University of Southern California Dissertations and Theses

The relationship of students' self-regulation and self-efficacy in an online learning environment

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The relationship of students' self-regulation and self-efficacy in an online learning environment

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Content Running head: SELF-REGULATION/SELF-EFFICACY ONLINE

1

THE RELATIONSHIP OF STUDENTS’ SELF-REGULATION AND SELF-EFFICACY IN
AN ONLINE LEARNING ENVIRONMENT

by

Victoria Viernes

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

August 2014

Copyright 2014 Victoria Viernes

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Dedication
I dedicate this work to my late, Tatay Jose and Nanay Marcelina Sumajit, who have
raised me to become the person I am today. I am grateful for their sacrifices and support always.
My past in-laws, Monico and Albina Viernes who have supported me in their humbleness and
caring ways.
Finally, my husband Michael, your unconditional love and support throughout this
journey made this experience possible. Thank you for encouraging me to seize the opportunity.
My children, Megan and Micah who make me strive to be a better person and parent—this is for
you and to remind you to “always do your best!” Mommy did it!
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Acknowledgements
I would like to thank my committee members, Advisor Dr. Brandon Martinez, Dr. Bob
Keim, and Dr. Robert Rueda for your knowledge and expertise in making this a valuable
learning experience! Principal Alfredo Carganilla for supporting me in my endeavors.
My study gang, Alohilani Okamura, Abby Halston, Sonny Rolden, and Chen Wei. You
all have made this learning journey a fun and exciting experience. Your enthusiasm and
motivation helped me make it through our ups and downs.
I’m so thankful that I have all my family and friends to share this accomplishment!

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Table of Contents
Dedication 2

Acknowledgements 3

List of Tables 6

Abstract 7

Chapter One: Overview Of The Study 8

Introduction 8

Distance Learning 10

Self-Regulation 11

Self-efficacy 12

Statement of the Problem 13

Purpose of the study 15

Importance of Study 16

Definition of Terms 18

Chapter Two: Literature Review 20

Distance Education 20

Credit Recovery 23

Costs and Credit Recovery 24

Pedagogy of Credit Recovery 25

Bandura’s Theoretical Framework 27

Social Cognitive Theory 28

Figure 1. Social Cognitive Theory (Bandura, 1997) 28

Self-efficacy and Motivation 30

Social Cognitive Perspective in Self-regulation for Online Learning 32

Self-Regulatory Attributes for Online Learning Success 33

Motivation 34

Internet Self-Efficacy 36

Time Management 37

Study Environment Management 37

Learning Assistance Management (Help Seeking) 38

Summary 40

Chapter Three: Methodology 42

Research Design 42

Participants 43

Procedures and Instrumentation 43

Variables and Definition of Scales 47

Data Analysis 49

Methodological Assumptions and Limitations 49

Summary 50

Chapter Four: Data Results 51

Descriptive Characteristics of Sample Participants 51

Treatment of Data 52

Summary of Findings 55

Chapter Five: Discussion 57

Implications 57

Recommendations 60

References 64

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Appendix A 73

Appendix B 76

Appendix C 78

Appendix D 80

Appendix E 82

Appendix F 85

Appendix G 90

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List of Tables
Table 1 Selected self-regulatory attributes and related psychological processes comprising
online learner autonomy (Lynch & Dembo, 2004) 34

Table 2 Subscales and Internal Reliability Coefficients for Motivation Strategies for
Learning Questionnaires (Modified from Pintrich et al., 1993, pp. 808) 46

Table 3 Definition of demographic items and subscales us in the modified MSLQ 47

Table 4 Demographic Characteristics of Participants 51

Table 5 Cronbach’s Alpha For Each of the Proposed Subscales 53

Table 6 Summary of correlation for self-regulation subscales and course achievement n=62 54

Table 7 Summary of correlation for self-efficacy and self-regulation attributes n=62 55

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Abstract
One of greatest challenges for educators is motivating students to learn. Unmotivated students
lead to high dropout rates. The purpose of the non-experimental, descriptive-correlational study
was to investigate the relationship between students’ self-regulation and their course
achievement and self-efficacy in an online credit recovery program. The Modified Motivation
Scale Learning Questionnaire (MSLQ) was used in the study as measuring two different
constructs: motivation and learning strategies. A total 62 respondents in an online credit
recovery program participated in the study. Descriptive statistics and the Pearson correlation
were employed to analyze data. Results of the study indicated no direct relationship between
students’ self-regulation and final grade. Students’ self-efficacy and self-regulation levels
support previous research on relationships among self-efficacy, self-regulation and one’s
academic experiences. Self-efficacy and task value emerged as significant in terms of at-risk
students in the online credit recovery program. While it appears that students’ self-regulation in
online learning is not strongly related to academic achievement, this finding does not negate the
importance of self-regulatory learning behaviors. Rather, it informs the need for more research
on online instruction and course design to address the needs of at-risk learners.

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CHAPTER ONE: OVERVIEW OF THE STUDY
Introduction
In most classroom settings, some students are seen as naturally enthusiastic about
learning while others will need or expect their teachers to inspire, challenge, and encourage
them. Motivation is a fundamental concern among teachers, as they continuously strive to seek
what motivates their students (Linnenbrink & Pintrich, 2003). Students who are reluctant to
learning oftentimes do not finish their assignments and sometimes avoid tasks. These reluctant
learners may also be content with just getting by in school. One common factor among reluctant
learners is their perception of themselves, known as self-efficacy. When students’ self-efficacy
is low, then their motivation to perform will be low (Sancore, 2008). Consequently, their self-
esteem and self-efficacy can diminish, especially if students are berated with negative comments.
Students’ reluctance to learn can also be affected by the types of assignments teachers create.
Students presented with too easy or too difficult material will eventually become bored and
unmotivated to succeed. In order to motivate students, teachers should encourage and challenge
their students and help students maintain high self-efficacy (Linnenbrink & Pintrich, 2003;
Sancore, 2008).
Motivating students to learn is one of the greatest challenges for educators who may have
an impact on the community. Unmotivated students may drop out, and this may pose distressing
personal problems and societal consequences. The latest reported dropout rates in United States
by the National Center for Educational Statistics (2007) show that 3.9% of all students (aged 16
to 24) are no longer in school. This translates to approximately 430,000 students dropping out in
one year and 3,462,000 students in total who are classified as dropouts.
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Socially and personally, the consequences of school dropout can be catastrophic, often
resulting in lower income, delinquency, dependence on public assistance, alcohol and drug
involvement, and imprisonment (Ballerand, Fortier & Guay, 2007; Dynarski & Gleason, 2002).
School dropout is a mystery in that many academically capable adolescents view school as being
meaningless and punishing. In a 2002 survey of sophomore dropouts, the top five reasons for
leaving school were that they missed too many days (43.5%); thought it is easier to get a GED
(General Education Diploma) (40.5%); were getting poor grades/failing (38%); did not like
school (36.6%); and could not keep up with the school work (32.1%) (National Center for
Education Statistics, 2006). Although these multiple reasons exist for dropout behavior, the link
is undeniable between motivation and performance within a learning context. Poor academic
motivation is an inevitable precursor on underachieving school performances, and
underachievement is a strong predictor of school dropout (Vallerand et al., 1997).
Researchers used a large-scale investigation to study factors that predict school dropout.
Lan and Lanthier (2003) followed students from 8
th
through 12
th
grade who eventually dropped
out and concluded that the transition to high school in the 9
th
grade is a “critical yet neglected
time” for at-risk students (p.309). Students who drop out typically fall behind in measures of
academic achievement. Rumberger and Palardy (2005) found that schools that were effective in
raising test scores were not effective in reducing dropout rates, which seems to indicate that
schools focused on the outcome of the majority do not reach the students who are dropout risks.
In Hawaii, approximately 20% of students do not graduate on time (DOE AYP Report,
2010). Also, the revised Hawaii State Strategic Plan states, “65% of all jobs in Hawaii will
require some post-secondary training beyond high school by 2018.” It also states, of the 205,400
job vacancies, only 13,000 will be available for high school dropouts.
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Thus, there is a great need to understand how to motivate students who are disengaged in
the classroom and who are at risk of dropping out of school. A focus on academic achievement
outcomes may neglect learning processes (e.g. academic motivation, student engagement) and
context (e.g. safety, sense of belonging, social support). Educators must place more attention on
the learning processes and contexts with students in attempts to reverse the long-standing
patterns of school failure (Alderman, 2004).
Twenty years ago, the General Accounting Office reported “the social costs of the
dropout problem include an under skilled labor force, lower productivity, lost taxes, and
increased public assistance and crime” (p.6). All those factors are still true today, and students
who leav their education prematurely remain an enormous problem for the public school system.
One advantage, 20 years later, is the promise that online learning holds as a tool for engaging
these students (Watson & Gemin, 2008).
Distance Learning
The dropout epidemic across the United States is concentrated disproportionately among
racial and ethnic minorities of low socioeconomic status who are invariably enrolled in schools
with high dropout rates (Shore & Shore, 2009). America continues to face this challenging
social issue but is also equipped with potential solutions resulting from technological growth.
These promising solutions involve presenting potential dropouts with innovative, technology-
driven opportunities that hold great promise for addressing the dropout rate in the United States
(Blueprint for Reform, 2009; Duncan, 2010; Gates Foundation, 2009; Watson & Gemin, 2008).
It is imperative that educators, policymakers, and all stakeholders take immediate steps to offer
students at risk of dropping out of school the technological intervention of online credit recovery.
Credit deficiency may lead to students’ decisions to drop out of high school (Bridgeland, DiIulio,
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& Burke Morison, 2006; Institute of Education Sciences [IES], 2009). High school students are
expected to earn credits for each class completed and, traditionally, when they do not pass a
class, they have been required to retake the same face-to-face class in the same amount of time.
Online credit recovery interventions enable students to retake courses that they previously failed
and for which they, therefore, did not earn credit (Watson & Gemin, 2008). This opportunity
provides students at risk of dropping out of high school due to severe credit deficiency a great
opportunity, as they are able to work at their own pace, gain confidence, and potentially recover
more credits than would be possible in the traditional classroom (Watson & Gemin, 2008).
Online credit recovery programs have the potential to increase the engagement and
achievement of at-risk students through technological interaction, personalization, and feedback
(Biesinger & Crippen, 2008; e2020, 2009; Watson & Gemin, 2008). If online credit recovery
programs are successful in providing socioeconomically and academically disadvantaged at-risk
students with opportunities to recover credits and, thus, avoid dropping out, online credit
recovery opportunities must continue to spread across public schools with low graduation rates.
The importance of self-regulation in improving learning outcomes in online and face-to-face
formats cannot be overstated. Research literature has concluded that students who are more able
to regulate their learning perform better than those students who are less able to regulate their
learning (Schunk & Zimmerman, 1998; Zimmerman & Schunk, 2001).
Self-Regulation
Self-regulated learning refers to “learning that occurs largely from the influence of
students’ self-generated thoughts, feelings, strategies, and behaviors, which are oriented toward
the attainment of goals” (Schunk & Zimmerman, 1998, p. viii). Also referred to as academic
self-regulation, SRL has been studied in traditional classrooms as a means of understanding how
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successful students adapt their thoughts, feelings, and actions to improve learning. In general,
investigations have consistently found that students with adaptive self-regulatory beliefs,
emotions, and behaviors outperform their less-adaptive counterparts (for a review, see Pintrich,
1999).
Although various conceptualizations of academic self-regulation exist (for a review, see
Boekaerts et al., 2000), several scholars have found social cognitive models to be particularly
useful in analyzing student success in online contexts (Artino, 2007b; Hodges, 2005; Militiadou
& Savenye, 2003). Social cognitive models of self-regulation distinguish themselves from purely
cognitive theories in that they focus on the links between students’ personal perceptions and their
use of self-generated learning strategies (Pintrich, 1999; Zimmerman, 2000). Moreover, social
cognitive models are concerned with explaining how these personal perceptions and associated
behaviors are ultimately influenced by contextual features of the learning environment (Pintrich,
2000; Zimmerman, 2000). While most self-regulation learning theorists acknowledge the
influence self-regulation, existing research (Pintrich & DeGroot, 1990; Schunk, 2005) has also
suggested the relationship between students’ academic self-efficacy and their use of self-
regulating learning strategies.
Self-efficacy
Self-efficacy is defined as “beliefs in one's capabilities to organize and execute the
courses of action required to produce given attainments” (Bandura, 1997, p. 3). That is, self-
efficacy beliefs allow someone to answer the question, Can I do this? The “this”, of course, is
situation-specific, and individuals may find self-efficacy beliefs varying from situation to
situation. Peterson and Arnn (2005) argue that self-efficacy is the foundation of human
performance. They conclude that research in the area of self-efficacy may provide information
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that will improve workplace performance. In the context of human learning, Zimmerman and
Schunk (2003) agree, remarking, "the predictive power of self efficacy beliefs on students'
academic functioning has been extensively verified" (p. 446). Peterson and Arnn (2005) also
describe the four major sources of self-efficacy as developed by Bandura (1997) and posit that
human performance technologists must consider those sources as they develop training
interventions.
Statement of the Problem
Although research involving K-12 online learning has explored various purposes and
populations, there is still a dearth of research involving online learning with the specific purpose
of credit recovery for at-risk students (IES, 2009; Stillwell, 2009). Online learning is growing
rapidly in the K-12 sector (Bonk, 2009; Patrick & Powell, 2009; Watson et al. 2009) but has
been most prevalent in higher learning, where the bulk of research has been conducted (Means et
al., 2009). Previously, Cavanaugh et al. (2005) published a meta-analysis focused on K-12
distance learning and found that virtual classrooms produced comparable or better levels of
student achievement than similar traditional classrooms. These results, however, were exclusive
to virtual classrooms and were not exclusively concerned with at-risk students. Patrick and
Powell (2009) revealed similar results in examining the effectiveness of online learning. Online
learning was found to have as good as or better outcomes, but these results were also not
exclusively concerned with at-risk students.
Extensive research has explored the status of virtual schools and the growth of online
learning in the K-12 population (Means et al., 2007; Watson & Gemin, 2009; Watson et al.,
2008) but little exploration has concerned online learning with students at risk of dropping out of
high school. The need for research has been established, but the evidence is lacking. Online
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learning holds great potential for all students (Bonk, 2009; Watson et al., 2009) but research is
needed to establish how the online format interacts specifically with at-risk students.
Prior to the proliferation of the Internet, few studies were performed regarding self-
efficacy and computers (Oliver & Shapiro, 1993). Most of the studies cited by Oliver and
Shapiro (1993) are unpublished doctoral dissertations, which emphasize the dearth of research on
the subject at the time of their review of the literature. The research on self-efficacy and
computers is primarily related to people's confidence in using technology.
It has been shown that perceived efficacy for using computers leads to a higher likelihood
of using them (Ertmer, Evenbeck, Cennamo, & Lehman, 1994; Hill, Smith, & Mann, 1987;
Jorde-Bloom, 1988). The studies by Hill and colleagues (1987) and Ertmer and coauthors (1994),
determined that the quality of the experience (that is, positive experience), not simply any prior
experience, increased self-efficacy for computers and, thus, influenced future usage. Several
studies have shown that anxiety toward computer use is a major obstacle in educators' adoption
of computers (Hakkinen, 1995; Mclnerney, Mclnerney, & Sinclair, 1994; Reed & Overbaugh,
1993; Stimmel, Connor, McCaskill, & Durrett 1981).
Most self-efficacy research conducted in online learning environments has used college-
aged participants. This is a noticeable difference from the literature on academic self-efficacy in
traditional and online learning environments. As can be observed earlier in this discussion,
research on self-efficacy in traditional learning environments has had time to establish the
breadth and depth of studies at all age levels of learners. This breadth has not yet been
established in the study of self-efficacy in online learning environments.

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Purpose of the study
As online distance learning has grown, so, too, has interest in self-regulated learning
(Boekaerts, Pintrich, & Zeidner, 2000). Self-regulated learning (SRL) has been defined as “an
active, constructive process whereby learners set goals for their learning and then attempt to
monitor, regulate, and control their cognition, motivation, and behavior, guided and constrained
by their goals and the contextual features of the environment” (Pintrich, 2000, p. 453).
In the last 10 years, several educational psychologists (Dabbagh & Kitsantas, 2004;
Hartley & Bendixen, 2001; Schunk & Zimmerman, 1998) have suggested that students require
considerable motivation and self-regulation to stay engaged, guide their cognition, and regulate
their effort in online situations. This suggestion stems from the belief that learning on the Web
tends to be much more autonomous and self-directed (Allen & Seaman, 2007). The highly
independent nature of online learning is thought to be due, in part, to the lack of structure and
guidance that normally comes from face-to-face, social interactions with an instructor and other
students (Moore & Kearsley, 2005).
The purpose of this study was to examine the self-regulation and self-efficacy of students
who are enrolled in an online credit recovery lab program and to determine the benefits from the
online learning environment. Alfred Bandura’s social cognitive theory was used as a theoretical
framework in understanding the online environment. Then, a discussion on self-regulation of
motivation model is presented and, finally, literature on online credit recovery is examined. The
aim of this study was to address the following research questions:
1) What is the relationship between students' self-regulation in an online credit recovery program
and their course achievement?
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2) What is the relationship between students' self-efficacy and their self-regulation in an online
credit recovery program?
Importance of Study
The consequences of failing core academic courses during the first year of high school
are dire. Research by the Consortium on Chicago School Research demonstrates that students
who fall “off track” during the first year of high school have a substantially lower probability of
graduating than students who stay “on track” (Allensworth & Easton, 2005).
Credit recovery is one strategy to deal with high failure rates. The primary goal of credit
recovery programs is to give students an opportunity to retake classes they failed in an effort to
get them back on track and keep them in school (Watson & Gemin, 2008). Most recently, as
schools across the nation struggle to keep students on track and re-engage students who are off
track, online learning has emerged as a promising and increasing popular strategy for credit
recovery; more than half of respondents from a national survey of administrators from 2,500
school districts reported using online learning in their schools for credit recovery with just over a
fifth (22%) reporting “wide use” of online learning this this purpose (Greaves & Hayes, 2008).
Despite the growing use of online courses for credit recovery, the evidence base is thin (Watson
& Gemin, 2008).
Studies about online learning and the at-risk high school learner have been few and are
needed to better understand learning needs of this population and to assist educators in making
program decisions for these at-risk learners. The concepts of students’ self-efficacy and self-
regulation are an essential component in student learning and achievement. This study is
intended as a contribution to investigating the role of learner self-regulation and self-efficacy in
an online credit recovery context.
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As online enrollments continue to grow at the high school level, and as some states
require online experiences as part of their graduation requirements, an understanding of unique
characteristics and learning needs of at-risk high school students in online credit recovery
settings may effectively begin to promote and support a successful online experience for at-risk
learners.
This study explored one such academic intervention, a computer-based credit recovery
system for at-risk high school students. The study explored whether students’ self-regulation and
their self-efficacy are related to course achievement in an online credit recovery program. From
the results of this study, administrators may be able to create effective decisions, in terms of
student success, supported by research, surrounding the implementation of an online credit
recovery tool in the high school setting. School administrators may begin to make data-driven
decisions regarding the number of classes scheduled into the academic day for computer-based
credit recovery and what teacher allotments are needed to schedule for student success.
Ultimately, if it can be shown that computer-based courses, such as an online credit recovery
program, increase at-risk student learning outcomes, then it would be feasible for administrators
to look into expanding and instilling research that focuses on the kinds of courses that optimize
facilitation flexibility, students’ self-efficacy, self-regulation and increased engagement in a way
that makes fiscal sense to unique districts situations. Graduation rates could increase and
dropout rates could decrease as students recover lost credits faster and remain on track to
graduate n a flexible environment.

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Definition of Terms
The following definitions refer to terms used throughout this study:
At-risk: The U.S. Department of Education (1992) defined an “at-risk” student as one who is
likely to fail at school. School failure is typically seen as dropping out of school before
graduation. The Department of Education report examines seven sets of variables associated
with at-risk students: basic demographic characteristics; family and personal background
characteristics; the amount of parental involvement in the student’s education; the student’s
academic history; student behavioral factors; teacher perceptions of the student; and the
characteristics of the student’s school.
Credit recovery: The most common definition of credit recovery is simply “a structured means
for students to earn missed credit in order to graduate.” (Center for Public Education, 2012)
Credit Recovery Program: This term includes any educational program with the central intent of
assisting students in finishing classes and gaining credits toward graduation (Watson & Gemin,
2008).
Distance Education: Moore and Kearsley (2005) defined the “planned learning that normally
occurs in a different place from teaching, requiring special course design and instructional
techniques, communication through various technologies and special organizational and
administrative arrangements” as distance learning (p. 2).
Dropout: This term refers to a student who left high school between the beginning of one year
and the following year without earning a diploma or taking an equivalency test degree (NCIS,
2009).
Face-to-Face Learning: This term is defined as a teacher-student in-person interaction and may
be used interchangeably with “traditional” classroom.
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Online Learning: This term will be used interchangeably with “e-learning,” “distance learning,”
and “virtual learning,” and includes a variety of platforms and instructional structure, all of
which are delivered via the Internet.
Self-efficacy: people’s beliefs about their capabilities to produce designated levels of
performance that exercise influence over events that affect their lives. Self-efficacy beliefs
determine how people feel, think, motivate themselves, and behave. Such beliefs produce these
diverse effects through four major processes. They include cognitive, motivational, affective, and
selection processes (Bandura, 1994).
Self-Regulation: Zimmerman (1989) described self-regulation as the degree to which
“individuals are meta-cognitively, motivationally and behaviorally active participants in their
own learning experiences” (p. 329).

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CHAPTER TWO: LITERATURE REVIEW
The concepts introduced in the overview below provide a framework for analyzing
results of the study. There are four main bodies of literature examined in Chapter 2 of this study:
distance education, credit recovery, Bandura’s theoretical framework, and self-regulatory
attributes for online learning success.
Distance Education
Distance education is hardly a new phenomenon. For example, in the United States,
correspondence courses have provided online learning to students around the country since the
creation of the postal service in the 19
th
century (Phipps & Merisotis, 1999). This trend continued
well into the 20
th
century with the advent of television and radio—media technologies that
allowed for expanded opportunities to learn at a distance (Moore & Kearsley, 2005). Today,
computer-mediated communications and the Internet have resulted in a rapid and explosive
interest in online distance education (Larreamendy-Joerns & Leinhardt, 2006).
Within the industrialized world, online education, either singly or as part of blended (part
online, part face-to-face) education models, has become increasingly extensive in a wide array of
learning domains (Bates, 2000, 1995; Edelson and Pittman, 2001; Kearsley, 2000). The dramatic
growth of online education is demonstrated by recent reports from the National Center for
Education Statistics (Sikora & Carroll, 2002) and the Council for Higher Education
Accreditation (2002).
Bates (2000) characterized online distance education as a continuum ranging from mixed
face-to-face and distance teaching/learning on one end to complete distance teaching/learning on
the other end. Distributed education represents an eclectic blend of technologies and modalities
to enable both synchronous (real time) and asynchronous (anytime) teacher-learner and learner-
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learner interactions in a single course or program. Blended education is a form of distributed
education, utilizing both distance and face-to-face modalities to deliver instruction.
According to the study by Smith, Clark and Blomeyer (2005), K-12 distance education in
the United States is “increasingly a tool of education reform” (p. 3); as of September, 2008, 44 of
the 50 U.S. states “offered significant online opportunities for students” (Watson, Gemin, &
Ryan, 2008, p. 8). Online learning in the United States was used initially to allow school districts
to offer courses they would not be able to offer otherwise (Watson & Gemin, 2008); today,
school districts are finding that online and blended courses are effective ways to reach at-risk
students, those who potentially may not graduate from high school (Watson & Gemin, 2008).
Lessons can be learned from research showing growth in post-secondary online education
and the exploration of the effectiveness of online courses for at-risk students at the high school
level. Some of these lessons regard addressing the needs of at-risk learners even earlier in
primary and secondary education. “With almost 4 million students or 22% of the higher
education population presently enrolled in fully online courses, it would be appropriate to
consider that online instruction is maturing in postsecondary education. The same cannot be said
about online learning in primary and secondary education where online instruction is still
considered to be in its nascent stages” (Picciano & Seaman, 2009, p. 2).
Despite growing enrollment and venues for offering online learning, a “pressing need”
exists “for efforts to organize and systematize research on the effectiveness of K- 12 online
learning” (Smith, Clark, & Blomeyer, 2005, p. 14). Cavanaugh (2005) concurred that, in the
midst of rapid changes in technology and the placement of education online, only a small body
of research exists “to guide instructors, planners, or developers” (p. 6) at the K-12 level.
Research has not kept pace with the growth of online learning at the K-12 level.
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Research involving online learning at levels other than college and adult falls into two
types. One typical study type investigates characteristics of successful online students and the
other looks at characteristics of students’ learning environments (Roblyer, Davis, Mills,
Marshall, & Pape, 2008; Scribner, 2007). To date, studies about online learning and at-risk high
school learners have been few, and there is a “dearth of information on the extent and nature of
online learning in K-12 schools” (Picciano & Seaman, 2009, p. 4) exists. Studies about online
learning and the at-risk high school learner are needed to better understand their learning needs
of and to assist in making program decisions for these fragile learners. One venue that may assist
with educational reform or change for at-risk learners is the online environment whereby school
districts provide educational opportunities for high school students in place of, or in addition to,
traditional face-to-face classrooms. Unlike a face-to-face course where an instructor is available
during a specified class time to clarify content and to adjust and modify instructional delivery,
creators and implementers of online courses must consider learners’ needs in advance of placing
the course online (Siemens, 2002). Watson and Gemin (2008) suggested that reaching at-risk
students using online learning “presents a specific set of issues” (p. 3) that should be considered.
Distance education institutions need to realize what makes learners at risk in order to
accommodate them. According to the Southwest Educational Development Laboratory (SEDL),
"a number of variables related to a student's family or personal background appears to contribute
to increasing the risk of failure in school" (2003, p. 1). Some of the most frequently cited factors
that contribute to being at risk are "single head of household, low socioeconomic status, minority
group status, limited English proficiency, low educational attainment of parents, disabilities,
psychosocial factors, and gender" (SEDL, 2003, p.1). Additionally, at-risk learners share certain
characteristics. They are "sensitive to failure, intimidated by faculty, unfamiliar with support
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systems," have "undeveloped work ethic, little exposure to smarter students," and they may be
"immature" (Mayberry, 2003, p. 4-5). Learning styles can have an impact as well. Diaz and
Carnal (1999) found that most online students are independent style learners, in contrast to
classroom learners, who are more dependent and collaborative. It is recommended that
instructors conduct a learning-style inventory because "knowledge of student learning
preferences can aid faculty in class preparation, designing class delivery methods, choosing
appropriate technologies, and developing sensitivity to differing student learning preferences
within the distance education environment" (Diaz & Carnal, 1999, p. 2). Wheeler, Miller, Halff, ,
Fernandez, Halff, Gibson, & Meyer (1999) assert that "at-risk students have the potential to
succeed if their needs are recognized and addressed" (p. 2).
Credit Recovery
High schools began implementing online credit recovery programs as a response to the
increased emphasis on on-time graduation rates. Credit recovery is traditionally defined as a way
to recover credit for a course in which a student was previously unsuccessful in earning
academic credit towards graduation (Watson & Gemin, 2008). Credit recovery programs, in
general, have a primary focus of helping students stay in school and graduate on time.
Historically, in a world of face-to-face learning, credit recovery was confined to retaking a
course during the regular school day or during summer school. Students who had previously
failed a course retook it in a similar setting and with similar teaching practices. While many
students benefited from these time-tested methods, a significant number did not (Tyler &
Lofstrom, 2009).
School systems investigating credit recovery programs for their students began looking
for alternatives to the historical models. According to Watson and Gemin (2008), a new platform
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for credit recovery was online learning. Having flexible, self-paced, online courses that students
could complete anytime and anywhere offered many advantages to schools and students.
Students could use the affordances of 21
st
century learning successfully to complete a course
they were unsuccessful in completing in a traditional classroom.
Interestingly, the use of online learning platforms for credit recovery appears to be
increasing. The 2008 report from America’s Digital Schools indicates that more than
60 percent of the 4,000 technology directors surveyed nationwide report usage of a digital
Learning Management Systems (LMS) for credit recovery applications (Greaves & Hayes, 2008)
Costs and Credit Recovery
For a growing number of districts, online and hybrid approaches to credit recovery are
helping to accomplish core goals for graduation without dramatic budget increases or heightened
demands on staff and facilities. From a budgeting perspective, online credit recovery often gives
schools a strategy to retain students in the school and, as a result, keep the funding associated
with those students. The before/after school models enable students to attend school full-time
and still make up the courses they failed previously. Additionally, when schools can identify at-
risk students and bring them back into full-time attendance, schools can restore full funding for
those students.
From a staffing and personnel standpoint, online credit recovery enables one teacher of
record to serve many schools within a district or across a wider geographic area. When schools
provide online credit recovery on-site, students can often be monitored and assisted by part-time
instructional assistants. If the issue is one of facilities, before and after school programs can
leverage existing classrooms, labs and library spaces without significantly adding costs.

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Pedagogy of Credit Recovery
According Picciano and Seaman (2009), curriculum experts frequently point out that the
integration of online learning strategies often opens new opportunities for success for students
enrolled in credit recovery programs. Online learning can provide an environment in which
instruction can be delivered in new or different formats for students who were previously
unsuccessful in a course. Online course delivery provides much needed flexibility and
dimension, including:
• Capacity to deliver multimodal, individualized, and self-paced learning experiences
• Technology to engage students in interactive content, using animation, simulations,
video, and audio, and to provide immediate feedback as students complete tasks and
assessments.
• Flexible pacing to support the learning momentum of each individual student
• Authentically personalized instruction that can focus on the needs of individual students,
so
• Those students do not need to repeat what they already know and can accomplish.
Because students can often learn at their own pace, review and repeat instruction as
needed, and can take advantage of the flexibility offered by a digital learning environment, they
are often better able to grasp the essential content and concepts required for satisfactory course
completion (Picciano & Seaman 2009).
In 2004, a synthesis of new research on K-12 online learning reported results across five
major meta-analyses. The authors reported that students, on average, perform equally or better
academically in formal online learning situations as students under traditional settings (Metri
Group, 2006). Beyond the technological “chic” of online programs, many online courses allow
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students to proceed at their own pace and in the comfort and security of their homes. Many
online credit recovery programs are also individualized and/or modularized so that teachers can
customize the online courses to the specific needs of students and not simply have them retake
the entire course as is typically done in the face-to-face instructional model.
Picciano (Eduviews, 2009) also underscored the impact of changing pedagogical
approaches and their value in online credit recovery stating, “Individualized instruction,
modularization, multimedia infusion, and on-going assessment are some of the pedagogical
techniques that typify many online credit recovery courses. In addition, there is a growing appeal
to using blended learning techniques that attempt to apply the best of both formats to help
students who are struggling with a particular course or content” (p.8). Picciano pointed out that
online credit recovery can also work to remove the stigma for students who have to repeat
courses and can alleviate some of the strain teachers’ experience when re-teaching students who
were unsuccessful the first time. Picciano specifically cited the value of scaffolding modular
approaches to concept and skill mastery, stating that the online environment can be ideally suited
for this kind of instructional approach. Scaffolding involves providing learners with more
structure during the early stages of a learning activity and gradually turning responsibility over to
them as they internalize and master the skills needed to engage in higher cognitive functioning
(Palincsar, 1986; Rosenshine and Meister, 1992). He also noted that online learning can be used
to address and capitalize on a student’s individual learning style and make it possible to
differentiate learning more effectively, based on continuous assessment and adjustments to
individual student needs (Eduviews, 2009). During the 2006-07 school year, Florida Virtual
School (FLVS) students who self-reported taking courses for credit recovery had a passing rate
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of 90.2 percent, similar to the 91.2 percent passing rate for the entire FLVS student population
(Eduviews, 2009).
In summary, online credit recovery expands and optimizes instructional capacity,
leverages available resources, and provides significant opportunities to students who may be at
risk for failing specific courses or dropping out of school entirely, all without adding significant
burdens to staff and facilities costs. When students drop out of school, they are disengaged,
disconnected, and too often discouraged about their in-school experiences. Online credit
recovery is a way to stem the tide of students’ dropping out.
In fact, 80 percent of the students responding to The Silent Epidemic survey said their
chances of staying in school would have increased if classes were more interesting and provided
opportunities for real-world learning (Watson & Gemin, 2008). Online credit recovery
approaches can, indeed, address 21st century students’ needs for engaging learning experiences
in a real-world environment. This approach to credit recovery will not answer the entire problem
of high dropout rates. It will, however, deliver new answers to more students and provide new
and needed opportunities for students to stay in and finish their high school educations (Watson
& Gemin, 2008).
Bandura’s Theoretical Framework
Much of online distance education research has been theoretical and focused on three
general areas: descriptive studies of distance education programs, group academic outcomes
comparison studies (distance class versus face-to-face class), and studies matching individual
learner traits with media variables (Perraton, 2000, 1995; Saba, 2000). These approaches, while
necessary and valuable in their own right, have generally lacked a pedagogically relevant
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theoretical underpinning and have not generated advances in teaching/ learning theory that have
served to benefit both distance teachers and learners (Diaz, 2000; Phipps and Merisotis, 1999).
Alfred Bandura’s social cognitive theory provides an understanding of the learning in an
online learning context. According to Bandura (1986, 1995), human learning occurs when
individuals observe the behaviors of others, abstract information from those behaviors, make
decisions as to which ones to adopt, and, later, enact those selected behaviors. While the meta-
cognitive skills are essential, the affective factors such as beliefs, expectations, introspections
(forethought), and even persistence play major roles in learning. In the social cognitive view,
personal and social change relies extensively on the empowerment of the individual. People can
effect change in themselves through their own efforts. Change is dependent on one's perceived
belief about one’s ability to exercise control. Evaluations of one's performances, resulting in
consequences, play a critical role in changing behavior. Successful consequences tend to be
repeated and retained; failure consequences are discarded (Schunk, 1996). In this regard, social
systems are created by human activity and, in turn, impose constraints and provide resources for
development and everyday functioning (Figure 1).
Social Cognitive Theory

Figure 1. Social Cognitive Theory (Bandura, 1997)
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Social cognitive theory establishes that human behavior is influenced and affected by the
individual, his/her behavior, and his/her environment. Each person affects as well as is affected
by this triadic relationship. The theory establishes that each individual possesses the capacity to
symbolize, develop self-directed forethought, and learn from his or her and others’ individual
experiences (Schunk & Pajares, 2001). According to social-cognitive theory, each individual
possesses a self-regulating system that affects motivation and learner differentiation. This self-
regulating system represents a triadic reciprocity process that is affected by a bi-directional and
interdependent relationship between behavior, personal experiences, and environment (Bandura,
2001). This relationship becomes a triadic interrelation that influences motivation and self-
beliefs. The self-system is a part of the self-regulatory system that each individual possesses. The
self-regulatory system aids in the development of beliefs and behaviors that will enable or
discount actions. Research has shown that self-regulatory behavior can account for academic
achievement (Pajares 1996; 2001a; 2001b; 2002; Pajares & Schunk, 2001). As part of this self-
regulatory system, Bandura introduced the concept of self-efficacy. He defines self-efficacy as
an essential part of human functioning, reciprocally motivating and perpetuating the individual’s
behavior (Bandura, 2001). The concept of self-efficacy can be considered as the theoretical
foundation to determine the individual differences in an online credit recovery context.
Bandura (2001) explains the process of thought and action as regulated by a self-system
that enables individuals to exercise control of their thoughts, feelings, and actions. Pajares (1996)
describes the self-system as one that “houses one’s cognitive and affective structures and
includes the ability to symbolize, learn from others, plan alternative strategies, regulate one’s
own behavior, and engage in self-reflection” (p. 1). The self-system is a self-regulatory
subsystem that mediates the influences of each of the triadic parts of an individual’s behavior,
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thoughts, feelings, and motivation. Based on the results of the interactions among environment,
personal characteristic, and beliefs, the individual’s likelihood of taking similar actions increases.
Each person affects his or her environment and is influenced by his or her actions. The thoughts
resulting from this interrelationship become a mediator between knowledge and behavior
(Pajares, 1996).
Each person’s experience forms an important part in the development of self-regulation
(Bandura, 2001; Pajares, 1996). The individual is, therefore, accumulating perception about his
performances that influence his or her self-belief. Through this bi-directional reciprocal process,
the individual is in control of his thoughts, environment, and behavior. The self-system is
composed of experiences and beliefs that each person forms of his or her abilities. According to
Bandura (2001), self-efficacy is the concept by which each person’s experiences, abilities, and
thoughts merges into one road. This concept could account for the online learner level of
motivation in an online credit recovery context.
Self-efficacy and Motivation
Bandura (2001) defines self-efficacy as “people’s judgment of their capabilities to
organize and execute courses of action required to attain designated types of performances” (p.
2). Self-efficacy regulates the way in which an individual perceives his or her competency. This
perception influences an individual’s ability to complete a task and a set, attainable goal (Pajares
& Schunk, 2001). This perception also affects the level of motivation and resilience the
individual develops. Each individual develops a visualization of self, creating what Bandura calls
a self-system. This self-system provides cognitive and affective information basic to the control
of thoughts, feelings, and actions. An individual perception activates the self-system and
provides information regarding past experiences, accomplishments, and failures. These
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experiences are processed, stored, and used by the self-efficacy beliefs system, which, in turn,
affects experiences, thoughts, behavior, and environment. The self-system then forms
conceptualizations of the individual’s abilities. These perceptions become the motivational drive
that accompanies action.
Self-efficacy directly affects the levels of motivation in terms of individuals’ active
choices. By determining what activities they are more likely to accomplish, the adult learner
engages in actions in which s/he is more likely to succeed. According to Pajares (2001a, 2001b)
and Schunk and Pajares (2001), research studies have demonstrated that self-efficacy affects the
levels of motivation, learning, and achievement. Social cognitive theory proposes a bi-directional
interrelation between each part of the individual’s experience and the cognitive summary of the
experiences each individual accumulates through the years. Each individual forms a set of self-
efficacy beliefs that account for his or her motivation and resilience in completing an activity.
Students’ perceptions are based on information obtained from “actual performances, their
vicarious experiences, and the persuasions they receive from others and their physiological
reactions” (p. 2). High self-efficacy contributes to beliefs in the individual’s capacity to learn,
motivating experiences and the efforts placed on learning based on information obtained from
“actual performances, their vicarious experiences, and the persuasions they receive from others
and their physiological reactions” (p. 2). High self-efficacy contributes to beliefs in the
individual’s capacity to learn, motivating experiences and the efforts placed on learning.
Self-efficacy is not only a self-judgment of an individual’s ability, but also the beliefs
that an individual develops regarding his or her ability to successfully complete a task. The
development of self-efficacy is the result of the triadic interrelationship among environment,
personal characteristics, and behavior. Self-efficacy influences an individual’s will to complete a
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task, to perform an action, or engage in an activity. This perception of self-efficacy interrelates
with the individual’s ability to complete a task. According to social cognitive theory, each
individual’s motivation is driven by self-efficacy beliefs as well as other self-regulatory
characteristics (e.g. self-esteem). Self-efficacy influences motivation through the individual’s
perception of his or her ability. An individual can have a high level of motivation and self-
efficacy on a learning task, but his or her actual experience may affect the individual’s belief of
his or her ability to complete such a task. For example, a student may be highly motivated and
confident that he can pass his driving test; however, he may end up failing. The learning process
is then mediated by self-efficacy, which motivates and affects the effectiveness of self-directed
behavior (Pajares, 1996).
In summary, self-efficacy is an essential part of learning that affects the individual’s
belief that it is possible to engage and complete a task. If self-efficacy affects learning and
achievement in an online credit recovery context, teachers can look into building self-efficacy in
the teaching practices of online credit recovery programs, which may enhance the learning of at-
risk students.
Social Cognitive Perspective in Self-regulation for Online Learning
Numerous online distance education researchers have identified learner autonomy as an
important factor in academic success (Holmberg, 1995; Jung, 2001; Kearsley, 2000; Keegan,
1996; Peters, 1998). Merely knowing the importance of this factor in online distance learning,
however, does not help in understanding precisely how autonomous online learners function,
how they exercise their autonomy effectively, or what specific factors are involved in successful
autonomous online distance learning.
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Based on the literature review, the social cognitive perspective of self-regulation provides
a framework for online education research that can offer insight as to the functioning of
autonomous learners. Working within this perspective, Zimmerman (1989) defined academic
self-regulation as the extent to which learners are meta-cognitively, motivationally, and
behaviorally active in achieving their learning goals. Self-regulated learners set task-specific
learning goals and employ appropriate strategies to attain those goals. They monitor and evaluate
their progress and adjust their learning strategies as necessary. They motivate themselves and
focus on learning in the face of distractions. They seek assistance as necessary and ensure that
their learning environment is conducive to learning. In short, self-regulated learners are active,
adaptive constructors of meaning who control important aspects of their cognition, behavior, and
environment in attaining their learning goals (Pintrich, 2000).
Zimmerman (1998, 1994) argued that a learner’s personal choice and control are defining
conditions for self-regulation. This emphasis on personal choice and control, important elements
of learner autonomy, is significant for distance learners (Doherty, 1998). Zimmerman (2002)
pointed out that self-regulation is also important because it addresses a major educational goal in
that it enables the development of lifelong learning skills. The advent of online education has
provided a context ideally suited to this pursuit of on-going education.
Self-Regulatory Attributes for Online Learning Success
There are five self-regulatory attributes that were selected as being important for online
distance learning success (Lynch & Dembo, 2004): motivation (self-efficacy and goal
orientation), Internet self-efficacy, time-management, study environment management, and
learning assistance. Each of these self-regulatory attributes and related psychological processes
are discussed below in Table 1.
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Table 1
Selected self-regulatory attributes and related psychological processes comprising online
learner autonomy (Lynch & Dembo, 2004)
Self-Regulatory Attributes Psychological Processes
Motivation’ Efficacy beliefs: confidence in ability and
skills to successfully perform specific learning
tasks
Goal orientation: reasons why learner engages
in learning task
Experience with internet technology Internet self-efficacy: confidence in using
and/or learning the technologies employed in
online education
Time management skills The ability to manage and structure learning
time effectively and productively
Study environmental management skills The ability to ensure that the study
environment is conducive to learning and
restructure is necessary
Learning assistance management skills (help
seeking)
The ability to know when help is needed,
identify sources of help, obtain help, and
evaluate the help received.

Motivation
Motivation for learning focuses on why learners choose to learn (Pintrich and Schunk,
1996), and is a dimension of distance learner autonomy frequently cited in the distance education
literature (Bates, 1995; Holmberg, 1995; Kearsley, 2000; Keegan, 1996; Moore, 1998; Olgren,
1998; Schrum and Hong, 2002). Simply knowing that motivation is an important variable in
successful distance learner autonomy, however, is not particularly helpful. It is necessary to
isolate specific components of motivation that can contribute to learner autonomy. Two
important components of motivation are beliefs about one’s personal efficacy (ability) for
mastering a specific task and the personal goal orientation one brings to a course of study.
Personal perceptions of self-efficacy are a critical element of motivation (Bandura, 1997;
Pintrich and Schunk, 1996). Bandura (1997) defined self-efficacy as individuals’ judgments of
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his/her ability to plan and carry out the necessary behaviors to achieve specific goals.
Linnenbrink and Pintrich (2002) pointed out that adaptive self-efficacy beliefs could function as
enablers of academic success. Learners with high self-efficacy are likely to employ adaptive self-
regulatory learning strategies and study skills. Learner perceptions of personal efficacy,
therefore, have a reciprocal relationship with the self-regulatory processes that affect motivation
and performance. A high sense of self-regulatory efficacy enhances task performance efficacy,
which, in turn, motivates further self-regulation in pursuit of further academic attainment.
Self-efficacy has been noted as important in successful distance learning (Gibson, 1998).
A study of online learners by Wang and Newlin (2002a, 2002b) found that self-efficacy for
course content as well as self-efficacy for technology skills was predictive of learner
performance in the class. A study by Joo, Bong, and Choi (2000) indicated that self-efficacy for
self-regulated learning related significantly though indirectly (through more specific self-efficacy
variables) to student performance. A study by Zhang, Li, Duan, and Wu (2001) found that self-
efficacy was positively related to students’ goal orientation and self-regulatory learning skills.
A second component of motivation is a learner’s personal goal orientation. Pintrich,
Smith, Garcia, and McKeachie (1991) defined goal orientation as a learner’s general goals or
orientation toward a course. Intrinsic goal orientation is defined as the degree to which a learner
participates in a learning task in order to meet a personal challenge, satisfy personal curiosity,
and/or attain personal mastery over the elements of the task. Task performance, therefore, is an
end in itself and not a means to an end. Intrinsic goal orientation contrasts with extrinsic goal
orientation in that the latter signifies participation in a task as a means to an end (such as grades
or rewards) and not as an end in itself.
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Beatty-Guenter (2001), in reviewing the literature on course completion rates for distance
students in Canadian community colleges, identified goal orientation as a significant attribute of
those learners who completed their courses. Thompson (1998) noted that the fact that distance
learners set clear goals is an important element of performance. Gibson (1998) suggested that it
is important for distance learners to be able to assume control over their learning goals, methods,
and evaluation strategies. Several research studies have found that goal setting by distance
learners contributes to performance (Curry, Haderlie, and Ku, 1999; Schrum and Hong, 2002;
Whipp and Chiarelli, 2001). Learners who are goal oriented (either intrinsically or extrinsically)
are more likely to set specific learning goals than learners with poor goal orientation. Those
learners with an intrinsic goal orientation, however, are more likely to set mastery oriented goals.
Motivation, then, is a key element of autonomous learning. One component of motivation
is self-efficacy, or learners’ judgments about their ability to accomplish a task, as well as their
confidence that they possess the skills to perform the task. Another component is a learner’s goal
orientation, either intrinsic or extrinsic. It is important to note that there are other components of
motivation not so prominent in the distance education literature, such as the value learners
ascribe to specific learning tasks (how important, interesting, or useful they are to the learner),
control of learning beliefs (the learner’s belief that success in performing a task is determined by
his or her own efforts and not by an external agent), and affective factors (e.g., test anxiety).
Internet Self-Efficacy
Experience with technology is another important element of success for online learners
(Schrum and Hong, 2002). Wang and Newlin (2002a, 2002b) found that both self-efficacy for
learning course content as well as self-efficacy for technology skills were predictive of learner
performance. Joo, Bong, and Choi (2000) found that Internet self-efficacy was an important
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variable in online learner success. Developing positive beliefs (self-efficacy) about one’s ability
to work effectively with Internet technology is, in part, a result of successful experience using
that technology. Ensuring that online learners are both comfortable and competent with using the
technological tools central to their study experience is an important consideration in online
learning.
Time Management
A third important element of distance learner success is the ability to effectively manage
learning time (Kearsley, 2000; Phipps and Merisotis, 1999). Palloff and Pratt (1999) pointed out
that interacting in a Web-based course could require two to three times the amount of time
investment than a face-to-face course. Roblyer (1999) noted that students who have difficulty
managing time are more likely to achieve less in a distance course or drop out altogether. Gibson
(1998) pointed out that a key construct relating to distance learners’ persistence is their self-
efficacy for learning at a distance and that personal perceptions of competence (self-efficacy) are
related to learners’ perceptions of their ability to manage time effectively.
Students who use their time efficiently are more likely to learn and/or perform better than
students who do not have good time management skills. Self-regulated learners know how to
manage their time because they are aware of deadlines and of how long it will take to complete
each assignment. They prioritize learning tasks, separating the more difficult from the easier
tasks in terms of the time required to complete them. They are aware of the need to evaluate how
their study time is spent and to reprioritize as necessary (Zimmerman and Risemberg, 1997).
Study Environment Management
Self-regulated learners are proactive in managing not only their study time, but also their
study environment (Zimmerman & Martinez-Pons, 1986). They are sensitive to their
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environment and resourceful in altering or changing it as necessary. Since they do not study in a
structured and controlled classroom context, online learners must be able to structure their own
physical learning environment, whether at home or elsewhere. Whipp and Chiarelli (2001) found
that social environmental structuring strategies were important attributes of successful online
learners.
In terms of physical space, online learners generally have the option of accessing their
courses via computers at home or elsewhere, such as a library or computer lab. If they are
working at home, they have the option of where the computer is situated – a quiet place such as a
den or bedroom, or a louder more distracting environment, such as a living room or kitchen. If
learners are unable to restructure their learning environment at home, they can access their
course from a university or library computer. Learners must also ensure that they have access to
and are proficient at using the equipment they require in order to study effectively. This
equipment may include a computer of sufficient RAM and with the necessary software to access
course materials, whether text, video, and/ or graphic. Mastery of these elements contributes to
the learner’s control over the virtual space within which online learning occurs.
Learning Assistance Management (Help Seeking)
Self-regulated learners also are aware of the important role other people can play in their
learning. One of their distinguishing characteristics is their ability to seek academic assistance in
an adaptive manner to optimize learning. Several authors have noted the importance of help-
seeking behavior in distance learning (Hara and Kling, 2000; Holmberg, 1995; Wang and
Newlin, 2002a, 2002b). Autonomous distance learners seek appropriate learning help from
others. Since an element of online education is social isolation from classmates and instructors,
online learners need to be proactive in employing the technology, through email, chat rooms,
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bulletin boards, as well as occasional face-to-face meetings, to lessen the social distance
involved in their learning situation.
Henderson and Cunningham (1994) argued that effective use of instructional technology
systems requires that the learner be sufficiently motivated and self-regulated to effectively and
efficiently utilize the features of the technology. In an online learning context, this means that
learners either have or mindfully develop skills in using the specific elements of the technology
that permit interaction with other learners and with instructors. Online learners must be able to
determine where and how to seek help and make decisions concerning the most appropriate
sources for such help.
It is important to note, however, that these are not the only variables that contribute to
self-regulatory behavior, but are merely those that have been selected for investigation in this
study based upon their prominence in the distance education literature. There are other self-
regulatory attributes, both motivational and behavioral, that comprise self-regulated behavior.
These include such components of motivation as the value learners assign to specific tasks, locus
of control beliefs, and affective factors. They also include cognitive and meta-cognitive learning
strategies such as rehearsal, organization, critical thinking, and elaboration, among others. Any
of these self-regulatory attributes also may be potentially significant aspects of online learning
success (Lynch & Dembo, 2004).
In summary, there are a number of self-regulatory learning attributes that contribute to
learner autonomy in online learning contexts (Table 1). A critical component is motivation for
learning. Two elements of motivation are efficacy beliefs and goal orientation. Efficacy beliefs
reflect a learner’s confidence to successfully accomplish a learning task. Goal orientation refers
to the reasons a learner engages in a learning task. A second component of online learner
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autonomy is experience with Internet technologies, which contributes to the learner’s confidence
(self-efficacy) in effectively using the technology in order to learn. A third component of
autonomy is the learner’s ability to manage study time effectively and productively along with
the other time demands in his or her life. A fourth component is the learner’s ability to manage
his/herstudy environment to ensure that it is supportive of learning and to restructure it as
necessary. A fifth component of autonomy is the learner’s ability to seek learning assistance
when necessary and in the appropriate manner through the relevant channels. This latter
component involves knowing when help is needed, knowing where to seek that help, knowing
how to request the help, and knowing how to evaluate the effectiveness of the help received
(Aleven, Stahl, Schworm, Fischer, & Wallace, 2003).
These five components of learner autonomy are self-regulatory learning attributes that
have been identified in the self-regulation literature as important factors in classroom-based
learning. They have also been cited in the distance education literature as important elements of
online learning success.
Summary
To recapitulate, the challenge of implementing more rigorous graduation and college
readiness standards while at the same time increasing state and local graduation rates, has left
many educational practitioners and policymakers searching for innovative programs to meet the
needs of at-risk students. Researchers have suggested that the success of dropout prevention
efforts depends greatly upon the types of strategies implemented, making it essential that
selected approaches have been proven effective for the identified risk factors of those being
targeted (Black, 2002; Diplomas Count, 2010; Fitzpatrick & Yoels, 1992). Distance education
continues to be a growing area of study among researchers and educators. Online learning is one
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strategy aimed at closing the achievement gap and increasing graduation rates. The use of online
learning platforms for credit recovery appears to be increasing. However, research on K-12 has
been limitied and is needed to understand the nature of today’s learners, specifically those
deemed to be at-risk learners. Literature on Bandura’s theoretical framework for online learning
provided an understanding on individual learning and beliefs. Finally, a discussion on self-
regulatory attributes for online success demonstrated the factors associated with online learning.
The aim of this study was to address the following research questions:
1) What is the relationship between students' self-regulation in an online credit recovery course
and their course achievement?
2) What is the relationship between students' self-efficacy and their self-regulation in an online
credit recovery program?

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CHAPTER THREE: METHODOLOGY
This chapter describes the research methodology and procedures used to collect and
analyze data for the study in order to answer the research questions. The methodology is divided
into the following subsections: a) research design, b) participants, c) procedures and
instrumentation, d) operational definition of variables, e) data analysis, f) methodological
assumptions and limitations, and f) summary.
The following research questions guided the study:
1) What is the relationship between students' self-regulation in an online credit recovery
program and their course achievement?
The research hypothesis is the higher the level of students’ self-regulation, the more
positive or likelihood the students’ will have positive course achievement.
2) What is the relationship between students' self-efficacy and their self-regulation in an
online credit recovery program?
The research hypothesis is the higher the level of students’ self-efficacy, the higher the level of
their self-regulation.
Research Design
A non-experimental, descriptive-correlational research design with a self-report survey
via online and paper and pencil was used in this study. According to Charles and Mertler (2002),
non-experimental research tends to be the rule rather than the exception in educational research,
as it is used to “(1) depict people, events, situations, conditions, and relationships as they
currently exist or once existed; (2) evaluate products or processes; and (3) develop innovations”
(p.30). A non-experimental approach is appropriate due to the study parameter that will include
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comparing students’ self-regulation and computer self-efficacy during an online credit recovery
course.
Participants
The research study population was a convenience sample that consisted of at-risk
students who were enrolled in an online credit recovery program during the fall of the 2013-14
school year at an urban high school in Southwestern United States. For the purpose of this study,
“at-risk” is a classification granted to those students who failed at least one traditional classroom
course and were likely to drop out of school or be held back from moving on to the next grade
level (Fortune, 2010).
The sample chosen for this study consisted of random sampling of about 100 students
who are in 10
th
to 12
th
grades at the school where the researcher is a staff member. Of the 100
students, a total of 62 students participated in the study. The format of the credit recovery
program consisted of a classroom lab where students were taking various subjects based on
individual circumstances. Students worked independently on their course subject using an online
curriculum purchased by the school. The credit recovery lab was monitored by a part-time
teacher whose primary role was to assist students in technical issues within the lab and act as a
tutor for the students.
Procedures and Instrumentation
Students identified as being at risk and who were enrolled in the online credit recovery
program were given the opportunity to be a part of the study through consent forms (Appendices
A,B, & C). To offset the possibility of a low-consent form return rate, all students were given
consent forms at the during the online credit recovery course. For non-experimental and
correlational research, 30 subjects are recommended as a minimal sample size. According to
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Alreck and Settle (2004) there are minimum and maximum sample sizes that apply to almost all
surveys. A sample of fewer than 30 respondents is normally considered too low in regard to
survey research. When possible, Alreck and Settle (2004) recommend a minimum of about 100
respondents.
This non-experimental study took place during the fall of the 2013-2014 school year
using a paper and pencil survey and an online survey. The survey was administered to 62
students who provided consent to participate in the study and who were enrolled in the online
credit recovery program. According to Myers, Botti, and Pompea (1997), the questionnaire
approach often brings light to important variables, processes, and interactions that deserve more
extensive attention. These provide clues and are often the source of fruitful hypotheses for
further study.
The primary instrument that was used to collect quantitative data in this study is a
modified version of the Motivated Strategies for Learning Questionnaire (MSLQ, presented in
Appendices e & F) created by Pintrich, Smith, Garcia, and McKeachie (1990). Online and paper
and pencil surveys were given to participants during the middle of the semester of the credit
recovery course. Participants responded to each item using a 5-point scale with options ranging
from “not at all true of me” (1) to “very true of me” (5). This modified MSLQ survey is a scaled
response format proven to be a reliable and useful tool that can be adapted for a number of
different purposes for researchers, instructors, and students (Garica-Duncan & McKeachie,
2005).
The MSLQ was developed by Pintrich et al. (1993) in order to understand college
students’ motivation and the learning strategies they used in a college course. It is a self-report,
seven-point Likert-type scale with 81 items and takes about 20 to 30 minutes to administer. The
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31-item motivation subscale consisted of three motivation constructs, expectancy (self-efficacy
and control belief), value (intrinsic or extrinsic goal and task value beliefs), and affect (task
anxiety). The 50-item learning subscale included three general types of constructs, cognitive
(basic and complex strategies, such as rehearsing, elaboration, organization and critical
thinking), metacognitive (planning, monitoring, and regulating), resource management (time
management and using proper place to study), and peer learning and help seeking. The subscales
and internal consistency are shown in Table 4. Pintrich et al. (1993) conducted two confirmatory
factor analyses, one for motivation subscale and one for learning strategies subscales, in order to
examine the fit between the MSLQ items and theoretical concepts. The predictive validity was
examined by the correlation between the MSLQ subscales scores and the standardized final
course grade. Both analyses suggested that the MSLQ is a valid measure for motivation and
learning strategies. The results from the confirmatory factor analysis of the motivation and the
learning strategies subscales indicated a model fit. The correlations between the MSLQ subscales
scores and standardized final course grade reached were statistically significant except for the
correlation between extrinsic motivation and final grade, between peer learning and final grade,
and between help-seeking and final grade (Table 2).

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Table 2
Subscales and Internal Reliability Coefficients for Motivation Strategies for Learning
Questionnaires (Modified from Pintrich et al., 1993, pp. 808)
Scale Coefficient Alpha
Motivation Scales
Intrinsic Goal 0.74
Extrinsic Goal 0.62
Task Value 0.90
Control of Learning Beliefs 0.68
Self-efficacy for Learning and Performance 0.93
Test Anxiety 0.80
Learning Strategies Scales
Rehearsal 0.69
Elaboration 0.75
Organization 0.64
Critical Thinking 0.80
Metacognitive Self-Regulation 0.79
Time and Study environment Management 0.76
Effort Regulation 0.69
Peer Learning 0.76
Help Seeking 0.52

After reviewing various tools used to assess self-regulation and self-efficacy, portions of
the MSLQ were selected as this questionnaire was the most widely used in studies investigating
self-regulation and contains several subscales that were of interest in this particular study:
namely self-efficacy for learning and performance and metacognitive self-regulation as they are
important to online learning and its easy translation into an assessment that can be modified to
address self-regulatory issues related to online learning. Furthermore, a demographic section
was added to the survey. Grade, sex, and grade level were asked to gain further foundational
information for the data analysis/reporting phase of the study. The dissertation committee, the
University of Southern California IRB, and the Department of Education IRB approved all
survey items and overall study (Appendix D & E).
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Variables and Definition of Scales
Each student was asked to complete the MSLQ during an online course credit recovery
course for which they were already assigned to begin within scheduled labs. Attached to the
survey was a short demographic data collection section that asked their age, sex, and current
grade level. At the completion of the online credit recovery course, the researcher collected the
students’ final grades as part of the analysis in the study.
A few specific variables were considered while conducting this non-experimental study.
Research question 1: What is the relationship of students’ self-regulation in an online credit
recovery program and their course achievement? The independent variable was the level of self-
regulation, and the dependent variable was the students’ course achievement, which was the final
grade. Research question 2: What is the relationship between students’ self-efficacy and their
self-regulation in an online credit recovery program? The independent variable was the level of
self-efficacy, and the dependent variable was the level of self-regulation. Table 3 presents the
definition of the items in the demographic sections and the subscales used in the MLSQ survey.
Table 3
Definition of demographic items and subscales us in the modified MSLQ
Demographic Definition
Age The students’ numerical stage of existence
Gender The student is either male or female
Use of computer-based course(s) The numerical count of computer-based
course(s) the student may have taken to date
Type of online credit recovery course The student will be either take English, math,
social studies, science, or other course

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Table 3, continued
Motivation and learning scales Definition
Self-efficacy for learning and performance The students’ belief in their own abilities to
achieve a goal or an outcome
Metacognitive strategies for self-regulation Metacognition refers to the awareness,
knowledge, and control of cognition. The three
general processes of self regulation include:
planning, monitoring, and regulating
Control beliefs about learning The students’ concerns the belief that outcome
are contingent on one’s own effort, in contrast
to external factors such as the teacher
Intrinsic goal orientation The students’ concerns on the degree to which
he/she perceives herself/himself to be
participating in a task for reasons such as
challenge, curiosity, and mastery
Extrinsic goal orientation The students’ concerns that the degrees to
which the student perceives his/herself to be
participating in a task for reasons such as
grades, rewards, performance, evaluation by
others, and completion.
Task Value Refers to students’ perceptions of the course
material in terms of interest, importance, and
utility
Time and study environment Refers to students’ time management that
involves scheduling, planning, and managing
one’s study time
Effort regulation Refers to students’ ability to control their effort
and attention in the face of distraction and
uninteresting tasks.

Reliability coefficients for the motivation scales and the learning strategies scales were
calculated by Pintrich, Smith, Garcia, and McKeachie (1993) with Cronbach’s alpha .68 and .62,
respectively. With the exception of extrinsic goal orientation, all other motivation subscales
illustrated significant correlations with final grade by Pintrich, Smith, Garcia, and McKeachie
(1993). Further analysis conducted to explore the predictive validity of the MSLQ found that
subscales of time and study management and effort regulation on the modified MSLQ for this
study were significantly correlated with final grades (Pintrich, Smith, Garcia, &McKeachie,
1993).
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Data Analysis
Data was entered by using Qualtrics software program and the Statistical Package for
Social Science (SPSS) version 21 was used as the statistical software to analyze the data.
Descriptive statistics (mean, standard deviation, responses, and counts) and correlational
statistics were used to address research questions 1 and 2. Participants were identified to the
researcher only through given student identification numbers. The questionnaire did not ask
students’ names, birthdates, or other specific and unsecured identifying traits. Only the
researcher had access to survey response data that displayed student identification numbers. The
researcher matched student identification numbers with student names collected through the
survey consent forms in order to analyze response by grade, level, and gender. All survey
engines were secured and data collection was backed up on disc through the researcher’s
password protected laptop. The student consent forms were stored in a locked file cabinet
drawer in the researcher’s office. The consent forms and backed up disc will be destroyed after
three years of storage.
Methodological Assumptions and Limitations
The purpose of the study was to collect data in regards to at-risk students who were
enrolled in an online credit recovery program. It is assumed that the study design may provide
insight as to the motivation and learning of at-risk students in the online credit recovery program.
Since participant survey selections/data remain confidential to all people except the researcher, it
is assumed that student survey collections were consistent with their true perceptions about the
questions. The study sample could be construed as having been representative of many diverse
urban schools across the country due to similar dropout dilemmas, comparable demographics,
and parallel instructional challenges. Yet, it must be noted that, even though these students’
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responses may have been representative of many urban schools across the country, the results
should not be considered 100% representative of all urban high schools. Not all urban high
schools have similar characteristics, instructional approaches, or the same student demographics.
The researcher expected between 60 and 80 students to complete survey. It is also assumed that
little technical difficulty or ill-timing of surveys occurred due to the fact that teachers let each
student know exactly when the survey needed to be taken while they were under teacher
supervision within the school lab(s).
Limitations of the study include the limited number of data sets available for the fall
2013-14 school year as well as using an urban school district as the convenience sample. Data
sources and instrumentation may include gaps and may not be fully comparable to every district.
While advantages and disadvantages of online learning for at-risk students is discussed, data may
be limited for at-risk students exclusively.
Summary
The purpose of this non-experimental, descriptive-correlational study was to investigate
whether there is a relationship between students’ self-regulation and their course achievement
and self-efficacy in an online credit recovery program. An adapted MSLQ was utilized to
compare variables within the study. The MSLQ was chosen due to its adaptability to this
specific study and due to its already proven reliability and validity in the field of education.
Descriptive and statistical data analysis tests were employed to help categorize and explain the
results gained from all data. Results obtained are presented in chapter four of this study. Finally,
chapter five presents implications and recommendations drawn from the results obtained in this
study.

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CHAPTER FOUR: DATA RESULTS
The purpose of the study was to analyze data from high school students enrolled in an
online credit recovery program and to answer the research questions: 1) What is the relationship
of students’ self-regulation in an online credit recovery program to their course achievement? 2)
What is the relationship of students’ self-efficacy and self-regulation in an online credit recovery
program? As school districts find that online and blended courses are effective ways to reach at-
risk students (Watson & Gemin, 2008), an investigation into students’ motivation and learning in
an online learning environment is warranted. This chapter provides a description of
characteristics of the participants in the study, the treatment of data, and, finally, data analysis.
Descriptive Characteristics of Sample Participants
Online and paper and pencil surveys were distributed to students taking online credit
recovery classes during the fall of 2013, in the middle of the semester, at an urban high school.
Of the expected 100 students, there were 62 who turned in consent forms and participated in the
study. An explanation of the demographic characteristics is presented in Table 4.
Table 4
Demographic Characteristics of Participants
Demographic Descriptive Statistics*
n 62
Age 16.9
Gender
Male 30 (48.4)
Female 32 (51.6)
Grade
11 21 (33.9)
12 41 (66.1)

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Table 4, continued
Class
Math 6 (9.7)
English 34 (54.8)
Social Studies 20 (32.3)
Science 1 (1.6)
Health 1 (1.6)
Overall expected grade 2.34 Mean
A 17 (26.4)
B 19 (30.6)
C 15 (24.2)
D 10 (16.1)
F 1 (1.6)
Overall final grade 2.61 Mean
A 14 (22.6)
B 14 (22.6)
C 17 (27.4)
D 16 (25.8)
F 1 (1.6)
Reported as M (SD) or n (valid %)

The participants were taking a variety of subjects in the online credit recovery lab setting.
The majority of the participants were taking English and Social studies and a majority of the
participants were in the 12
th
grade. Students showed expected mean of 2.34 and final mean of
2.61for their final grade.
Treatment of Data
Prior to the analysis, the data were examined for errors that may affect the results of the
survey. There were four students who were removed from the dataset on the basis of
inappropriate fill-in responses, missing data, and invariant responses to the entire survey.
Prior to aggregating individual scale items, internal consistency was assessed by
computing Cronbach’s alpha for each of the proposed subscales found in Table 5.

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Table 5
Cronbach’s Alpha For Each of the Proposed Subscales
Scale Coefficient Alpha No. items
Motivation Scales
Extrinsic Goal 0.746 4
Task Value 0.857 6
Control of Learning Beliefs 0.704 4
Self-efficacy for Learning and Performance 0.881 7
Learning Strategies Scales
Metacognitive Self-Regulation 0.646 12
Time and Study environment Management 0.711 4

In order to improve the internal consistency of each of the scales, an intra-class
correlation was calculated on the items in each of the metacognitive self-regulation and
time/study environment subscales and items were removed that correlated poorly with the items
in the scale. Any given items that correlated poorly with the items in the scale indicated that the
particular items did not reflect the same underlying construct as did the other items in the
subscale. Thus, by removing the items, the reliability increased in the subscales. The subscale
of intrinsic goal and effort regulation indicated that it was aweak scale, and, therefore, it was
removed from the analysis in the study.
In order to address the research questions, the Pearson’s Correlation statistics were used
to assess the strength of the relationships among the variables in the study. Research question 1:
What is the relationship of students’ self-regulation in an online credit recovery program and
their course achievement?
Table 6 provides a complete presentation of the correlation.

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Table 6
Summary of correlation for self-regulation subscales and course achievement n=62

Mean
(SD)
Self-
regulation
Time/
Study
mgmt.
Expected
grade
Final
grade
Self-
regulation
42.43
(7.42)

1

.238

-.224

-.200
Time/
Study
14.70
(3.15)

1

-.188

.045

Expected
grade
2.34
(1.10)

1

.623**
Final
Grade
2.61
(1.15)

1

The results indicated a negative relationship between students’ self-regulation and
students’ expected and final grades. Time and study management scale also indicated no
relationship between students’ expected and final grades. Self-regulation refers to the students’
awareness, knowledge, and control of cognition. The three general processes of self- regulation
include planning, monitoring, and regulating. Time and study management refers to students’
time management that involves scheduling, planning, and managing one’s study time. Although
there was a negative relationship among the self-regulation scales with the expected and final
grades, the results indicated a strong relationship between students’ expected and final grades.
Research question 2? What is the relationship between students' self-efficacy and their self-
regulation in an online credit recovery program?
Table 7 provides a presentation of the complete correlation.

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Table 7
Summary of correlation for self-efficacy and self-regulation attributes n=62
Mean
(SD)
Self-
efficacy
Self-
regulation
Extrinsic
Motivation
Task
value
Control
learning/
Beliefs
Self-
efficacy
28.51
(4.75)

1

.431**

.695**

.736**

.574**
Self-
regulation
42.43
(7.24)

1

.433**

.345**

.244
Extrinsic
Motivation
16.96
(2.83)

1

.627**

.584**
Task Value 23.80
(4.46)

1

.597**
Control
learning/
beliefs
16.33
(2.91)

1
**Correlation is significant at to .01 level (2-tailed)
*Correlation is significant at the .05 level (2-tailed)

Across every pair except for control learning/beliefs and self-regulation, there is a
statistically significant, positive correlation. In other words, students who showed higher scores
in these self-regulation measures also had higher scores on the other aspects of self-efficacy
subscales. Specifically, task value and self-efficacy indicate a very strong positive relationship.
Task value refers to students’ perceptions of the course material in terms of interest, importance,
and utility. Self-efficacy refers to the students’ beliefs in their own abilities to achieve a goal or
an outcome. Across the other pairs, the correlation was moderate to strong.
Summary of Findings
Existing data from students taking online credit recovery at an urban high school setting
were analyzed to determine the relationship, if any, between students’ self-regulation and course
achievement and the relationship between students’ computer self-efficacy and their self-
regulation. Existing data was reviewed to eliminate erroneous data. As a result, 62 surveys
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administered online and through paper and pencil were used for data analysis. A description of
demographic characteristics of the participants was presented. Internal consistency was assessed
by Cronbach alpha, indicating a majority of the subscales to be reliable, except for effort
regulation and intrinsic goal orientation, which was deleted from the final analysis. Finally, the
Pearson correlation was used to analyze the relationships among the variables in the study.
While students’ motivation, which consisted of self-regulation scale, indicated no relationship
with their final grades. Students were able to predict their final grades in the credit recovery
program. The results of the analysis indicated that students’ motivation, which consisted of
subscales extrinsic motivation, task value, and self-efficacy, had a strong positive to a very
strong positive relationship with students’ learning, which is the subscale of self-regulation.
The following chapter relates the findings of the current study to the past literature.
Chapter five includes implications and recommendations of practice.

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CHAPTER FIVE: DISCUSSION
Due to the high dropout rates, the current study examined at-risk students’ motivation and
learning. Studies about online learning and the at-risk high school learner have been few and are
needed to better understand their learning needs. The study was guided by two research
questions. Research question 1 asked, what is the relationship of students’ self-regulation in an
online credit recovery program and their course achievement. Research question 2 asked, what
is the relationship between students’ self-efficacy and their self-regulation in an online credit
recovery program?
A non-experimental, descriptive-correlational quantitative design was employed at an
urban high school. A Modified Learning Strategy Questionnaire (MSLQ) was given to students
who were enrolled in a credit recovery program via online and paper and pencil during the fall of
2013 and resulted in 62 participants. Descriptive statistics and Pearson correlation were used to
analyze the relationships among students’ self-regulation, course achievement, and self-efficacy
in the credit recovery program.
Implications
Data pertaining to research question one indicated no direct relationship of students’ self-
regulation with course achievement in the online credit recovery program. Although the results
indicated no direct relationship with students’ level of self-regulation and final grades, there was
a positive relationship with students’ expected grade and final grades. This suggests that,
although students possess performance expectations throughout most courses, there is no
evidence that suggests that these expectations remain unchanged. Indeed, it is logical to expect
that the grade expectations of many students will change over time, especially as students
become more acquainted with the requirements of each course and with each instructor's
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expectations and grading style. Specifically, it is likely that grade expectations made at the
beginning of a course and grade evaluations made at the end of the course (immediately prior to
the final exam) are similar, but it is also likely that the expectations will have changed during the
course.
In this study, students were given the survey at the middle of the semester, which
suggests that they have become familiar with course format and expectation. Grade expectations
at the end of a course, immediately prior to taking the final exam, can be more accurate
predictors of students' final course grades than grade expectations at the start of the course
(Koriat, 1997). By the time of the final exam, students have typically taken at least one previous
exam and are acquainted with the form and the coverage of exams in the course. They have also
received feedback on performances on previous exam(s), so they are acquainted with the
performance outcomes and of any shortcoming, which they experienced (Hacker, Boi, Horgan &
Rakow, 2000). Furthermore, they are familiar with the material to be covered on the final exam
and with how well they are prepared for it.
Data pertaining to research question two of the current study supports previous literature
on the relationships among self-efficacy, self-regulation and one’s academic experiences. One’s
perception of self-efficacy has been shown to be an important component of one’s academic
success in online formats (Pintrich & DeGroot, 1991; Lynch & Dembo, 2004). Efficacy beliefs
affect people’s feelings, their thought processes and their behavior (Bandura, 1986, 1993).
Those who feel confident about their ability to handle the material in a specific class are likely to
perform better in that class than someone with less confidence (Joo et al., 2000). Increased self-
efficacy positively relates to the successful use of learning strategies. Pintrich and DeGroot
(1990) found that self-efficacy was related to cognitive and metacognitive engagement. Students
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who believed they were more capable were more likely to utilize cognitive strategies such as
taking a test.
Personal perceptions of self-efficacy are a critical element of motivation (Bandura, 1997;
Pintrich and Schunk, 1996). Bandura (1997) defined self-efficacy as individuals’ judgments of
their abilities to plan and carry out the necessary behaviors to achieve specific goals.
Linnenbrink and Pintrich (2002) pointed out that adaptive self-efficacy beliefs could function as
enablers of academic success. Learners with high self-efficacy are likely to employ adaptive self-
regulatory learning strategies and study skills. Learner perceptions of personal efficacy,
therefore, have a reciprocal relationship with the self-regulatory processes that affect motivation
and performance.
The results of the study should be interpreted with caution. Although self-efficacy and
task value emerged as significant, due to the sample size, study design, and strong correlation on
the attributes’, the study can’t clearly identify the unique power of each subscale. It is likely that
other self-regulation attributes or variables that affect students’ online learning are also important
in the online learning experience. As mentioned in previous literature, there are other self-
regulatory attributes, both motivational and behavioral, that comprise self-regulated behavior.
These include such components of motivation as the value learners assign to specific tasks, locus
of control beliefs, and affective factors. They also include cognitive and meta-cognitive learning
strategies such as rehearsal, organization, critical thinking, and elaboration, among others. Any
of these self-regulatory attributes also may be potentially significant aspects of online learning
success (Lynch & Dembo, 2004). However, the results of the current study showed the
importance between students’ self-regulation and their course achievement and self-efficacy in
an online learning environment..
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Recommendations
The survey data analyzed at-risk students’ self-regulation and self-efficacy in an online
learning environment. The recent practice of the high school’s offering online independent study
courses to at-risk students with a history of difficulty with attendance, coursework completion,
and who may have insufficient prior learning emerges as a concern for discussion. Areas of
future research and discussions may be the literacy of students, the demands of the courses, and
inconsistent study and work skills of at-risk students. The high school credit recovery program
can also work towards improving the support systems and program implementation practices that
may leave at-risk students without sufficient support. Staffing the lab with a knowledgeable
adult who has been trained to provide assistance in the product and the assessments of learning
for more meaningful learning is critical to maintaining rigor, personalization, and support for the
at-risk learners.
While it appears that the findings of students’ self-regulation in online learning was not
determined with their academic achievement, this finding does not negate the importance of self-
regulatory learning behaviors, but informs online instruction and course design. A suggestion is
that students must first have positive perceptions of online course communication and
collaboration in order to engage in self-regulated learning in the online classroom to a sufficient
degree that it may positively influence academic achievement as measured by their grade. Other
variables not examined may also function as additional prerequisites to self-regulation in online
learning. Instructors and designers of online course curricula can be especially concerned with
creating learning environments where positive perceptions toward online course communication
and collaboration may be formed and fostered. Future research can look into the relationship of
self-regulation in online learning between student perceptions of online course communication
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and collaboration and academic achievement as measured by their grade so as to further validate
findings.
In the current study, the students took online classes for credit recovery in a computer
lab, with each of them taking classes in different subjects. In the computer lab, the teacher
functions more as an “as needed tutor”, since the content for the online courses already exists
within the program. Therefore, the students work largely independently. As a result of this and
previous research, an implication is that some students may not be ready to handle more
autonomous work if they have low self-efficacy for that particular subject. Perhaps some type of
analysis should be done before a student undertakes online coursework to determine his/her
readiness and self-efficacy for that particular subject. This may help to identify those who may
need additional assistance with the course. iNACOL, which stands for International Association
for K-12 Online Learning, is a non-profit organization focused on research; developing policy
for student-centered education to ensure equity and access; developing quality standards for
emerging learning models using online, blended, and competency-based education; and
supporting the ongoing professional development of classroom, school, district and state leaders
for new learning models. iNacol’s research committee recommends the following areas for
future research (Archambault et. al., 2010):
• Explore how the identification of at-risk students affects the attrition and course
completion rates in virtual schools and what measures virtual schools take once a student
has been identified as being at-risk.
• Identify the assessment and prediction tools, models, and instruments used to remediate
students’ knowledge, skills and abilities to enable success in the online environment.
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In the literature, Picciano (Eduviews, 2009) discussed the impact of changing
pedagogical approaches and their value in online credit recovery, stating, “Individualized
instruction, modularization, multimedia infusion, and on-going assessment are some of the
pedagogical techniques that typify many online credit recovery courses. In addition, there is a
growing appeal to using blended learning techniques that attempt to apply the best of both
formats to help students who are struggling with a particular course or content” (p.8). In addition
to independent study, hybrid/blended online courses could be developed with structured course
attendance, using software to deliver content with differentiation in products to showcase
learning.
Blended/hybrid learning is usually defined as the combination of multiple approaches to
teaching. It can also be defined as an educational process which involves the deployment of a
diversity of methods and resources or to learning experiences, which are derived from more than
one kind of information source. Examples include combining technology-based materials and
traditional print materials, group and individual study, structured pace study and self-paced
study, or tutorial and coaching (Blended Learning, 2005).
Within hybrid/blended learning, an example is the student-centered approach also known
as flexible learning, which requires different teaching methodologies and also different
relationships between teachers and students. In comparison to traditional educational models,
flexible learning is broadly characterized by:
• Less reliance on face-to-face teaching and more emphasis on guided independent
learning; teachers become facilitators of the learning process directing students to
appropriate resources, tasks and learning outcomes.
• Greater reliance on high quality learning resources using a range of technologies (e.g.,
SELF-REGULATION/SELF-EFFICACY ONLINE
63

print, CD-ROM, video, audio, the Internet)
• Greater opportunities to communicate outside traditional teaching times.
• An increasing use of information technology (IT). Flexible learning is not synonymous
with the use of IT but IT is often central to much of the implementation of flexible
learning, for example in delivering learning resources, providing a communications
facility, administering units and student assessment, and hosting student support systems.
• The deployment of multi-skilled teams. Rather than the academics responsible
undertaking all stages of unit planning, development, delivery, assessment and
maintenance, other professionals are often required to provide specific skills, for example
in instructional design, desktop publishing, web development and administration and
maintenance of programs (Centre for Flexible Learning, 2005).
Flexible learning is the main concept and reasoning behind blended learning. The point of
creating blended learning was to allow flexible learning for the students. This term, then, is also
a major aspect of blended learning. It needs to be recognized and understood by users in order to
better create and deliver a blended learning unit to students. As can be seen from the above
example and explanation, technology may not be a given to blended learning, but an addition.
Instead, blended learning can be used as a method outside the traditional classroom and teaching
to help increase interest and reach all learners.
Online learning continues to be popular trend because of its potential for providing more
flexible access to content and instruction at any time and from any place. Educators making
decisions about online learning need rigorous research examining the learning needs of different
types of students as well as the effectiveness of different online practices.

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64

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Appendix A
Parent Consent for Participation in
University Southern California Doctorate in Education Program
Victoria Viernes
Primary Investigator

My name is Victoria Viernes and I am a Doctorate student in the Rossier School of Education
program at the University of Southern California and I am also a counselor at this school. It is
important for you to know that my role as a counselor will not interfere or impact the study.
With the permission from my Principal, my r ole as a researcher requests you to permit your
child to participate in an educational research study in the credit recovery program at Farrington
High School. The purpose of the study is to examine students’ motivation and learning in the
credit recovery program.

Goal: The desired outcome of the study is to gain knowledge and understanding the concepts of
students' motivation and learning in an online credit recovery environment because they are
essential components in student learning and achievement.

Study activities: The administration of the survey will take place in the credit recovery
classroom lab on a scheduled date and time. At the beginning of the credit recovery period, your
child’s participation will consist of answering about 55 multiple-choice style questions via e-
mail, which ask about his/her motivation and learning in the online credit recovery course. The
following data will also be collected from your child: grade level, gender, credit recovery course,
and final grade. Completion of this survey should take about 30 to 45 minutes.

Data use and confidentiality: Primary investigator will collect your child’s self-reported data
and demographic data such as grade level, gender, credit recovery course, and final grades. With
permission from Hawaii Department of Education, I will access your child’s final grade through
our school data at the end of the term.

When findings from this study are presented in public (dissertation committee, high school), we
will not use your child’s real name or other personally identifying information. Instead, your
child will be assigned random numbers for identification purposes only and to analyze your
child’s self-reported data for the primary investigator. Personal information will be kept private
and stored by the primary investigator

During this research study, I will keep all information private. All digital information will be
protected by a password on my laptop computer. All paper documents will be kept in a locked
file cabinet in my office. I will be the only person with access to the information. Although
legal authorities, including the University of Southern California and Hawaii Department of
Education, have the right to review research records.

When the study is complete I will shred any paper documents. All data will be protected
electronically and destroyed when the study is complete.

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Benefits and risks of participation: We believe that there is little or no risk to your child.
However, if he/she feel stressed or uncomfortable with any part of the study, including the
survey questions your child can skip the question, take a break, or stop participating in the study
altogether. The results of this study might help the school administrators, other teachers, and
researchers learn more about students in a credit recovery program.

Voluntary Participation: Your child’s participation in the study is voluntary and will not affect
his or her grade or academic standing and his or her interactions with the teacher/staff member.

Questions: If you would like a copy of my data collection instruments or my research study, and
other questions, please contact me at 808-753-6761 or email at viernes@usc.edu. If you have
any questions regarding your child’s rights as a research participant, please contact the UPIRB at
213-821-5272 or email at upirb@usc.edu.

Please keep this portion of this consent form for your records.
If your consent for your child to participate in this study please sign the following page and give
to your child to return to me.

Parent/Guardian’s Consent for Child to Participate in Research Study
“The relationship of students’ self-regulation and computers self-efficacy in an online learning
environment.

Signature(s) for Consent:

Please mark an X for the following:

__________ I give permission for my child to participate in the research study entitled, “The
relationship of students’ self-regulation and computers self-efficacy in an online learning
environment.

__________ I give permission to use my child’s demographic information and final grade from
the Hawaii Department of Education for the purpose of analyzing the data.

__________ I understand that, in order to participate in this study, my child must also agree to
participate.

__________ I understand that my child and /or I can change our minds about participation, at
any time by notifying the researcher of our decision to end participation in this study.

OR

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75

__________ I do not give permission for my child to participate in the research study entitled,
“The relationship of students’ self-regulation and computers self-efficacy in an online learning
environment.

Name of Child (Print):
_________________________________________________________________

Child’s email:
___________________________________________________________________________

Name of Parent/Guardian (Print):
___________________________________________________

Parent/Guardian’s Signature: _____________________________________Date:
______________

Primary Investigator (Print): ________________________________________________

Primary Investigator Signature: ____________________________________Date:
_____________

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Appendix B
Student Assent for Participation in
“The relationship of students’ self-regulation and computer self-efficacy in an online
learning environment”
Victoria Viernes Primary Investigator

My name is Victoria Viernes and I am a Doctorate student in the Rossier School of Education
program at the University of Southern California. I am also a counselor at this school. It is
important for you to know that my role as a counselor will not interfere or impact the study. I
am asking you to participate in an educational research study in the credit recovery program at
Farrington High School. The purpose of the study is to learn about your motivation and learning
in the credit recovery program. Before you decide whether to take part in this study, it is
important that you know that:

• It is your choice to be a part of it or not;
• If you decide to be a part of the study, you can stop at any time;
• Your decision to participate will not affect your grades or your relationship with
teacher/staff member; and
• Your parent or guardian must also agree for you to participate

Goal: The goal of the study is to learn and understand about students’ motivation and learning in
an online credit recovery environment because they are important to your learning and
achievement.

Study activities: On a scheduled date and time, you will be given an online survey via email at
the beginning of your credit recovery class. You will answer about 55 multiple-choice questions
that will ask about your motivation and learning in the credit recovery program. I will also be
asking for your grade level, gender, credit recovery course, and final grade. Completion of this
survey should take about 30 to 45 minutes.

Data use and confidentiality: When findings from this study are presented in public
(dissertation committee, high school), we will not use your real name or other personally
identifying information about you. Instead, you will be assigned random numbers for
identification purposes only so that I am able to analyze your responses for my study. Personal
information about you will be kept private and stored by the primary investigator. All data
including hard copies and electronic data will be shred and destroyed when study is complete.

Benefits and risks of participation: We believe that there is little or no risk to you when
participate in the study. However, if you feel stressed or uncomfortable with any part of the
study, including the survey questions, you can skip the question, take a break, or stop
participating in the study altogether. The results of this study might help the school
administrators, other teachers, and researchers learn more about students in a credit recovery
program.

SELF-REGULATION/SELF-EFFICACY ONLINE
77

Voluntary Participation: Your participation in the study is voluntary and will not affect your
grade or academic standing and your interactions with the teacher/staff member. You can
withdraw at any time.

Questions: If you would like a copy of my data collection instruments or my research study, and
other questions, please contact me at 808-753-6761 or email at viernes@usc.edu. If you have
any questions regarding the study rights as a research participant, please contact the UPIRB at
213-821-5272 or email at upirb@usc.edu.

Agreement to take part in study:
Signing your name at the bottom of this form means that you agree to be a part of the study. You
will be given a copy of this form after you have signed it.

_________________________________________
___________________________________ _____________
Your Name (printed) Your signature Date

Your email:_______________________________________________________

_________________________________________
___________________________________ _____________
Researcher Name Researcher signature Date

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78

Appendix C
Student Consent for Participation in
“The relationship of students’ self-regulation and computer self-efficacy in an online
learning environment”
Victoria Viernes Primary Investigator

My name is Victoria Viernes and I am a Doctorate student in the Rossier School of Education
program at the University of Southern California. I am also a counselor at this school. It is
important for you to know that my role as a counselor will not interfere or impact the study. I
am asking you to participate in an educational research study in the credit recovery program at
Farrington High School. The purpose of the study is to learn about your motivation and learning
in the credit recovery program. Before you decide whether to take part in this study, it is
important that you know that:

• It is your choice to be a part of it or not;
• If you decide to be a part of the study, you can stop at any time;
• Your decision to participate will not affect your grades or your relationship with
teacher/staff member; and
• Your parent or guardian must also agree for you to participate

Goal: The goal of the study is to learn and understand about students’ motivation and learning in
an online credit recovery environment because they are important to your learning and
achievement.

Study activities: On a scheduled date and time, you will be given an online survey via email at
the beginning of your credit recovery class. You will answer about 55 multiple-choice questions
that will ask about your motivation and learning in the credit recovery program. I will also be
asking for your grade level, gender, credit recovery course, and final grade. Completion of this
survey should take about 30 to 45 minutes.

Data use and confidentiality: When findings from this study are presented in public
(dissertation committee, high school), we will not use your real name or other personally
identifying information about you. Instead, you will be assigned random numbers for
identification purposes only so that I am able to analyze your responses for my study. Personal
information about you will be kept private and stored by the primary investigator. All data
including hard copies and electronic data will be shred and destroyed when study is complete.

Benefits and risks of participation: We believe that there is little or no risk to you when
participate in the study. However, if you feel stressed or uncomfortable with any part of the
study, including the survey questions, you can skip the question, take a break, or stop
participating in the study altogether. The results of this study might help the school
administrators, other teachers, and researchers learn more about students in a credit recovery
program.

SELF-REGULATION/SELF-EFFICACY ONLINE
79

Voluntary Participation: Your participation in the study is voluntary and will not affect your
grade or academic standing and your interactions with the teacher/staff member. You can
withdraw at any time.

Questions: If you would like a copy of my data collection instruments or my research study, and
other questions, please contact me at 808-753-6761 or email at viernes@usc.edu. If you have
any questions regarding the study rights as a research participant, please contact the UPIRB at
213-821-5272 or email at upirb@usc.edu.

Agreement to take part in study:
Signing your name at the bottom of this form means that you agree to be a part of the study. You
will be given a copy of this form after you have signed it.

_________________________________________
___________________________________ _____________
Your Name (printed) Your signature Date

Your email:_______________________________________________________

_________________________________________
___________________________________ _____________
Researcher Name Researcher signature Date

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80

Appendix D

8/19/13 11:49 PM
https://istar.usc.edu/iStar/Doc/0/IIA2QB41KT9KBF5TKMGCDK600F/fromString.html Page 1 of 2
!
!
UNIVERSITY OF SOUTHERN
CALIFORNIA UNIVERSITY
PARK INSTITUTIONAL
REVIEW BOARD FWA 0000709
!
!
!
!
!
!
!
!
Date: Aug 15, 2013, 11:59am!
Principal Investigator: Maria Viernes!
ROSSIER
SCHOOL OF EDUCATION
Faculty Advisor: Brandon
Martinez!
ROSSIER SCHOOL OF EDUCATION!
Co-Investigators:!
Project Title: Self-regulation and computer self-efficacy
in an online credit recovery program!
USC UPIRB # UP-13-00343!
!
!
!
!
!
!
The iStar application and attachments were reviewed by UPIRB staff on
8/15/2013.
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82

Appendix E
Hawaii
Department
of
Education
IRB

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Appendix F
Modified MSLQ
Demographic Information
_________________________

1. What is your student ID number? ________________________________________

2. What is your grade level? Circle one 9 10 11 12

3. What is your age 14 15 16 17 18

4. Gender? Circle one Male Female

5. Number of online credit recovery course taken before? 0 1 2 3 4

6. What credit recovery course did you take last semester?:

Math? Yes No
English? Yes No
Social Studies? Yes No
Science? Yes No
Health? Yes No
Elective Yes No

Motivation Learning Scale Questionnaire
Motivation Scale
The following questions ask about your motivation for and attitudes about this class. Remember there is
no right or wrong answers, just answer as accurately as possible. Use the scale below to answer the
questions. If you the statement is very true of you, choose 5; if a statement is not at all true of you, choos
1. It the statement is more or lees true of you, find the number between 1 and 5 that best describes you.

1 2 3 4 5
not at all very true
true of me of me

not at allvery true
true of m of me
7. In a class like this, I prefer course material 1 2 3 4 5
that really challenges me so I can learn new things

8. In a class like this, I prefer course material that arouses my 1 2 3 4 5
curiosity, even if is difficult to learn.

9. The most satisfying thing for me in this course is trying to 1 2 3 4 5
understand the content thoroughly as possible.

10. When I have the opportunity in this class, I choose course 1 2 3 4 5
assignment that I can learn from even if I don’t guarantee
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a good grade.

11. Getting a good grade in this class is the most satisfying 1 2 3 4 5
thing for me right now.

12. The most important thing for me right now is improving 1 2 3 4 5
my overall grade point average, so my main concern in this
class is getting a good grade.

13. If I can, I want to get better grades in this class than most 1 2 3 4 5
of the other students.

14. I want to do well in this class because it is important to 1 2 3 4 5
show my ability to my family, friends, employer, or others.

15. I think I will be able to use what I learn in this course in 1 2 3 4 5
other courses.

16. It is important for me to learn the course material in this class. 1 2 3 4 5

17. I am very interested in the content areas of this course. 1 2 3 4 5

18. I think the course material in this class is useful for me to 1 2 3 4 5
learn.

19. I like the subject matter of this course. 1 2 3 4 5

20. Understanding the subject matter of this course is very 1 2 3 4 5
important to me.

21. If I study in appropriate ways, then I will be able to learn 1 2 3 4 5
the material in this course.

22. It is my own fault if I don’t learn the material in this course. 1 2 3 4 5

23. If I try hard enough, then I will understand the course 1 2 3 4 5
material.

24. If I don’t understand the course material, it is because I 1 2 3 4 5
didn’t try hard enough.

25 I believe I will receive an excellent grade in this class. 1 2 3 4 5

not at all very true
true of me of me

26. I am certain I can understand the basic concepts taught 1 2 3 4 5
in this course.

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27. I’m confident I can understand the most complex material 1 2 3 4 5
presented by the instructor in this course.

28. I’m confident I can do an excellent job on the assignments 1 2 3 4 5
and tests in this course.

29. I expect to do well in this class. 1 2 3 4 5

30. I’m certain I can master the skills being taught in this class. 1 2 3 4 5

31. Considering the difficulty of this course, the teacher, and 1 2 3 4 5
my skills, I think I will do well in this class.

Learning Strategies Scale
The following questions ask about your learning strategies and study skills for this class. Again, there are
no right or wrong answers. Answer the questions about how you study in this class as accurately as
possible. Use the same scale to answer the remaining questions. If you think the statement is very true of
you, circle 5; if a statement is not true of you, circle 1. if a statement is not at all true of you, circle 1. It
the statement is more or lees true of you, find the number between 1 and 5 that best describes you.

1 2 3 4 5
not at all very true
true of me of me

not all very true
t rue of me of me
32. During class time I often miss important point because of 1 2 3 4 5
other things. (reversed)

33. When reading for this course, I make up question to help 1 2 3 4 5
focus my reading.

34. When I become confused about something I’m reading for 1 2 3 4 5
this class, I go back and try to figure it out.

35. If course materials are difficult to understand, I change the 1 2 3 4 5
way I read the material.

36. Before I study new course material thoroughly, I often skim 1 2 3 4 5
it to see how it is organized.
not at all very true
true of of me

37. I ask myself questions to make sure I understand the material 1 2 3 4 5
I have been studying in this class.

38. I try to change the way I study in order to fit the course. 1 2 3 4 5

39. I often find that I have been reading for class but don’t 1 2 3 4 5
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know what it was all about. (reversed)

40. I try to think through a topic and decide what I am 1 2 3 4 5
supposed to learn from it rather than just reading it over
when studying.

41. When studying for this course I try to determine which 1 2 3 4 5
concepts I don’t understand well.

42. When I study for this class, I set goals for myself in order 1 2 3 4 5
to direct my activities in each study period.

43. If I get confused taking notes in class, I make sure I sort it 1 2 3 4 5
out afterwards.

44. I usually study in a place where I can concentrate on my 1 2 3 4 5
course work.

45. I make good use of my study time for this course. 1 2 3 4 5

46. I find it hard to stick to a study schedule. (reverse) 1 2 3 4 5

47. I have a regular place set aside for studying. 1 2 3 4 5

48. I make sure I keep up with the weekly readings and 1 2 3 4 5
assignments for this course.

49. I attend class regularly. 1 2 3 4 5

50. I often find that I don’t spend very much time on this 1 2 3 4 5
course because of other activities. (reversed).

51. I rarely find time to review my notes or reading before 1 2 3 4 5
an exam (reversed).
Not all very
satisfied satisfied

52. I often feel so lazy or bored when I study this class that 1 2 3 4 5
I quit before I finish what I planned to do. (reversed)

53. I work hard to do well in this class even if I don’t like 1 2 3 4 5
what we are doing.

54. When course work is difficult, I give up or on only study 1 2 3 4 5
the easy parts (reversed).

55. Even when course materials are dull and uninteresting, 1 2 3 4 5
I manage to keep working until I finish.
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56. I am satisfied of my performance in this course 1 2 3 4 5

57. What is your expected final grade for this class? A B C D F

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Abstract (if available)

Abstract One of greatest challenges for educators is motivating students to learn. Unmotivated students lead to high dropout rates. The purpose of the non‐experimental, descriptive‐correlational study was to investigate the relationship between students’ self‐regulation and their course achievement and self‐efficacy in an online credit recovery program. The Modified Motivation Scale Learning Questionnaire (MSLQ) was used in the study as measuring two different constructs: motivation and learning strategies. A total 62 respondents in an online credit recovery program participated in the study. Descriptive statistics and the Pearson correlation were employed to analyze data. Results of the study indicated no direct relationship between students’ self‐regulation and final grade. Students’ self‐efficacy and self‐regulation levels support previous research on relationships among self‐efficacy, self‐regulation and one’s academic experiences. Self‐efficacy and task value emerged as significant in terms of at‐risk students in the online credit recovery program. While it appears that students’ self‐regulation in online learning is not strongly related to academic achievement, this finding does not negate the importance of self‐regulatory learning behaviors. Rather, it informs the need for more research on online instruction and course design to address the needs of at‐risk learners.

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Asset Metadata

Creator Viernes, Victoria (author)

Core Title The relationship of students' self-regulation and self-efficacy in an online learning environment

School Rossier School of Education

Degree Doctor of Education

Degree Program Education (Leadership)

Publication Date 06/06/2014

Defense Date 03/15/2014

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

Tag credit recovery,OAI-PMH Harvest,online learning,self‐efficacy,self‐regulation

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Martinez, Brandon (committee chair), Keim, Robert G (committee member), Rueda, Robert (committee member)

Creator Email viernes@usc.edu,vviernes723@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-417728

Unique identifier UC11295176

Identifier etd-ViernesVic-2532.pdf (filename),usctheses-c3-417728 (legacy record id)

Legacy Identifier etd-ViernesVic-2532.pdf

Dmrecord 417728

Document Type Dissertation

Format application/pdf (imt)

Rights Viernes, Victoria

Type texts

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

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

Repository Name University of Southern California Digital Library

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