to establish answers to both the
evaluative question and the process question (McEwan & McEwan, 2003), as well as functioning
as both research and evaluation (Alkin, 2011). The first three questions serve the research
function, with the first question determines the evaluative question of the extent to which the
intervention is efficacious and questions two and three answering the process question of how
the intervention is working, or not.

22
Methodological Framework
The methodological approach used address the described research questions will be a
descriptive, quantitative methods approach. It will draw on field-validated instruments to target
broader concepts in academic success research, ideally giving the study the ability to answer
both the evaluative and the process questions. The use of field-validated instruments allows the
study to delve into broader psychosocial indicators while maintaining reliability and validity
within the limits of the study.
The first research question is evaluative in nature and is designed to describe the retention
outcomes of stakeholders participating in the intervention. The academic coaching intervention
being tested was initiated in the Spring of 2019 and targets a clearly defined population of
second semester freshmen, namely students achieving less than a 2.0 GPA in their first semester.
As such, there exists significant longitudinal data from previous cohorts that allow for a
correlation analysis between students with the intervention and students without the intervention.
Creswell (2018) describes this type of question as best suited to a quantitative evaluation due to
its application to both a control and treatment group. He goes on to describe some
considerations the researcher should acknowledge, including the quasi-experimental nature of
the study due to differing cohorts from year to year, as well as a number of threats to internal
validity including history, regression to the mean, and experimental mortality which would be
expressed in this study as varying dropout rates. In an effort to mitigate these threats to validity,
the research question regarding intervention efficacy is stated as broadly as possible and
integrates experimental mortality into the description of efficacy. This research question will be
addressed through observations of prior cohorts’ persistence rates followed by observations of
persistence rates for cohorts upon which the intervention was applied. Because persistence, or

23
lack thereof, is describing the experimental mortality it seeks to eliminate this threat to validity
from the first research question. Standard quantitative methods related to the null and research
hypothesis, as described by Salkind (2014), will be used to confirm or refute the null hypothesis
through statistical analysis of cohort performance both before and after the treatment was in
effect.
The second and third research questions are well suited to a validated quantitative
analysis as they seek to correlate specific interventions with the elimination of existing
knowledge, motivation, and organizational gaps present in stakeholders (Creswell, 2018). These
research questions rely heavily on the literature review as a foundation to describe the universe
of mechanisms affecting the knowledge, motivation, and organizational influences of
academically at-risk freshmen. Malloy (2011) cautions that such quantitative studies that rely on
previous literature to establish the framework of the study should take care to ensure the
literature review is comprehensive so as to not exclude relevant literature on the subject. The
academic coaching initiative implemented at the institution is designed to address a number of
previously identified factors in the literature, which would categorize this intervention as
research-based (Duke & Martin, 2011).
This form of quantitative study would be considered a quantitative correlational study as
it seeks to establish the efficacy of the intervention through a quantitative analysis of longitudinal
data from the treatment group. The researcher approaches this study from a pragmatic
worldview, whereas the purpose of the study is to help solve the real-world problem of retention
of academically at-risk freshmen at the researcher’s home institution (Creswell, 2018). It must
also be noted, however, that the researcher is anticipating some constructivist-based outcomes
when examining the intervention participants themselves. Students attending the institution

24
come from around the country, and from a variety of walks of life, the researcher anticipates that
each participant will encounter and respond to static knowledge, motivation and organizational
influences in a variety of ways based on their own personal histories (Creswell, 2018). This
variety of responses, and the interventions most efficacious to each particular response, is an area
of interest for the study and will contribute to the fourth research question.
Definitions
The following definitions of common terms is provided so as to produce a lingua franca
for the study.
Academic Capital: A term meant to denote a narrower form of the broader sociological
concept of cultural capital, and is defined as social processes that build family knowledge of
educational and career options and support navigation through educational systems and
professional organizations (Bourdieu & Passeron, 1977; St. John, Hu, & Fisher, 2011).
Academic Coaching: An advising approach that can empower the student to reflect and
act upon the range of goals, interests, and passions available in higher education utilizing
coaches to help students develop their abilities to think critically, solve problems, overcome
personal obstacles, and discover their strengths (McClellan & Moser, 2011).
Academic Coaching Center: An academic support division housing academic coaches
and charged with programmatic responsibilities related to the retention of academically at-risk
freshmen (AU, 2018).
Academically at-risk: Defined in this study to mean any freshman student who finished
their first semester with a cumulative GPA of less than 2.0 (AU, 2018)

25
Contentiousness (Big Five personality assessment): Defined as a tendency to respond in
certain ways under certain circumstances; the tendency to think, feel, and behave in a relatively
enduring and consistent fashion across time in trait-affording situations (Roberts et. al, 2009).
Retention: Defined as continued enrollment within the same higher education institution
in the fall semesters of a student’s first and second year (National Student Clearinghouse, 2020).
Organization of the Project
This dissertation is organized as a five-chapter study. Chapter One frames key concepts
related to student success generally, challenges that make certain students academically at-risk,
and interventions that seek to target this population. Chapter Two reviews the existing literature
on student success and specifically on Pell-eligible, academically at-risk students. Chapter Three
describes the study’s knowledge, motivation, and organizational influences, as well as study
methodology. Chapter Four provides the data analysis and results of the study, and finally,
Chapter Five offers recommendations for future applications to improve the retention of
academically, at-risk freshmen.

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CHAPTER TWO: REVIEW OF THE LITERATURE
In this chapter, literature on student success and departure will be examined to develop a
framework to understand the factors of student retention loss. There has been significant
development of student success theory over the years, with various researchers developing a
number of models to help explain the phenomenon. This chapter will review a number of these
models, and more specifically, academic underachievement. Ultimately, focus will be given to
models best adapted to this study’s stakeholder group of focus, Pell-eligible, academically at-risk
freshmen. The review will then examine the knowledge, motivation, and organizational
influences that specifically affect this student population, as well as various interventions that
research has shown to have promise with this population.
Influences on the Problem of Practice
While there is widespread agreement on the need to address retention and graduation
rates, the particular factors that influence those retention and graduation rates are of more debate.
This chapter will examine the evolution of student success theory and explore various theoretical
frameworks to determine the most appropriate influences on the study’s stakeholder group of
focus, Pell-eligible, academically at-risk freshmen. Upon determining an appropriate theoretical
framework, the chapter will then examine recent research studies to elucidate salient influences
for this specific population. Finally, the chapter will review several research-proven
interventions and ultimately focus on the academic coaching intervention that is the subject of
this study.
Evolution of Student Success Theory
Student success in higher education has been a topic of significant inquiry for many
years, with articles on the subject dating as far back as 1924 and continuing to present day

27
(MacPhail, 1924; McGinn et al., 2018). As far back as 1910, researchers began to define student
success, beginning with conceptions such as passing classes emblemizing success (Snedden,
1910). As research and theory development progressed, the concept of student success evolved
into a more holistic inquiry into student perceptions and outcomes along multiple dimensions.
This includes conceptions of knowledge gaps, personality traits, motivation factors, and
qualitative outcome factors such as intellectual growth, broadened worldview and personal
development (Higbee, 2005; McGinn et al., 2018; Richardson, Abraham, & Bond, 2012;
Yazedjian et al., 2008).
Current research in the field of student success acknowledges that a web of intersecting
factors influences student success outcomes related to academic and social integration (Tinto,
1987; Wolf-Wendel, Ward, & Kinsie, 2009). Through continued research into the field, a
number of scholars have begun to develop models of student success informed by previous
scholarship on the subject. These models seek to identify the key dimensions upon which
prediction of student success can be modeled.
Models of Student Success Theory
Student success theories attempt to provide theoretical frameworks upon which the
varying measures of student success can be elucidated, while seeking predictive capacity with
respect to the degree of attainment for those measures. Toward that end, a number of researchers
have proposed various models, providing centrality to differing factors and seeking to target
differing populations with their predictive capability. Student success research initially sought
unified models, but have more recently sought to target specific populations in their models.
One such theory, pioneered by Randi Levitz and by Lee Noel, sought to frame student
success as persistent retention and ultimate graduation, with a practical focus on retention as the

28
necessary precursor to graduation (Noel, Levitz & Saluri, 1985). This model took an all-of-the-
above approach to student retention, seeking out student barriers in academic, financial, and
social realms. Noel and Levitz continue their work contemporaneously through Ruffalo, Noel,
Levitz (RNL), a consulting and services firm that operates in the higher education market and
serves to help universities increase their student retention through a variety of interventions
targeting barriers discovered through their research.
Contemporaneously with Noel and Levitz, Vincent Tinto (1987) published his first book
on the subject of student departure, research that would become a seminal work in the field of
student retention. This first work began development of what would become the most widely
cited work in the field of student success, and framed success much in the same way as Noel and
Levitz, namely as persistence to graduation. Tinto’s work focused on the various causes and
influences upon student departure in an effort to stake out the boundaries of influences in an
effort to begin developing responses to those influences. The main theory embedded in Tinto’s
work revolves around the notion that students enter the collegiate environment with certain
characteristics that impact both their goal and institutional commitments, as shown in Figure 2.

29

Figure 2. Vincent Tinto’s explanatory model of the dropout process. Adapted from “Dropouts
from higher education: A theoretical synthesis of recent research,” by V. Tinto, 1975, Review of
Educational Research, 45, p. 95.
Upon entry, those students are impacted by the academic system in various ways, each of
those impacts affecting their academic integration, social integration, or both. Those impacts
subsequently affect their current goal and institutional commitments. For those students whose
experiences lower their commitments, there exists an inflection point below which student
departure is effectuated (Tinto, 1993). Tinto’s work in the field has formed the foundation of
future student success research and will be used as the foundation of this study as well.
Following on Tinto’s work and focusing on the integration components, Alexander Astin
(1984, 1993, 1999) developed a model of student involvement that has been widely researched

30
and utilized in the field of Student Affairs. Astin’s work focuses student involvement as the key
driver of maintaining student commitment to the institution, thus increasing student retention.
This model of student retention focuses on the observable behaviors that students engage in,
from their academic help-seeking behaviors to their social bonding behaviors. This theory
acknowledges students differing starting points in the collegiate experience, but only to the
extent that they affect which behaviors are necessary for student retention.
Following Astin’s work, a more targeted theory of student success has been proposed that
seeks to integrate both ex ante predictors as well as observed behaviors in an effort to predict
future retention outcomes (Cabrera, Castaneda, Nora & Hengstler, 1992). Cabrera’s (1992)
model relies on an assessment of both motivation and academic ability, combined with
observations of academic and social characteristics, to develop the predictive capabilities of the
model. This model has been less widely researched compared to both Tinto (1987) and Astin
(1984), however, it begins to develop notions related to broader psychosocial concepts such as
motivation and personality as being important in the conversation about student success.
Subsequent researchers expand and move this idea forward with significant research into the
broader psychosocial influences on student retention, influences such as cultural capital, sense of
belonging, and life stressors as predictors of retention (Langhout, Drake, & Roselli, 2009;
Mattson, 2014; Walpole, 2003).
As Tinto’s (1987) theory of student departure solidified as the foundation of student
success research, various scholars began to examine how Tinto’s model played out in specific
populations. This research sought to determine whether all students were subject to the same
forces, or whether specific populations experienced specific barriers that may inform institutional
intervention. Two such theorists were John Bean and Barbara Metzner (1984), who pioneered

31
population-specific research with their work around non-traditional students. Bean and Metzner
(1984) found that non-traditional students were far more susceptible to outside environmental
forces than to forces related to social integration. This work opened up the notion that Tinto’s
model of student departure might still hold in theory, but that specific populations may be more
susceptible to certain forces than they are to others, based on the unique characteristics of that
population. It is in this spirit that subsequent research into specific populations both reinforced,
as well as extended, Tinto’s theory of student retention.
More recently, there has been focus directed at gaps in access and completion for
minority and low-income students, highlighted in the Spellings Commission Report (U.S.
Department of Education, 2006). This governmental report, along with elements included in the
reauthorization of the Higher Education Opportunity Act in 2008, began placing more pressure
on colleges and universities to better serve these populations. Some required actions included
creating net price calculators, FAFSA4caster for estimating assistance amounts, College
Navigator, and the White House Scorecard, which provided retention and graduation rates
(Castleman, 2013). In addition to increased governmental focus on access and completion, a
number of non-governmental actors began to increasingly focus on equity gaps in achievement
among minority and low-income students. In 2007, the Equity Scorecard began assessing
institutions based on their service to students of color, noting that “postsecondary institutions are
rich in data but poor in the means and know-how of organizational learning,” (Harris &
Bensimon, 2007). The intent of the tool was to develop awareness of racial inequities and instill
institutional responsibility in addressing structures, policies, and practices that fall short. This
increase in pressure from observers of higher education has made it increasingly important for

32
institutions to create an environment that supports the success of all students, with particular
focus on minority and low-income students.
Institutions have responded to these calls with a myriad of new student support services
seeking to boost retention and persistence among vulnerable populations such as first-generation
students, racial minority students, and low-socioeconomic students (Cataldi, Bennett & Chen,
2018; Niu, 2015; Sandoz, Kellum, & Wilson, 2017). This effort recently expanded to include
males, who have been shown to lag behind their female peers in preparation and graduation
(Chen, 2016). While different institutions have responded with different solutions, the Education
Trust (2015) found that common strategies included behavior modeling by leadership, engaging
practitioners in the problem-solving process, ensuring accurate data that was accessible,
investigating student pathways, personalizing student services, and promoting a culture of
inquiry and development. For low-income and first-generation students, positive outcomes
resulted from efforts that increased students’ perception that the campus was supportive of their
efforts (Filkins & Doyle, 2002). Research has also found that campus administrators’ mindsets
play a part in program development. Coylar (2011) described a common intervention, summer
bridge programs, as often targeting low-income students as at-risk based perceived deficit
models that normalize higher-income students’ behavior as the standard, thus risking alienation
of low-income students. Institutions may instead use writing assignments that outline past and
future characteristics of classroom success and utilize the outcomes of these assignments to
identify students that are academically at-risk (Colyar & Stitch, 2011). These assignments
provide an opportunity for students to reflect, and to identify academic success behaviors, which
can then be used to assess students’ capabilities in those skills rather than make assumptions
based solely on income level.

33
Additional research related to programs that seek to improve social and cultural capital
among low-income students has yielded promising results (Hannon, Faas, & O’Sullivan, 2017).
Their study found that students who participated in “leadership through service” activities helped
students feel more empowered and more able to take control of their situation. This type of
student development helps build the self-efficacy shown to positively correlate with academic
success (Bandura, 2000).
While there have been numerous university programs that have sought to capitalize on
these research findings, one relatively new entrant into that universe is academic coaching. The
National Advising Association (2017) describes academic coaching as a collaborative
relationship between an academic coach and student that focuses on the student’s personal and
professional goals through the development of self-awareness, strength building, academic
planning, and honing the student’s purpose, interests, and values in order to promote degree
completion. The coach serves as a liaison to institutional resources, helping bridge gaps in
cultural capital, to support students’ overall academic success. Additionally, coaches help
students focus on those personal values that brought the student to university, and to highlight
those values in service of increasing motivation to pursue academic success behaviors.
Coaches also serve as accountability partners to help encourage students to complete the
goals they have set for themselves, thus improving students’ self-efficacy and leading to a
virtuous spiral of success. Students that participated in academic coaching and noted an
effective experience with their mentor reported better outcomes in college and beyond (Smith,
2009). While mentoring, and academic coaching specifically, is a relatively new concept in
higher education it has proven successful with specific populations (Perez, 2014; Robinson,
2015; Robinson & Galahan, 2010). Academic coaching can be differentiated from other student

34
support programs such as advising, tutoring or counseling by its sustained interactions with
mentees, as coaches meet with their students at regularly scheduled times throughout the
intervention to create a more holistic atmosphere of support. This sustained interaction supports
goal-setting and accountability that is often lacking in other approaches that address more acute
concerns. However, coaching does not supplant these other interventions, but rather supports
their efficacy by serving as a gateway to those services in times where they would be most
beneficial.
History of Academic Underachievement
Academic underachievement, or failure to complete one’s chosen course of study, has
been a focus of institutions of higher education since their founding. Historical research on the
topic has focused on student deficit models whereas more recent research has taken a more
sociological approach to academic underachievement (Arceneaux & Hood, 1990; Ashcraft,
1967; Lee, 2017; Miner, 1910). The initial focus of research around academic underachievement
sought to uncover the missing variables not currently considered in the admissions process. The
early work in this field sought to identify deficits not readily apparent in the GPAs and test
scores. This early work focused on pre-college preparation, both in the academic and social
senses of the word (Ashcraft, 1967; Borow, 1945; Miner, 1910). This earlier work operated
under the premise that if only institutions could select the right student, then successful outcomes
were fait accompli.
Subsequent research in the field, augmented by more robust student success theories,
began to uncover additional variables that possessed predictive capabilities with respect to
academic achievement, and theorists began to contemplate whether over/under achievement was
simply a function of measurement error (Arceneaux & Hood, 1990). More recent research into

35
academic achievement has begun to acknowledge the psychosocial and constructivist aspects of
academic achievement and have begun to explain the confounding variables found in earlier
research. By examining prior knowledge, motivational influences, and organizational contexts,
researchers have begun to identify new variables related to individual-level contexts that affect
academic achievement (Biggers & Croghan, 1990; Howard, 2001; Lee, 2017). Building upon the
work of Tinto (1987), Bean (1984), and others, these subsequent researchers began to understand
academic achievement as an outcome based on a complex web of pre-college factors, academic
experiences, and social interactions, that each impact students’ commitment in unique ways
based on their personal, lived experience. It is this constructivist lens that has led student success
researchers to target more narrow populations in an effort to find sufficient commonality within a
population so as to support effective interventions.
Challenges Specific to Socioeconomic Status
Higher education institutions have long sought to graduate the most students they
possibly could, however, changing enrollment demographics have yielded uneven results for
various populations within the college community. Existing research on these unique populations
provides some guidance to institutions seeking to increase success rates for these students (Gray,
2013). Research indicates that college students entering university from a low socioeconomic
status (SES) household have an increased likelihood of presenting a number of co-occurring
indicators that are negatively correlated to academic success. These indicators include academic
preparation, reliance on financial aid support, and social and cultural capital. Once enrolled,
research shows low socioeconomic status students are also more sensitive to variances in their
sense of belonging, the presence or absence of student support programs, and institutional
efficiency (Browman et al., 2017, Linnehan, Weer & Stonely, 2011; Mattson, 2014; Reason,

36
2009). Each variable presents a risk factor for decreased academic performance, and research
indicates that low socioeconomic status students present an increased sensitivity to these risk
factors. Throughout the research literature, low socioeconomic status continues to emerge as
highly predictive of academic underachievement, second only in its predictive capability to high
school GPA (Reason, 2009). Inquiries into the influences upon underachievement have been
many and varied, however, common themes have emerged as scholars have pursued differing
lines of inquiry.
Building on the work of Tinto (1987), Astin (1984), and Pascarella and Terenzini (1983),
researchers have delved into how experiences and involvement are affected by the
socioeconomic background of the student. Studies found that students from low socioeconomic
backgrounds engaged in very different behaviors than their peers from high socioeconomic
backgrounds (Berger, Milem, & Paulsen, 1998; Hossler, 1984; Hossler, Schmit & Vesper, 1999).
This research indicated that students’ financial history and status, as manifested by their
behaviors, had impacts on both their academic and social integration within the institution. These
behaviors include things like time spent working, contact with faculty, time spent studying, and
group affiliation and participation.
Academic capital. Later, holistic research sought to encapsulate the effects of a
student’s environmental context into a coherent concept drawn from sociology known as cultural
capital (Bourdieu, Bourdieu, & Kreckel, 1983; Putnam, 1995; Walpole, 2003). In the context of
higher education, this concept is meant to represent the accumulated knowledge, resources, and
experience a student brings to the collegiate experience. In order to narrow the focus of this
broad sociological construct, this study will focus on a specialized form of cultural capital
pertinent to the collegiate experience, which will be referred to as academic capital. This could

37
include knowledge of study habits gleaned from older siblings, course scheduling tips from
parents who attended college, or familiarity with terms such as Registrar or Bursar. It also
includes resources like a vehicle or meal plan, as well as experiential knowledge of things like
wayfinding or applying for a loan.
This knowledge, resource access, and experience, or lack thereof, expresses itself in the
individual choices that students make when presented with new situations in the college
environment. Differing levels of academic capital impacts the way students respond to the
academic system, driving how students respond to events such as academic struggle and social
isolation, among others (Mattson, 2014; Walpole, 2003). Current research into academic capital
has focused on the various knowledge gaps produced by variance in socioeconomic status, and
how institutions might devise interventions to address those knowledge gaps.
Sense of belonging. Another dimension of socioeconomic status that appears frequently
in the literature is the psychosocial concept of sense of belonging (Langhout, Drake & Roselli,
2009; Mattson, 2014; Reason, 2009; Walpole, 2003). While related to academic capital in the
context that it represents an accumulation of experience that manifests itself in the environment,
the literature demonstrates that a student’s sense of belonging is more closely linked to
dimensions of motivation including expectancy-value, self-efficacy and attribution (Bandura,
2000; Eccles, 2000; Wigfield & Cambria, 2010; Rueda, 2011). Research indicates that a strong
sense of belonging increases motivation, which subsequently increases retention and graduation
outcomes, and conversely, a weak sense of belonging is predictive of lower GPA and retention
(Mattson, 2014). Socioeconomic status has been shown to correlate to a weaker sense of
belonging (Reason, 2009; Walpole, 2003), therefore it stands to reason that Pell-eligible students,

38
who are among the financially neediest students at an institution, would be most sensitive to
these effects.
Student life stressors. A final dimension of socioeconomic status that the literature has
shown to be indicative of academic risk are the myriad of life stressors faced by college students.
These include a unique set of academic, social and emotional stresses that are generated by the
collegiate environment (DeBerard, Spielmans, & Julka, 2012; Karimshah et al., 2013; Walpole,
2003). These stressors have been found to be impactful along multiple dimensions, including
frequency and intensity, meaning that both a student’s total number of stressors, as well as their
ability to cope with them, affects a student’s overall risk level (Langhout, Drake, & Roselli,
2009; Reason, 2009). These findings seem to indicate that students from low socioeconomic
backgrounds face increased risk along multiple fronts. The mere lack of resources provides
additional opportunities for stress occurrences within their environment and makes them more
likely to be in need of financial support, and their relative lack of experiencing navigating
financial transactions is likely to increase the perceived intensity of these events due to their lack
of familiarity. Combined, these effects prove powerful enough to make a measurable impact on
these students’ ability to persist and graduate (DeBerard, Spielmans, & Julka, 2004; Karimshah
et al., 2013). These effects speak to the organizational environment in which students find
themselves, and ultimately to how organizational influences impact academically at-risk
students’ ability to persist.
Knowledge, Motivation and Organizational Influences Framework
In order to provide a unifying conceptual framework for the study topics, this study
employed a gap analysis framework as enumerated by Clark and Estes (2008). Clark and Estes
(2008) provide a systematic, analytic framework that seeks to clarify organizational and

39
stakeholder performance goals and identifies the gap between the actual performance level and
the performance goal. Once the gap is identified, this framework specifically examines the
stakeholder knowledge, motivation and organizational influences that may impact performance
gaps (Clark & Estes, 2008). Knowledge and skills identified by Krathwohl (2002) are divided
into four types: (a) factual; (b) conceptual; (c) procedural; and (d) metacognitive.
These knowledge types are used to determine if stakeholders know how to achieve a
performance goal. Motivation influences include the choice to consider goal achievement,
continuing to work towards the goal and the mental effort to accomplish the goal (Clark & Estes,
2008; Rueda, 2011). Motivational principles such as self-efficacy, attributions, values and goals
can be considered when analyzing the performance gap (Rueda, 2011). Finally, organizational
influences on stakeholder performance to consider may include work processes, resources and
institutional culture (Clark & Estes, 2008). Additionally, cultural settings and cultural models are
both examined in an effort to understand workplace culture (Gallimore and Goldenberg, 2001).
Each of these elements of Clark and Estes’ (2008) gap analysis will be addressed below
in terms of the academically at-risk students’ knowledge, motivation and organizational needs to
meet their stakeholder performance goal of persisting into their sophomore year. The first section
will be a discussion of assumed influences on the stakeholder performance goal in the context of
knowledge and skills. Next, assumed influences on the attainment of the stakeholder goal from
the perspective of motivation will be considered. Finally, assumed organizational influences on
achievement of the stakeholder goal will be explored. Each of these assumed stakeholder
knowledge, motivation and organizational influences on performance will then be examined
through the methodology discussed in Chapter Three.

40
Stakeholder Knowledge, Motivation, and Organizational Influences
Knowledge and Skills
In this section, literature regarding the knowledge and skills necessary for at-risk
freshmen will be examined in order to determine the pertinent components related to the
stakeholder goal of retention of Pell-eligible, academically at-risk freshmen into sophomore year.
Student persistence is a performance problem whose root cause can be attributed to some
combination of failures along the dimensions of knowledge, motivation or organizational assets
(Clark & Estes, 2008). The knowledge dimension has been shown to be a key component of
learning, and ultimately, the ability for the learner to transfer that learning to new contexts
(Blume et al., 2010). This section seeks to explore the existing literature related to the
knowledge dimension of learning, and specifically, knowledge requisites related to the
stakeholder goal of retention of academically at-risk freshmen.
As a performance problem, retention of Pell-eligible, academically at-risk freshmen is
particularly sensitive to the knowledge dimension in that the stakeholder group is subject to the
knowledge dimension along two separate axes. The first axis is related to academic integration,
and the requisite knowledge necessary for that performance problem. The second axis is related
more generally to student life and social integration, and the requisite knowledge necessary for
that performance problem. Academically at-risk freshmen are in danger of failure along both
axes, and failure along either axis would result in student departure (Tinto, 1993). The
complexity and inter-relatedness of these axes necessitates a solid foundation in the prerequisite
knowledge necessary for execution of the stakeholder goal. Through an examination of the
literature on at-risk freshman retention, various components of knowledge dimension will be
identified and categorized based on the knowledge framework offered by Krathwohl (2002).

41
These categorizations will situate each requisite knowledge component as declarative, procedural
or metacognitive (Krathwohl, 2002).
Declarative knowledge is defined as discrete bits of information, either tangible factual
knowledge or intangible conceptual knowledge (Krathwohl, 2002). These discrete bits of
knowledge could be factual, such as the location of one’s classroom, or more conceptual, such as
methods for studying for an exam. In either case, declarative knowledge is discrete and
assessable (Krathwohl, 2002). Procedural knowledge represents a collection of bits of
declarative knowledge, with the added feature of the collection itself containing meaning.
Procedural knowledge would include things such as knowing how to make an advising
appointment or knowing where to find a social event. This knowledge may contain a number of
factual components; however, the collection contains greater meaning that the composite parts in
that the sequencing of the components produces a unique outcome. Finally, metacognitive
knowledge represents the epistemological dimension, as metacognitive knowledge requires not
only the foundational declarative or procedural knowledge, but also requires a reflective
component that examines the nature of that knowledge (Krathwohl, 2002).
It is important to note that these types of knowledge build upon one another, existence of
procedural knowledge being impossible without factual, existence of metacognitive knowledge
being impossible without procedural and factual. These distinctions are intended to assist with
framing the stakeholder’s knowledge, selecting assessment instruments and, ultimately, assisting
in remediation strategy selection. Misalignment of approach has been shown to significantly
impact the efficacy of reform programs (Rueda, 2011), therefore, proper categorization of
knowledge is necessary to increase the likelihood of success.

42
Knowledge Influences
Persistence and retention of Pell-eligible, academically at-risk students is a complex,
multi-faceted problem that many colleges and universities face, and one whose solutions are
equally complex and multi-faceted (Jones & Watson, 1990). In an effort to provide a framework
for examining the knowledge influences affecting academically at-risk freshmen, this study will
employ Vincent Tinto’s Model of Student Departure (1993) to categorize and enumerate the
different dimensions of student departure. Tinto’s model focuses on two primary factors, goal
commitment and institutional commitment. Goal commitment, in this context, refers to the
degree to which the student is committed to academic success. Similarly, institutional
commitment refers to the degree to which the student is committed to the particular institution
they are attending. Additionally, students’ goal and institutional commitments are further
affected by their prior knowledge, self and family attributes, and experiences that occur outside
of the college setting (Tinto, 1993).
For the purpose of this study, pre-college characteristics will be largely ignored except to
the extent that they impact and inform processes occurring during the students’ college
experience. In order to examine the knowledge influences at work in the stakeholder group, it is
important to elucidate the processes affecting the stakeholder group. Tinto (1993) describes two
primary processes, each one affecting a different commitment identified in the model. Goal
commitment and institutional commitments are strengthened or weakened through a process
Tinto describes as academic and social integration. While all college freshmen are subject to
both processes, the stakeholder group in this study is identified as academically at-risk, so
particular attention will be paid to the academic integration processes at work.

43
Academic integration refers to both formal academic performance and informal faculty
and staff interactions (Tinto, 1993). In order for academically at-risk freshmen to be successful
in persisting to their sophomore year, they must know and understand the knowledge influences
that affect their academic integration. In order to achieve this integration, students must know
and understand how to access their academic capital in order to facilitate integration into the
collegiate environment.
Knowledge influence 1 – procedural. Pell-eligible, academically at-risk freshmen must
understand how to navigate institutional systems. In the context of this study, navigation of
institutional systems relates to students’ procedural knowledge (Krathwohl, 2002) of how to
access institutional resources as it relates to their ability to interact with institutional processes.
A number of researchers have found that students’ ability to navigate institutional systems is
predictive of academic success (Ishitani, 2006; Mattson, 2014; Yazedjian et al., 2008). Research
in the field has also indicated that the level of academic capital a student brings in this domain is
predictive of the overall capability a student will exhibit along this knowledge dimension
(Mattson, 2014; Walpole, 2003).
Knowledge influence 2 – procedural. Pell-eligible, academically at-risk freshmen must
be able to create systems of support. Systems of support can take many forms for college
students, including institutional resources dedicated to student support, familial support, and
even community support (Mattson, 2014; Schaffling et al., 2018). According the Krathwohl
(2002), this procedural knowledge must be applied at the highest level of Bloom’s taxonomy
(Bloom, 1956) through the application of this knowledge to create systems of support. Research
on academic capital has shown that the strength and accessibility of these systems of support are
predictive of students’ ability to persist in the collegiate environment (Linnehan, Weer, &

44
Stonely, 2011; Mattson, 2014; Schaffling et al., 2018). These support systems offer a variety of
support mechanisms, from academic tutoring to motivational support and trustworthy
information, but each system serves a role in providing resources to assist in confronting
challenges encountered during the collegiate experience.
Knowledge influence 3 – metacognitive. Pell-eligible, academically at-risk freshmen
should understand their financial circumstances. While cost concerns are a factor for many
college students, Pell-eligible students are particularly susceptible to the consequences of their
financial circumstances and therefore should be particularly aware of their situation. Knowledge
of these requirements would be categorized as metacognitive knowledge (Krathwohl, 2002),
which would indicate that this knowledge requires that students understand the ramifications of
their financial circumstances, however, studies show many college students are not
knowledgeable of their financial aid package, its maintenance requirements, or how those
requirements impact their ability to stay in school (Brock, 2010).
Because Pell-eligible students in the academically at-risk category are also identified as
low socioeconomic status (Heisserer & Parette, 2002), maintaining their financial aid package,
and the academic requirements that come with it, is as important to maintaining their status at the
institution as is meeting the university’s academic requirements. These layered requirements
provide an additional potential failure point, and as such, it is critical that students understand
their financial situation and how it can impact their ability to persist at the institution. These
layered requirements require that students not only have knowledge of the financial facts of their
situation, but also the knowledge of how to plan for the impact of those facts on outcomes.
Due to the complexity and individualized nature of students’ financial aid packages, it is
important for the institution to coordinate services between financial aid counselors, academic

45
advisers and academic coaches such that correct information is transferred to the student.
Studies have shown that effective coordination of student services has a positive impact on
student retention (Tinto, 2004). This coordination, and subsequent knowledge transfer, is of
particular import to academically at-risk freshmen due to the overrepresentation of low
socioeconomic status among the stakeholder group (Swail, 2004).
Table 1
Knowledge Influences, Types, and Assessments for Knowledge Gap Analysis

Table 1 highlights the organizational mission and goal as well as the stakeholder goal.
Additionally, the identified influencers, types, and assessments provide a summary of the
Organizational Mission
Axios University is a public comprehensive liberal arts institution that seeks to develop
knowledgeable and productive students.
Organizational Global Goal
AU will connect programs and services key to timely degree completion and postgraduate
preparation to create one service superstructure to ensure student success.
Stakeholder Goal
Retention of 50% of Pell-eligible, academically at-risk freshmen into their sophomore year.
Knowledge Influence Knowledge Type Knowledge Influence
Assessment
Pell-eligible, academically at-
risk freshmen must
understand how to navigate
institutional systems.
Procedural Knowledge Assessment 1
(see Appendix A)
Pell-eligible, academically at-
risk freshmen must be able to
create systems of support.
Procedural Knowledge Assessment 2
(see Appendix A)
Pell-eligible, academically at-
risk freshmen should
understand their financial
circumstances.
Metacognitive Knowledge Assessment 3
(see Appendix A)

46
knowledge related factors affecting the retention of academically, at-risk freshmen at Axios
University.
Motivation
In conjunction with knowledge influences, motivational influences form key factors
contributing to academic performance problems. This section will review the literature related to
motivational theories and influences as they pertain to retention of academically at-risk
freshmen. It is important to note here that the motivational theories described in this section
represent the dominate theories in the literature and are not intended to fully encompass all
motivational theories at work in the domain, but rather seek to provide a framework through
which the stakeholder’s performance problem can be evaluated. These theories seek to explain
individuals’ actions as they relate to active choice, persistence, and mental effort in furtherance
of their performance goals (Clark & Estes, 2008, p. 44). Each theory offers some research-
backed insights into the mechanisms affecting Pell-eligible, academically at-risk freshmen’s
motivation for achieving their performance goal, namely, persistence into their sophomore year.
Throughout the literature related to the success and retention of academically at-risk
freshmen, there exists a focus on gap analysis whereby those academically at-risk students are
evaluated against a range of theories to assess what is missing in unsuccessful students as
compared to their peers. The existing research is underpinned by a crucial assumption that goes
largely unnamed, which is the assumption that students admitted to an institution are equally
likely to succeed. Significant research attention has been given to motivational factors related to
performance (Anderman & Anderman, 2006; Bandura, 2000; Eccles, 2006), which would seem
to support the notion that researchers see motivational factors as the most likely culprit for non-
performance in this stakeholder group. Among the motivational theories prevalent in the

47
literature, expectancy-value theory provides an explanation of active choice within the
stakeholder group (Wigfield & Cambria, 2010). Active choice relates to an individual’s decision
to begin doing something (Clark & Estes, 2008), and expectancy-value theory speaks to the
motivational effects one’s valuation of the expected outcome plays on active choice (Bandura,
2000). In evaluating persistence, attribution theory has been linked to students’ choices as they
pertain continuing on a course of action (Anderman & Anderman, 2006). Persistence speaks to
an individual’s ability to continue until one’s objective is achieved (Clark & Estes, 2008), and
attribution theory provides some insights into the motivational effects of locus of control upon
persistence. In the research on motivation as it relates to mental effort, self-efficacy theory has
shown promise in predicting student behavior (Bandura, 2000). An individual’s assessment of
their ability to be successful in the future plays a motivating factor in their choice to apply
mental effort in pursuit of their goals. In total, these theories seek to explain the motivational
influences that affect stakeholder behavior and elucidate a course of action.
Motivation influence 1 – expectancy-value theory. Pell-eligible, academically at-risk
freshmen should understand the value of outcomes emanating from their academic behaviors
such as class attendance and studying. The first hurdle of any motivation-related problem is the
task of beginning. The literature on motivational theory refers to this as active choice, namely,
the decision on the part of the subject to begin a task (Clark & Estes, 2008). In the context of
academically at-risk freshmen, active choice plays an integral role as many failure points for this
stakeholder group revolve around students failing to engage in critical activities such as class
attendance, studying, homework completion, and help-seeking (Kitsantas, Winsler, & Huie,
2008; Stripling, Roberts, & Israel, 2013). Expectancy-value theory provides a theoretical
framework upon which an assessment of these non-actions can be evaluated. Wigfield and

48
Eccles (2000) have done significant research in this area, and their results lead one to the
conclusion that students’ decision on whether or not to begin a task related to their academic
success rests on two primary determinations, as outlined in expectancy-value theory as first
articulated by Vroom (1964).
The first of these determinations is a subjective valuation of the outcome of the task. In
an academic context, this is defined as academic success in the course. This valuation is further
broken down along the theoretical lines of attainment value, intrinsic value, utility and cost
(Wigfield & Cambria, 2010). In evaluating academically at-risk students, these subdomains of
value are of particular interest as individual valuations along each line may vary from student to
student. In the context of academic success, these valuations closely hew towards students’
evaluation of the importance of a good grade, how much they enjoy class, how they see a good
grade furthering broader goals, and how difficult it is to achieve said result. Each of these are
testable hypotheses within the stakeholder group and may yield significant insights to direct the
institutional response.
Following a valuation determination, students continue their internal deliberations to
develop a likelihood of achieving their desired outcome. This determination, described in the
literature as an expectancy valuation (Eccles, 2006), is the student’s measure of how likely the
outcome is to occur. There is significant literature (Eccles, 2006; Friedman & Mandel, 2010;
Wigfield & Cambria, 2010) on the litany of contributing factors that go into individuals’
assessments of the likelihood of an event, which include past experiences, self-perceptions, and
action-control beliefs (Pekrun, 1993).
Taken together, individual students are making constant evaluations on the value of
academic success and the likelihood that it will occur. Based on those evaluations, students are

49
making determinations on whether or not to engage in the task-specific behaviors associated with
academic success. These determinations are one target for academic coaches as they seek to
remediate stakeholder performance in the upcoming semester. If it proves possible to change
students’ valuations, then it may be possible to engage students’ intrinsic motivation for
academic success. As such, the tenets of expectancy-value theory should be considered when
constructing interventions for academically at-risk freshmen.
Motivation influence 2 – attribution theory. Pell-eligible, academically at-risk
freshmen should believe they have control over their own academic outcomes. Due to the
intentional definition of probationary status related to academically at-risk freshmen, there exists
commonality among the stakeholder group which appears at a significantly higher rate, namely
an experience of academic failure. Because this commonality exists among the stakeholder
group, it becomes important to address the reality of that failure and its impact on future
behavior. As discussed in the previous section, past experiences contribute to evaluations of
potential future outcomes. In the prior section, the motivational factor of active choice was
discussed as it relates to academic success behaviors. Moving past active choice, it becomes
important to address the issue of persistence among Pell-eligible, academically at-risk freshmen.
Due to the aforementioned academic failure, it is more likely for this stakeholder group to resist
beginning academic success behaviors, and if begun, to persist in those behaviors to their
conclusion.
In this context, attribution theory can provide some significant insights to possible
interventions. Attribution theory, as first described by Heider (1958), posits three dimensions of
attribution: locus of control, stability, and controllability. Bernard Weiner (1979) further
developed this theory in the context of education. This work provides some particularly valuable

50
insights when applied to Pell-eligible, academically at-risk freshmen, due to their definitional
status as not currently academically successful. When evaluating persistence issues related to
academic non-performance, the dimensions of attribution theory become particularly insightful.
In his research, Weiner (1985) found that the particular attribution (cause) of non-performance
was not as important as the individual dimensional assessments. Weiner (1985, p. 559) found
that motivational predictors included the respondent’s evaluation that circumstances involved an
internal locus of control, unstable conditions, and controllable outcomes. If a respondent
believed that their academic outcome was related to circumstances that could change, that were
controllable, and that they could control, then the respondent was much more likely to be
motivated to persist in those tasks in the future (Anderman & Anderman, 2006).
These results are particularly important for this stakeholder group, as they are statistically
more likely to hold beliefs that posit an external locus of control, a fatalism regarding the
changeability of circumstances, and a lack of belief in their ability to control outcomes (Graham,
1997). Fortunately, there exists substantive literature on processes and procedures around
attribution re-training (Kallenbach & Zafft, 2004), which seeks to modify maladaptive
attributions and re-focus respondents’ attributions towards those more conducive to academic
success. As such, existing literature in this area is of substantive value to Axios University and
the Academic Coaching Center as it seeks to modify persistence behaviors in the stakeholder
group in an effort to alter the current academic trajectory for its at-risk freshman students.
Motivation influence 3 – self-efficacy theory. Pell-eligible, academically at-risk
freshmen should have confidence in their ability to succeed academically. In the context of
motivational theory, previous sections have addressed active choice and persistence behaviors,
however, of equal value are questions of mental effort. While active choice and persistence are

51
externally observable, mental effort exists more in the domain of the students themselves. As
such, university administrators must use corollary assessments to determine the likelihood of a
stakeholder member putting forth the mental effort necessary for academic success. In service of
this goal, self-efficacy theory may be able to provide a sufficient framework for assessing that
likelihood, and potentially increasing it as well. In the context of educational success, Albert
Bandura (1977, 2000) has done significant work with respect to self-efficacy and its role in
predicting and promoting student success. At its essence, self-efficacy is an individual’s belief
that they can execute a course of action to obtain a desired result. As discussed in the previous
section, the controllability of an outcome determines whether or not an individual believes there
exists a course of action that can affect an outcome, however, it is the individual’s self-efficacy
beliefs that drive their contribution of mental effort towards that course of action.
Fortunately, among the various beliefs that exist within an individual’s schema, self-
efficacy has been shown to be malleable, at least in some respects (Bean & Eaton, 2001).
Additionally, Carol Dweck (2010, p. 26) has contributed substantially to the literature on self-
efficacy, showing that a growth mindset is not only supportive of academic success, but can also
be cultivated by learning organizations through specific interventions. This notion of a growth
mindset is particularly relevant to motivational aspects of mental effort, as those with a growth
mindset tend to put forth sufficient mental effort so as not to mitigate the learning that comes
from failure. Without sufficient mental effort, it becomes impossible to assess the cause of an
academic failure and change course in future iterations. As such, mental effort becomes more of
a prerequisite for the truly valued outcome, academic success.
Taken in total, motivational influences related to the academic success of at-risk
freshmen are likely to be key contributing factors to any subsequent interventions. There exists

52
an assumption among many higher education administrators that academic admission criteria
adequately predict student success, and as such, non-performance must likely be due to non-
academic factors such as motivation. While a testing of this assumption is outside the scope of
this study, further research should be conducted to assess whether or not additional, non-
academic factors such as motivation should be assessed as admission criteria, alongside existing
academic criteria.
Table 2
Motivation Influences, Types, and Assessments for Knowledge Gap Analysis

Table 2 highlights the organizational mission and goal as well as the stakeholder goal.
Additionally, the identified influencers, types, and assessments provides a summary of the
Organizational Mission
Axios University is a public comprehensive liberal arts institution that seeks to develop
knowledgeable and productive students.
Organizational Global Goal
AU will connect programs and services key to timely degree completion and postgraduate
preparation to create one service superstructure to ensure student success.
Stakeholder Goal
Retention of 50% of Pell-eligible, academically at-risk freshmen into their sophomore year.
Assumed Motivation Influences Motivational Influence Assessment
Pell-eligible, academically at-risk freshmen
should understand the value of outcomes
emanating from their academic behaviors such
as class attendance and studying.
Motivation Assessment 1 (see Appendix A)
Pell-eligible, academically at-risk freshmen
should believe they have control over their own
academic outcomes.
Motivation Assessment 2 (see Appendix A)
Pell-eligible, academically at-risk freshmen
should have confidence in their ability to
succeed academically.
Motivation Assessment 3 (see Appendix A)

53
motivation related factors affecting the retention of academically, at-risk freshmen at Axios
University.
Organization
In order to provide a schema for classifying the myriad of environmental influences, this
study classifies organizational influences into two categories, either cultural models or cultural
settings. This schema is derived from the work of Ed Schein (2017) and defines cultural models
as those invisible things that make up an organizational environment, whereas cultural settings
are those observable actions or physical things that make up the organizational environment.
Cultural models would include only indirectly observable things like group norms, habits of
thinking, shared meaning, and identity (Schein, 2017). Cultural settings would include directly
observable things like employee behaviors, physical layout, training programs, and even formal
mission or values (Schein, 2017). The purpose of this classification schema is to clarify which
organizational influences can be directly assessed and which will need indirect assessment via
student survey.
Examining organizational influences provides benefits to practitioners as it provides
actionable information to the organization on key knowledge and motivation gaps in a
stakeholder group. This information allows organizations to develop specific interventions,
policies, settings and practices to address those gaps. By connecting educational research with
the potential for purposeful changes in the organizational environment this study seeks to
continue what Gallimore and Goldenberg (2001) perceived to be a vital function of educational
research.
Key organizational influences. In order to focus the examination of organizational
influences, a review of the literature on academically at-risk freshmen populations identified

54
several key knowledge and motivation gaps that may be affected by the campus environment.
These key organizational influences represent cultural models present at the institution and are
based on unwritten and unspoken norms that exist at the institution. In addition to these cultural
models, there exist a number of observable cultural settings that also affect students’ knowledge
and motivation gaps.
Cultural models. Looking first at cultural models present in the campus environment, a
review of the research literature on student self-efficacy (Bandura, 1977, 2000), growth mindset
(Dweck, 2010), and attribution (Anderman & Anderman, 2006; Weiner, 1985) provide evidence
that the campus culture has effects on student motivation. Numerous studies have shown that
academically at-risk populations are more likely to suffer from a lack of a sense of belonging
(Mattson, 2014) and feelings of being an imposter (Davis, 2010). These emotions can erode a
student’s belief in their own self-efficacy, thus negatively affecting their motivation to persist in
the face of difficulty. The institution needs to value the impact of sense of belonging has on
students’ motivation.
Cultural settings. In addition to campus culture, there are a number of specific, observable
environmental factors that emanate from students’ knowledge and motivation. These various
environmental circumstances produce stressor events for students, and the frequency and
intensity of these stressor events subsequently influence students’ mental and emotional state in
ways that negatively impact their academic success (Karimshah et al., 2013; Langhout, Drake &
Roselli, 2009). Assessing the frequency and intensity of stressor events over the course of the
collegiate experience has been shown to help institutions tailor their organizational decision-
making and resource allocation to those events within their power to control, and to also provide
additional institutional supports to students experiencing events outside the institution’s control.

55
For Pell-eligible, academically at-risk students, research shows that these students are more
likely to come academically unprepared (Davis, 2010) and more likely to have financial and
housing concerns (Peralta & Klonowski, 2017). These additional knowledge and motivation
gaps, combined with the general stressor events all students are subjected to, generate a host of
matters that may prove stressful to Pell-eligible, academically at-risk freshmen (Karimshah et al.,
2013; Langhout, Drake, & Roselli, 2009). The institution needs to provide resources to address
student life stressors.
Table 3 highlights the organizational mission and goal as well as the stakeholder goal.
Additionally, the identified influencers, types, and assessments provide a summary of the
organization related factors affecting the retention of academically, at-risk freshmen at Axios
University.
Table 3
Organizational Influences, Types, and Assessments for Knowledge Gap Analysis

Organizational Mission
Axios University is a public comprehensive liberal arts institution that seeks to develop
knowledgeable and productive students.
Organizational Global Goal
AU will connect programs and services key to timely degree completion and postgraduate
preparation to create one service superstructure to ensure student success.
Stakeholder Goal
Retention of 50% of Pell-eligible, academically at-risk freshmen into their sophomore year.
Assumed Motivation Influences Motivational Influence Assessment
The institution needs to value the impact of
sense of belonging has on student motivation
(Cultural Models).
Psychological Sense of School Membership
Scale-adapted (see Appendix B)
The institution needs to provide resources to
address student life stressors (Cultural Settings).
Student Life Stressor Scale-adapted (see
Appendix C)

56
Conceptual Framework: Knowledge, Motivation, and Organizational Influences
In an effort to provide focus, the study uses Clark and Estes’ (2008) gap analysis to
provide a conceptual framework that targets specific knowledge, motivation, and organizational
influences that affect the problem being researched. The conceptual framework seeks to convey
the system of concepts, assumptions, beliefs and expectations that informs the specific research
questions (Maxwell, 2013). In doing so, it also seeks to elucidate the researcher’s understanding
of the problem space, including the conceptual mechanics at work, the anticipated interaction
effects among variables, and even the philosophical worldview of the researcher. While
knowledge, motivation, and organizational influences are enumerated separately for the purpose
of research design, it should be noted that these variables are not perceived to be acting in
isolation. The conceptual framework is intended to provide a roadmap for how these interactions
might occur, and how this framework has affected all aspects of the research design (Merriam &
Tisdell, 2016).
For this study, the base conceptual framework used will be Clark and Estes (2008) gap
analysis. The gap analysis identifies knowledge, motivation, and organizational influences that
have been shown to influence outcomes. This study’s identified knowledge, motivation, and
organizational influences are informed by the research of Vincent Tinto (1988, 1993, 2004) and
his work on student departure theory, which establishes persistence as a function of student
commitment acted upon by the system to foster academic and social integration. In his work,
Tinto (1988) describes a number of pre-college markers such as family background, individual
attributes, and pre-college schooling as factors contributing to individual students’ goal and
institutional commitments. These commitments then manifest within the university setting in the
form of academic and social integration into the academic system, and the degree of these

57
integrations lead to an individual student’s likelihood to persist (Tinto, 1993). It is this base
theoretical framework that provides the base concepts upon which this study is constructed. The
idea that individual students’ knowledge, motivation, and organizational influences affects either
their academic or social integration forms the foundational assumption of the study.
Building upon these foundational concepts, the study’s conceptual framework then
focuses more specifically on first-generation, Pell-eligible probationary students, the stakeholder
group that is the subject of the study. Research on this specific stakeholder group has shown
challenges related to pre-college preparation, study skills, reliance on financial aid, sense of
belonging and cultural capital (Browman et al., 2017; Davis, 2010; Linnehan, Weer, & Stonely,
2011; Mattson, 2014).
The conceptual framework, illustrated in Figure 3, details the specific knowledge,
motivation, and organizational influences the study seeks to describe. The specific knowledge
influences, such as the stakeholder’s knowledge of success criteria, academic and social
resources, and study skills are all drawn directly from existing research on the stakeholder group.
Previous research has shown that first-generation, Pell-eligible students exhibit lower knowledge
levels related to study skills, as well as the cultural capital necessary to know where institutional
support exists to mitigate these deficiencies (Davis, 2010; Mattson, 2014). Additional research
has highlighted a number of motivational issues present in the stakeholder group related to
expectancy value (Wigfield & Eccles, 2000), self-efficacy (Bandura, 1977, 2000), and attribution
(Weiner, 1985). Expectancy-value theory, as contextualized by Wigfield and Eccles (2000),
provides a number of potential predictors of successful academic behaviors such as class
attendance and studying. Albert Bandura’s work (1977, 2000) related to academic self-efficacy
informs the study’s examination of stakeholders’ beliefs that they can affect a course of action to

58
achieve a desired result, namely continued persistence at the institution. Weiner’s work (1985)
on attribution theory in educational contexts provides a framework for understanding how the
stakeholders might take in the negative feedback associated with their placement on probation
and how that feedback might affect future behavior. Within the context of these individual
spheres of activity there exists the university, the organizational context that seeks to construct
an educational environment designed to support student success. This organizational context is
experienced differently by different constituents, and this study seeks to identify the cultural
models and settings that impact the persistence of first-generation, Pell-eligible students. It
should be noted that while each of these knowledge, motivation, and organizational influences
have been listed independently, they are inextricably linked in their impacts upon each other.

59

Figure 3. Conceptual Framework
The conceptual framework used in this study situates the students’ knowledge and
motivation influences within the context of the cultural models and settings present in the
collegiate environment.
A cultural setting, student life stressors, can affect a student’s behavioral choices due to
their lack of knowledge of the importance of those choices, and the consequences of those
choices can then affect their motivation to persist beyond that failure. Similarly, motivational

60
gaps can affect knowledge-seeking behaviors, which can be further impacted by cultural models
held by students. Each of these factors exist within the ecosystem that is the university
environment, therefore it is not possible to completely isolate the variables such that they are
completely independent from each other. This conceptual framework seeks to provide an
overarching lens through which these variables, and their interactions between and among each
other, can be examined in an effort to improve the persistence of Pell-eligible, academically at-
risk students at Axios University.
Conclusion
This study seeks to evaluate the efficacy of academic coaching in the context of Pell-
eligible, academically at-risk students. The current literature on this population indicates that
their at-risk status emanates from deficits in academic capital and a sense of belonging, as well
as increased frequency and intensity of student life stressors. From this existing literature
presented in Chapter 2, this study seeks to identify key knowledge, motivation, and
organizational influences that impact Pell-eligible, academically at-risk freshmen. These
influences include knowledge of institutional and support systems, motivation related to
expectancy-value, attribution, and self-efficacy, cultural models related to students’ sense of
belonging, and cultural settings related to students’ experience with student life stressors.
Students experience these knowledge, motivation, and organizational influences within the
context of the collegiate experience, therefore outcomes in one domain have impacts upon other
domains, ultimately leading to either persistence or retention loss. Chapter 3 will present this
study’s methodological approach to assessing and analyzing these influences.

61
CHAPTER THREE: METHODS
This study was designed to evaluate the efficacy of an academic coaching intervention
targeting Pell-eligible, academically at-risk freshmen. The study targeted three main concepts,
shown in the literature, which effect this population. Those include academic capital, sense of
belonging, and student life stressors. These psychosocial indicators have been shown to
influence Pell-eligible, academically at-risk students’ knowledge and motivation as it relates to
their ability to persist to sophomore year. The intention of this chapter is to provide detail
describing the stakeholder group of focus, the methods and manner in which the identified
indicators will be assessed and interpreted, and to explore the limitations of the study.
Participating Stakeholders
For the purpose of this study, the stakeholder group of focus was Pell-eligible,
academically at-risk freshmen participating in the academic coaching intervention during their
Spring semester at the institution. All students at the institution that fit into this category were
required to participate in the academic coaching intervention. The intent of the study was to
evaluate the effects of this intervention on those Pell-eligible students. For the study itself, all
academically at-risk freshmen were selected to participate in the data collection, yielding a
census sampling of the stakeholder group. The intent of selecting all participants in the academic
coaching intervention was to mitigate any deleterious effects of specifically targeting only Pell-
eligible, academically at-risk freshmen. Due to the electronic nature of survey delivery, it was
decided that the potential negative effects of explaining why some students were being surveyed
and some not was more costly than delivering the survey to all participants. The primary reason
for using census sampling was to provide as much validity to the results as is possible in this
context. Due to their participation in the academic coaching program, all academically at-risk

62
students were accessible and could be surveyed, and the quantity of results does not inhibit
analysis of Pell-eligible participants. It should be noted that Pell-eligible, academically at-risk
freshmen are not the only participants in the academic coaching program but are the stakeholder
group of focus for this study.
Survey Sampling Criteria and Rationale
For this study, the identified survey instruments were delivered to a census sample of
Pell-eligible, academically at-risk freshmen. The sampling rationale is predicated on there being
between 100 and 150 qualifying students in a given cohort, and that this number of students can
reasonably be given an electronic survey without significant cost or intrusion. By soliciting
responses from the total population, results can eliminate one potential source of error introduced
by sampling (Merriam & Tisdell, 2016).
Survey Sampling (Recruitment) Strategy and Rationale
Survey administration was attempted at the beginning of the academic coaching program
during the month of January, and again at the conclusion of the academic coaching program
during the month of May. Surveys were distributed via the Snap Survey web-based survey
administration tool and went to all participants in the academic coaching program. The
distribution list was established at the commencement of the program, and post-program surveys
were attempted delivery regardless of the student’s enrollment status at the time of survey. In
all, it is estimated that there were between 100 and 150 Pell-eligible, academically at-risk
freshmen participating in the academic coaching program (AU, 2018). The intention of this
study is to evaluate the academic coaching intervention and determine, to what degree, the
program was effective in affecting students’ knowledge, motivation, and/or organizational
influences as they relate to persistence into sophomore year. The intention of surveying the

63
entire population is to provide a large enough sample to determine if known factors of retention
loss among this population are impacted by the academic coaching intervention, and if those
effects are predictive of retention.
The intention of the survey methodology was to capture assessments of knowledge,
motivation and organizational influence levels. This assessment was intended to provide
multiple dimensions shown in research to be relevant in the student departure decision (Tinto,
2004). In conjunction with the assessment data, institutional data on retention and demographics
was incorporated to tie assessment data to retention outcomes to determine any correlations.
Data Collection and Instrumentation
The academic coaching intervention implemented at Axios University targeted
academically at-risk freshmen, with the study concentrating its focus on the Pell-eligible
students, a subset of academically at-risk freshmen. In the context of this focus, the study seeks
to ascertain the knowledge, motivation, and organizational influences that affects Pell-eligible,
academically at-risk freshmen’s persistence into sophomore year. Through a review of the
current literature in the field, three main drivers of retention in this population emerged. The
drivers include academic capital, a specialized form of cultural capital present in a university
context; students’ sense of belonging, as well as the various stressors that affect their academic
performance. With respect to stressors, while many stressors have external origins, for the
purpose of this study the focus will be the impact of those stressors on academic outcomes as
reported by the students themselves.
This study used knowledge and motivation assessments developed from academic
success literature and specifically targeted questions associated with identified knowledge and
motivation influences. Additionally, organizational influences were assessed using existing

64
instruments, adapted for the context of this study, to determine sense of belonging and student
life stressor responses. Academic success is a robust research field, and as such, the instruments
selected have been developed by other researchers and externally validated. These instruments
include the College Student Stress Scale (Feldt, 2008), adapted to target specific stressor
categories and assess institutional resource utilization, and the Psychological Sense of School
Membership Scale (Pittman & Richmond, 2008). The College Student Stress Scale contains
numerous research-derived stressor areas shown to relate to academic success and has been
adapted to further inquire about organizational supports in response to those stressor areas. The
Psychological Sense of School Membership Scale is intended to represent the organization’s
effectiveness in creating a sense of community in the students’ minds. The degree to which that
sense of community is felt by the stakeholder was intended to serve as a proxy for organizational
influences affecting academic performance.
Surveys
Survey instruments. The survey instrument was a concatenated version of the
developed knowledge and motivation assessments, the Psychological Sense of School
Membership Scale (Pittman & Richmond, 2008) and the College Student Stress Scale (Feldt,
2008), which was adapted to target specific stressor categories and determine institutional
resource utilization. Assessment items were broken up into sections, with each section
correlating to a specific knowledge, motivation, or organizational influence assessment.
The knowledge and motivation assessments are developed from research-based factors
associated with academic success. The survey items are measured on a 6-point Likert interval
scale, spanning Strongly Disagree to Strongly Agree, with no option for ambivalence. The
survey items included a number of questions that sought to pinpoint the knowledge and

65
motivation influences affecting collegiate performance. These survey questions include
knowledge assessments such as “I am aware of the resources at my school that can help me be a
more successful student.” as well as motivation assessments such as “Going to class every day is
important as it affects the outcome of my final grade.” In total, the knowledge and motivation
assessments sought to establish a student’s “starting point” prior to any intervention on the part
of the institution.
The Psychological Sense of School Membership Scale by Pittman and Richmond (2008)
is an adapted version of the widely used PSSM scale originally developed by Carol Goodenow
(1993). The scale was originally intended to assess adolescents’ perceived sense of belonging
and has been adapted by Pittman and Richmond to a collegiate environment. The scale is an 18-
item instrument rated on a three-point Likert scale ranging between Never, Sometimes, and
Always. The instrument items were modified from Pittman and Richmond’s scale, replacing
references to their institution, University of North Carolina-Greensboro, with references to Axios
University. The scale is designed to assess students’ sense of belonging and affiliation with the
school community and uses questions such as “I feel like a real part of AU” and “I am treated
with as much respect as other students.” to assess the student’s sense of belonging. The
assessment items from PSSM were included in the overall survey instrument items and
administered along with the other assessment instruments.
The College Student Stress Scale (Feldt, 2008) is a 6-item instrument that measures
levels of stress and anxiety across a number of research-based dimensions of life stress,
including family, finances, housing, academics, and other areas. This instrument was adapted to
rate on a never/sometimes/often scale with additional questioning presented relating to campus
resource use if the respondent experienced the stressor. The survey does not address frequency

66
or intensity of individual stressor events, but rather focuses on cumulative impact felt by the
student. Question items in this scale include “felt anxious or distressed about financial matters”
and “felt anxious or distressed about academic matters.” These survey items, along with the
other four items, are intended to assess frequency of distress felt by the student in various
domains and their usage of organizational resources in response to that distress. This component
of the survey was administered in conjunction with other survey components, and captured stress
experienced over the course of the semester of collegiate experience.
Survey procedures. The assessment questions for this survey were combined with the
assessment questions for all three surveys, and they were administered at the conclusion of the
program. As the academic coaching program is an existing initiative within the university, there
already existed test assessment points for all students in the program. These individual survey
items were appended to the existing assessment checkpoints to provide a seamless integration
into the existing assessment framework.
The combined assessment was administered through an electronic survey instrument
delivered by the university’s Institutional Research office, which already conducts assessments
for this program. This delivery mechanism minimized the researcher’s access to raw data and
provided the survey respondents with another level of assurance in the anonymity of their
responses (Merriam & Tisdale, 2016). The additional individual survey items were integrated
into this existing delivery platform to minimize test fatigue from the students (Maxwell, 2013).
Data Analysis
Data analytics for this research study combined student data on knowledge and
motivation influences, sense of belonging, and student life stressors obtained through the survey
instrument, as well as student data obtained directly from the institution related to student

67
demographics and outcome data related to individual student persistence. The combined data
allowed for analysis of psychosocial measures and any correlation to student outcomes.
For psychosocial assessments, descriptive statistics were calculated for the sample
population participating in the academic coaching intervention, including mean values for each
item indicator. Additionally, Pearson’s correlation calculations were performed to determine any
correlations between psychosocial measures. Statistical analysis was done following the
collection of all survey results. After the start of the following semester, outcome data was
incorporated into the dataset and a correlational analysis of psychosocial indicators and outcomes
was performed to determine any correlation between indicators and subsequent outcomes.
Validity and Reliability
The stakeholder group of focus for this study is Pell-eligible, academically at-risk
freshmen. Due to the nature of this stakeholder group, combined with the existing literature on
academic performance in this population, the knowledge, motivation, and organizational
influences most related to this population are more psychometric and psychosocial in nature.
Due to the complexity of generating valid and reliable instrumentation that targets sense of
belonging, and student stressors, this study used existing instrumentation that has already been
externally tested and found to be valid and reliable. Maxwell (2013) suggests this approach for
beginning researchers that seek to target higher-order concepts. In doing so, the novice
researcher eliminates a potential source of error in instrumentation development and focuses
their research on the topic at hand.
This approach is supported by Merriam and Tisdale (2016), who note that robust research
fields often have an array of useful assessment instruments that have been rigorously examined
through peer-reviewed research into their validity and reliability. Due to the depth of research

68
available on collegiate academic success, development of assessment instruments was limited to
those knowledge and motivational influences to be directly assessed, and for which there existed
no independent instrumentation. The knowledge, motivation, and organizational influences
themselves are not in question in this study, there exists sufficient literature to confirm that those
influences are, in fact, demonstrably meaningful. What is more pertinent to this evaluative study
is which specific influences are present, and whether the academic coaching intervention has any
measurable effect on those influences.
The combined survey questions for this study was incorporated into the existing
assessment framework already in place for this program. As such, this study will utilize the
existing participant selection, survey delivery, and response follow-up protocols already in place
within the program assessment architecture. Maxwell (2013) would encourage a choice such as
this, as it minimizes student disruption by integrating survey items into an existing framework
which should minimize cognitive load on students’ responses. This methodology sought to
ensure maximum response rates, as the university’s Institutional Research office is best
positioned to deliver and follow up on survey assessments.
Ethics
Due to the nature of the study involving human subjects, it is important to acknowledge a
number of responsibilities incumbent upon the researcher. First, informed consent from the
survey participants is necessary, and that consent was obtained as a component survey
instrument delivery process. This consent was delivered in electronic format, with the consent
form detailing the participants’ rights with respect to the voluntary nature of the research,
confidentiality of the data and of their participation, as well as assurances with respect to data
storage and usage (Glesne, 2011).

69
To ensure the safety of the participants, the researcher submitted the study to the
University of Southern California Institutional Review Board (IRB) and followed their rules and
guidelines regarding the protection of the rights and welfare of the participants in this study. As
this study was conducted at another higher education institution, the survey was also submitted
to the Axios University Institutional Review Board (IRB). To ensure compliance, all
participants signed consent forms. It was verbally reiterated that this study is voluntary, and
their identity would be kept confidential. Due to the positionality between the researcher and the
participants, separation between the researcher and the students was particularly important for
the study and the researcher respected participants’ wishes if, at any time, they decided to
withdraw from the study.
Due to the researcher’s positionality within the institution as an assistant provost at the
university, the survey assessment was administered through the university’s Institutional
Research Office, which conducts all institutional surveys administered to students. The intent of
this delivery mechanism is to further distance the researcher from the participants in order to
minimize any perceived impact the researcher’s institutional position may have on the study
participants. Additionally, the survey items were included in an already-existing assessment
framework used by the academic coaching program for internal program assessment. The
intention for inclusion in this existing assessment instrument was to further disassociate the
researcher from the survey responses in an effort to get truthful, frank answers from survey
participants (Merriam & Tisdell, 2016).
It should be noted that the survey instrument was delivered in an electronic format, and
that it is possible that not all participants are equally versed in electronic survey delivery, which
may affect response rates. Given that electronic delivery is a component of contemporary higher

70
education, the assessment of academic capital may prove useful in forming hypotheses were a
non-random non-response pattern detected. It should also be noted that the individual
assessment instruments were selected from a stable of like-type instruments, with specific care
given to find instruments that present their assessment in the most neutral fashion. Many of the
instruments, especially those related to belonging and stress, appeared to have individual items
geared towards negativity. For example, the College Student Stress Scale, the instrument used in
this study, presents its question as “felt anxious or distress about financial matters” and asks the
participant to rate this item from Never to Often. Similar instruments ask the question such as “I
feel stressed about financial matters” and ask the participant to rate the statement from Strongly
disagree to Strongly Agree, however, it is believed that this wording primes the participant
towards a negative orientation due to the original statement being framed as a negative outcome.
Given that the participants are from a vulnerable population and have achieved suboptimal
academic outcomes in their first semester, the decision was made to seek assessment instruments
that framed the items in the most neutral fashion possible. Additionally, survey results were
catalogued by randomized id by the Institutional Research Office, further anonymizing the
results while still allowing the study to draw conclusions survey responses.

71
CHAPTER FOUR: FINDINGS
Introduction
The purpose of this study was to investigate the overall efficacy of academic coaching as
a retention intervention for Pell-eligible, academically at-risk freshmen, and further, to
investigate the key knowledge, motivation, and organizational influences at work. Additionally,
this study identifies statistically significant findings that could inform the development of
institutional actions to support the stakeholder group.
The study sought to answer three research questions related to the stakeholder group and
their participation in an academic coaching intervention:
1. To what extent are participants in the academic coaching initiative retained into their
sophomore year?
2. What is the knowledge and motivation of Pell-eligible, academically at-risk freshmen
related to their ability to persist to sophomore year?
3. What is the interaction between academic and social campus culture and context and the
knowledge and motivation of academically at-risk freshmen in relation to their ability to
persist to sophomore year?
These research questions sought to establish answers to both the evaluative question and the
process question (McEwan & McEwan, 2003), as well as functioning as both research and
evaluation (Alkin, 2011). This research in practice utilizes study results to formulate
recommendations that inform continuous improvement in the program providing administrators
data upon which they may base any future iterations of program development.

72
Participating Stakeholders
The stakeholders for this study were Pell-eligible, academically at-risk freshmen taking
part in a university retention intervention centered around academic coaching. All students in the
study were first-time freshmen in the Fall of 2019, finished the fall semester with a cumulative
GPA of less than 2.0, and were enrolled in a mandatory academic coaching intervention in the
Spring of 2020. During the Spring 2020 semester the institution moved to an all-remote modality
in mid-March 2020 in response to the national COVID-19 pandemic outbreak. The cohort
offered survey participation consisted of all enrollees in the academic coaching intervention (N =
255), which included both Pell-eligible students as well as those who were not Pell-eligible.
Among the 255 students who initially enrolled in the academic coaching initiative, 44%
were Pell-eligible (N = 111). Additionally, 56% were White (N = 143) and 44% were non-White
(N = 112), with the majority of the non-White population consisting of African-American
students (N = 73). Among the 255 initial participants, 64% (N = 163) were found to have
persisted into Sophomore year.
Of the 255 initial participants, 32 students responded to the survey instrument. Among
survey respondents, 53% were Pell-eligible (N = 17), a slight over-representation from the group
as a whole. Additionally, 41% of respondents were White (N = 13) and 59% were non-White (N
= 19), with the majority of non-White survey respondents consisting of African-American
students (N = 9). Notably, Hispanic respondents made up roughly 19% of the survey respondents
(N = 6) while only representing 5% of the total population (N = 14). Additionally, 78% of survey
respondents (N = 25) were retained to Sophomore year, substantively exceeding the retention
rate of the overall participant population.

73
Results and Findings
The following results and findings presented relate to each research question, followed by
analyses seeking to contextualize the findings in ways more meaningful to institutional
administrators developing or improving similar intervention strategies. The first research
question is evaluative in nature, oriented towards addressing the overall efficacy of the program
in its stated goal, namely the retention of academically at-risk freshmen. The second and third
research questions are process-oriented, seeking to uncover the salient elements embedded
within the program delivery to inform continuous improvement of the program itself. These
research questions evaluate key knowledge, motivation, and organizational influences identified
within the literature as material factors impacting the program goal of retaining Pell-eligible,
academically at-risk freshmen.
Quantitative Analysis Overview
The quantitative analysis overview articulates the statistical analysis methodology
utilized in this chapter. Outcome data related to population retention was examined to determine
overall program effectiveness. For specific knowledge, motivation, and organizational influences
assessed through the survey instrument, Cronbach’s alpha serves to underscore the overall
reliability of the survey and Pearson’s correlation coefficients enhance the understanding of
KMO relationships. Additionally, demographic variances among the total sample are provided
where statistically significant. The findings are presented as they relate to the specific research
questions posed in Chapter Two.
The survey instrument utilized multiple assessment questions targeting individual KMO
influences. The survey was administered by the university’s Institutional Research office using
Snap Survey software. For the knowledge and motivation assessments, each influence was

74
assessed using three to 7 questions scored on a 6-point Likert scale ranging from strongly
disagree to strongly agree, with no neutral response available. Numeric transformation of the
response (strongly disagree = 1; strongly agree = 6) allowed for a simplified statistical analysis
of survey data. The cultural model assessment used a modified sense of belonging assessment
that rated 18 measures of belonging as either never, sometimes, or always (never = 1; always =
3). The cultural setting assessment used a college student stress scale with 6 measures of stress
with responses never, sometime, or often (same coding as the cultural model responses).
As the first research question is evaluative in nature, program outcomes related to
persistence to sophomore year were the focus of the data analysis. The data set includes 5 years
of retention data for the target population, freshmen who finished their first semester under a 2.0
GPA. This five-year data set includes three cohorts prior to the implementation of the
intervention. These cohorts are intended to establish a baseline outcome range for the population,
prior to any intervention.
For the second question, which focus on knowledge and motivation influences identified
in the literature review, descriptive and inferential statistics encompassing the individual KMOs
described key differences between outcome groups. The research instrument assessed each KMO
influence using three to 7 individual assessment questions. The third research question, which
focused on organizational influences related to cultural models and settings, utilized an adapted
sense of belonging scale based on 18 measures of belonging and a 6-measure college student life
stressor scale. Loading of results by individual KMO factor allowed for the calculation of
Cronbach’s alphas (Table 4) within IBM SPSS Statistics 27.

75
Table 4
KMO Influencer Cronbach’s Alpha
Measure Cronbach’s Alpha
1. Navigating Institutional Systems .83
2. Creating Systems of Support .86
3. Understanding Financial Circumstances* .56
4. Expectancy-Value** .83
5. Attribution .79
6. Self-Efficacy .88
7. Sense of Belonging .76
8. Student Life Stressors .77
Note. N = 32. *Understanding Financial Circumstances assessment 3 and 4 discarded to improve
Cronbach’s alpha from .46 to .56. **Expectancy-Value assessment 5 discarded to improve
Cronbach’s alpha from .73 to .83.
Tavakol and Dennick (2011) suggest values ranging from .70 to .95 are acceptable for internal
consistency. Statistical analysis utilized inter-phase comparisons of single means using a paired-
samples t-test with a 95% confidence interval (CI) to determine statistical significance.
Research Question 1: Academic Coaching Participant Retention
Retention for students participating in academic coaching increased relative to those who
did not participate in the program. Students who become academically at-risk following the
completion of their Fall semester has remained fairly stable over the last 5 years. Table 5 shows
the most recent 5 years of freshmen cohorts, and the number and percentage of those cohorts that
became academically at-risk following the conclusion of the Fall semester.

76
Table 5
Academically At-Risk Freshmen Retention Data
Cohort N Percent of Cohort
2015 365 15.5%
2016 414 18.4%
2017 383 16.1%
2018 400 17.3%
2019 382 16.6%
Average 389 16.7%
Note: N represents the number of students in each freshman cohort who finished their first
semester under a 2.0 GPA (academically at-risk).
Program participation. The institution must address operational processes related to
student participation in academic coaching. As shown in Table 6, for the 2 years in which
academic coaching was mandated for all freshmen who finished their first semester under a 2.0
GPA, between 75-80% of students required to participate in academic coaching actually enrolled
in the program.
Table 6
Academic Coaching Enrollment and Retention Data
Cohort N
Enrolled in
following
Spring
Enrolled in
Academic
Coaching
Enrollment
Percentage
Participant
Retention
Rate
Non-
participant
Retention
Rate
2018 400 271 205 75.6% 38.0% 25.8%
2019 382 265 213 80.4% 62.0% 42.3%
Note: N represents the number of students in each freshman cohort who finished their first
semester under a 2.0 GPA (academically at-risk). Enrollment in academic coaching was
mandatory.

77
Until institution addresses the participation rate, longitudinal studies of program
effectiveness will continue to maintain a confounding variable related to self-selection bias
associated with participation.
Early intervention. Earlier intervention may be an effective strategy to further increase
the retention rate of academically at-risk freshmen. Table 7 indicates that between 28-32% of
academically at-risk freshmen do not enroll in the next semester, one in which academic
coaching is currently delivered. While current outcomes give an indication of the efficaciousness
of academic coaching, students who fail to enroll in the spring semester are not present to
participate in the academic coaching intervention. This suggests a need for earlier indicators of
academic distress available prior to the semester GPA currently used to identify students for
academic coaching.
Table 7
Academic Coaching Enrollment and Retention Data
Cohort N
Enrolled in
following
Spring
Fall-to-
Spring
Retention
Loss
Enrolled in
Academic
Coaching
Enrollment
Percentage
2015 365 255 30.1% – –
2016 414 300 27.5% – –
2017 383 268 30.0% – –
2018 400 271 32.2% 205 75.6%
2019 382 265 30.2% 213 80.4%
Note: N represents the number of students in each freshman cohort who finished their first
semester under a 2.0 GPA (academically at-risk). Enrollment in academic coaching was
mandatory.

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Gender variances. Retention of female-identifying students exceeds that of male-
identifying students across all race and socioeconomic subgroups. Table 8 highlights the large
variance in retention rates between the gender-based subgroup within the academically at-risk
population. When sorted by retention outcome, chi-square hypothesis testing supported the
statistical significance associated with the higher female retention compared to that of their male
counterparts, even after inclusion of race and Pell status. This finding highlights a statistically
significant gap in program outcomes by gender, which offers a focus for program improvements.
Table 8
Contingency Table – Continued Enrollment and Gender
Gender Data Element Not Enrolled Enrolled Total
Female Count 32 82 114
Expected Count 41.1 72.9 114
% of Total 12.5% 32.2% 44.7%
Adjusted Residual -2.4 -0.5
Male Count 60 81 141
Expected Count 50.9 90.1 141
% of Total 23.5% 31.8% 55.3%
Adjusted Residual 2.4 -2.4
Chi-Square Tests Value df Asymptotic
Significance*
Pearson Chi-Square 5.733 1 .017
Likelihood Ratio 5.800 1 .016
N of Valid Cases 255
Note. N = 255. *Two-side value at the 95% confidence level.
Research Question 2: Knowledge and Motivation
Survey respondents within the total sample showed weak correlation among individual
KMO assessments. Statistical analysis utilizing Pearson’s correlation of individual KMO means

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sought to establish any statistically significant relationships between individual KMO influences
and identify variations between demographic subgroups. Table 9 highlights statistically
significant correlations between individual KMO means for the total respondent sample, which
includes both outcomes (retained and not retained), as well as both Pell and non-Pell students.
The results highlighted in Table 9 show a moderate degree of correlation among 6 of the
28 KMO pairs, suggestive of a relatively weak cohesiveness among KMO influences across the
total sample. These moderate correlations are more meaningful when viewed in the context of
similar correlations disaggregated by retention outcome and Pell status.
Table 9
Pearson’s Correlation Coefficients – Total Sample
Measure 1 2 3 4 5 6 7 8
1. Navigating
Institutional Systems
– .23 -.08 .11 .44* .01 -.10 .34
2. Creating Systems of
Support
– -.56 .35* .24 .27 .40* .04
3. Understanding of
Financial
Circumstances
– .12 .03 .28 -.03 -.24
4. Expectancy-Value – .16 .37* .26 .15
5. Attribution – .41* .07 .29
6. Self-Efficacy – .29 -.35*
7. Sense of Belonging – -.14
8. Student Life Stressors –
Note. N = 32. *p < .05. **p < .01.
Pell students who retained into their sophomore year develop systems of support that
positively affect their self-efficacy and reduce the impact of student life stressors. Table 10

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shows strong correlations between the KMO influences of creating systems of support, self-
efficacy, and student life stressors. The high degree of positive correlation between systems of
support and self-efficacy among retained students, as well as strong negative correlations
between systems of support and stress, is predicted within the existing literature (Torres &
Solberg, 2001). The presence of these correlations within literature further confirms the survey
findings in the research study.
Table 10
Pearson’s Correlation Coefficients – Returning Students Fall 2020 (Pell)
Measure 1 2 3 4 5 6 7 8
1. Navigating
Institutional Systems
– .06 -.02 .04 .62* .31 .13 .17
2. Creating Systems of
Support
– .50 .54 .04 .73** .22 -.71**
3. Understanding of
Financial
Circumstances
– .27 -.20 .37 .07 -.55
4. Expectancy-Value – -.05 .59* .28 -.20
5. Attribution – .35 .25 .22
6. Self-Efficacy – .41 -.57*
7. Sense of Belonging – -.25
8. Student Life Stressors –
Note. N = 32. *p < .05. **p < .01.
The knowledge influence assessed under the heading of creating systems of support closely
tracks with existing literature highlighting gaps in knowledge among students from low
socioeconomic backgrounds, and is known collectively within the literature as cultural capital.
While not the only previously known knowledge gap present within this student population, the

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results of the research study show the ability to create systems of support to be a key factor when
predicting the likelihood of persistence.
Non-Pell retained students’ ability to navigate institutional systems enhances related
measures of knowledge and motivation influences. As indicated in Table 11, non-Pell students
retained into sophomore year exhibit strong correlation between their ability to navigate
institutional systems and motivation influences of expectancy-value and attribution. Recent
literature based on socioeconomic status (Mattson, 2014) suggests a positive correlation between
socioeconomic status and cultural capital, a key component of knowledge related to life at a
university.
Table 11
Pearson’s Correlation Coefficients – Returning Students Fall 2020 (Non-Pell)
Measure 1 2 3 4 5 6 7 8
1. Navigating
Institutional Systems
– .71** .42 .76** .71** .51 .19 .43
2. Creating Systems of
Support
– .35 .71* .79** .74** .20 .53
3. Understanding of
Financial
Circumstances
– .32 .26 .37 .69* .50
4. Expectancy-Value – .58* .25 .27 .58*
5. Attribution – .88** .18 .17
6. Self-Efficacy – .26 .08
7. Sense of Belonging – .40
8. Student Life Stressors –
Note. N = 32. *p < .05. **p < .01.

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The data indicates increased familiarity with institutional operations can positively affect
students’ motivational influences as they relate to persistence. Study results point to the
importance of familiarizing students with the nature and operation of a university environment,
with those more familiar being better able to effectuate the actions needed to promote academic
success.
Navigating institutional systems. Pell-eligible students reported slight-to-moderate
perceived ability to navigate institutional systems, which was lower than their non-Pell
counterparts. Pell students reported slightly positive perceptions (M = 4.66) of their ability to
navigate institutional systems, however they also exhibited greater variance in their responses
(SD = 1.22) than their non-Pell peers (SD = 0.85). The data in Table 12 suggests a wider range of
prerequisite knowledge as it pertains to operating within an institutional environment. Results
suggest that academic coaching has a positive impact on Pell students’ knowledge of navigating
institutional systems, but the academic coaching program must make additional improvements to
ensure that it is not leaving individual students behind in the development of this knowledge set.
Gender disparities. Females report a higher belief in their ability to navigate institutional
systems than do their male counterparts. As shown in Table 12, females reported a moderately
high confidence in their perceived ability to navigate institutional systems (M = 5.08) as
compared to their male counterparts, who reported only a slight confidence in their ability (M =
4.20) in addition to their greater variability within the group. These findings are statistically
significant (p = .02) indicating a meaningful disparity in perceived knowledge of navigating
institutional systems by gender.

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Table 12
Descriptive and Means Comparisons – Navigating Institutional Systems
Comparison n M SD df t F p
TS 32 4.78 1.06
P
NP
17
15
4.66
4.91
1.22
0.85
1 -0.67 0.45 .51
M
F
11
21
4.20
5.08
1.33
0.75
1 -2.41 5.83 .02
Note. Total sample (TS), Pell recipients (P), non-Pell recipients (NP), Male (M), and Female (F).
95% confidence interval utilized for calculating p values.
Locus of control. Students who perceive a stronger ability to navigate institutional
systems are more likely to attribute a self-focused locus of control as it relates to their academic
performance. As shown in Table 13, retained Pell-eligible students exhibited an increase in r-
value (r = .62) from the total sample value (r = .44), indicating that a correlation between
navigating institutional systems and attribution positively influences retention outcomes among
Pell-eligible, academically at-risk students.
Table 13
Pearson’s Correlation Coefficients – Study Participants Fall 2020
Measure Attribution
1. Navigating Institutional Systems (TS) .44*
2. Navigating Institutional Systems (P) .62*
3. Navigating Institutional Systems (NP) .71**
Note. N = 32. Total Sample (TS), Returning Pell (P), Returning Non-Pell (NP). *p < .05. **p <
.01.

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Additionally, an even stronger correlation between navigating institutional systems and
self-efficacy is present among retained non-Pell students, as expressed by an r value (r = .71)
with a statistically significant p-value (p < .01). This follows literature related to cultural capital
that posits familiarity with environments and systems bolters persistence likelihood (Mattson,
2014).
Creating systems of support. Pell-eligible, academically at-risk students are highly
cognizant of the relationship between creating systems of support and academic success. Survey
results, highlighted in Table 14, indicate that Pell students moderately to strongly agree (M =
5.47) with statements suggesting the importance of systems of support. Total sample results were
nearly as strong (M = 5.38), indicating a widespread acceptance of the need for strong systems of
support as it relates to academic success.
Table 14
Descriptive Statistics and Means Comparisons – Creating Systems of Support
Comparison n M SD df t F p
TS 32 5.38 0.67
P
NP
17
15
5.47
5.27
0.61
0.74
1 0.85 0.72 .40
M
F
11
21
5.38
5.39
0.79
0.62
1 -0.15 0.02 .88
Note. Total sample (TS), Pell recipients (P), non-Pell recipients (NP), Male (M), and Female (F).
95% confidence interval utilized for calculating p values.
Systems of support and self-efficacy. Academically at-risk students’ understanding of the
importance of systems of support increases their motivational self-efficacy. Among retained
students, both Pell and Non-Pell, perceptions of increased self-efficacy were highly correlated to
positive perceptions of the importance of systems of support. As shown in Table 15, both Pell

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and Non-Pell students that persisted to sophomore year exhibited strong correlations to measures
of self-efficacy.
Table 15
Pearson’s Correlation Coefficients – Returning Students Fall 2020
Measure n Attribution Self-Efficacy
1. Creating Systems of Support (P) 17 .04 .73**
2. Creating Systems of Support (NP) 15 .79** .74**
Note. Pell (P) and Non-Pell (NP). *p < .05. **p < .01.
Systems of support and attribution. Notably, correlations to a self-focused locus of
control existed only among Non-Pell students. This could potentially result from the newer
systems of support developed by Pell students relative to their Non-Pell peers. The findings
suggest that non-Pell students are more likely to be relying on existing systems of support and
are therefore more cognizant of the positive benefits strong support networks have on one’s
ability to control their environment. Pell-eligible students may develop these correlations after
extended utilization of newly formed support networks illuminate the environmental control
benefits granted to an individual by strong support networks.
Systems of support and student life stressors. Creating systems of support significantly
reduces the impact of student life stressors on Pell-eligible student’s likelihood of persistence to
sophomore year. As a whole, Pell-eligible students are subject to the same array of stressor
events as all other students in a university environment, but also have the added domain of
financial stressors in addition to those present throughout the student body as a whole. For Pell-
eligible students that persisted, a strong negative correlation exists between creating systems of
support and the reported impact of student life stressors.

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This statistically significant relationship is illuminating with respect to the search for
interventions that produce the cascading effects on student retention as described in the literature
(Bandura, 1977; Tinto, 1987; Torres & Solberg, 2001). The literature predicts that systems of
support foster increased self-efficacy and reduce the impact of stressor events, and survey
responses among Pell-eligible students reflected these predictions. Interestingly, non-Pell
students in the survey did not generate similar negative correlations, which may be a result of the
reduced impact of financial events as a source of stressors.
Table 16 highlights the Pearson’s correlation coefficients for returning students and
reflects the challenges Pell-eligible students face with incorporating effective supports to address
life stressors.
Table 16
Pearson’s Correlation Coefficients – Returning Students Fall 2020
Measure n Self-Efficacy
Student Life
Stressors
1. Creating Systems of Support (P) 17 .73** -.71**
2. Creating Systems of Support (NP) 15 .74** .53
Note. Pell (P) and Non-Pell (NP). *p < .05. **p < .01.
Understanding financial circumstances. Academically at-risk freshmen are not
confident in their ability to complete their college degree without added financial support. Across
the total survey sample, respondents reported slight to moderate disagreement (M = 2.64) with
statements related to their ability to attend and complete college without financial assistance. As
shown in Table 17, this outcome was notably consistent across the entire sample, with little
variance between Pell (M = 2.65) and Non-Pell (M = 2.63) students.

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Table 17
Descriptive Statistics and Means Comparisons – Understanding Financial Circumstances
Comparison n M SD df t F p
TS 32 2.64 1.68
P
NP
17
15
2.65
2.63
1.78
1.61
1 0.02 0.00 .98
M
F
11
21
2.55
2.69
1.63
1.74
1 -0.22 0.05 .82
Note. Total sample (TS), Pell recipients (P), non-Pell recipients (NP), Male (M), and Female (F).
95% confidence interval utilized for calculating p values.
While Pell status objectively delineates socioeconomic status, the survey findings
indicate a broader uneasiness with college students’ financial circumstances. These survey
results run contrary to other, literature-supported findings in the study, which could potentially
be a result of survey methodology. While literature related to Pell students suggests that financial
concerns are a contributing factor in their collegiate experience, data from the research study
found no such evidence.
Expectancy-value. Academically at-risk students perceive significant value in academic
success behaviors. Study participants reported strong overall agreement (M = 5.70) with
statements related to the positive impacts of academic success behaviors. Across the total
sample, students reported strong agreement with statements such as “going to class every day is
important as it affects the outcome of my final grade.” and “it is important to me to complete all
the assigned homework as it contributes to the success I have in a course.” This strong agreement
was present across all demographic subgroups, with Pell students exhibiting slightly higher
agreement with such statements (M = 5.82) as compared to their non-Pell peers (M = 5.57).

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Table 18
Descriptive Statistics and Means Comparisons – Expectancy-Value
Comparison n M SD df t F p
TS 32 5.70 0.52
P
NP
17
15
5.82
5.57
0.40
0.61
1 1.42 2.02 .16
M
F
11
21
5.45
5.83
0.75
0.29
1 -2.07 4.27 .05
Note. Total sample (TS), Pell recipients (P), non-Pell recipients (NP), Male (M), and Female (F).
95% confidence interval utilized for calculating p values.
Table 18 suggests students maintain a place a high value on academic success and
perceive the effort to be worth the reward (M = 5.70). Additionally, the low standard deviation
(SD = 0.52) supports the effective knowledge transfer within the academic coaching intervention.
Gender disparities. Female study participants perceive greater expectancy-value from
academic success behaviors than do their male counterparts. Females reported higher average
agreement with statements related to valuing academic success behaviors (M = 5.83) as
compared to their male counterparts (M = 5.45). Additionally, female respondents exhibited a
tighter response cluster to the mean (SD = 0.29) as compared to their male counterparts (SD =
0.75), indicating a broader agreement among the demographic subgroup. This finding is
statistically significant (p = .05), which should prompt further study into gender variances as it
relates to the impact of academic coaching on expectancy-value perceptions. This finding, in
conjunction with previously enumerated findings along gender lines, indicates that there exist
substantive differences in response to programmatic interventions.
Expectancy-value and self-efficacy. Pell-eligible students benefit from increased
perceptions of expectancy-value through cascading effects on persistence and increased

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measures of self-efficacy. Throughout the total sample, as shown in Table 19, slight positive
correlations between measures of expectancy-value and self-efficacy emerge (r = .37). This
general correlation is predicted by literature related to expectancy-value (Eccles, 2001), however,
among retained Pell students this positive correlation becomes stronger (r = .59) and remains
statistically significant (p = .05). Notably, among retained non-Pell students this positive
correlation weakens (r = .25) and loses its statistical significance. This could potentially be a
result of lower reported perceptions of expectancy-value among non-Pell students (M = 5.57),
which may in turn be driven by the lower overall contribution of academic success behaviors to
non-Pell students’ ability to return their sophomore year. Since socioeconomic factors are a less
important driver of ability to attend among non-Pell students, this subgroup may be less inclined
to perceive academic success, and the behaviors associated with academic success, as high value
activities. These diminished associations may, in turn, reduce correlation to self-efficacy as they
are legitimately less impactful as it relates to non-Pell student’s ability to continue at the
institution.
Table 19
Pearson’s Correlation Coefficients – Study Participants Fall 2020
Measure n Self-Efficacy
1. Expectancy-Value (TS) 32 .37*
2. Expectancy-Value (P) 17 .59*
3. Expectancy-Value (NP) 15 .25
Note. Total Sample (TS) Returning Pell (P) and Returning Non-Pell (NP). *p < .05. **p < .01.
Attribution. Pell-eligible students perceive an internal locus of control with respect to
their ability to achieve academic success. Across the total sample, respondents reported a

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moderate to strong internal attribution (M = 5.30) as it relates to their perceived control over
academic success. Among Pell students, this perception was slightly higher (M = 5.45),
indicating a high degree of internal attribution with respect to questions such as “I have control
over seeking solutions to academic obstacles that would prevent me from being a successful
student” and “I believe I have control over seeking the knowledge I need to succeed
academically.”
Existing literature related to attribution in an educational context posits that attribution of
non-performance is pertinent in the context of students’ assessment of locus of control (Weiner,
1985). Survey findings point to academic coaching as an effective intervention to bolster
students’ internal attribution, further increasing their likelihood of persistence to sophomore
year.
Table 20
Descriptive Statistics and Means Comparisons – Attribution
Comparison n M SD df t F p
TS 32 5.30 0.72
P
NP
17
15
5.45
5.13
0.56
0.84
1 1.27 1.60 .22
Note. Total sample (TS), Pell recipients (P), non-Pell recipients (NP). 95% confidence interval
utilized for calculating p values.
Among Pell-eligible students, study results showed that attribution had strong positive
correlations to identified knowledge influences. However, Table 21 suggests those strong
positive correlations did not hold when evaluating relationships to motivational influences.

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Table 21
Pearson’s Correlation Coefficients – Study Participants Fall 2020
Measure n Self-Efficacy
1. Attribution (TS) 32 .41*
2. Attribution (P) 17 .35
3. Attribution (NP) 15 .88**
Note. Total Sample (TS) Returning Pell (P) and Returning Non-Pell (NP). *p < .05. **p < .01.
The results run counter to existing literature on the effects of attribution on other
motivational influences (Anderman & Anderman, 2006). Kallenbach and Zafft (2004) highlight
a number of potential processes and procedures for re-training as it relates to maladaptive
attributions, however, the study results indicate that such additional programmatic interventions
may prove ineffective in ultimately improving persistence outcomes. Interestingly, non-Pell
students exhibited high correlation between internal attribution and self-efficacy (r = .88),
however, reported lower overall perceptions of their internal attribution (M = 5.13). This result
could potentially emanate from non-Pell students’ reliance on existing networks of support, thus
reducing measures of self-reliance.
Self-efficacy. Students’ perceptions of their own self-efficacy are unmoored from
observable behavior linked to self-efficacy. Table 22 highlights students across the total sample
reported moderate belief in their own self-efficacy, as reflected in their responses to questions
such as “I have confidence in my ability to seek help if I need it.” and “I am confident that I have
the ability to succeed academically in the face of challenges.” Such statements serve to gauge
respondents’ belief in their own self-efficacy, but also enumerate research-based academic
success behaviors shown to increase persistence. However, retained students reported lower

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perceived self-efficacy (M = 4.92) than did their non-retained peers (M = 5.05). This result runs
contrary to all research on academic success behaviors, which posit that increases in self-efficacy
should increase retention. Notably, perceptions of self-efficacy reported across the total sample
(M = 4.95) remained consistent within demographic subgroups. When disaggregated by Pell
status, both Pell (M = 4.94) and non-Pell (M = 4.96) reported strikingly similar levels of self-
efficacy. This trend continued when disaggregating by gender, with males (M = 4.88) reporting
only slightly lower perceptions of self-efficacy than their female counterparts (M = 4.98).
Self-efficacy and persistence. One anomaly among subgroups was disaggregation along
the dimension of persistence. While existing literature (Bandura, 2000) shows that self-efficacy
is positively correlated with academic persistence, survey respondents who persisted into the Fall
2020 semester reported lower levels of self-efficacy than did their peers who did not persist to
their sophomore year. This result, as indicated in Table 22, runs counter to prevailing thought
related to the impact of self-efficacy, and therefore may be an excellent candidate for further
study.
Table 22
Descriptive Statistics and Means Comparisons – Self-Efficacy
Comparison n M SD df t F p
TS 32 4.95 1.10
P
NP
17
15
4.94
4.96
1.16
1.06
1 -0.04 0.00 .97
M
F
11
21
4.88
4.98
1.67
1.09
1 -0.25 0.06 .80
R
NR
25
7
4.92
5.05
0.22
0.42
1 -0.27 0.07 .79
Note. Total sample (TS), Pell recipients (P), non-Pell recipients (NP), Male (M), Female (F),
Retained (R) and Non-retained (NR). 95% confidence interval utilized for calculating p values.

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One explanation might be the Dunning-Kruger effect, a cognitive bias whereby an
individual with lower ability at a task overestimates their ability. A more in-depth study targeting
this cognitive bias would delineate these subgroups, allowing program administrators a more
granular view of the relationship between perceived self-efficacy and competence.
Self-efficacy and student life stressors. Increases in self-efficacy among Pell-eligible
students produced cascading beneficial effects on life stressors that may contribute to increased
persistence. As shown in Table 23, respondents reported a slight, negative correlation (r = -.35)
between self-efficacy and student life stressors. Literature on self-efficacy in educational settings
(Bandura, 2000) predicts such correlations, however, survey results showed this correlation
strength increasing for Pell students (r = -.57) but disappearing for non-Pell students.
One potential explanation for the disappearance of this correlation, as posited previously,
may be the relatively smaller impact student life stressors play within the context of persistence
likelihood among non-Pell students.
Table 23
Pearson’s Correlation Coefficients – Study Participants Fall 2020
Measure n Student Life Stressors
1. Self-Efficacy (TS) 32 -.35*
2. Self-Efficacy (P) 17 -.57*
3. Self-Efficacy (NP) 15 .08
Note. Total Sample (TS) Returning Pell (P) and Returning Non-Pell (NP). *p < .05. **p < .01.
Research Question 3: Organizational Influences
Study results showed little correlation between organizational influences and other
knowledge and motivational influences despite significant foundations in existing literature
(Bandura, 2000; Dweck, 2010, Weiner, 1985). As shown in Table 24, survey respondents in the

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total sample showed statistically significant correlation with only one knowledge influence,
creating systems of support.
Table 24
Pearson’s Correlation Coefficients – Survey Respondents Fall 2020
Measure
Sense of
Belonging
(TS)
Sense of
Belonging
(P)
Sense of
Belonging
(NP)
1. Navigating Institutional Systems -.10 .13 .19
2. Creating Systems of Support .40* .22 .20
3. Understanding of Financial Circumstances -.03 .07 .69*
4. Expectancy-Value .26 .28 .27
5. Attribution .07 .25 .18
6. Self-Efficacy .29 .41 .26
Note. N = 32. Total Sample (TS) Returning Pell (P) and Returning Non-Pell (NP). *p < .05. **p
< .01.
Additionally, this correlation was slight (r = .40) within the total sample and disappeared
altogether when disaggregating retained Pell and non-Pell students. While non-Pell students did
present positive correlations between understanding financial circumstances and sense of
belonging, potential issues with survey responses related to understanding of financial
circumstances may limit the validity of this correlation.
Pell-eligible students must possess resources upon which they can rely to mitigate the
negative effects of stressor events on academic performance. Whether self-reliant, or reliant on
others, the availability of solution-generating resources is a crucial factor in persistence. Survey
results on student life stressors, highlighted in Table 25, were in line with predictions emanating

95
from existing literature, with strong correlations emerging between creating systems of support
and self-efficacy.
Table 25
Pearson’s Correlation Coefficients – Survey Respondents Fall 2020
Measure
Student Life
Stressors (TS)
Student Life
Stressors (P)
Student Life
Stressors (NP)
1. Navigating Institutional Systems .34 .17 .43
2. Creating Systems of Support .04 -.71** .53
3. Understanding of Financial
Circumstances
-.24 -.55 .50
4. Expectancy-Value .15 -.20 .58*
5. Attribution .29 .22 .17
6. Self-Efficacy -.35* -.57* .08
7. Sense of Belonging -.14 -.25 .40
Note. N = 32. *p < .05. **p < .01.
These results are predicted in empirical research by Karimshah et al. (2013), whose work
showed that the deleterious effects of stressor events can be mitigated by strong systems of
support. Additionally, Pell-eligible students are more likely to be academically unprepared
(Davis, 2010) and are more likely to have financial and housing concerns (Peralta & Klonowski,
2017). These added stressors make systems of support a key factor in mitigating the negative
effects of these stressor events. Similarly, work by Bandura (2000) showed that increases in self-
efficacy could also mitigate the negative effects of stressor events. Bandura’s research shows that
an increased belief in one’s ability to succeed is often a self-fulfilling prophecy, with increases in
measures self-efficacy leading to increased performance, and increased performance leading to
further increases in measures of self-efficacy.

96
Sense of belonging. Academically at-risk students show only moderate levels of
affiliation with the institution. Table 26 highlights the results indicating academically at-risk
students reported a moderate sense of membership within the university community (M = 2.20)
with low variation within the group (SD = 0.23). Numerous empirical research studies (Davis,
2010; Mattson, 2014) have shown that academically at-risk students are more likely to suffer
from a diminished sense of belonging. This finding held true within demographic subgroups,
with Pell-eligible students (M = 2.20) reporting nearly identical levels of school affiliation as did
their non-Pell peers (M = 2.21). Interestingly, survey results showed little variation in responses
from retained (M = 2.18) and not-retained students (M = 2.28) despite a significant body of
literature that indicates that sense of belonging plays an important role in student motivation and
ultimately persistence (Anderman & Anderman, 2006; Bandura, 2000; Dweck, 2010; Weiner,
1985).
Table 26
Descriptive Statistics and Means Comparisons – Sense of Belonging
Comparison n M SD df t F p
TS 32 2.20 0.23
P
NP
17
15
2.20
2.21
0.23
0.22
1 0.22 0.05 .82
R
NR
25
7
2.18
2.28
0.23
0.22
1 1.08 1.16 .29
Note. Total sample (TS), Pell recipients (P), non-Pell recipients (NP), Retained (R) and Not-
Retained (NR). 95% confidence interval utilized for calculating p values.
The results could potentially be explained by the timing of the assessment, in that the
entire survey population was already academically at-risk, therefore the population may have
self-sorted to a more homogenous group as it relates to feelings of belonging. Another possible

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explanation could be the unique conditions of the Spring 2020 semester, as survey respondents
were completing the assessment after 6 weeks of remote learning induced by the coronavirus
pandemic, an event that touched the lives of every participant.
Student life stressors. Survey results found statistically significant variance between
demographic subgroups in the sample. A deeper evaluation of KMO correlations shows a strong
negative correlation among retained Pell students between systems of support, self-efficacy, and
levels of anxiety about stressor events. The survey instrument used in the research study utilized
statements such as “felt anxious or distressed about personal relationships” and “felt anxious or
distressed about academic matters” with respondents rating these statements on a range between
never and often. This instrument sought not to quantify the number of stressor events
themselves, but to quantify their impact on students’ stress levels. This distinction may be
important in explaining the variations found between demographic subgroups.
Pell status. Pell-eligible students are more able to cope with stressor events despite their
exposure to a broader range of events over the academic semester. As shown in Table 27, Pell-
eligible students reported lower overall distress from stressor events (M = 2.08) than did their
non-Pell peers (M = 2.31). Research related to Pell-eligible students (Davis, 2010; Peralta &
Klonowski, 2017; Karimshah et al., 2013) suggests that these students are more likely to
experience additional stressor events related to financial concerns, however, survey responses
show lower levels of distress. These results are in line with other, related survey results
inquiring about systems of support. Pell-eligible students reported higher knowledge of systems
of support (M = 5.47) as related to their non-Pell peers (M = 5.27). Given that systems of support
are known to reduce the impact of stressor events (Schaffling et al., 2018), it is possible that the
effects these enhanced support systems manifest in reduced reports of distress.

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Gender variances. Females are more able to compartmentalize stress and anxiety as it
relates to their ability to engage in academic success behaviors. Across numerous measures of
academic success behaviors, females reported higher efficacy as it related to knowledge and
motivation influences than did their male peers. These measures included navigating institutional
systems (ΔM = +0.88), understanding financial circumstances (ΔM = +0.14), expectancy-value
(ΔM = +0.38) and self-efficacy (ΔM = +0.10). These findings stand in contrast to the survey
results related to student life stressors, with females reporting higher distress and anxiety related
to stressor events (M = 2.33) than their male counterparts (M = 1.91). The study findings suggest
that females may be more able to compartmentalize distress, or that they have a higher baseline
of latent anxiety and therefore exhibit a higher level of functioning even while reporting a higher
absolute anxiety level.
Table 27
Descriptive Statistics and Means Comparisons – Student Life Stressors
Comparison n M SD df t F p
TS 32 2.19 0.49
P
NP
17
15
2.08
2.31
0.52
0.43
1 -1.37 1.88 .18
M
F
11
21
1.91
2.33
0.33
0.50
1 -2.54 6.47 .02
NW
W
19
13
2.02
2.44
0.48
0.39
1 2.61 6.79 .01
Note. Total sample (TS), Pell recipients (P), non-Pell recipients (NP), Male (M), Female (F),
Non-White binned (NW), and White (W). *p < .05. **p < .01.
One potential explanation for higher persistence impact on males, despite lower reported
anxiety, is that males may be experiencing a greater differential between matriculation and the

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end of their freshman year. This larger delta among males may explain both the empirical survey
findings as well as the persistence findings in a concomitant manner.
Race variances. Academically at-risk students that self-identified as White are more
likely to experience anxiety related to life stressor events occurring during the academic
semester. Survey findings showed a statistically significant difference in reported levels of
anxiety among White students (M = 2.44) compared to their non-White peers (M = 2.02). These
findings point to meaningful variations in stressor event response between the two groups. When
taken in conjunction with persistence rates across demographic subgroups, these results suggest
that anxiety related to stressor events may potentially be a driver of a student’s ultimate
persistence likelihood. As with gender variances, one possible explanation might be that the
change in anxiety levels over time is a more telling indicator of persistence impact, rather than
the nominal, perceived anxiety level itself.
Summary
Academic coaching is an effective intervention to increase persistence for academically
at-risk students. Students participating in academic coaching were nearly 50% more likely to be
retained as sophomores than similarly situated peers that did not participate in academic
coaching. Study results indicate that there is additional opportunity with increasing program
participation among eligible students, that even earlier intervention may prove efficacious, and
that male students across all demographic subgroups are at higher risk of non-retention than are
their female counterparts.
Research results show that academically at-risk students who were able to increase their
performance along knowledge dimensions influenced their motivation positively. As a group,
academically at-risk students have all achieved a first-semester GPA of less than 2.0, which

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provided negative feedback as it relates to their ability to succeed academically. This feedback
negatively affects their sense of self-efficacy, which in turn affects their motivation as it relates
to their persistence in academic success behaviors (Bandura, 2000), which further negatively
affects their likelihood to persist to sophomore year. Results showed that positive shifts in
knowledge related to academic success behaviors correlated to positive shifts in motivational
influences such as expectancy-value and self-efficacy. Additionally, Pell-eligible students
reported a more pronounced correlation, suggesting programmatic interventions such as
academic coaching are particularly efficacious within this demographic subgroup.
Among Pell-eligible students, the study showed that programmatic interventions that
increase self-efficacy positively affect other influences that affect persistence. Students that
reported moderate to high levels of self-efficacy also exhibited higher levels of knowledge of
academic success behaviors as well as lower impacts of distress from student life stressors.
Taken in conjunction, literature predicts that these influences positively affect persistence
likelihood.
Based on assessed influences, study results related understanding of financial
circumstances showed little to no impact on other influences or persistence overall. Given that
Pell-eligible students are classified as lower socioeconomic status, this finding was unexpected
as it explicitly dealt with socioeconomic considerations. Knowledge influences related to
academic success behaviors were more reliable in predicting persistence and were also found to
be more impactful on motivational influences known to increase persistence likelihood.

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CHAPTER FIVE: RECOMMENDATIONS
Discussion of Findings
The intent of this study was to assess the efficacy of academic coaching as a retention
intervention targeting Pell-eligible, academically at-risk students. The study results were
confounded by the COVID-19 pandemic; however, the findings point to a number of key
elements that can be used to further improve the academic coaching intervention at the heart of
the study. In the following chapter, the findings are examined, and recommendations are made
based on those findings, in an effort to further refine and improve the effectiveness of academic
coaching as a retention intervention at Axios University.
Recommendations for Practice to Address KMO Influences
Knowledge Recommendations
The literature review revealed a nuanced landscape of student success research, with
recent research projecting a more constructivist view of student success interventions, whereby
population context affects the efficacy of given interventions. In the context of Pell-eligible,
academically at-risk freshmen, the literature suggests three key knowledge influences: students’
understanding of how to navigate institutional systems, their ability to create systems of support,
and their understanding of their financial circumstances (Brock, 2010; Bourdieu, Bourdieu,
& Kreckel, 1983; Ishitani, 2006; Mattson, 2014; Walpole, 2003). The data from a survey of
Pell-eligible, academically at-risk freshmen participating in an institutional intervention featuring
academic coaching is highly likely to suggest that these three knowledge influences are needed
for the population, and their absence likely a contributing factor to their success rates. These
knowledge influences are procedural and metacognitive knowledge types, as described by
Krathwohl (2002), and recommendations for remediation are made based on Clark and Estes

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(2008) types of assistance organizations can provide to mitigate knowledge gaps. Table 28
describes the knowledge influences identified in the literature as needs and aligns those
influences with associated learning and implementation principles identified to support context-
specific recommendations for the organization to address the stakeholder goal.
Improving navigation of institutional systems. The research study and supporting
literature indicates that students’ understanding of how to navigate collegiate institutional
systems is a procedural knowledge gap among Pell-eligible, academically at-risk freshmen, and
this gap is critical to Pell-eligible students due to their decreased levels of cultural capital
(Bourdieu, Bourdieu, & Kreckel, 1983; Walpole, 2003). Findings from the research study
support the existence of a gap in knowledge among Pell-eligible, academically at-risk freshmen.
A recommendation that has roots in both sociocultural theory as well as information
processing systems theory seeks to close this procedural knowledge gap by reframing
information based on prior knowledge and providing scaffolding in the form of job aids to help
connect the new knowledge to prior knowledge, thus enhancing learning. Gallimore and
Goldenberg (2001) found that learning tasks similar to a familiar cultural setting enhanced
learning and transfer. Additionally, Schraw and McCrudden (2006) suggest information learned
meaningfully and connected to prior knowledge is recalled more quickly and accurately. This
suggests efforts to convey institutional procedural knowledge in a manner that is more culturally
contextual to Pell-eligible, academically at-risk freshmen would result in greater recall of
procedural usage as well as greater accuracy. To implement these principles, Scott and Palinscar
(2013) provide strategies, including providing sufficient modeling, practice and feedback to
facilitate learning and performance. Schraw and McCrudden (2006) encourage usage of
modeling effective strategy to provide examples of how and when to use particular strategies.

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Table 28
Summary of Knowledge Influences and Recommendations
Assumed Knowledge
Influence
Principles Context-Specific Recommendation
Pell-eligible,
academically at-risk
freshmen must
understand how to
navigate institutional
systems.
Learning tasks common to the
individual’s familiar cultural
settings promotes learning and
transfer (Gallimore &
Goldenberg, 2001).

Scaffolding and tools facilitate
learning and performance
(Scott& Palinscar, 2013).
Deliver academic coaching support
with peer mentoring, training aids,
and toolkits explaining institutional
systems.

Provide opportunities to practice
these skills, utilizing academic
coaches as well as opportunities for
outcome reflection
Pell-eligible,
academically at-risk
freshmen must be able
to create systems of
support.
Continued practice promotes
automaticity and takes less
capacity in working memory
(Schraw & McCrudden, 2006).

Model effective strategy use
(Schraw & McCrudden, 2006).

Provide timely feedback that
links use of learning strategies
with improved performance
(Shute, 2008).
Provide an initial network of support
including assigned peer and faculty
mentors that model networking
strategies, provide a structured
environment to practice those
networking strategies and provide
feedback to students.
Pell-eligible,
academically at-risk
freshmen should
understand their
financial circumstances.
Learning and motivation are
enhanced when learners set goals,
monitor performance, and
evaluate progress towards goal
achievement (Ambrose et al.,
2012; Mayer, 2011).
Utilize peer mentoring and targeted
financial counseling; structure
counseling to promote financial
management understanding prior to
discussing future actions

Segment financial understanding
into smaller components and
encourage students to reflect on how
each component affects the others.

Provide opportunities for students to
debrief and reflect on the learning
shortly afterwards.

Provide financial counselors to
discuss best practices associated
with financial management.

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The recommendation is to institute academic coaching support that incorporates peer
mentoring to provide peer models for how and when to use institutional systems in contexts
more familiar due to the peer relationship. Additionally, coaching support provides opportunities
to practice these skills, utilizing academic coaches to provide feedback, as well as opportunity
for reflection on the outcomes from those practice opportunities.
Fostering the creation of systems of support. This research study found that creating
systems of support was a knowledge gap critical to improving persistence among Pell-eligible,
academically at-risk freshmen. As a recommendation, peer mentors represent an opportunity to
seed a system of support for these Pell-eligible, academically at-risk students. Seminal research
by Bourdeiu, Bourdieu, and Kreckel (1983) established cultural capital theory as a potential
explanation for achievement gaps. Cultural capital has been defined to varying degrees, with
Putnam (1995) referring to it as the moral obligations and norms, social values, and social
networks of an individual, and Bourdieu (1977) viewing it through the lens of class as an
aggregation of three interrelated forms of capital: economic, cultural and social.
This research study focused on the social network component of social capital,
recognizing that students from different backgrounds bring different social networks to collegiate
life. Moschetti and Hudley (2015), Mattson (2014), and Walpole (2003) all expanded upon this
theoretical work and framed their research within the context of higher education and cultural
capital’s effect on academic underachievement. In the context of higher education, cultural
capital includes an understanding of financial transactions, access to friends and family that have
been through a collegiate experience, and experience checking out books from a library, along
with a litany of other factors relevant in the collegiate experience. Collectively, gaps in this
cultural capital, referred to in previous chapters as academic capital, hinders individual students’

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ability to overcome obstacles and impedes their ability to access available resources within the
institutional environment. Enhanced social networks provide a system of support to help bridge
the gaps encountered by students, thus enhancing their likelihood to persist and graduate. Peer
mentors provide an opportunity for students to connect with someone more similar to themselves
than would be an adult academic coach, creating a more culturally familiar setting. Research by
Gallimore and Goldenberg (2001) indicated learning is enhanced when done in a culturally
familiar setting, therefore peer mentoring is an important component of the recommended
intervention. Additionally, purposeful selection of the peer mentors to pair mentors and mentees
from culturally similar backgrounds is critically important to the learning and transfer process.
Research by Schraw and McCrudden (2006) suggested information meaningfully
connected to prior knowledge is more readily recalled. In their research, Schraw and McCrudden
(2006) found that meaningful connections to prior knowledge facilitated the encoding process
that moves new information into long-term memory. Their research posited all humans have
limited attentional resources, and that connections to prior knowledge reduced the amount of
attentional resources necessary to perform the encoding function. This research further supports
the use of culturally familiar peer mentors, who maintain a longer-term relationship with the
mentees and convey institutional knowledge through the lens of their own experiences, which are
more likely to be culturally relevant due to the purposeful selection of mentors for each mentee.
Based on research by Scott and Palinscar (2013), academic coaches support the peer mentoring
process by providing scaffolding, giving students the opportunity to use their knowledge and
experience to model navigating institutional systems in the context of specific student issues,
give students the opportunity to practice those skills, and provide feedback based on the outcome
of those practice opportunities. Scott and Palinscar’s (2013) research, based on sociocultural

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theory put forward by Vygotsky (1978), posits that learning, thinking and knowing are relations
among people and therefore learning is more effective when engaged within this context. Their
research found this iterative learning process is more effective with strong modeling and
feedback loops available to the learner. At the conclusion of this process, students are given
opportunities to reflect on the outcomes from this practice to further cement the learning process.
Recent empirical research by Reddick et al. (2011) suggests a program of combined peer
mentoring and institutional student support can be effective in closing the gap in students’
cultural capital, making them more effective in navigating institutional systems. This research is
supported by the findings in this study, which found that students who developed a system of
support were more likely to persist to sophomore year.
Ensuring understanding of their financial circumstances. While this research study
did not find any significant gap, existing student success literature indicates that Pell-eligible
freshmen are particularly susceptible to student life stressors related to their socioeconomic
status (DeBerard, Spielmans, & Julka, 2012; Karimshah et al., 2013; Walpole, 2003). A
recommendation with a basis in metacognitive theory is appropriate for helping to close a
knowledge gap related to metacognition. A metacognitive approach to students’ understanding of
their financial circumstances is intended to mitigate the stressor effects of students’
socioeconomic status on their retention likelihood. Metacognitive theory suggests that learning
and motivation are enhanced when learners set goals, monitor their performance, and evaluate
progress towards achieving those goals (Ambrose et al., 2012; Mayer, 2011). Existing research
suggests several implementation strategies proven to improve metacognition, including having
learners identify prior knowledge before a learning task (Mayer, 2011), helping individuals
connect new knowledge to prior knowledge and construct meaning (Schraw & McCrudden,

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2006), modeling metacognitive processes by speaking out loud and assessing strengths and
weaknesses (Baker, 2006), breaking down complex tasks and encourage individuals to think
about content in strategic ways (Schraw & McCrudden, 2006), and ultimately providing learners
with an opportunity to debrief following a learning task (Baker, 2006).
Using these implementation strategies, a recommendation for addressing metacognitive
knowledge gaps in Pell-eligible students’ understanding of their financial situation would include
utilizing peer mentoring and targeted financial counseling. Counseling would be structured to
help students identify what factual information they already know about their financial
circumstances, break down complex ideas into more discrete concepts, and provide students
opportunities to think about the ramifications of these complex ideas on their personal situation.
Additionally, financial counseling would provide the opportunity for peer mentors to model how
they think about similar circumstances, talking through how their metacognitive processes
worked. Finally, students would be given an opportunity to debrief on their own metacognitive
processes, giving counselors an opportunity to understand students’ thinking and allowing for
formative assessment of the learning process.
While findings in the research study did not exhibit a knowledge gap in understanding
financial circumstances, existing literature has previously identified metacognitive gaps in
understanding, which creates additional pitfalls for these students (Langhout, Drake, & Roselli,
2009; Reason, 2009). Krathwohl (2002) describes this knowledge gap as metacognitive
knowledge in that students may know about their socioeconomic status, but the knowledge gap is
in knowing how that knowledge impacts other circumstances. Empirical studies related to low
socioeconomic status have shown additional pitfalls generated by that status are sufficient
enough to produce measurable variations in student persistence and graduation (DeBerard,

108
Spielmans, & Julka, 2004; Karimshah et al., 2013). Research has shown that institutional support
has proven to be effective in mitigating some effects of this knowledge gap (Reddick et al.,
2011) and specific, targeted counseling for low socioeconomic students has a positive correlation
with persistence and graduation (Karimshah et al., 2013). Additionally, Benson et al. (2009)
found peer support correlated positively with student success among students from low
socioeconomic backgrounds. Thalluri (2016) noted peer mentors were better able to
contextualize information for low socioeconomic students, likely due to cultural similarities in
their circumstances. Evidence from generalized metacognitive learning research by Baker (2006)
and Schraw and McCrudden (2006) support the potential efficacy of these interventions,
highlighting the learning processes related to meaning making and cultural context, respectively.
Motivation Recommendations
There exists a prolific, and varied, amount of research surrounding the motivational
influences affecting student success. A literature review of this research leads one back to several
foundational research theories emanating from more psychological research, which is then
contextualized in a higher education setting. Table 29 describes the motivation influences
identified in the literature and aligns those influences with associated learning and
implementation principles identified to support context-specific recommendations for the
organization to address the stakeholder goal. Among these more seminal research theories are
expectancy-value theory, with Wigfield and Eccles (2000) doing a significant amount of research
in this area. Expectancy-value has been shown to play a key role in what Clark and Estes (2008)
refer to as active choice decisions, namely the moment an individual makes the choice to begin a
task. Moving past the decision to begin a task, Clark and Estes (2008) describe the next stage of
motivation as persistence, or the ability to continue on in that task.

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Table 29
Summary of Motivation Influences and Recommendations
Assumed Motivation
Influence
Principles Context-Specific Recommendation
Pell-eligible,
academically at-risk
freshmen should
understand the value of
outcomes emanating
from their academic
behaviors such as class
attendance and
studying (expectancy-
value theory).
Rationales with a discussion of the
importance and utility value of the
work or learning can help learners
develop positive values (Eccles,
2006; Pintrich, 2003).

Learning and motivation improve
if the learner values the task
(Eccles, 2006).

Models who are credible and
similar can foster positive values
(Pajares, 2006).
Utilize peer mentors emphasizing
the rationale for engaging in
academic behaviors such as
attending class and studying for
exams, which are associated with
positive academic outcomes.
Pell-eligible,
academically at-risk
freshmen should
believe they have
control over their own
academic outcomes
(attribution theory).
Learning and motivation improve
when individuals attribute outcomes
to effort rather than ability
(Anderman & Anderman, 2009).

Identify missing skills/knowledge,
communicate skills/knowledge can
be learned, and teach those skills
and knowledge (Anderman &
Anderman, 2009).

Provide opportunities to exercise
choice and control (Pintrich, 2003).
Educate students on their ability to
control their outcomes through
their actions, stress the process of
learning over any particular grade,
and provide feedback and time for
reflection on strategies used up to
the present.
Pell-eligible,
academically at-risk
freshmen should have
confidence in their
ability to succeed
academically (self-
efficacy theory).
High self-efficacy can positively
influence motivation (Pajares,
2006).

Feedback and modeling increase
self-efficacy (Pajares, 2006).
Educate students on the perception
of student ability to increase
confidence in their ability to
succeed academically.

Additional programming should
provide scaffolded goal setting
with reflective components.

Engage peer mentors to model
success-generating behaviors.

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Psychological research on persistence often centers on attribution theory, first described by
Heider (1958) and later refined by Weiner (1979) in the context of education. This theory is
particularly insightful with respect to Pell-eligible, academically at-risk freshmen, as they are
more likely to have previous experiences that are deleterious to their motivation to persist in the
future. If students are able to succeed in active choice and persistence, Clark and Estes (2008)
indicate that they also need to succeed in putting forth mental effort to ultimately achieve their
goal. Psychological research into self-efficacy has proven informative in this context. Building
off work by Bandura (1977, 2000), and contextualizing this research into the field of education
through the research by Carol Dweck (2010) and Bean and Eaton (2001), self-efficacy emerged
as an important driver to motivational success in Pell-eligible, academically at-risk freshmen.
Valuing academic success behaviors. The results from this research study, supported
by literature, suggest Pell-eligible, academically at-risk freshmen suffer from a motivation gap as
it relates to valuing academic behaviors that lead to success. According to Eccles (2006),
learning and motivation are enhanced when a learner values the task. Her theories are grounded
in psychological expectancy-value theory, which posits that motivation to engage in active
choice and persistence are directly affected by the subjective task value a learner assigns to a
task. Additionally, Eccles (2006) and Pintrich (2003) found rationales that included a discussion
of the importance and utility value of the work can help learners value the task. This effect is
produced by modifying a learner’s subjective task value for those tasks known to be efficacious.
To develop an effective intervention, such a program should include rationales about the
importance and utility value of the task (Pintrich, 2003) and be delivered by models who are
credible and similar (e.g. gender, culturally appropriate) in order to foster positive valuation
(Pajares, 2006). These research studies indicate proper mentor selection is critical to creating

111
positive valuations associated with academic behaviors like class attendance and studying. In
order to facilitate an understanding of the value of outcomes associated with academic behaviors,
the program should utilize peer mentors that will emphasize the rationale for engaging in
academic behaviors such as attending class and studying for exams, which are associated with
positive academic outcomes.
The programmatic recommendation for using peer mentors to increase the perceived
value of academic success behaviors is grounded in substantial research in the fields of both
psychology and education. Seminal psychological research by Vroom (1964) defined the
theoretical framework for expectancy-value theory, namely, that an individual is more motivated
to start and complete a task when they (a) expect a positive outcome from that task, and (b) value
that task’s contribution to that outcome. Building upon that theoretical framework, Eccles (2000,
2006) began applying that research in an education setting, looking for ways that learners could
be motivated to engage in academic success behaviors. Clark and Estes (2008) describe the
necessary steps as active choice and persistence, with active choice representing the decision to
begin the behavior and persistence describing the continuation of effort to completion. Empirical
research by Kitsantas, Winsler, and Huie (2008) further confirmed the efficacy of such
approaches, focusing on the initial step of active choice. In their research, students with low
expectancy-value metrics were less likely to begin academic success behaviors such as class
attendance, studying, homework completion, and help-seeking. Furthermore, additional research
in the field found that a student's subjective task value was critically important to maintaining the
motivation necessary to persist in a behavior in order to see it through to conclusion (Wigfield &
Cambria, 2010). This research concluded that students’ evaluation of a task’s contribution to a
positive outcome played a significant role in ultimately determining their motivation levels for

112
the task. Beyond valuing the task, itself, research by Pekrun (1993) noted students’ past
experiences played a substantial role in determining whether they believed such actions would
produce a positive outcome, further substantiating the need for credible role models from
culturally similar backgrounds to help convince Pell-eligible, academically at-risk students that
academic success behaviors can work for them as well.
Promoting an internal locus of control. The results of this research study, supported by
existing literature, indicates that a motivation gap exists related to Pell-eligible, academically at-
risk freshmen need to believe they have control over their academic outcomes. The presence of
this motivation gap is supported by psychological research, as well as educational research, in
the area of attribution theory. To address this gap, research by Anderman and Anderman (2009)
indicates learning and motivation are enhanced when individuals attribute success or failure to
their effort rather than their ability. It is imperative that any intervention ensure that these
students are able to attribute that failure to changeable influences (effort) instead of immutable
influences (ability). Programmatic interventions should educate students on their ability to
control their outcomes through their actions, stress the process of learning over any particular
grade, and provide feedback and time for reflection on strategies used up to the present. This can
be achieved through regular peer mentor interactions that review the academic behaviors leading
up to the grade event, discussion of which behaviors were beneficial, and reflection on how
adjustments will be made for the next event.
Programmatic recommendations to stress the process of learning over any particular
grade or academic outcome are firmly rooted in educational theory, which is built upon more
generalizable psychological attribution theory. Heider (1958) posited three dimensions of
attribution: locus of control, stability, and controllability. In this general context, Heider

113
proposed that each of these dimensions impacted an individual’s motivation in different ways.
When an individual felt they had no control (locus of control), they were less likely to engage in
activities to alter the outcome in the future. Similarly, when an individual felt the outcome was
fixed and immutable (stability), they were also less likely to engage in activities to alter the
outcome in the future. Finally, if an individual felt that individual actions, more generally, could
not control an outcome then they were less likely to engage in activities to alter the outcome in
the future. Building upon this work, Bernard Weiner (1979) brought Heider’s theory into an
educational setting to improve academic outcomes. Weiner (1985) found that, with respect to
predicting future behavior, a student’s particular attribution of non-performance was not as
important as the student’s individual dimensional assessments. Further research along this line
found that if a student believed that a situation was under their control, changeable, and
controllable, they were much more likely to be motivated to persist in the future (Anderman &
Anderman, 2006). These results are particularly important for this stakeholder group, as they are
statistically more likely to hold beliefs that posit an external locus of control, a fatalism regarding
the changeability of circumstances, and a lack of belief in their ability to control outcomes
(Graham, 1997). Fortunately, there exists substantive literature on processes and procedures
around attribution re-training (Kallenbach & Zafft, 2004), which seeks to modify maladaptive
attributions and re-focus respondents’ attributions towards those more conducive to academic
success. This research further validates the programmatic recommendation to focus on the
process of learning, thus de-emphasizing particular academic outcomes and instead emphasizing
growth over time.
Building self-efficacy around academic success. Based on literature in the field,
supported by the results of this student, a motivational gap exists in Pell-eligible, academically

114
at-risk freshmen’s confidence in their own ability to succeed academically. Theoretical
foundations for this motivation gap lie in self-efficacy theory, which has been shown by Bandura
(1977, 2000) to increase mental effort towards a task. Research suggests high self-efficacy can
positively influence motivation (Pajares, 2006) and that feedback and modeling increase self-
efficacy (Pajares, 2006). Recommendations for interventions are to educate students as to the
university’s perception of student ability (i.e. the student was admitted) to increase students’
confidence in their own ability to succeed academically, as well as provide scaffolded goal-
setting with reflective components engaging peer mentors that can model success-generating
behaviors, including reflective elements examining near-term goals with the intent to provide
feedback about success and challenges in achieving those goals. In practice, this type of
intervention involves a sustained relationship with academic coaches and peer mentors who are
able to provide ongoing support that utilizes reflection and goal setting in an iterative fashion.
Programmatic recommendations for increasing students’ confidence are grounded in
extensive educational research. Seminal research by Albert Bandura (1977, 2000) laid the
foundation for application of self-efficacy theory in the context of education. Bandura has spent
decades researching how measures of self-efficacy are predictive of academic success and how
promoting and improving self-efficacy can increase academic success. At its core, self-efficacy
is an individual’s belief that they can execute a course of action to obtain a desired result. In the
context of Pell-eligible, academically at-risk students, self-efficacy is particularly important due
to students’ prior experience with academic failures, which serve to erode their belief in their
own self-efficacy. Fortunately, among the various beliefs that exist within an individual’s
schema, self-efficacy has been shown to be malleable, at least in some respects (Bean & Eaton,
2001). By providing incremental goal setting, in conjunction with appropriate scaffolding,

115
programmatic interventions have been shown to build self-efficacy through small, progressive
victories (Pajares, 2006). Additionally, opportunities for feedback between goals has proven to
be efficacious, given that those providing feedback carefully balance strengths and challenges
(Borgogni et al., 2011). Taken in total, there is substantial research, both theoretical and
empirical, on the efficacy of programmatic interventions.
Organization Recommendations
Recommendations for organizational changes were funneled through the academic
coaching center’s program director, for inclusion into the center’s continuous improvement
cycle. In evaluating organizational influences on Pell-eligible, academically at-risk freshmen, a
review of the literature, supported by the findings from this research study, show that
environmental influences can be broken down into two main segments. Based on the work by
Shein (2017), this research study breaks organizational influences down into cultural models and
cultural settings, with models representing indirectly observable things such as norms, habits of
thinking, and shared meaning and settings representing those observable things such as
behaviors, physical layouts, and formal programs. Using this framework, the literature identifies
a key influence in each area. By looking at cultural model influences within the context of higher
education and building on self-efficacy research by Bandura (1977, 2000), growth mindset
research by Dweck (2010), and attribution research by Anderman and Anderman (2006), one
finds that academically at-risk populations are significantly influenced by sense of belonging
(Mattson, 2014; Davis, 2010). As such, the institution should value the impact sense of
belonging has on student motivation. In seeking cultural setting influences, the relevant literature
is related to how environmental factors affect student behavior. Research by Karimshah et al.
(2013) explores how student life stressors influence students’ emotional state in ways that

116
negatively impact their academic behaviors. Additional research by Langhout, Drake, and
Roselli (2009) further supports these findings. The results of this research study further solidify
the findings as they relate to student life stressors’ impact on academic outcomes. As such, the
institution should provide resource, to mitigate the effects of these student life stressors.
Table 30
Summary of Organization Influences and Recommendations
Assumed Organization
Influence
Principle and Citation
Context-Specific
Recommendation
The institution needs to
value the impact sense
of belonging has on
student motivation
(cultural model).
Effective leaders demonstrate a
commitment to valuing diversity through
inclusive action. They promote a culture
that promotes equity and inclusion and
cultivates an atmosphere where diversity is
viewed as an asset to the organization and
its stakeholders (Angeline, 2011; Prieto,
Phipps & Osiri, 2009).

Positive emotional environments support
motivation (Clark & Estes, 2008).
The organization should
cultivate an inclusive
environment demonstrating
the institutional value of
diversity and inclusion and
promotes positive
emotional connection to
the campus community.
The institution needs to
provide resources to
address student life
stressors (cultural
setting).
Effective change efforts ensure everyone
has the resources needed to do their job
and resources align with organizational
priorities (Clark and Estes, 2008).

Organizational effectiveness increases
when leaders monitor and evaluate the
effectiveness of all aspects of their
organization and use valid and reliable data
to drive decision-making.

Motivation, learning, and performance
improve when the participant perceives the
anticipated outcome will be positive and
fair (Vroom, 1964)

Monitoring performance of all staff and
students produces higher learning
outcomes (Waters, Marzano & McNulty,
2003).
The organization should
provide resources to
mitigate the negative
impact of student life
stressors on academic
behaviors through
diagnostic assessments of
those life stressors.

Adjust organizational
resources into specific,
prioritized areas to
minimize student stress
levels.

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Table 30 describes the organizational influences identified in the literature and aligns
those influences with associated learning and implementation principles identified to support
context-specific recommendations for the organization to address the stakeholder goal.
Improving sense of belonging. While research findings did not support the research
literature in identifying sense of belonging as an organizational gap, the depth and breadth of
research on the topic warrants some acknowledgement on the part of the institution. In viewing
organizational influences through the schema described by Schein (2017), students’ sense of
belonging is identified as an organizational gap that presents itself within the stakeholder group.
Organizations should demonstrate a commitment to valuing diversity through inclusive action.
They should promote an organizational culture that values equity and inclusion and cultivates an
atmosphere where diversity is viewed as an asset to the organization and its stakeholders
(Angeline, 2011; Prieto, Phipps & Osiri, 2009). Clark and Estes (2008) found that positive
emotional environments support motivation. In order to achieve such an organizational culture,
the organization should cultivate an inclusive environment that demonstrates institutional value
of diversity and inclusion and promotes positive emotional connection to the campus community
by seeking out opinions that differ from their own, including diverse voices in decision-making,
and increasing individual outcome expectancies by avoiding competitive structures.
Pell-eligible, academically at-risk students present with lower levels of belonging,
including feelings of not belonging and of feeling like an imposter, both of which erode a
positive emotional environment (Mattson, 2014; Davis, 2010). Creating a positive motivational
environment centered around belonging is a particularly useful approach to Pell-eligible,
academically at-risk populations, as these students are disproportionately from low
socioeconomic backgrounds and underrepresented households (Browman et al., 2017; Davis,

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2010; Linnehan, Weer, & Stonely, 2011; Mattson, 2014). By cultivating an inclusive
environment that demonstrates institutional value of diversity and inclusion and promotes
positive emotional connection to the campus community by seeking out opinions that differ from
their own, including diverse voices in decision-making, the organization will likely increase the
stakeholders’ sense of belonging and increase individual outcome expectancies by avoiding
competitive structures. This could include having diverse representation on campus decision-
making bodies, dedicated physical spaces for specific student groups, as well as including
campus employees from similar backgrounds on working groups designed to support these
students.
Mitigating the effects of student life stressors. In contrast to cultural models, which
require indirect observation, cultural settings are more directly observable and measurable.
Current literature, in conjunction with this study’s findings, demonstrated student life stressors
constitute an organizational gap for institutions working with Pell-eligible, academically at-risk
freshmen. Clark and Estes (2008) note that effective change efforts ensure that everyone has the
resources (equipment, personnel, time, etc) needed to do their job, and that if there are resource
shortages, then resources are aligned with organizational priorities. Generally speaking, student
life stressors create events where there are resource shortages, be they financial, emotional, time,
or some other necessary resource. Due to unique populations within a given institution, it is
important to monitor the specific stressor types within an institution’s student population.
Organizational effectiveness increases when leaders monitor and evaluate the effectiveness of all
aspects of their organization and use valid and reliable data to drive decision-making (Vroom,
1964; Waters, Marzano & McNulty,2003). This implies organizational leaders should
consistently monitor potential student life stressors within their population and focus resources

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on those stressors most prevalent within their particular community. Institutions should provide
resources that help mitigate the negative motivational impact of student life stressors on
students’ academic behavior through diagnostic assessments of student life stressors that allow
the institution to target organizational resources into specific, prioritized areas most impactful to
students’ stress levels. Within the context of the institution that is the site of the research study,
financial stressors topped the list of concerns for their specific student body, which would
indicate that limited institutional resources should be directed towards financial aid in the form
of tuition and cost of attendance support.
Substantial educational research investigated potential non-academic factors that
contribute to academic challenges. Among this research includes specific research on life
stressors that can affect student outcomes (Karimshah et al., 2013; Langhout, Drake & Roselli,
2009). This research found that the frequency and intensity of student life stressors affected
students’ mental and emotional states, which subsequently had an impact on their academic
performance. For Pell-eligible, academically at-risk students, research shows that these students
are more likely to come academically unprepared (Davis, 2010) and more likely to have financial
and housing concerns (Peralta & Klonowski, 2017), further adding to the potential list of
potential student life stressors. Based on this research, an institution should assess the frequency
and intensity of stressor events in their particular population over the course of the collegiate
experience. The institution should then use that data to tailor organizational decision-making and
resource allocation to those events within their power to control, and to also provide additional
institutional supports to students experiencing events outside the institution’s control.

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Kirkpatrick and Kirkpatrick Training Evaluation
The Kirkpatrick Model of training evaluation has its roots in the doctoral dissertation of
Dr. Donald Kirkpatrick, who wrote is 1954 Ph.D. dissertation on evaluation of training for
industrial supervisors (Kirkpatrick & Kirkpatrick, 2016). Dr, Kirkpatrick’s investigations into
training evaluation were rooted in the desire to know if training was making a difference for the
supervisors that were trained. In 1959 Dr. Kirkpatrick authored four articles reflecting on his
dissertation work, entitled Reaction, Learning, Behavior, and Results. These four articles were
eventually concatenated and described as levels, and the Kirkpatrick Model was born. Since that
time, Dr. Kirkpatrick’s model for training evaluation has become an industry standard and his
son, Dr. Jim Kirkpatrick has continued to expand upon his father’s foundational elements.
The Kirkpatrick Model has gone through several iterations, with the most current being
the New World Kirkpatrick Model. While the model has undergone a number of edits, the
foundational principles remain the same. The first principle, that the end is the beginning,
implies that trainers must begin with an end goal in mind before designing the training. Without
the desired outcome as an anchor point, the training is likely to fail to affect the attitudes,
knowledge, and skills relevant in achieving the outcome. The second principle posits that return
on expectation is the ultimate indicator of value. This principle is related to the stakeholder
expectations, including the expectations of those who commissioned the training as well as the
expectations of participants in the training. The third principle relates to establishing partnerships
to bring about positive return on expectations. While most learning professionals focus on
assessments of reaction and learning, the substantive returns are generated when trainings
modify behavior and subsequently bring about the desired results. These results represent the
fourth principle of the model, which is that value must be created before it can be demonstrated.

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This principle implies that trainings must create change in the organization before that change
can be evaluated to determine if the training provided a positive return. In this same vein, the
fifth principle posits that a compelling chain of evidence demonstrates a training’s bottom-line
value. This suggests that results must not only be created, but there must also be a clear line
between the effects of the training and the subsequent results for the training to be considered
valuable.
Building off these foundational principles, this research study employs the New World
Kirkpatrick Model in an effort to align organizational behavior in such a way as to produce
tangible, verifiable benefits to the organization. The New World Model makes some
adjustments to previous iterations, incorporating current research on self-efficacy, organizational
drivers, and leading indicators to improve the feedback loop that allows an organization to
iteratively improve the training program. Equally important, the New World Model flips the
presentation of levels from one through four, instead inverting them to begin with level four,
further emphasizing the need to begin with the end in mind. The following sections utilize the
Kirkpatrick New World Model to provide a conceptual framework for articulating a training
program to improve the retention of Pell-eligible, academically at-risk freshmen.
Level 4: Results and Leading Indicators
When designing programmatic interventions, it is important to begin with the end in mind
(Kirkpatrick & Kirkpatrick, 2016). By focusing first on the desired results and corresponding
leading indicators, one can ensure that one is using the proper metrics to accurately measure
those indicators. Leading indicators can be broken down into two categories, internal outcomes,
and external outcomes (Kirkpatrick & Kirkpatrick, 2016). External outcomes are those outcomes
affect, and are observable by, outside parties. Internal outcomes, in contrast, are primarily only

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observable by the organization and its stakeholders (Kirkpatrick & Kirkpatrick, 2016). Internal
and external outcomes are presented in the table below, with the understanding that meeting
these outcomes will require the use of job aids, training, peer mentoring, opportunities to practice
implementation, and organizational support for students. It is also assumed that internal
outcomes directly influence external outcomes.
Table 31
Outcomes, Metrics, and Methods for External and Internal Outcomes
Outcome Metric(s) Method(s)
External Outcomes
Increased persistence of
Pell-eligible, academically
at-risk freshmen
Collect data every semester to
determine if Pell-eligible,
academically at-risk freshmen re-
enroll in the next semester
Compare semester reports
obtained from Institutional
Research
Increased average GPA
rates of Pell-eligible,
academically at-risk
freshmen
Collect data annually to determine
the average GPA of Pell-eligible,
academically at-risk freshmen
Compare annual reports
obtained from Institutional
Research
Increased graduation of
Pell-eligible, academically
at-risk freshmen
Collect data every semester on the
number of Pell-eligible,
academically at-risk freshmen
that graduate from the institution
Compare annual graduation
reports obtained from
Institutional Research
Internal Outcomes
Increased usage of peer
mentor support in academic
coaching intervention
program
The number of students assigned
to a peer mentor over the number
of participants enrolled in class-
based support (all students in
stakeholder group)
Compare annual enrollments in
class-based support, anchor
course for academic coaching
intervention, and students
assigned a peer mentor
Improved academic skills
of Pell-eligible,
academically at-risk
freshmen
Complete formative and
summative evaluations of Pell-
eligible, academically at-risk
freshmen before and after
academic coaching intervention
Review surveys every semester
to recommend additional
training sessions or their job aids
to improve academic coaching
intervention
Increased usage of financial
support resources by Pell-
eligible, academically at-
risk freshmen
Total financial aid dollars
awarded from identified programs
to Pell-eligible, academically at-
risk freshmen
Semester report of all financial
aid from identified programs
distributed to Pell-eligible,
academically at-risk students

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Table 31 articulates the desired externally observable outcomes and internal program
outcomes, establishes the metrics by which those outcomes might be assessed, and provides
methods for that assessment.
Level 3: Behavior
Critical behaviors. Kirkpatrick and Kirkpatrick (2016) explain that critical behaviors are
those few, crucial behaviors that are most impactful to program outcomes. To maintain their
efficacy, Kirkpatrick and Kirkpatrick (2016) stress that critical behaviors should be specific,
observable, and measurable.
Table 32
Critical Behaviors, Metrics, Methods, and Timing for Evaluation
Critical Behavior Metric(s) Method(s) Timing
1. Increase utilization
of peer support
resources for Pell-
eligible,
academically at-
risk students
The ratio of Pell-
eligible, academically
at-risk students
assigned to dedicated
peer mentors over total
number of Pell-eligible,
academically at-risk
students
Count of Pell-eligible,
academically at-risk
students divided by count
of active peer mentors
Every academic term
2. Increase usage of
financial resources
for Pell-eligible,
academically at-
risk students
The ratio of total
financial aid dollars
distributed from
identified programs to
Pell-eligible,
academically at-risk
students to the number
of those students
enrolled
Identification and
summation of all
distributed funding in all
applicable financial aid
programs divided by
count of Pell-eligible,
academically at-risk
students
Every academic year,
forward-looking
3. Increase utilization
of best practices to
support academic
success behaviors
in Pell-eligible,
academically at-
risk students
The percentage of Pell-
eligible, academically
at-risk students that
report utilizing
academic success
behaviors based on
summative assessments
Results of academic
coaching summative
assessment tool based on
response rate to questions
centered on academic
success behaviors
Every academic term

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Table 32 describes the critical behaviors necessary to achieving successful training, as
well as metrics for assessing that success, methods for assessment, and recommended timing of
those assessments.
As this research study focuses on Pell-eligible, academically at-risk freshmen, the critical
behaviors should also focus on the observable behaviors from this stakeholder group. These
critical behaviors, supported by research literature and the results of this study, include an
increase in the usage of peer mentor support resources, an increase in the utilization of financial
aid resources available to the stakeholder group, and an increase in the utilization of best practice
academic success behaviors among the stakeholder group.
Required drivers. Kirkpatrick and Kirkpatrick (2016) describe the systems and
processes put in place to reinforce, encourage, rewarding and monitoring critical behaviors as
required drivers. These required drivers are divided into two main categories, support and
accountability (Kirkpatrick & Kirkpatrick, 2016). The support category encompasses activities
that reinforce, encourage, and reward, while accountability encompasses monitoring activities.
Ensuring that Pell-eligible, academically at-risk students increase their utilization of peer mentor
networks, financial aid resources, and best practice academic success behaviors requires a mix of
required drivers that include both support and accountability. Table 33 describes the required
drivers necessary to supporting the critical behaviors described earlier, as well as the timing of
those drivers and the critical behaviors they are intended to support.
Organizational support. For Pell-eligible, academically at-risk freshmen to be
successful and persist at Axios University, the institution must establish and continuously
improve the support structures available to these students, as detailed in Table 33.

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Table 33
Required Drivers to Support Critical Behaviors
Method(s) Timing Critical Behaviors Supported
Reinforcing
Job aids and toolkits Every academic term 1, 2, 3
Website listing financial aid
programs
Every academic term 2

Checklist of best practices for
academic success behaviors
Every academic term 3
Encouraging
Feedback and mentoring on
best practices in the classroom
Ongoing 3

Mentoring on financial aid
programs
Ongoing 2

Feedback and mentoring
during coaching sessions
Ongoing 1, 2, 3
Rewarding
Recognition for matching peer
mentors
Ongoing 1

Recognition for use of
academic success behaviors
during coaching sessions
Ongoing 3
Monitoring
Survey of students to monitor
usage of peer mentor networks,
financial aid support, and
academic success behaviors
Every academic term 1, 2, 3

Observation of students in the
classroom to determine if best
practices are utilized
Ongoing 3

Review of student assessments
of student life stressors to
determine if financial aid
programs meet student needs
Every academic year 2

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The university already has a framework support program in its academic coaching
initiative; therefore, the institution should utilize this support framework to continue to monitor
the critical behaviors that lead to persistence while also utilizing the required drivers to
determine where scarce resources should be allocated to maximize their utilization and efficacy.
Level 2: Learning
Learning goals. Kirkpatrick and Kirkpatrick (2016) describe learning goals as helpful in
determining whether stakeholders are acquiring the requisite knowledge, skills, and motivation
to reach the desired results. Anderson and Krathwohl (2001) describe learning goals within the
context of Bloom’s taxonomy, with discrete factual knowledge as the lowest level and abstract,
metacognitive knowledge at the highest level. These distinctions are helpful when designing
assessments that target those specific learning goals. By the conclusion of the academic coaching
intervention, Pell-eligible, academically at-risk students should be able to:
1. Understand best practice academic success behaviors (Conceptual knowledge dimension)
2. Recognize financial aid programs applicable to their circumstances (Conceptual
knowledge dimension)
3. Carry out best practice academic success behaviors (Procedural knowledge dimension)
4. Implement financial aid support in their tuition payment plans (Procedural knowledge
dimension)
5. Value their presence as a member of the academic community (Motivation expectancy-
value dimension)
6. Believe in self-efficacy in engaging academic success behaviors (Motivation self-efficacy
dimension)

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Program. The four knowledge learning goals, as well as the two additional motivation
goals, will be accomplished through an integration of new programmatic elements into the
existing academic coaching intervention, which engages Pell-eligible, academically at-risk
freshmen during the entirety of their second semester at university. The primary vehicles for
dissemination of knowledge, behavior modeling, and feedback and reflection will come through
three primary components of the academic coaching initiative. These included an academic
support course in which all Pell-eligible, academically at-risk freshmen are required to take,
integration into existing academic coaching sessions, and augmentation of a nascent peer
mentoring program.
Three pillars mark the boundaries of the scope of this program, which include academic
success behaviors, financial aid literacy and utilization, and motivation factors that enable these
knowledge influences. The program seeks to integrate these pillars throughout the program
timeframe, beginning with initial assessments of knowledge and motivation at the beginning of
the semester, learning related to identified knowledge gaps throughout the semester, and ongoing
community-building through peer mentor networks that provide opportunities for modeling,
feedback and reflection.
With respect to base knowledge gaps that relate to students’ understanding of academic
success behaviors, the program seeks to address those gaps with repeated exposure to the
requisite knowledge. First, through class-based activities, all students in the stakeholder group
will be exposed to learning around the topic of academic success behaviors. These behaviors will
be discussed during class, and formative assessment will be used to check for understanding
throughout the semester. These same learning goals will be repeated during monthly academic
coaching sessions, as well as being reinforced during peer mentoring sessions. The intent behind

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the repetitive exposure in different contexts is to exemplify the importance of these academic
success behaviors. Building on the knowledge exposure, peer mentors will model these academic
success behaviors through companionship during the semester’s collegiate experience, including
walking with students to class and participating in study sessions prior to exams. Through this
sustained relationship, students will be afforded the opportunity to see these behaviors modeled,
have a forum to provide feedback, and be allowed to reflect on their own behaviors with
individuals that are culturally similar to themselves.
Programmatic efforts addressing knowledge gaps related to financial aid literacy will
follow a similar framework, utilizing a combination of class-based activities to provide
knowledge exposure, reinforcement and accountability opportunities with academic coaches, and
supportive resources in the peer mentor network. A key element, with respect to utilizing
financial aid resources, will be the inclusion of financial aid counseling during the semester. The
intention of this targeted counseling is to recognize that Pell-eligible, academically at-risk
students face unique financial challenges and for the institution to have the opportunity to
provide customized advice from knowledgeable professionals in the context of the students’
particular circumstances. This counseling is augmented by peer mentor support from mentors
who, themselves, were in similar situations earlier in their collegiate careers. The combination of
expert advice, along with a culturally similar figure that can help translate that advice, is
intended to ultimately increase Pell-eligible students’ utilization of existing financial aid support
programs designed for their needs.
While knowledge gaps in the stakeholder group are key drivers to successful outcomes,
those gaps are more effectively addressed with direct programmatic action. Motivation gaps,
however, can prove to be more difficult to address through scripted programmatic action.

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Nonetheless, motivation gaps are equally important in ultimately driving critical behaviors that
lead to success.
The program seeks to address two primary motivation gaps identified by the research
study. The first of these gaps relates to a lack of motivation related to value. Many Pell-eligible,
academically at-risk students come from underrepresented groups in higher education, and this
can manifest as a deficit in one’s sense of belonging within the academic community. The
program seeks to address this motivation gap through a number of actions designed to reinforce
students’ belief that they are a deserving and welcomed member of the university community.
These actions should enhance students’ sense of belonging and increase their motivation to
address their knowledge gaps. Programmatic actions include affirmations from class instructors
and academic coaches that students do, indeed, belong at the university and that their academic
abilities are sufficient for them to succeed there. Additionally, academic coaches will seek to
guide students to clubs and activities that will help build a sense of community for the student,
providing a support structure they can draw on during times of stress. The most important
component of programmatic action, however, will come in the curated pairing of students with
an appropriate peer mentor. The peer mentor component is crucially important in addressing this
motivation gap because it provides a culturally similar role model that can act as a model for
success while connecting to the students’ circumstances in ways that are impossible for
professional staff. The student’s peer mentor is designed to provide the first anchor point in
establishing a sense of community that enhances students’ sense of belonging at the institution.
Peer mentors play an equally important role in addressing the second motivation gap
present in the stakeholder group, namely the self-efficacy to act upon the new knowledge they
have gained throughout the program. While addressing knowledge gaps are critical, it is also

130
crucially important for the program to address the deficit in confidence that leads many students
to fail to persist in behaviors integral to their success. The program seeks to address this deficit
with a combination of support and accountability interwoven throughout the program. First,
class-based instructors will be able to observe students’ actual academic success behaviors in a
classroom setting, with the ability to provide feedback and opportunities to practice. To support
these more infrequent interactions, peer mentors will provide students ample opportunity to
observe academic success behaviors through informal meetups and study sessions. These peer
mentors also provide an exemplar of the potential outcomes from such behaviors due to their
more senior status in the university as well as their culturally similar backgrounds. The
combination of these effects is intended to increase the students’ confidence in their own abilities
to persist in academic success behaviors. The sustained peer mentor relationship is important in
addressing setbacks students may encounter, providing them opportunities to reflect on what
happened and discuss how they might address similar situations in the future.
Evaluation of the components of learning. Kirkpatrick and Kirkpatrick (2016) identify
five components that contribute to Level 2 learning, including knowledge, skill, attitude,
confidence, and commitment. These various components can be evaluated using a variety of
assessments, with timing being crucial to success as most assessments will be formative
(Kirkpatrick & Kirkpatrick, 2016). Table 34 enumerates the various types of assessments used,
as well as the timing of those assessments. Additionally, the table articulates the evaluation plans
for the different components of the program, describing the nature of the assessment and its
proposed timing.
The evaluation of learning components addresses student knowledge, motivation, and
persistence within the program (formative), at the end of the program (summative), and at

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designated check points throughout the academic year to gauge retention of the principles and
information delivered. As such, the evaluation provides direct feedback to program
administrators on overall effectiveness as well as signaling areas for improvement (Kirkpatrick
& Kirkpatrick, 2016).
Table 34
Evaluation of the Components of Learning for the Program
Method(s) or Activity(ies) Timing
Declarative Knowledge “I know it.”
Pre-intervention assessment of knowledge Start of semester
Formative class-based knowledge checks First half of semester
Informal knowledge checks by coaches and mentors Throughout the semester
Procedural Skills “I can do it right now.”
Observations by class instructors Throughout the semester
Informal assessments by peer mentors Throughout the semester
Attitude “I believe this is worthwhile.”
Observations by class instructors Throughout the semester
Feedback and discussion with peer mentors Throughout the semester
Feedback and discussion with academic coaches Throughout the semester
Confidence “I think I can do it on the job.”
Discussion with peer mentors Throughout the semester
Discussion with academic coaches Throughout the semester
Commitment “I will do it on the job.”
Formal action plan developed in class First month of semester
Informal discussion with peer mentors Throughout the semester

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Level 1: Reaction
Kirkpatrick and Kirkpatrick (2016) describe three components to Level 1 reactions,
engagement, relevance and customer satisfaction. Due to the nature of the academic coaching
intervention being structured as a sustained relationship, as opposed to a discrete training
session, Level 1 reactions to the program will be both formal and informal, both formative and
summative. Examples of informal, formative reactions include instructor, academic coach, and
peer mentor observations of students’ engagement with the program. These informal assessments
are designed to provide feedback about the dynamics of the program and participant action
(Kirkpatrick & Kirkpatrick, 2016). More formal assessments of engagement will include
attendance at class sessions, academic coaching appointments, and peer mentor opportunities.
More summative assessments will be used to measure relevance and customer satisfaction as the
program relies on sustained relationships to build these components.
Table 35
Components to Measure Reactions to the Program
Method(s) or Tool(s) Timing
Engagement
Informal observations from class instructors Ongoing during engagement opportunities
Informal observations from academic coaches Ongoing during engagement opportunities
Informal observations from peer mentors Ongoing during engagement opportunities
Formal attendance at program activities Ongoing as activities occur
Relevance
Formal, summative assessment End of semester
Customer Satisfaction End of semester
Formal, summative assessment End of semester
These assessments will be a component of the end-of-program assessment as they require
more resources and time to complete (Kirkpatrick & Kirkpatrick, 2016). Table 35 describes the

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methods by which program reactions will be assessed, including any tools used in the assessment
and its proposed timing.
Evaluation Tools
Immediately following the program implementation. As this programmatic
intervention is designed to run over the course of a semester, Level 1 assessments as those
described by Kirkpatrick and Kirkpatrick (2016) are intended to be a mix of both formative and
summative assessment. Assessments that are intended to capture engagement are necessarily
formative due to the extended program period, however, assessments of relevance and customer
satisfaction are intended to be provided immediately at the conclusion of the semester. Level 2
assessments, which are designed to assess knowledge, skill, confidence, attitude, and
commitment (Kirkpatrick & Kirkpatrick, 2016), are more appropriate in this context when
delivered earlier in the program. Because of the program length, these assessments provide
valuable course-correction data for program administrators, allowing them to iterate and modify
program delivery much faster than traditional summative assessments. Because of the continuing
relationship between program providers, coaches, teachers and mentors, the assessment process
for Level 1 and Level 2 assessments is designed to be as non-invasive as possible utilizing
observation data and participant feedback during sessions already scheduled for such dialogue.
The intention of this methodology is to minimize survey-fatigue in the participants while still
gathering valuable data that allows program administrators to pivot if the desired outcomes are
not being achieved. A sample observation and feedback assessment form is included in appendix
D. This form is designed to be used by an observer, be that a class-based instructor, academic
coach, or peer mentor, to quickly capture their assessment of demonstrated behavior as it relates
to Level 1 and Level 2 assessment items.

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Delayed for a period after the program implementation. While formative assessments
are important in gauging participant engagement and perceptions, they are not a substitute for
substantive feedback from those participants. Whereas these formative assessments are primarily
utilized to make in-progress, real-time adjustments to program delivery, more substantive
summative assessments are designed to dig deeper into program outcomes in an effort to make
more structural changes in program delivery in future iterations. While Level 3 (critical
behaviors) and Level 4 (internal and external outcomes) are assessed using institutional outcome
data, it is important to supplement those assessments with substantive assessment of Level 1 and
Level 2 benchmarks. These more substantive assessments are designed to be delivered to
participants as they near the conclusion of the program, and are intended to draw out the
participant’s view of the program as they experienced it. Kirkpatrick and Kirkpatrick (2016)
describe this process as necessary to determine what knowledge was actually acquired and able
to be transferred to real-world applications during the course of their semester. These assessment
results will be evaluated in conjunction with corresponding institutional assessments of Level 3
and Level 4 outcomes to determine alignment between the various levels. It is anticipated that
students who report increased knowledge transference and application will ultimately show
correlated increases in Level 3 and Level 4 outcomes. Any disconnect between participants’
perceptions of their learning and their ultimate outcomes will provide program administrators
information as to where the program can be improved in future iterations. A sample of a
summative assessment instrument is included in appendix E.
Data Analysis and Reporting
Formative assessment data collected through observation and feedback interactions will
be immediately available to program administrators for the purpose of making in-progress course

135
corrections in program delivery. At the conclusion of the semester, these assessments will be
delivered to the Office of Institutional Research for collation and inclusion in the larger
assessment regime for the program. Immediately following the conclusion of the summative
assessment period, which typically occurs in conjunction with the conclusion of the semester in
which students participate in the program, all assessment results will be collated by the Office of
Institutional Research and made available to program administrators for an initial feedback
period. This result delivery period is designed to allow program administrators some initial
insights into participants’ post-program views of the program, and allows program administrators
time to consider and plan program adjustments for consideration by administration. These
results, however, are not sufficient alone in warranting substantive program modification, and
are therefore considered preliminary results. As the program is designed to be a retention
intervention, final program modifications are only considered after these assessment results are
integrated with actual programmatic outcomes based on participants’ persistence data is
available, which is typically at the start of the Fall semester. Following the beginning of the Fall
semester, after which final participant outcome data is available, the Office of Institutional
Research will prepare a final program assessment report that integrates formative assessment
data, participant summative assessment data, and programmatic outcome data, to produce a fuller
picture of program outcomes and those intermediate assessment points that build towards those
outcomes. This report will be delivered to program administrators, as well as the Office of the
Provost. Assuming participant feedback aligns with programmatic outcomes for those same
participants, only then will program modifications be considered in consultation with the
Provost. Program administrators will have the Fall semester to prepare the next iteration of the
program delivery for a new participant cohort to begin the program in the Spring semester.

136
Limitations and Delimitations
All studies face limitations, be they access to participants, resources, or external factors,
these limitations expose the study to the risk of faulty, inaccurate, or unrepresentative data
(Merriam & Tisdell, 2016). The purpose of this study was to evaluate the effectiveness of
academic coaching as a retention intervention for Pell-eligible, academically at-risk freshmen
and to examine the knowledge, motivation, and organizational influences that affected that
persistence likelihood.
The first, and broadest, limitation to this research study is inherent in the scope of the
study design. Student persistence is a complex issue impacted by myriad factors and this study
focused solely on the student population as the single stakeholder. By the very nature of this
design choice, additional stakeholders were excluded from examination in an effort to produce a
research study that could be effectively executed. This choice was made intentionally, based on
the premise the students are ultimately the determiners of whether they return the following year.
This choice, however, must acknowledge the influential impact faculty, program administrators,
academic coaches, and student peers have on a student’s ultimate decision to stay in school.
Within the context of this study, those impacts are largely unexamined except to the extent that
they present as KMO influences in the student responses.
A second limitation emerged as a result of the study execution itself. The study was
originally designed as a paired-response, interrupted time series study, whereby study
participants would provide survey responses to KMO influences pre-intervention, then would
participate in the academic coaching intervention, and finally provide survey responses to the
same KMO influences post-intervention. Pairing responses to participants would provide a
granular assessment of the impact of academic coaching on individual KMO influences at the

137
individual student level. This study design did not properly gauge the willingness of study
participants to engage with the assessment instrument. Despite numerous offerings of
participation, robust support from program administrators, and repeated entreaties to participate,
only 16 of 255 potential participants completed the pre-intervention survey. Similarly, only 32 of
255 completed the post-intervention survey, with only two of the 32 participants having
completed both pre and post surveys. This reality fundamentally changed the results analysis
methodology as the original design was unworkable given the actual responses. Ultimately, pre-
survey responses were discarded and all analysis was done using the 32 post- survey responses
as the only survey data included in the analysis. In retrospect, this was potentially more
predictable than the researcher originally anticipated. After the low response from the pre-
intervention survey, follow-up inquiries with program administrators revealed that assessment
response has been historically difficult with this student population, and that additional measures
should be taken in the future to bolter response. This fundamental shift in results analysis,
combined with the relatively low response rate, may explain some of the anomalous findings in
the results. Several KMO influences supported by theoretical and empirical research were found
in the study to be largely irrelevant, despite their strong grounding in literature. These findings
may be a result of survey responses being skewed by respondent self-selection.
A third, and completely novel, limitation was the timing of the research study itself. This
study was designed to assess the effectiveness of a retention intervention and the data collection
occurred during the Spring of 2020. This specific semester was also the semester that COVID-19
emerged as a global pandemic that affected nearly every aspect of life. Students participating in
the Spring 2020 semester had all experiential travel opportunities halted by February, and the
campus at Axios University shut down by mid-March with all students sent home and all

138
instruction continued via an ad hoc virtual delivery mechanism. Due to the disruption, the
college also instituted a Pass/Fail grading scheme for the semester and suspended its academic
suspension policies. Given that the participant student population was already academically at-
risk, this series of events are of a scope and gravity as to make the entire findings unique to the
moment in time. Study participants, who were already targeted with additional academic
supports, were thrust into an online learning modality mid-semester, taught by instructors not
regularly accustomed to such modalities, and limited in their access to additional academic
supports. These factors would generally portend a deleterious impact on their persistence
likelihood. In response to the impacts of COVID-19 on the student population, the institution
authorized a Pass/Fail grading scheme for the semester, allowing students with a “D” or better to
choose a “Pass” grade, which had the effect of fulfilling any degree requirement and eliminating
any GPA impact related to the course. Additionally, the probation and suspension policies were
suspended for the semester, meaning any student whose GPA would have otherwise resulted in a
suspension to continue their studies in the Fall semester. These policy changes would likely have
contributed to a higher persistence rate than would otherwise be expected based on historical
trends. Taken in conjunction, there is likely no real way to determine the net effect of these
confounding events on the persistence rates measured by the research study conducted in the
Spring of 2020. All persistence data from the study should be taken cautiously, however, since
some academically at-risk students did not participate in academic coaching, there exists a
contextual baseline for determining relative persistence within the confines of the Spring 2020
semester cohort.

139
Recommendations for Future Research
Due to the extraordinary nature of the environment during the time of the research study,
additional replication of study metrics is necessary to validate or refute initial findings.
Additional future research seeking to generalize the study results will find seeds of those
research lines in the study findings, including examination of additional stakeholders,
development of cultural capital, the relationship between motivation and behavior, and financial
considerations.
This research study focused on student participants using a literature-based foundation of
assumed KMO influences. Further research focusing on other stakeholders such as program
developers and academic coaches may try to understand which specific influences the program
seeks to address and compare that intent with research literature. Future studies may jettison
assumed influences and focus more keenly on those influences program creators intend to affect,
which may provide a better assessment of actual program performance against desired
performance, rather than assessing program performance against assumed influences.
Additionally, study findings showed the substantive impact of knowledge influences, and
particularly knowledge influences related to what the research literature generally describes as
cultural capital. Further study should expand upon this idea and broaden the range of knowledge
influences that would fit under this umbrella in an effort to identify additional, salient knowledge
factors related to student persistence. A successfully implemented interrupted time series study
could elucidate the specific impact of academic coaching on the development of these
knowledge domains, as academic coaching’s sustained interaction model is uniquely designed to
facilitate this type of development. Additionally, longitudinal study of this specific cohort group
could help provide additional illumination upon key factors affecting student persistence. Such a

140
longitudinal study could incorporate additional, qualitative components that sought to identify
the key drivers of student persistence or departure. A longitudinal study would also allow for the
tracking of organizational implementation of study recommendations, which may help determine
the efficacy of these recommendations as well as any potential institutional hurdles to
implementation.
Building from the study findings, additional research in the motivation domain may
prove fruitful in uncovering the complex interactions between motivation and behavior. This
study focused on students’ perceptions of their motivation levels; however, future research may
further delve into the links between self-reported motivation and observable behavior. Initial
findings suggested a strong link between motivation and persistence, however, links between
motivation and observable academic success behaviors went unexamined. Additional studies
might focus more intently on those links between motivation and behavior in an effort to further
inform program development, monitoring activities, and remediation.
Finally, further study is needed to investigate the links, or lack thereof, between financial
circumstances and persistence. Given that collegiate attendance is inextricably linked to financial
support, it seems self-evident that financial implications would play a substantive role in student
persistence. However, initial research findings showed weak, if any, connections between a
student’s financial circumstances and their ultimate persistence likelihood. Additional research
should further delve into the ways in which finances do, or do not, affect persistence among
students from low socioeconomic backgrounds. This research line is particularly potent as it is
likely to inform institutional resource allocation between financial aid and student support
services. A fuller understanding of the linkages between these various options and their impact

141
on persistence likelihood would be especially helpful for institutions that are having to allocate
scarce resources in the service of student success.
Summary
Substantial research has been done in the area of student success, and overwhelmingly
that research points to specific knowledge, motivation, and organizational gaps that affect the
academic success of Pell-eligible, academically at-risk students. The programmatic intervention
described in this study is designed to address those gaps, however providing training for the sake
of training is not encouraged, as learning outcomes and metrics to determine results should be at
the core of training planning (Kirkpatrick & Kirkpatrick, 2016). The New World Kirkpatrick
Model provides an excellent framework for evaluating the effectiveness of this program by first
focusing on the intended outcomes, identifying critical behaviors that lead to those outcomes,
and drilling down to the drivers that affect those behaviors. By disaggregating program
outcomes, from the very broad to the very specific, and linking those outcomes together, it
becomes possible to assess the program along multiple dimensions to determine where
adjustments need to be made. This approach moves beyond the traditional training evaluation
model whereby training is simply assessed through engagement, and instead links those initial
assessments to the broader program goals. Conversely, more formative assessment allows
program administrators insight into checkpoints that should ultimately lead to program
outcomes. This is informative when program outcomes are not as desired, as they provide more
granular data on the performance at various checkpoints.
This study, and studies like it, are important tools for administrators seeking to improve
their institutional effectiveness. For universities, especially public universities, to live up to the
ideals upon which they were founded they must continue to improve upon their ability to educate

142
every student they accept. This awesome responsibility to educate, laid upon universities, cannot
be understated as it serves as a central pillar of American democracy. Thomas Jefferson (1820)
spoke of this responsibility to educate by stating:
I know no safe depository of the ultimate powers of the society, but the people
themselves: and if we think them not enlightened enough to exercise their control with a
wholesome discretion, the remedy is, not to take it from them, but to inform their
discretion by education. (September 28, 1820)
This idea requires that we educate our whole society, so therefore when universities are failing
segments of our population, we remain a dream unfulfilled. Through critical examination of
institutional performance, we can continue to improve upon our ability to educate and enlighten
our citizenry, expanding the promise and privilege of education. Such a goal requires that we
continually review our performance and make efforts to improve success rates with populations
exhibiting achievement gaps. This work requires an in-depth understanding of the knowledge,
motivation, and organizational influences impacting students, and a proactive response from
institutional actors to address those influences. Through this study, and others like it, institutions
may gather insight into those areas where improvement is likely and take action to fulfill the
promises upon which they were founded.

143
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163
Appendices
APPENDIX A: Knowledge and Motivation Assessments
Knowledge Assessment 1 (navigating institutional systems)
I am aware of the resources at my school that can help me to be a more
successful student.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

I know how to add and drop courses from my schedule.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

I know how to use the different student support services offered by my college.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

I know how to appeal a university decision.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

I know how to use the different academic support services offered by my
college.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

I know how to use my financial aid.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

164
Knowledge Assessment 2 (creating systems of support)

It is important to have a network of support.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

When I struggle in college, I know that I have someone to turn to for help.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

I feel comfortable seeking information from those who work at my college.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

I have people in my life who believe I have what it takes to succeed in
college.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

I have the emotional support that I need to get through college.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

There are people I trust who believe I can finishing college.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

There are people in my life that see the value in college.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

165
Knowledge Assessment 3 (understanding of financial circumstances)
I can continue to attend my college without financial aid.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

I can financially afford to finish my college degree without additional support.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

My concerns about college costs limit what activities I can participate in.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

I understand the relationship between my grades and my scholarship.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

Motivation Assessment 1 (expectancy-value)
Going to class every day is important as it affects the outcome of my final grade.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

It is important to me to complete all assigned homework as it contributes to the success I
have in a course.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

It is important for me to study for exams.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

When I don't understand something, I actively seek help.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

166
Motivation Assessment 2 (attribution)
I have control over seeking solutions to academic obstacles that would
prevent me from being a successful student.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

I believe I have control over the academic outcomes I achieve.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

I believe I have control over seeking the knowledge I need to succeed
academically.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

Motivation Assessment 3 (self-efficacy)
I have confidence in my ability to seek help if I need it.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

I am confident I can utilize academic strategies for success.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

I am confident that I have the ability to succeed academically in the face of
challenges.
Strongly Moderately Slightly Slightly Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

167
APPENDIX B: Psychological Sense of School Membership Scale-Adapted
I feel like a real part of Axios University (AU).
Never Sometimes Always
People at Axios University (AU) notice when I’m
good at something.
Never Sometimes Always
It is hard for people like me to be accepted at Axios
University (AU).
Never Sometimes Always
Other students at Axios University (AU) take my
opinions seriously.
Never Sometimes Always
Most instructors at Axios University (AU) are
interested in me.
Never Sometimes Always
Sometimes I feel as if I don’t belong at Axios
University (AU).
Never Sometimes Always
There’s at least one instructor or staff member at
Axios University (AU) I can talk to if I have a
problem.
Never Sometimes Always
People at Axios University (AU) are friendly to me.
Never Sometimes Always
Instructors at Axios University (AU) are not
interested in people like me.
Never Sometimes Always
I am included in lots of activities at Axios University
(AU).
Never Sometimes Always
I am treated with as much respect as other students.
Never Sometimes Always
I feel very different from most other students at
Axios University (AU).
Never Sometimes Always
I can really be myself at Axios University (AU).
Never Sometimes Always
The instructors at Axios University (AU) respect me.
Never Sometimes Always
People at Axios University (AU) know I can do good
work.
Never Sometimes Always
I wish I were at a different school.
Never Sometimes Always
I feel proud of belonging to Axios University (AU).
Never Sometimes Always
Other students here at Axios University (AU) like the
way I am.
Never Sometimes Always

168
APPENDIX C: College Student Stress Scale
For the following items, report how often each has occurred this semester using the following scale:
Never Sometimes Often
0 1 2

For non-zero responses (Sometimes or Often), present a free text response block with the statement
“Please describe any campus resources you utilized to address this matter”

1. felt anxious or distressed about personal relationships _____
2. felt anxious or distressed about family matters _____
3. felt anxious or distressed about financial matters _____
4. felt anxious or distressed about academic matters _____
5. felt anxious or distressed about housing matters _____
6. felt anxious or distressed about being away from home _____

169
APPENDIX D: Sample Observation/Feedback Assessment Instrument
Strongly
Disagree
Somewhat
Disagree
Neither Agree
nor Disagree
Somewhat
Agree
Strongly
Agree
Shows engagement with
learning goals
1 2 3 4 5
Shows understanding of
learning goals
1 2 3 4 5
Shows ability to execute
learning goals
1 2 3 4 5
Displays positive
attitude towards
learning goals
1 2 3 4 5
Displays confidence in
achieving learning goals
1 2 3 4 5
Displays commitment
towards achieving
learning goals
1 2 3 4 5

170
APPENDIX E: Sample Summative Assessment Instrument
Strongly
Disagree
Somewhat
Disagree
Neither Agree
nor Disagree
Somewhat
Agree
Strongly
Agree
Overall, this program
helped me succeed
academically
1 2 3 4 5
This program provided
information and tools
that were relevant to my
success academically
this semester
1 2 3 4 5
This program helped me
understand what things
I needed to do to
succeed academically
1 2 3 4 5
This program helped me
understand how to
utilize resources that
can help me succeed
academically
1 2 3 4 5
Using the things I was
taught would help me
succeed academically
this semester
1 2 3 4 5
I could succeed
academically this
semester
1 2 3 4 5
I engaged in the
behaviors that would
help me succeed
academically this
semester
1 2 3 4 5

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Asset Metadata

Creator Yancey, Jonathan David (author)

Core Title Academic coaching for Pell-eligible, academically at-risk freshmen: an evaluation study

School Rossier School of Education

Degree Doctor of Education

Degree Program Organizational Change and Leadership (On Line)

Publication Date 02/17/2021

Defense Date 12/08/2020

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

Tag academic coaching,academic development,at-risk students,cultural capital,expectancy-value,OAI-PMH Harvest,Pell,self-efficacy,vulnerable populations

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Muraszewski, Alison (committee chair), Stowe, Kathy (committee member), Tobey, Patricia (committee member)

Creator Email jdyancey@usc.edu

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