University of Southern California Dissertations and Theses

Self-regulation and online course satisfaction in high school

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Self-regulation and online course satisfaction in high school

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SELF-REGULATION AND ONLINE COURSE SATISFACTION IN HIGH SCHOOL

by

Sara Peterson
_______________________________________________________________________

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

May 2011

Copyright 2011 Sara Peterson
ii
TABLE OF CONTENTS

List of Tables iv

Abstract v

CHAPTER ONE- Introduction to the Study 1
Online Course Attrition 1
Theoretical Framework 3
Online Learning and Self-Regulation 3
Dimensions of Self-Regulated Learning 4
Self-Regulation Subprocesses 4
Self-Regulation Strategies 6
Purpose of the Study 9
Research Question 10

CHAPTER TWO: Literature Review 11
Online Learning 11
Online Credit Recovery 12
Self-regulatory Attributes 14
Motivation: Self-efficacy and Goal Orientation 14
Internet Self-Efficacy 19
Time Management 21
Study Environment Management 24
Help Seeking 25

CHAPTER THREE: Research Design and Methodology 30
Participants and Sample 30
Data Collection and Instrumentation 31
Motivated Strategies for Learning Questionnaire 32
Internet Self Efficacy Scale 35
Data Analysis 35
Limitations of the Study 36
Significance 37

CHAPTER FOUR: Analysis of the Findings 39
Presentation of Descriptive Characteristics of Respondents 40
Treatment of the Data 41
Data Cleaning 41
Scale Validation 41
Data Analysis 42

iii
CHAPTER FIVE: Conclusions and Recommendations 46
Summary of Findings 47
Implications and Recommendations 49
Future Research 51
Conclusion 52

References 55

Appendices:
APPENDIX A: MSLQ Item List 63
APPENDIX B: MSLQ Subscales and Corresponding Alphas 70
APPENDIX C: Internet Self-efficacy Scale Item List 71

iv
LIST OF TABLES

Table 1: Dimensions of Self-regulated Learning 7

Table 2: Components of the MSLQ 33

Table 3: Demographic Characteristics of Sample Students 40

Table 4: Summary of Correlations 43

Table 5: Summary of Hierarchical Linear Regression 44

v
ABSTRACT
The purpose of the current study was to investigate the potential impact of
students’ self-regulatory attributes on their experiences with online classes. The six self-
regulatory attributes of subject specific self-efficacy, goal orientation, Internet self-
efficacy, study environment management, time management and help seeking were
examined to determine which of these attributes were predictive of a student’s
willingness to enroll in future online classes. The Centinela Valley Union High School
District surveyed students enrolled in online credit recovery classes during the fall of
2010. Data was gathered with the Motivated Strategies for Learning Questionnaire and
the Internet Self-efficacy Scale in the fall of 2010. This existing data was then analyzed
to determine if any relationships existed between the six self-regulatory attributes and a
student’s satisfaction with online classes, as measured by the students’ willingness to
enroll in future online classes. Data analysis consisted of simple correlations and
hierarchical linear regression.
The results revealed that self-efficacy for a specific subject was responsible for
10% of the observed variance in students’ willingness to enroll in future online classes.
The findings from this study suggest that a student’s level of confidence with the
particular subject they are taking may influence their experience in an online class. The
results of the study also suggest that the six self-regulation attributes are a closely related
group of variables that can impact students’ experiences with online classes.
1
CHAPTER ONE- INTRODUCTION TO THE STUDY
Online Course Attrition
With the increased availability of the Internet, computers and other forms of
technology, distance education, delivered specifically in an online format, has burgeoned
(Beatty-Guenter, 2001). For the purposes of this dissertation, online learning is defined as
learning where at least 80% of the content is delivered online (Allen & Seaman, 2008).
Although this relatively new form of course delivery offers promise for students and
institutions, online courses are not without problems that should be addressed (Xenos,
2004). One major problem with online education is the high attrition rate (Carter, 1996;
Diaz, 2002; Holder, 2007; Parker, 1999; Roblyer, 2006; Willging & Johnson, 2004;
Xenos, 2004). Due to the fact that online learning is being used more and more, and for
multiple purposes, research into the issues surrounding online learning is valuable.
The dropout rate for online courses is significantly higher than for face-to-face
(F2F) classes. Xenos (2004) suggests that the dropout rate for online courses in higher
education is 25%-40%, compared to 10%-20% in F2F classes. Online learning in high
school shows a similar pattern of attrition, with some online programs reporting dropout
rates exceeding 60% (Roblyer, 2006). Dropping out of an online course, may signal that
one is dissatisfied with the online learning experience, which could affect whether or not
one chooses online learning in the future.
Virtual or online schooling is one of the fastest growing segments of K-12
education in the United States. The National Center for Education Statistics found that as
2
of 2003, 36% of school districts had students in virtual courses servicing more than
300,000 students (Roblyer, 2006). The increase in online education has benefited both
students and institutions alike by making courses more cost effective, convenient and
amenable to the needs of the individual student. With more students taking online classes,
the higher attrition rate (Carter, 1996; Diaz, 2002; Holder, 2007; Parker, 1999; Roblyer,
2006; Willging & Johnson, 2004; Xenos, 2004) related to online classes raises concerns.
Levy (2004) found that students who do not complete an online class reported not
being satisfied with the “e-learning” experience, and that dissatisfaction within the first
two weeks of classes accurately predicted one’s likelihood to drop out of an online course
and affected their willingness to take another class online. Shea, Pickett and Pelz (2003)
discovered that student’s satisfaction with online learning is highly correlated with issues
of pedagogy, course design and organization of the course.
Dropout from distance or online classes is perhaps bad for the specific student
who drops out, but it is also potentially bad for the institution and society at large. Not
completing a distance course may prevent a student from enrolling in another distance
course (Poellhuber, Chomienne, & Karsenti, 2008). It may also adversely affect one’s
self-esteem and feelings of self-efficacy, and therefore whether or not one persists in
education. Determining which issues most contribute to satisfaction and completion of
online classes is important for online course designers, instructors and students.
Issues of self-regulation may contribute to online course attrition and impact a
student’s willingness to take online classes in the future. Six self-regulatory attributes
3
will be studied to explore the predictive validity of a student’s willingness to enroll in
future online classes.
Theoretical Framework
Online Learning and Self-Regulation
The social cognitive theory of self-regulation in learning is the theoretical
underpinning of this study. Because much online learning is independent in nature, it is
possible that students in online classes need to be more self-regulating in order to
complete the course. Due to the specialized nature of learning in an online environment,
there are certain issues that are integral to success in online classes and therefore whether
or not one chooses to engage in online learning in the future. Qualities that contribute to
increased success in online classes are the ability to work independently, maintain focus
on personal and academic goals, sustain motivation in the face of life’s demands and
display computer proficiency (Holder, 2007).
Moore (1993) developed a theory to explain the components of distance
education. Transactional distance theory consists of three variables that are present in
different forms of distance education. These are interaction, structure and autonomy.
The first two are predominately the domain of course designers, as they deal with the
manner in which information is transmitted and how the course is arranged. The last
variable of autonomy deals with distance education learners and their ability to monitor
and regulate their own learning behavior.
Due to the amount of self-direction and independent work involved in online
classes, autonomy has been found to be an important component of online or distance
4
learning (Holmberg, 1995; Jung, 2001; Kearsley, 2000). However the general concept of
autonomy does not shed light on the specific strategies and aspects of autonomy that a
distance learner must possess and implement to achieve academic success in an online
format.
Dimensions of Self-Regulated Learning
The social cognitive theory of self-regulation provides a lens through which the
specific aspects of learner autonomy necessary for success in online classes can be
explored in greater detail. Self-regulated learning is a proactive process that students use
to acquire specific academic skills (Zimmerman, 2002; Zimmerman 2008). Self-
regulation deals with self-generated thoughts, feelings and behaviors that are directed
toward the attainment of goals. Self-regulation subprocesses (Zimmerman, 1989) and
Self-regulation strategies (Pintrich, 1999) are two descriptions of self-regulation that will
be explored.
Self-Regulation Subprocesses
Social cognitive theorists assume that self-regulation is comprised of three
categories of subprocesses: self-observation, self-judgment and self-reaction
(Zimmerman, 1989). These subprocesses must be developed and used to facilitate self-
directed change (Bandura, 1991). One should be aware of cognitions and actions, be able
to judge if those actions are in accordance with attaining goals, and be able to react
accordingly to that information, by altering behavior if necessary. The specific context
can also help or hinder a student’s self-regulatory abilities (Winters, Greene & Costich,
5
2008). This is particularly relevant with online courses. The design of the course, as
well as the manner and frequency of interaction between instructors and students can help
to facilitate self-regulation and success in online classes (Jung, Choi, Lim & Leem,
2002).
These three categories of subprocesses interact with each other in a reciprocal
fashion, meaning that they all influence one another bidirectionally. As one completes an
assignment, one must observe progress, make judgments about the accuracy and quality
of the work, and react and direct future behavior accordingly. The actions taken as a
result of the analysis, in turn influence future cognitions and actions. This reciprocal
influence reflects the social cognitive idea of triadic reciprocality, whereby personal
(cognitive and affective), environmental and behavioral factors influence one another in
an ongoing process (Bandura, 1986).
Self-regulated students are meta-cognitively, motivationally and behaviorally
active in their own learning processes (Zimmerman, 1989). They direct their own
learning, instead of being guided by a teacher or parent. According to Zimmerman
(1989), self-regulated learners employ specific learning strategies to achieve goals. They
evaluate and monitor their progress, and adjust their learning behavior when necessary.
They are motivated, and persevere even in the face of distractions. Pintrich (2003) found
that self-regulated students also tend to be more academically motivated and display
better learning outcomes.
Due to successful learning methods, and possibly their superior motivation, self-
regulated students are not only more likely to succeed academically, but they are also
6
more likely to have an optimistic view of their future (Zimmerman, 2002). Successful
self-monitoring reveals potential learning gains to the student. Even if the gains are
minimal, the self-regulated learner is able to recognize their progress. This increases
self-efficacy, which is one’s feeling of what can be accomplished within a given domain.
Efficacy beliefs affect one’s motivation to continue with the task.
According to Zimmerman (2002) self-regulation is not a single trait that one does
or does not have. Self-regulation is a family of skills that must be utilized and
customized to complete certain tasks. Components of self-regulation include, but are not
limited to setting specific goals, designing strategies to achieve those goals, monitoring
one’s performance and one’s environment to ensure compatibility with goal attainment,
and managing one’s time efficiently.
Self-Regulation Strategies
Pintrich (1999) describes a model of self-regulated learning that is comprised of
three general categories of strategies: 1) cognitive learning strategies, 2) self-regulatory
strategies to control cognition, and 3) resource management strategies (Pintrich, 1989;
Pintrich & DeGroot, 1990; Pintrich & Garcia, 1991). An elaboration of each follows.
Table 1 contains a brief overview of the different dimensions of self-regulated learning.

7
TABLE 1
Dimensions of Self-Regulated Learning
Self-regulation Subprocesses
(Zimmerman, 1989)

Self-regulation Strategies
(Pintrich, 1999)
1. Self-Observation

1. Cognitive Learning Strategies
2. Self-Judgment

2. Self-regulatory Strategies to Control
Cognition
3. Self-Reaction

3. Resource Management Strategies

Cognitive Learning Strategies
The cognitive strategies that have been identified as important to classroom
performance are rehearsal, elaboration and organizational strategies (Pintrich & Garcia,
1991; Pintrich, 1999). Rehearsal deals with tasks like repeating the items to be learned,
and is a more passive strategy which results in relatively shallow processing of the
material. Elaboration requires deeper processing, with tasks like summarizing or
paraphrasing. Organizational strategies, like outlining a chapter, or selecting the main
idea also result in deeper processing of material. Deeper processing of content is
important in any discipline. However, in an online context it takes on added importance.
Because the online learner will have to decode much information on their own, the more
proficient and conversant they are with strategies that lead to a more in depth
investigation of the material, the more likely they are to perform well.
Self-regulatory Strategies to Control Cognition
The strategies used to control cognition can be divided into two types: knowledge
about cognition and self-regulation of cognition (Pintrich, 1999). Knowledge about
8
cognition is an awareness of one’s thinking patterns and thought processes, whereas self-
regulation of cognition deals with planning, monitoring and regulating behavior. The
former is simply an awareness of cognitions, whereas the latter is about taking that
awareness and transforming it into action to control one’s behavior. Theoretically this
type of “active awareness” would benefit the online student, by enhancing their ability to
monitor and control their academic behavior.
Resource Management Strategies
The resource management strategies involved in self-regulation are time
management, effort management, and environment management (Pintrich, 1999). Self-
regulation includes an individual’s ability to monitor and manage the amount of time they
need to spend to complete a certain task, the amount of effort required and the ability to
gauge the type of environment most conducive to completing the task. Due to the
independent nature of online learning, the self-regulation strategies described above
would theoretically assist the online student as they endeavor to succeed in a
nontraditional form of learning.
Students who are successful in online courses engage in strategies that less
successful students do not use, or do not use effectively (Azevedo, Guthrie & Seibert,
2004). Undergraduate students who used planning and forethought activities and
learning strategies, such as summarizing and making inferences, made larger gains in
conceptual understanding in online classes than students who didn’t perform those
activities, as measured by verbal protocol data gathered while students used a hypermedia
environment to learn about the circulatory system.
9
Eom and Reiser (2000) conducted a study of middle school students who took
online classes. First the researchers established if the students were “high self-
regulating” or “low self-regulating”. Then students were randomly assigned to either a
learner-controlled condition, which consisted of greater autonomy, or a program-
controlled condition with more structure. The results of the study showed that students
(both high and low self-regulating students) in a program-controlled condition scored
significantly higher on a posttest than the students in the learner-controlled condition.
However, students classified as high self-regulating, scored high in both conditions.
Thus the low self-regulating students benefited from the increased structure of the
program-controlled condition.
In sum, online learning is growing at a rapid rate. Although online learning
benefits many people, the high attrition rate causes concern. Aspects of self-regulation
can be important to success in any academic pursuit. However due to the increased
autonomy, students in online classes may need to be more self-regulating than students in
traditional classroom settings. The importance of self-regulation in an online format
cannot be overstated, since the learning is very often completed independently. One
could argue that the more awareness and control students are able to exert over their
behavior and learning environment, the more likely they are to have a positive online
learning experience.
Purpose of the Study
The purpose of the current study is to investigate specific self-regulatory
attributes to determine if any are predictive of a student’s willingness to enroll in future
10
online courses. Because online learning potentially involves a great amount of
independent work, students in online classes may need to employ specific self-regulatory
strategies, such as time and study environment management, in order to have a positive
experience in online classes, and therefore choose to enroll in online classes in the future.
Research Question
Of the self-regulation attributes of self-efficacy for learning and performance,
goal orientation, Internet self-efficacy, time management, study environment
management, and help seeking which are most predictive of a student’s willingness to
enroll in future online classes?

11
CHAPTER TWO: LITERATURE REVIEW
This dissertation explores the self-regulatory attributes that are most important to
a student’s willingness to enroll in future online courses at the high school level. Due to
the high attrition rate of online courses, research is needed to explore the factors that
contribute to student satisfaction in online courses. The theoretical framework for this
literature review is the social cognitive theory of self-regulation. According to the
research, the six self-regulatory attributes that are most likely to impact success in an
online format are self-efficacy for learning and performance, goal orientation, Internet
self-efficacy, time management, study environment management and help seeking. After
an investigation into aspects of online learning, the six self-regulatory attributes will be
explored.
Online Learning
Online learning is the latest incarnation of what has been a long-standing tradition
of distance education. Distance education began with correspondence style courses in the
first part of the twentieth century and has developed into courses that are partially or
completely mediated by the use of technology (Keegan, 1998). Online learning has
allowed students to overcome barriers to education by making learning flexible and
accessible (Burbules & Callister, 2000). Using a supply of electricity, a computer and an
Internet connection, online education has made learning possible from anywhere in the
world (Jung et al., 2002).
Online learning can take many different forms that incorporate multiple
technologies and different types of media to engage the student in the learning experience
12
(Burbules & Callister, 2000; Caywood & Duckett, 2003; Heckman & Annabi, 2006;
Keegan, 1998). Online learning is also used for multiple purposes. One recent use of
online courses is for the purpose of credit recovery in the high school setting. The
following section explores the uses, purposes, benefits and issues with online credit
recovery.
Online Credit Recovery
One way of utilizing online learning within high schools is for the purpose of
credit recovery. Online credit recovery is usually defined as an in-school program that
allows students to retrieve credits they did not successfully earn in a traditional F2F class
(Trotter, 2008). Rather than work online at home or some other off-campus environment,
the students take the classes in a computer lab at school. All students work independently
on different classes. In the online credit recovery labs, the teacher functions as a
facilitator, since the students progress through separate courses largely on their own.
With more and more students failing classes in high school, the need to offer quick,
flexible options to recover credits is becoming increasingly important, especially in large
urban high schools (Martin & Brand, 2006).
Although the structure of online credit recovery programs varies, most of them
share similar characteristics (Trotter, 2008). Most begin with a diagnostic test to
determine the student’s level of knowledge. Once that is determined, the course is
customized based on what the student still needs to learn. The course consists of brief
lectures, Internet research, homework activities, readings, quizzes and tests. Online
credit recovery courses are also designed to address students’ different learning styles
13
with varied modes of delivery that make use of audio, video, graphics, images and
animation.
Online credit recovery provides many benefits to both schools and students in
high schools with large populations who need to recover credits (Trotter, 2008). Online
credit recovery allows students to recover credits in a short amount of time. Because the
students work at their own pace, they are able to recover more credits than in a traditional
classroom setting (Roblyer, 2006). Instead of only being able to make up one or two
classes in a semester, students can complete as many classes as they can devote time.
This flexibility allows students who may otherwise have dropped out, a chance to
graduate.
Failing to graduate from high school creates a host of problems for the individual,
as well as for society. High school dropouts have a difficult time finding a job that pays a
living wage (Lehr, Hansen, Sinclair, & Christenson, 2003). They are also 72% more
likely to be unemployed (McKeon, 2006) and have more health problems than high
school graduates (Laird, Cataldi, KewalRamani & Chapman, 2008). Also the more
education one has completed, the less likely one is to be involved in crime (Belfield &
Levin, 2007; Watson & Gemin, 2008).
The use of online credit recovery programs has financial implications for schools
and states alike. Online credit recovery programs allow the student to remain in school,
which enables the school to collect attendance revenue. Belfield and Levin (2007), in
their study of California’s dropout problem, outlined the financial impact of high school
dropouts. According to their estimates, high school graduates create revenue gains for
14
the state in the form of state taxes, healthcare savings, crime savings and welfare savings
for a total savings of $53,580 for each student who graduates in California. The financial
benefits of graduating as many students as possible are clear.
Even though there are many benefits of credit recovery through online learning, it
is not a panacea. One pervasive problem is the high attrition rate of online classes (Carter,
1996; Diaz, 2002; Holder, 2007; Parker, 1999; Roblyer, 2006; Willging & Johnson,
2004; Xenos, 2004). The high attrition rates are an indication that many students may be
dissatisfied with online learning and therefore have trouble succeeding in that
environment (Roblyer, 2006). Although the reasons for the high attrition rate are
undeniably varied, determining which issues impact student’s satisfaction with online
classes is crucial to the continued development of online learning.
In sum, online learning can benefit schools and students alike. Credit recovery in
high school is a recent use of online learning. Although there are many benefits of online
credit recovery, the high attrition rates associated with online learning raise issues about
student satisfaction while learning in an online environment.
Self-regulatory Attributes
Motivation: Self-Efficacy and Goal Orientation
Motivation for learning focuses on why one chooses to learn and execute the
activities necessary for that learning (Pintrich & Garcia, 1991; Garcia & Pintrich, 1991;
Pintrich & Schunk, 1996). Assumptions about this construction of motivation within a
social cognitive framework include between-and within-context variability, multi-
dimensionality and between- and within-individual variability. Students cannot really be
15
classified as either “motivated or unmotivated”. Motivation is not a stable personality
trait that crosses all domains (Linnenbrink & Pintrich, 2002). Although this makes
motivation difficult to study, it provides hope for teachers and students about the
possibilities of increasing motivation to improve student performance within the specific
domain of interest. The motivational constructs of subject specific self-efficacy for
learning and performance and goal orientation will be examined.
Subject Specific Self-Efficacy for Learning and Performance
One’s personal perceptions of efficacy are an important element of motivation
(Pintrich & DeGroot, 1991; Lynch & Dembo, 2004), and consequently self-regulation.
Subject specific self-efficacy for learning and performance is what a person believes they
are able to accomplish within a given subject or domain with the skills they possess.
Efficacy beliefs affect people’s feelings, their thought processes and their behavior
(Bandura, 1986; Bandura, 1993). Self-efficacy is important when one is learning new
material and performing new tasks. One’s perceived self-efficacy can affect the goals
one sets and the motivation to persist with those goals, especially in the face of adversity
(Zimmerman, 2000; Zimmerman, Bandura & Martinez-Pons, 1992).
Because self-efficacy is very subject and task specific, (Bandura, 1986) the
content of an online class, and how confident the student is in their abilities in that
particular subject are important factors that affect performance. Those who feel
confident about their ability to handle the material are likely to perform better in that
class than someone with less confidence (Joo, Bong & Choi, 2000). Zimmerman et al.
(1992) found that students in a middle school social studies class who had higher
16
perceptions of self-efficacy for the content of the course received better grades. Self-
efficacy for achievement and the goals the students set for themselves accounted for 31%
of the variance of final grades. Lynch and Dembo (2004) also found in their study of
blended (online plus a F2F component) classes that self-efficacy for learning the specific
content was a significant predictor of final grades.
Students who believe they can achieve, even if that belief is not based on superior
ability, tend to set higher goals for themselves and perform at higher levels than their
peers who possess equal ability, but lower efficacy beliefs (Wang & Newlin, 2002).
Those with higher self-efficacy also tend to be more committed to the goals they have set
for themselves (Bandura, 1993). Students with high self-efficacy respond more
positively to criticism and negative feedback than students with low self-efficacy (Seijts,
Latham, Tasa & Latham, 2004).
Pintrich and DeGroot (1990), in their study of 173 seventh graders found that self-
efficacy was related to cognitive and metacognitive engagement. Students who believed
they were more capable were more likely to utilize cognitive strategies, such as
meaningful organization of information when studying for a test. Employing these
techniques increases the likelihood that one will succeed in a class. This success could
also lead to increased self-efficacy, which could in turn motivate one to continue with
education (Bandura, 1986). Self-efficacy was also positively correlated with intrinsic
value and use of self-regulatory strategies, and negatively correlated with test anxiety.
Wolters (2003) found that students with higher degrees of self-efficacy also were less
likely to procrastinate when completing academic tasks.
17
In sum, self-efficacy has been found to be a significant predictor of academic
success. Students who are more efficacious tend to employ strategies that increase their
likelihood for success. The more successful a student is, the more likely they are to be
satisfied with that experience. Self-efficacy is an important motivational variable that can
affect one’s choices, level of persistence and goal setting. The more confidence students
have in the subject they are taking, the more likely they are to persist, be satisfied with
and succeed in their online class.
Goal Orientation
Setting goals is an important part of any academic undertaking. Goal orientation
refers to the reasons one engages in a particular activity (Eccles & Wigfield, 2002).
People engage in behaviors for multiple reasons. There are many different types of goal
orientations, for the purposes of this literature review, intrinsic and extrinsic goal
orientations will be explored.
One can be motivated to do well in a course because they want to look smart, or
perhaps they are mainly interested in getting a good grade. These are extrinsic reasons,
or motivation that is based on outcomes outside or external to the individual (Eccles &
Wigfield, 2002). In contrast to students who are extrinsically motivated, there are
students who are motivated to study hard and do well in a class because they are
genuinely interested in the material, and simply want to engage in the learning for its own
sake (Pintrich & Garcia, 1991). These students can be classified as intrinsically
motivated. Intrinsic and extrinsic motives can occur simultaneously and are not
necessarily at odds with one another. A student can genuinely enjoy learning in a class
18
as well as wanting to get a good grade. However, students who possess higher intrinsic
motivation tend to perform better academically than students who are more focused on
external factors (Lyke & Kelaher Young, 2006). Students with an intrinsic goal
orientation also generally value the use of deeper cognitive strategies, which leads to
deeper processing of material. This deeper processing then leads to better learning
outcomes. Abar and Loken (2010) also found that college-bound high school students
with intrinsic goal orientations were significantly better at self-regulated learning, and
therefore performed better than students who had adopted extrinsic goal orientations.
Beatty-Guenter (2001) found that goal orientation was an important factor for students
who completed online courses in Canadian community colleges. Zhang, Li, Duan and
Wu (2001) in their study of 112 university students found that students with higher
intrinsic motivation and self-regulation skills tended to have more positive beliefs about
the effectiveness of online classes.
Lin, McKeachie and Kim (2003) conducted a study of college students to
ascertain the interaction between intrinsic and extrinsic motivation, and how that
interaction would impact course grades. The research showed that students who had high
intrinsic motivation and medium extrinsic motivation had the highest grades, leading the
researchers to conclude that this was the best combination of intrinsic and extrinsic
motives, and reinforcing the idea that the two motivations are not incompatible with one
another. Upon deeper investigation into this group, they found that the students with the
highest grades were also low in test anxiety and high in self-efficacy. These high
performing students also made better use of elaboration and organization strategies.
19
In sum, there is evidence to suggest that specific learning goals, whether intrinsic
or extrinsic, lead to better performance on complex tasks, than vague or abstract goals.
Setting specific, proximal goals leads to increased academic success. Goals are important
to any academic pursuit. In general, intrinsic goal orientations were associated with
deeper cognitive processing, use of self-regulation strategies and better academic
outcomes.
Internet Self-Efficacy
Due to the fact that online courses are delivered through the mode of technology,
one’s efficacy beliefs about their ability to manage technology and their Internet skills
could play an important role in online class satisfaction. Internet self-efficacy is not
necessarily based on what one has done in the past, but is based on what one believes
they can accomplish in the future with respect to the Internet and computer technology
(Compeau & Higgins, 1995).
Eastin and LaRose (2000) in their study of 171 undergraduate students in a
communications class found that prior Internet experience, Internet use and positive
outcome expectations as a result of Internet use, were positively correlated with Internet
self-efficacy, while self-disparagement and Internet stress were negatively correlated with
Internet self-efficacy. Prior Internet experience was the strongest predictor of Internet
self-efficacy. Those who had two years or more Internet experience were less likely to
experience stress with Internet usage and were more satisfied with their Internet abilities.
Eastin and LaRose (2000) also found a reciprocal relationship between Internet
self-efficacy and Internet use. The more efficacious people felt about using the Internet,
20
the more they actually used the Internet. The more they used the Internet, the more
efficacious they felt. Joo, Bong and Choi (2000) found that self-efficacy for computer
and Internet use significantly contributed to success in an online course. They also found
that self-efficacy for self-regulated learning related positively to Internet self-efficacy.
Willging and Johnson (2004) administered a survey to drop outs of an online master’s
degree program at the University of Illinois. Students reported that technological
difficulties were a major reason for deciding not to continue with the program.
For online students who are new to technology, they have a double task: learn the
content and learn the technology. This can make taking an online class difficult. Schrum
and Hong (2002) found that students with little or no experience with technology, found
online courses to be challenging. However faculty was able to overcome that challenge
by offering support in the form of guided practice of common tasks, like checking email
and navigating through web pages.
Compeau and Higgins (1995) divide the idea of Internet or computer self-efficacy
into three dimensions: magnitude, strength and generalizability. Self-efficacy magnitude
is the level of ability that an individual believes they have. Students with high computer
self-efficacy magnitude would be expected to try to accomplish more difficult computing
tasks than someone with lower computer self-efficacy magnitude. Self-efficacy strength
deals with how confident one is in assessments of one’s own computer self-efficacy. Self-
efficacy generalizability deals with how limited one’s assessment of one’s own abilities is
to a particular domain. Those with high self-efficacy generalizability would be expected
to successfully use different aspects of computer software and hardware.
21
Compeau and Higgins (1995), working within the reciprocal framework of social
cognitive theory, found that one’s technological self-efficacy determined how much one
enjoyed working with technology and therefore how much time one was engaged with
technology. The more time the subjects spent at the computer, the more enjoyment and
less anxiety they felt from the experience.
Zhang et al. (2001) described a link between technological self-efficacy and a
student’s self-regulation learning skills. The study found learners’ self-regulation skills
were closely related to one’s technological and Internet self-efficacy. Learners with
better self-regulatory skills managed and organized their learning more effectively, which
in turn increased their efficacy for completing assignments in an online environment.
In sum, comfort and experience with the Internet and computer technology were
important to success in an online format in the aforementioned studies. The more
students used technology, the more comfortable they were with it, and the more
enjoyment they experienced. The increased use, comfort and enjoyment contributed
positively to the online course experience.
Time Management
Successful time management is important to academic success. This includes but is
not limited to leaving enough time to complete assignments, study for exams and seek
assistance from instructors or peers if one needs clarification on the material before one
can proceed with an assignment and complete it in the time given (Zimmerman, 2002).
In an online classroom, the ability to manage one’s time is pivotal since most of the
learning is independent in nature. In a study of college students and distance learning,
22
Roblyer (1999) found that an important characteristic of successful distance or online
learners was the ability to effectively manage one’s time. Holder (2007) in his research
of students taking online courses at the university level found that students who were
better able to manage their time were more likely to be satisfied with the online
experience and persist in an online class.
Self-regulated learners use their time efficiently (Zimmerman, 2002). In order to
complete assignments on time, students must analyze and evaluate their time
expenditures and modify them as necessary. Regulating one’s time requires cognitive
and metacognitive strategies (Wolters, 2003). Students who are more self-regulated are
better at managing their time. Managing one’s time effectively is related to the self-
regulatory idea of self-monitoring. In order to complete assignments one must plan,
organize and evaluate progress; none of this can occur if the student is not monitoring
their behavior on a regular basis.
Three important phases of self-regulated learning are related to time management.
They are the forethought phase, the performance phase and the self-reflection phase
(Zimmerman, 2002). The forethought phase occurs before one undertakes a specific task.
One of the subcategories within the forethought phase is task analysis. Task analysis
consists of strategic planning. Before beginning an assignment, a student must know
approximately how much time will be required to complete the assignment, and be able
to allot the necessary amount of time. Without this stage, the student may find that they
have not allowed enough time to complete the assignment or to complete it well.
The next phase is the performance phase (Zimmerman, 2002). Within the
23
performance phase are the subcategories of self-control and self-observation. Self-
control refers to utilizing those strategies that were elucidated in the forethought phase,
like beginning an assignment early enough to ensure that there is adequate time to
complete it, or performing certain cognitive tasks that are necessary to the assignment.
Self-observation pertains to paying attention to strategies used and time spent on specific
tasks to be able to analyze how efficiently one’s time is spent. For example, as a student
completes an assignment, they should be aware of how much time they are spending on
tasks within the assignment to ensure that they are being as productive as possible. Self-
observation is one’s cognitive tracking of academic or personal functions.
The last phase of self-regulation relevant to time management is the self-reflection
phase. Within the self-reflection phase is the subcategory of self-judgment (Zimmerman,
2002). One form of self-judgment involves causal attributions. If a student is unable to
finish an assignment in the time given, the student can attempt to understand why they
didn’t finish in time. Perhaps they started too late or spent too much time on one section.
Whatever the reason, the student can analyze the past in hopes of making wiser choices
with their time in the future.
Successful time management strategies are correlated with success in online
courses (Kitsantas, Winsler & Huie; Phipps & Merisotis, 1999; Holder, 2007). Students
who rated themselves high in terms of time management skills generally had enough time
to complete their assignments. Song, Singleton, Hill and Hwa Koh (2004) in their study
of graduate students taking online classes, found that time management was important for
success. The graduate students in the study rated time management as one of the most
24
important factors for completing an online class.
In sum, managing one’s time is an important aspect of academic satisfaction and
success. Students who were more highly self-regulated exhibited better time management
strategies. Those who were better able to manage their time, were more likely to persist
in courses, be satisfied with the online experience and achieve at a higher level. In the
online class environment, time management is crucial because the student must complete
the assignments while potentially receiving less external cues than students in F2F
classroom environments.
Study Environment Management
The ability to manage one’s study environment is important to academic success.
Since online learning takes place almost entirely outside of the traditional F2F classroom,
the idea of a “classroom” takes on new meaning. Students in online classes work at
home, in libraries, in coffee shops and pretty much anywhere a reliable Internet
connection can be found (Whipp & Chiarelli, 2004). Because students in online classes
complete assignments mostly on their own, they need to be aware of how to manage their
environments to maximize achievement.
Within the social cognitive framework of triadic reciprocality, personal,
behavioral and environmental influences continually interact to create our experiences
(Bandura, 1986). The issue of environment management fits well within the
environmental sphere. The structure of one’s environment can help or hinder
productivity (Zimmerman, 1989), thereby affecting one’s emotions, cognitions and future
behavior.
25
In their case study of graduate students, Whipp and Chiarelli (2004) examined the
factors that contributed to success in online courses. The ability for the graduate students
to monitor and adjust their environments when necessary was important to their success
in the course. Some of the students in the study found quiet places in their homes to
work, so they wouldn’t be bothered by distractions. Other students completed
assignments at a local library during times when the library wasn’t busy. Many of the
students reported that they needed to create a psychological space for themselves where
they felt they were in class on a consistent schedule. One student announced, as she
entered a home office that she was “going to school” and was not to be disturbed until the
“class” was over. In their study of undergraduate students, Kitsantas et al. (2008) found
that time and environment management showed the strongest correlations with academic
performance. Students who are able to put themselves in environments that help foster
learning, have higher GPAs and are better adjusted in college than students who don’t
possess these skills.
In sum, online students who were able to successfully manage their study and
learning environments performed better than students who were less able to create a
productive learning environment. Because online students are outside of the traditional
classroom, finding learning environments that promote study productivity can mean the
difference between success or failure in an online format.
Help Seeking
Help seeking addresses the questions of if, when and from whom a student seeks
assistance when they have difficulty with an academic task (Karabenick, 2004). In any
26
academic setting, a student’s comfort with and readiness to ask for help when they need it
is important to their success in the course. This is especially true of online courses, since
the online student completes much of their work independently. In order for students in
online classes to be successful, they must be able and willing to seek help when it is
necessary.
Help seeking is an important aspect of self-regulated behavior (Aleven, Stahl,
Schworm, Fischer & Wallace, 2003). Help seeking is distinct from other self-regulatory
processes because it directly involves other people. Because help seeking is social in
nature, one’s social as well as achievement goals will influence help seeking behavior.
Ryan and Pintrich (1997) found that high school students who considered themselves
more socially competent were less likely to be threatened by the prospect and potential
consequences of seeking help and therefore more likely to ask for help.
For the purposes of this literature review two types of help seeking will be
examined. They are instrumental and executive help seeking. Instrumental (also called
adaptive) help seeking decreases a student’s need for future help (Karabenick, 2004). For
example, a student may ask a teacher to elaborate on a specific set of instructions so that
they can proceed more efficiently on their own. This type of help seeking is referred to
as instrumental because the help one receives is necessary to complete the task (Arbreton,
1998). Instrumental help seeking is when a student enlists a more knowledgeable person
to assist them with comprehension so they feel confident to complete the task
independently. This type of help seeking facilitates further work by the student. It would
be expected that a student seeking instrumental help might prefer a teacher rather than a
27
peer, since the teacher would usually possess more experience and expertise, and would
therefore be more useful to the student who wanted to proceed on their own.
Another type of help seeking is called executive (or expedient) help seeking.
Executive help seeking is used when a student wants to avoid having to complete the
work on his or her own (Karabenick, 2004). Students elicit executive help seeking so
that someone else can finish the work or complete the problem for them (Arbreton,
1998). It would be expected that a student seeking executive help might prefer to seek
help from a peer rather than a teacher. A peer would be more likely to provide a simple
answer to a problem, instead of a lengthy explanation of how to find the correct answer
on one’s own.
Students choose to seek help for a variety of personal and situational reasons.
One well-documented reason that students avoid seeking help is that asking for help is a
perceived threat to their self-esteem (Karabenick & Knapp, 1991; Aleven et al., 2003;
Karabenick, 2004). In a study of middle school students by Ryan and Pintrich (1997)
more threatened students were less likely to approach their teachers (a formal source) for
help, but were willing to approach a peer (an informal source). In college students this
seems to be reversed. For college students threat appears not to be related to help seeking
from formal sources, but is inversely related to help seeking from informal sources. In
other words, threatened college students are less comfortable seeking help from peers
than they are from teachers. Threats to one’s self-esteem affect patterns of help seeking
in complex ways and seem to be somewhat age specific.
28
In his study of help seeking behavior in large college classrooms, Karabenick
(2003) researched how the large and somewhat impersonal nature of college lectures
would affect a student’s help seeking behavior. He found that threat was positively
related to executive help seeking and inversely related to instrumental help seeking.
Also students who utilized instrumental help seeking strategies had higher course grades
than students who were help seeking avoidant.
Karabenick (2003) also investigated whether the particular goal structure of the
class (did the instructor focus more on performance or learning) had an influence on
student’s help seeking. The results of the study revealed that students who were in
classes that were perceived to be more performance oriented were more help avoidant.
Put another way, students who were in classes where the emphasis was on performance
rather than learning, were less likely to seek help. As might be expected, students with a
personal mastery goal orientation (those more focused on learning than performing for
some external outcome, like grades) were more likely to solicit help than those students
with a performance goal orientation (Arbreton, 1998; Karabenick, 2003; Karabenick,
2004). Help seeking has also been linked to the use of cognitive and metacognitive
strategies (Karabenick & Knapp, 1991).
In sum, the ability to recognize when one needs help, and to feel comfortable
enough to solicit help when necessary are important self-regulatory strategies that
contribute to success in classroom situations. Students who feel that their self-esteem
might be threatened are less likely to ask for help. Students who seek instrumental help
are more likely to utilize adaptive cognitive strategies and have higher grades.
29
The six self-regulatory attributes of self-efficacy for learning and performance,
goal orientation, Internet self-efficacy, time management, study environment
management and help seeking are factors that contribute to success in educational
settings from middle school through graduate school. The extent to which each is
important for success in online classes will certainly vary by student and specific
situation. However research indicates that the more a student is able to be aware of,
monitor and control their learning behavior, the more likely they are to successful in a
variety of educational environments.

30
CHAPTER THREE: RESEARCH DESIGN AND METHODOLOGY
Research Question
Due to the increases in enrollment (Allen & Seaman, 2008) and the high attrition
rate for online classes (Carter, 1996; Parker, 1999; Diaz, 2002; Xenos, 2004), an
investigation into the factors that are most predictive of student’s experiences with online
classes and therefore their willingness to enroll in online classes in the future is
warranted. This study examines six self-regulatory attributes and how those attributes, as
compared with one another, contribute to a student’s willingness to enroll in future online
classes.
In order to further investigate the self-regulatory attributes that are the best
predictors of a student’s willingness to enroll in future online courses, this study will
analyze existing data from students taking online classes within the Centinela Valley
Union High School District (CVUHSD) during the fall of 2010.
Participants and Sample
The participants for this study are students enrolled in online classes to recover
credits in a variety of subjects during the 2010 fall session at three high schools within
CVUHSD. The students are enrolled in a class called Education 2020 (E2020). E2020 is
an instructional program that offers online programs in various settings and to a wide
range of student academic levels and school implementation models since 1998
(Education 2020, 2008). The E2020 classes are aligned to national and state standards.
Schools use the E2020 program for a variety of purposes including core curriculum for
middle and high school education, credit recovery, academic learning centers, grade
31
recovery programs, fast track middle school programs, district virtual schools, and
various alternative school models. A significant number of schools utilizing E2020 as the
core curriculum have out-performed other traditional schools. E2020 currently services
over 40,000 students in 34 states.
The E2020 students in CVUHSD take online classes in a variety of subjects for the
purpose of credit recovery. The courses are customized according to the student’s level
of knowledge in the particular domain. The students complete the work independently
and primarily at school in their E2020 computer lab. Students also have access to the
system at home, and many of the students also complete assignments at home.
In the 2008-2009 academic year CVUHSD enrolled 7,333 students in grades 9-12
(Centinela Valley Union High School District, 2010). CVUHSD also serves a racially
diverse student population. The racial breakdown of the students is 70% Hispanic, 18.2%
African American, 11% White and 4.8% Asian/Pacific Islander.
Data Collection and Instrumentation
Existing student data will be analyzed for this study. The Centinela Valley High
School District administered an anonymous survey to their online credit recovery
students in the fall of 2010. The instruments used were specific subscales of the
Motivated Strategies for Learning Questionnaire (MSLQ) and the Internet Self Efficacy
Scale. Demographic information such as age, gender, prior experience with online
classes and grade point average were also collected. The following is an explanation of
each survey instrument that was used.

32
Motivated Strategies for Learning Questionnaire
The MSLQ is an 81 item, self-report instrument designed to measure college
students' motivational orientations and their use of various learning strategies (Pintrich,
Smith, Garcia, & McKeachie, 1991). Scores from the MSLQ have been widely used for
research in the areas of motivation and self-regulated learning. Scores have been used to
address the nature of motivation and its affect on the use of learning strategies, to
examine the relationships among the motivational constructs and evaluate the effects of
instructional interventions and less traditional course structures and educational
technologies, like online learning (Bong, 2001; Eom & Reiser, 2000; Duncan &
McKeachie, 2005; Wolters, 2004). In total, the MSLQ consists of 15 subscales, six
within the motivation section and nine within the learning strategies section (see
Appendix A for a list of MSLQ items). The instrument is adaptable allowing a researcher
to use the scales together or individually to suit their needs. Students completing the
MSLQ rate themselves on a Likert scale from 1 (not at all true of me) to 7 (very true of
me). The scores are computed by calculating the mean of each subscale.

33
TABLE 2
Components of the MSLQ

Part 1: Motivation Scales Part 2: Learning Strategies
Scales

Scale # of
items

Scale # of
items
1. Intrinsic Goal Orientation 4 1. Rehearsal 4
2. Extrinsic Goal Orientation 4 2. Elaboration 6
3. Task Value 6 3. Organization 4
4. Control of Learning Beliefs 4 4. Critical Thinking 5
5. Self-Efficacy for Learning 8 5. Metacognitive Self-Regulation 5
6. Test Anxiety 5 6. Time/Study Environment
Management
8
7. Effort Regulation 4
8. Peer Learning 3
9. Help Seeking 4

Total # of Items 31 Total # of Items 50

Some items within the MSLQ are negatively worded and must be reversed before a
student’s score can be computed. For example, the statement during class time I often
miss important points because I'm thinking of other things is designed to measure the
extent to which a student is metacognitively self-regulated. If a student circles a 1 on a
negatively worded question, this represents that the student is actually high in this
particular construct, not low. Therefore this item would be reverse scored and the 1
would become a 7. The simplest way to compute a reverse-coded item is to take the
original score and subtract it from 8 (Pintrich et al., 1991). Ultimately, the overall score
for each subscale represents the positive wording of all items within that scale and
therefore higher scores indicate higher levels of the construct being measured.

34
Reliability and Validity of the MSLQ
Following many data collection periods, statistical tests and modifications of the
MSLQ, the final version was completed in 1990 and presented formally in an article
published in Educational and Psychological Measurement (Pintrich, Smith, Garcia, &
McKeachie, 1993). The data presented results gathered from a sample of 380 students at
a public, 4-year university in the Midwest. The subjects were taken from 14 subject
domains and five disciplines, including natural science, humanities, social science,
computer science, and foreign language (Pintrich et al., 1991).
Using data from their sample of 380 students, the authors of the MSLQ completed a
number of statistical tests to determine the reliability and validity of their instrument.
First, the authors completed two confirmatory factor analyses. One confirmatory factor
analysis was completed for the set of motivational items and another for the learning
strategies items. Confirmatory factor analysis requires the identification of which items
(indicators) should fall onto which factors (latent variables). This confirmatory factor
analysis allowed the authors to quantitatively test their theoretical model (Pintrich et
al.,1993). Results indicated that the MSLQ showed reasonable factor validity (for
complete results see Pintrich et al., 1993).
The authors also calculated internal consistency estimates of reliability with
Cronbach’s alpha, as well as zero-order correlations between the different motivational
and learning scales (Pintrich et al., 1993). The majority of the Cronbach’s alphas for the
individual subscales were fairly strong, with most of them greater than .70, with the
largest one, self-efficacy for learning and performance, at .93. The Cronbach’s alphas for
35
the remainder of the subscales were below .70. Help seeking has the lowest Cronbach’s
alpha at .52. Appendix B includes a complete list of each subscale’s alpha. Overall,
these results suggested the MSLQ had relatively good internal reliability. The zero order
correlations for the various scales were also fairly strong, suggesting that the scales were
valid measures of the concepts of motivation and learning strategies.
Pintrich et al. (1993) correlated the MSLQ with student’s final course grades to
determine predictive validity. The correlations were significant and in the expected
direction and seemed to show sound predictive validity. Overall the MSLQ has been
used many times in many situations and seems to be a reliable and valid instrument.
Internet Self Efficacy Scale
The Internet Self efficacy Scale is an eight item self-report instrument (Eastin &
LaRose, 2000, Appendix C). It assesses a student’s confidence with using the Internet. It
measures a student’s overall feelings of efficacy when using technology.
Confirmatory factor analysis was conducted on the eight items to assess internal
consistency and factor loadings (Eastin & LaRose, 2000). The results were substantial
factor loadings and a standardized Cronbach’s alpha of 0.93.
Data Analysis
Data will be analyzed to examine the predictive value of the six independent
variables of self-efficacy for learning and performance, goal orientation, Internet self-
efficacy, time management, environment management and help seeking on the dependent
variable of willingness to enroll in future online classes. Simple correlations will be
conducted with all of the variables in the study to determine which of the independent
36
variables have significant correlations with each other.
The study will utilize descriptive and inferential statistics. The descriptive statistics
will include means, standard deviations and simple correlations. The inferential analysis
will be a hierarchical linear regression, to determine which of the self-regulatory
attributes are most predictive of one’s willingness to enroll in future online classes.
Limitations of the Study
A central limitation to the current study is the population of students. All of the
students in the current study are enrolled in online credit recovery classes. All of the
students have previously failed the classes they are currently taking online. Therefore the
conclusions drawn with credit recovery students may not generalize to students taking
online classes in subjects they haven’t failed. In addition, the structure of online credit
recovery classes is different than other online course in that all of the students are
working on their online courses in a school computer lab. Therefore a unique situation
exists where all of the content in online, but the students are working at school and have
immediate access to peers and the teacher.
Another limitation is the fact that this study will analyze data from students taking
online classes in a wide variety of subjects with different instructors. Due to these
differences there may be observed changes in self-regulatory skills that might be due to
the variations in course structure, content or instructor. A student’s experience with a
particular class could greatly affect their desire to take online classes in the future.
Since many variables affect one’s willingness to enroll in another online class,
another limitation to this study is the fact that none of those other issues will be addressed
37
or investigated. This study will focus only on the self-regulatory attributes and their
affect on one’s willingness to enroll in future online classes.
A final limitation that may exist relates to the population of the study. Since the
subjects of this study are high school students within one district, these findings may not
generalize to the greater population of students taking online classes.
Significance
If the research shows that specific self-regulatory attributes are significant
predictors of satisfaction with online courses as measured by willingness to take them in
the future, then it adds to the discussion of how online courses should be developed and
monitored. Perhaps one of the issues that contributes to the higher attrition rate
experienced by online courses as discussed earlier, is the fact that many students are not
prepared to take on work that is different in structure than traditional face to face
learning. If the research concludes that specific self-regulatory skills are predictive of
satisfaction in these online courses, then perhaps online course designers and instructors
need to build “helpers” into the course that assist students with skills like time
management. Online course developers, students and instructors need to be aware of
some of the potential difficulties online students might have and look into implementing
practices to try to ameliorate those issues to increase student satisfaction and willingness
to enroll in future online classes.
In sum, the MSLQ and the Internet Self-efficacy Scale were used by the Centinela
Valley Union High School District to gather data from high school students taking online
credit recovery classes during the fall of 2010. The existing data will then be analyzed.
38
Correlations and regression analyses will be performed to determine which self-
regulatory attributes are related to one another and which are most predictive of students’
willingness to enroll in future online classes. Although there are limitations to the current
study, the importance of gaining deeper understanding into the issues that affect
satisfaction with online courses is important for all involved with online learning.

39
CHAPTER FOUR: ANALYSIS OF THE FINDINGS
The purpose of the current study is to analyze existing data from high school
students taking online credit recovery classes to determine if there is a predictive
relationship between a student’s self-regulatory capabilities and their satisfaction with the
online learning experience, as measured by their willingness to enroll in online classes in
the future. With the higher attrition levels found in online learning, an investigation into
the causes is warranted (Carter, 1996; Diaz, 2002; Holder, 2007; Parker, 1999 Willging
& Johnson, 2004; Xenos, 2004). This chapter presents an analysis of the data in response
to the research question of which of the six self-regulation attributes of subject specific
self-efficacy for learning and performance, goal orientation, Internet self-efficacy, time
management, study environment management, and help seeking are most predictive of a
student’s willingness to enroll in future online classes?
The data used for this analysis were obtained from the Centinela Valley Union
High School District (CVUHSD). The data include students from the three high schools
within CVUHSD who were taking online credit recovery classes during the fall of 2010.
CVUHSD obtained the data with the use of the Motivated Strategies for Learning
Questionnaire and the Internet Self-Efficacy Scale (for discussion see pages 29-33).
This study focuses specifically on high school online credit recovery students and
how their level of self-regulation may interact with their satisfaction with online classes
and therefore their willingness to enroll in future online classes. The student
characteristics for this study are gender, age, ethnicity, grade-point average, and prior
experience with online classes.
40
Presentation of Descriptive Characteristics of Respondents
CVUHSD distributed a survey to students taking online credit recovery classes
during the fall of 2010. There were approximately 840 students taking online classes,
and 231 surveys were filled out. This represents a 27% response rate. Due to erroneous
or fictitious data, only 224 (N=124) surveys were used for this analysis. An explanation
of the demographic characteristics of the respondents follows and is presented in Table 3.
As shown in Table 3, the majority of respondents self identified as Hispanic, with

African American and White being the next two most frequent identities specified.

TABLE 3
Demographic Characteristics of Sample Students
Demographic Descriptive Statistics
a

n 215-224
Age 16.6 (0.98)
Sex
Male 121 (55.0%)
Female 99 (45.0%)
Race
White 16 (7.1%)
Black 24 (10.7%)
Asian 4 (1.8%)
Pacific Islander 6 (2.7%)
Hispanic 169 (75.1%)
Other 6 (2.7%)
Class
English 109 (49.8%)
Math 32 (14.6%)
Social Studies 39 (17.8%)
Science 32 (14.6%)
Electives 7 (3.2%)
Median Experience Online 1-2 classes
Median GPA 2.5-2.9
a
reported as M(SD) or n(valid %) unless otherwise noted
41
The respondents were taking a variety of subjects in the E2020 online credit recovery
class. The majority of students were taking English and Social Studies classes. Also most
students did not have extensive experience with online classes, as most of the respondents
had only taken one or two classes online. The respondents also had a median GPA of
2.5-2.9.
Research Question
The research question guiding the current study is which of the six self-regulatory
attributes of self-efficacy for learning and performance, goal orientation, Internet self-
efficacy, study environment management, time management and help seeking are most
predictive of a student’s willingness to take future online classes?
Treatment of the Data
Data Cleaning
Prior to the analysis, the data were examined for erroneous or fictitious data.
Three students were removed from the dataset on the basis of a combination of unrealistic
values provided for age, nonsensical fill-in responses, substantial missing data, or
invariant responses to the entire questionnaire.
Scale Validation
Prior to aggregating individual scale items, internal consistency was assessed by
computing Cronbach’s alpha for each of the six proposed scales. On the basis of this
analysis, it was decided to include all scale items for four of the scales. Specifically, an
adequate alpha value was obtained for Self-Efficacy (α = 0.84), Internet Self-Efficacy (α
= 0.85), Extrinsic Goal Orientation (α = 0.82), and Intrinsic Goal Orientation (α = 0.69).
42
For each of these four scales, single item deletions resulted in either no or substantively
negligible improvements to the scales’ internal consistencies.
For the remaining two scales, Time/Study Environment and Help Seeking, analyses
revealed a need to modify the original scales. For Time/Study Environment, on the basis
of Cronbach’s alpha all three reverse coded items were excluded, resulting in a 5-item
scale where alpha = 0.74.
Further, upon examining the internal consistency of the 3-items related to help
seeking, problems with the items’ wording were discovered, as reflected by a negative
alpha value. Because one item was reverse coded and another item referred to seeking
help from peers, something that might not be possible in the online environment, it was
decided to represent help seeking behaviors with the single item, “I ask the instructor to
explain concepts I don't understand well.”
Data Analysis
Scale Correlations
In order to assess the relationship among the six self-regulatory measures used in
the study, Pearson’s Correlation was calculated. Across every pair but one (help seeking
and extrinsic goal orientation) there is a statistically significant, positive correlation. In
other words, students who showed higher scores on any of these six self-regulation
measures also tended to have higher scores on the other aspects of self-regulation.
Across many of the pairs, the correlation was moderate to strong. For a complete
presentation of the correlations, see Table 4.

43
TABLE 4
Summary of Correlations for Six Self-regulatory Attributes (n = 144-187)

Mean
(SD)
Self-
Efficacy
Internet
Self-
Efficacy
Extrinsic
Goal
Orientation
Intrinsic
Goal
Orientation
Time/Study
Environment
Self-Efficacy
3.3
(.78)
-
Internet Self-
Efficacy
3.48
(.97)
0.77*** -
Extrinsic
Goal
Orientation
5.56
(1.40)
0.55*** 0.49*** -
Intrinsic Goal
Orientation
4.08
(1.29)
0.55*** 0.57*** 0.39*** -
Time/Study
Environment
5.02
(1.15)
0.66*** 0.62*** 0.67*** 0.55*** -
Help Seeking
3.97
(1.95)
0.27*** 0.33*** 0.11 0.19** 0.44***
** p ≤ 0.01. *** p ≤ 0.001

Regression Modeling
Hierarchical linear regression modeling was conducted to determine whether any
of the six self-regulatory attributes predict students’ intent to enroll in future online
courses after controlling for potential intractable covariates including gender, age, race,
prior experience with online courses, and GPA. For the complete regression model see
Table 5.
Due to the theoretical plausibility and lack of a priori hypothesis regarding the
role of the five covariates proposed, it was decided to force-enter the entire batch of
control variables into the model in the first block. Given the variety of scales assessed
and lack of a guiding theory for order of entry, the remaining six scales were entered into
the second block of analysis and selected using a stepwise methodology.
44
TABLE 5
Summary of Hierarchical Linear Regression for Six Self-Regulatory Attributes on Intent
to Enroll in Online Courses in the Future (N = 124)

Variable

B

SE B

β
Step 1
Gender -.40 .26 -.14
Age .03 .12 .02
Experience Online .25 .21 .12
GPA -.14 .10 -.13
Race
a

African American -.60 .42 -.13
Asian .29 .67 .04
Caucasian -1.11 .48 -.21*
Pacific Islander -.41 1.04 -.04
Other 1.17 .73 .14
Class
b

Math -.22 .42 -.05
Social Studies -.15 .43 -.03
Science .27 .40 -.06
Electives 1.17 .74 -.14
Model at Step 2
Gender -.43 .25 -.15
Age .05 .12 .04
Experience Online .21 .19 .10
GPA -.10 .10 -.09
Race
a

African American -.83 .39 -.18
Asian .10 .63 .01
Caucasian -1.20 .45 -.23**
Pacific Islander -.50 .98 -.04
Other 1.23 .69 .15
Class
b

Math .06 .40 .01
Social Studies .02 .40 .004
Science .14 .39 .03
Electives -1.26 .69 -.15
Self Efficacy .08 .02 .35***
Note. R
2
= .08 for Step 1 (p < 0.05); ΔR
2
= .10 (p < 0.001).
a
reference group = Hispanic;
b
reference group = English
* p ≤ 0.05 ** p ≤ 0.01. *** p ≤ 0.001

45
Of the six self-regulatory attributes, self-efficacy for learning and performance
within a particular subject emerged as the only factor predictive of a student’s willingness
to enroll in future online classes. Self-efficacy explains approximately 10% of the
observed variability in students’ likelihood to enroll in future online classes. Another
significant finding was in terms of race. According to this model, Caucasian students
were less likely to indicate that they would enroll in future online classes.
In sum, existing data collected from students enrolled in online credit recovery
classes in the fall of 2010 were analyzed to determine if any relationships existed
between students’ self-regulatory capabilities and their willingness to enroll in online
classes in the future. Pearson’s correlation and hierarchical linear regression modeling
were conducted. The results of the analysis indicated that many significant correlations
exist among the six self-regulatory variables and that subject specific self-efficacy for
learning and performance was a significant predictor of one’s willingness to enroll in
online classes in the future.
The following chapter will relate the findings of the current study to relevant
literature. Suggestions for future research and practice will also be included.

46
CHAPTER FIVE: CONCLUSIONS AND RECOMMENDATIONS
The current study was undertaken to explore issues surrounding the high attrition
rate of online classes. Due to the fact that online classes experience a higher dropout rate
than F2F classes (Carter, 1996; Diaz, 2002; Holder, 2007; Parker, 1999; Roblyer, 2006;
Willging & Johnson, 2004; Xenos, 2004), an attempt to understand what student factors
might be contributing to this attrition was the rationale behind this study. The research
question for this study is which of the six self-regulatory attributes of subject specific
self-efficacy for learning and performance, goal orientation, Internet self-efficacy, study
environment management, time management and help seeking are most predictive of a
student’s willingness to take future online classes?
The results of the current study are largely consistent with previous research 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 both F2F and online formats (Pintrich & DeGroot, 1991; Lynch &
Dembo, 2004). Efficacy beliefs affect people’s feelings, their thought processes and their
behavior (Bandura, 1986; Bandura, 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 who believed they were more capable were
47
more likely to utilize cognitive strategies when taking a test. Employing these techniques
increases the likelihood that one will succeed in a class.
Wolters (2003) found that students with higher degrees of self-efficacy were also
less likely to procrastinate when completing academic tasks. Staying focused and
completing assignments in a timely manner, helps to ensure course completion and
success. Zimmerman et al. (1992) found that students in a middle school social studies
class who had higher perceptions of self-efficacy for the content of the course received
better grades. Self-efficacy for achievement and the goals the students set for themselves
accounted for 31% of the variance of final grades.
In sum, the current study found that subject specific self-efficacy for learning and
performance significantly predicted a students’ willingness to take online classes in the
future. This finding is consistent with the literature on the relationship between self-
efficacy for learning and performance and students’ experiences with a particular subject.
An increase in comfort and confidence with specific subject matter seems to relate to a
positive experience with learning in an online format and in the current study was
predictive of one’s willingness to take online classes in the future.
Summary of Findings
Existing data from students taking online credit recovery classes within the
Centinela Valley Union High School District (CVUHSD) were analyzed to determine if
any of the six self-regulation attributes were predictive of students’ willingness to enroll
in future online classes. Existing data was reviewed to eliminate erroneous data. As a
result, 224 (N=224) surveys were used for data analysis. Pearson’s Correlations were
48
conducted to examine relationships among the variables. The results indicate that all
(except extrinsic goal orientation and help seeking) of the self-regulation variables are
highly correlated with one another. Hierarchical linear regression modeling was
conducted to determine whether any of the six self-regulation attributes predicted
students’ intent to enroll in future online courses, after controlling for gender, age, race,
prior experience with online courses, and GPA. After the analysis, subject specific self-
efficacy for learning and performance significantly predicted a student’s willingness to
enroll in future online classes.
The results of the current study should be interpreted with caution. Even though
self-efficacy for learning and performance emerged as significant, due to the limited
sample size, study design, and high risk of collinearity (revealed by the strong positive
correlations among the six attributes) the present study can’t clearly identify the unique
predictive power of each of the six self-regulation attributes. It is likely that the other
five self-regulation attributes are also important variables that impact a student’s
experience with online classes and therefore their willingness to enroll in online classes
in the future. However the results of the current study do point to the importance of the
constellation of these six factors as predictors of intent to enroll in future online classes.
These six factors are of interest not only because they’ve been shown to predict intent to
enroll in future online classes (explaining 10% of the variance in this construct), but also
because it’s theoretically plausible that interventions could be designed to make online
classes more appealing to online students.

49
Implications and Recommendations
The more prepared one is to undertake a course of study, the more likely one is to
feel efficacious and succeed. This is true in any educational endeavor, but is especially
true if the student is expected to process, understand and complete a course more
autonomously. Giving a struggling student additional help is an important aspect of
education.
Although there are many variations in online classes, one consistent component is
that the student completes the work in a nontraditional format with various levels of
assistance from the teacher. In the current study the students taking online classes for
credit recovery were mixed in a computer lab, with each of them taking classes in
different subjects. There was no “offline” instruction; the teacher answered sporadic
questions as they arose. In the E2020 classroom the teacher functions more as an “as
needed tutor”, since the content for the online courses already exists within E2020.
Therefore the students were working 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 their readiness and self-efficacy for that
particular subject. This analysis clearly should not be done to deny anyone access to an
online course, but only to identify those who may need additional assistance with the
course. This additional assistance could come in the form of periodic comprehension
checks by the teacher, enrichment activities in the course or additional help from “peer
50
experts”. One possible solution is to use vicarious learning (Bandura, 1986; Bandura,
1993; Margolis & Mccabe, 2006; Zimmerman, 2000). Online courses could design a
function that allows a student to see a demonstration of how to complete an assignment.
This could be transmitted through video, or presented through student work examples, or
even through peer assistance. This would allow the student to review a concrete example
of how to accomplish a task, and might increase the student’s self-efficacy in relation to
that specific assignment, and perhaps increase their self-efficacy for the course. In
addition, online courses could make information on learning strategies available to
students. Strategies like outlining, note taking and summarizing would help students to
understand the material. Perhaps some of the problem with a student’s self-efficacy
stems from the fact that they do not possess certain “tools” that make learning any
content easier.
Considering one’s readiness to learn particular material, and responding
accordingly is not a new concept. Many different populations of students are given extra
help when they need it. For example, students with processing difficulties are identified
so teachers can make accommodations to help them succeed. If a student struggles with a
particular subject or task, extra assistance is given to help ensure that the student can
handle the work. Since online learning is a newer form of learning that involves more
independent work, some students may need additional help to succeed with subjects in
which they struggle.
Due to the fact that the six self-regulation variables are so closely related,
interventions aimed at improving all of them would likely enhance the online learning
51
experience. For example, students could be sent reminder messages when there is an
important assignment due to help with time management. As part of a resource section
within the online course, there could be suggestions about how to successfully manage
one’s study environment to eliminate distractions. Online course designers and
instructors should be aware of the potential impact these attributes have on a student’s
experience with online learning.
In sum, even though the results of the current study should be interpreted with
caution, offering students with low self-efficacy additional help with online classes may
help them succeed. Due to the increased autonomy of online classes, students who
struggle with a specific subject, or with the other aspects of self-regulation would likely
benefit from interventions designed to help them understand concepts, complete
assignments and monitor their learning in an online environment.
Future Research
In order to help ameliorate online attrition rates and increase student satisfaction
with online classes, there are several suggestions for future research. First, future
research should focus on high school and college populations who are not taking classes
for the purposes of credit recovery to determine if the same issues apply. A study of
diverse populations would give a more complete picture in terms of online learning and
self-regulation. In addition, the specific attributes and overall quality of online classes
should be closely examined to determine if the specific characteristics of the online class
are contributing to student satisfaction levels and therefore higher levels of attrition.
Research that focuses on the practices of quality online learning should be undertaken to
52
guide the development of future online learning programs to ensure that online learning is
of a high quality.
Future research should also focus on specific interventions that help to increase
self-efficacy. Since research has shown that self-efficacy can be an important component
of a student’s experience in online classes, understanding which interventions most
improve self-efficacy could help to alleviate some of the problems students may have
with learning particular subjects online. This should also include research to determine
how teacher interventions and interventions built into the design of the course impact
student self-efficacy.
Another area meriting further research is on other factors that could impact one’s
experience with online classes. An investigation into the predictive value of other self-
regulatory factors that were not part of this research design, like learning strategies (e.g.,
critical thinking, elaboration, meta-cognitive self-regulation, effort regulation) and other
motivational variables (e.g., value beliefs, affective factors, control of learning beliefs)
would contribute to a deeper understanding of self-regulation and online classes. Further
research should also be conducted on how differences in course structure influence the
online learner. Factors related to content, mode of delivery, type and frequency of
assignments and levels of student-instructor and student-student interaction should be
studied to determine their potential influence on students’ online learning experiences.
Conclusion
Online learning is growing at a rapid rate. Although online learning offers
opportunity and flexibility, it is not without issues that should be addressed. One of the
53
issues with online learning is the high attrition rate when compared to F2F classes. The
purpose of the current research was to investigate those variables that might be related to
a student’s level of satisfaction with online learning as measured by a student’s
willingness to take online classes in the future. To investigate these ideas further, six
self-regulatory attributes were examined to determine if any were predictive of a
student’s willingness to enroll in future online classes. Existing data from students
enrolled in an online credit recovery program called E2020 was analyzed. After the
analysis, self-efficacy for learning and performance significantly predicted a student’s
willingness to enroll in future online classes.
The results of this study indicate that those with low self-efficacy for a particular
subject may have difficulty in the more independent format of online classes. Online
course designers and instructors should address this situation by assessing students’
levels of self-efficacy and offering interventions to help students who may struggle with a
certain subject. A variety of interventions were suggested, such as vicarious learning,
peer help and online resources to aid with the development of learning strategies.
In order to gain a deeper understanding of online learning and self-regulation,
future research should be undertaken to determine what other factors might influence
student experiences in online learning. Researchers should investigate other self-
regulatory variables that may impact students’ experiences with online learning. Since
the population of this research was high school students in online credit recovery classes,
an inquiry into diverse populations and increased sample sizes would also make an
important contribution to the accumulated knowledge concerning online learning.
54
Online learning serves an important function in our increasingly complex and busy
world, by allowing those who may not otherwise be able to graduate from high school, or
engage in higher education an opportunity to do so. The increased opportunities created
with online learning serve both institutions and individuals alike. In order for online
learning to continue to be productive, useful and of a high quality, designers and
instructors of online classes should attend to potential issues that may impact one’s
experience, satisfaction and therefore willingness to engage with online learning in the
future.

55
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APPENDIX A: MSLQ ITEM LIST

MSLQ Item List

The following is a list of items that make up the MSLQ (from Pintrich et al., 1991).

Part A. Motivation

The following questions ask about your motivation for and attitudes about this class.
Remember there are no right or wrong answers, just answer as accurately as possible. Use
the scale below to answer the questions. If you think the statement is very true of you,
circle 7; if a statement is not at all true of you, circle 1. If the statement is more or less
true of you, find the number between 1 and 7 that best describes you.
1 2 3 4 5 6 7
Not at all Very
true of me true
of me

1. In a class like this, I prefer course material that really challenges me so I can learn new
things.
2. If I study in appropriate ways, then I will be able to learn the material in this course.
3. When I take a test I think about how poorly I am doing compared with other students.
4. I think I will be able to use what I learn in this course in other courses.
5. I believe I will receive an excellent grade in this class.
6. I'm certain I can understand the most difficult material presented in the readings for
this course.
7. Getting a good grade in this class is the most satisfying thing for me right now.
8. When I take a test I think about items on other parts of the test I can't answer.
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9. It is my own fault if I don't learn the material in this course.
10. It is important for me to learn the course material in this class.
11. The most important thing for me right now is improving my overall grade point
average, so my main concern in this class is getting a good grade.
12. I'm confident I can learn the basic concepts taught in this course.
13. If I can, I want to get better grades in this class than most of the other students.
14. When I take tests I think of the consequences of failing.
15. I'm confident I can understand the most complex material presented by the instructor
in this course.
16. In a class like this, I prefer course material that arouses my curiosity, even if it is
difficult to learn.
17. I am very interested in the content area of this course.
18. If I try hard enough, then I will understand the course material.
19. I have an uneasy, upset feeling when I take an exam.
20. I'm confident I can do an excellent job on the assignments and tests in this course.
21. I expect to do well in this class.
22. The most satisfying thing for me in this course is trying to understand the content as
thoroughly as possible.
23. I think the course material in this class is useful for me to learn.
24. When I have the opportunity in this class, I choose course assignments that I can learn
from even if they don't guarantee a good grade.
25. If I don't understand the course material, it is because I didn't try hard enough.
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26. I like the subject matter of this course.
27. Understanding the subject matter of this course is very important to me.
28. I feel my heart beating fast when I take an exam.
29. I'm certain I can master the skills being taught in this class.
30. I want to do well in this class because it is important to show my ability to my family,
friends, employer, or others.
31. Considering the difficulty of this course, the teacher, and my skills, I think I will do
well in this class.
Part B. Learning Strategies
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 7; if a statement is not at all true of
you, circle 1. If the statement is more or less true of you, find the number between 1 and
7 that best describes you.

32. When I study the readings for this course, I outline the material to help me organize
my thoughts.
33. During class time I often miss important points because I'm thinking of other things.
(reverse coded)
34. When studying for this course, I often try to explain the material to a classmate or
friend.
35. I usually study in a place where I can concentrate on my course work.
66
36. When reading for this course, I make up questions to help focus my reading.
37. I often feel so lazy or bored when I study for this class that I quit before I finish what
I planned to do. (reverse coded)
38. I often find myself questioning things I hear or read in this course to decide if I find
them convincing.
39. When I study for this class, I practice saying the material to myself over and over.
40. Even if I have trouble learning the material in this class, I try to do the work on my
own, without help from anyone. (reverse coded)
41. When I become confused about something I'm reading for this class, I go back and try
to figure it out.
42. When I study for this course, I go through the readings and my class notes and try to
find the most important ideas.
43. I make good use of my study time for this course.
44. If course readings are difficult to understand, I change the way I read the material.
45. I try to work with other students from this class to complete the course assignments.
46. When studying for this course, I read my class notes and the course readings over and
over again.
47. When a theory, interpretation, or conclusion is presented in class or in the readings, I
try to decide if there is good supporting evidence.
48. I work hard to do well in this class even if I don't like what we are doing.
49. I make simple charts, diagrams, or tables to help me organize course material.
50. When studying for this course, I often set aside time to discuss course material with a
67
group
51. I treat the course material as a starting point and try to develop my own ideas about it.
52. I find it hard to stick to a study schedule. (reverse coded)
53. When I study for this class, I pull together information from different sources, such as
lectures, readings, and discussions.
54. Before I study new course material thoroughly, I often skim it to see how it is
organized.
55. I ask myself questions to make sure I understand the material I have been studying in
this class.
56. I try to change the way I study in order to fit the course requirements and the
instructor's teaching style.
57. I often find that I have been reading for this class but don't know what it was all
about. (reverse coded)
58. I ask the instructor to clarify concepts I don't understand well.
59. I memorize key words to remind me of important concepts in this class.
60. When course work is difficult, I either give up or only study the easy parts. (reverse
coded)
61. I try to think through a topic and decide what I am supposed to learn from it rather
than just reading it over when studying for this course.
62. I try to relate ideas in this subject to those in other courses whenever possible.
63. When I study for this course, I go over my class notes and make an outline of
important concepts.
68
64. When reading for this class, I try to relate the material to what I already know.
65. I have a regular place set aside for studying.
66. I try to play around with ideas of my own related to what I am learning in this course.
67. When I study for this course, I write brief summaries of the main ideas from the
readings and my class notes.
68. When I can't understand the material in this course, I ask another student in this class
for help.
69. I try to understand the material in this class by making connections between the
readings and the concepts from the lectures.
70. I make sure that I keep up with the weekly readings and assignments for this course.
71. Whenever I read or hear an assertion or conclusion in this class, I think about possible
alternatives.
72. I make lists of important items for this course and memorize the lists.
73. I attend this class regularly.
74. Even when course materials are dull and uninteresting, I manage to keep working
until I finish.
75. I try to identify students in this class whom I can ask for help if necessary.
76. When studying for this course I try to determine which concepts I don't understand
well.
77. I often find that I don't spend very much time on this course because of other
activities. (reverse coded)

69
78. When I study for this class, I set goals for myself in order to direct my activities in
each study period.
79. If I get confused taking notes in class, I make sure I sort it out afterwards.
80. I rarely find time to review my notes or readings before an exam. (reverse coded)
81. I try to apply ideas from course readings in other class activities such as lecture and
discussion.

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APPENDIX B: MSLQ SUBSCALES AND CORRESPONDING ALPHAS

Items within the 15 MSLQ Subscales and the Subscales’ Corresponding Coefficient
Alphas (Duncan & McKeachie, 2005)

Scale Items in the Scale alpha
Motivation Subscales
1. Intrinsic Goal Orientation 1, 16, 22, 24 .742
2. Extrinsic Goal
Orientation
7, 11, 13, 30 .62
3. Task Value 4, 10, 17, 23, 26, 27 .90
4. Control of Learning
Beliefs
2, 9, 18, 25 .68
5. Self-Efficacy for
Learning & Performance
5, 6, 12, 15, 20, 21, 29, 31 .93
6. Test Anxiety 3, 8, 14, 19, 28 .80

Learning Strategies
Subscales

1. Rehearsal 39, 46, 59, 72 .69
2. Elaboration 53, 62, 64, 67, 69, 81 .75
3. Organization 32, 42, 49, 63 .64
4. Critical Thinking 38, 47, 51, 66, 71 .80
5. Metacognitive Self-
Regulation
33r, 36, 41, 44, 54, 55,
56, 57r, 61, 76, 78, 79
.79
6. Time/Study Environment
Management
35, 43, 52r, 65, 70 .76
7. Effort Regulation 73r, 77r, 80r37r, 48, 60r, 74 .69
8. Peer Learning 34, 45, 50 .76
9. Help Seeking 40r, 58, 68, 74 .52

* Items marked with an “r” are reverse coded.

71
APPENDIX C: INTERNET SELF-EFFICACY SCALE ITEM LIST

Internet Self efficacy Scale Item List

______________________________________________________________________

Scale Item
______________________________________________________________________

I feel confident…

1…understanding terms/words related to Internet hardware.

2…understanding terms/words related to Internet software.

3…describing functions of Internet hardware.

4…trouble shooting Internet problems.

5…explaining why a task will not run on the Internet.

6…using the Internet to gather data.

7…learning advanced skills within a specific Internet program.

8…turning to an online discussion group when help is needed

_____________________________________________________________________
Standardized alpha = .93

Abstract (if available)

Abstract The purpose of the current study was to investigate the potential impact of students’ self-regulatory attributes on their experiences with online classes. The six selfregulatory attributes of subject specific self-efficacy, goal orientation, Internet selfefficacy, study environment management, time management and help seeking were examined to determine which of these attributes were predictive of a student’s willingness to enroll in future online classes. The Centinela Valley Union High School District surveyed students enrolled in online credit recovery classes during the fall of 2010. Data was gathered with the Motivated Strategies for Learning Questionnaire and the Internet Self-efficacy Scale in the fall of 2010. This existing data was then analyzed to determine if any relationships existed between the six self-regulatory attributes and a student’s satisfaction with online classes, as measured by the students’ willingness to enroll in future online classes. Data analysis consisted of simple correlations and hierarchical linear regression.

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Asset Metadata

Creator Peterson, Sara (author)

Core Title Self-regulation and online course satisfaction in high school

School Rossier School of Education

Degree Doctor of Education

Degree Program Education (Leadership)

Publication Date 02/01/2011

Defense Date 01/14/2011

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

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

Place Name California (states), Los Angeles (counties)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Hentschke, Guilbert C. (committee chair), Early, Sean (committee member), Riconscente, Michelle (committee member)

Creator Email sarapete@usc.edu,sjoypete@earthlink.net

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m3633

Unique identifier UC1271536

Identifier etd-Peterson-4269 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-427138 (legacy record id),usctheses-m3633 (legacy record id)

Legacy Identifier etd-Peterson-4269.pdf

Dmrecord 427138

Document Type Dissertation

Rights Peterson, Sara

Type texts

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

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu