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A comparison of student motivation by program delivery method: self-efficacy, goal orientation, and belongingness in a synchronous online and traditional face-to-face environment

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A comparison of student motivation by program delivery method: self-efficacy, goal orientation, and belongingness in a synchronous online and traditional face-to-face environment

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A Comparison of Student Motivation by Program Delivery Method:
Self-Efficacy, Goal Orientation, and Belongingness in a Synchronous Online and
Traditional Face-to-Face Environment
by
Soomin Chao
______________________________________________________________________________

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

May 2015

Copyright 2015 Soomin Chao

ii

Dedication

To
Jason,
Olivia
and
little
Violet,
my
greatest
sources
of
motivation.

Words
cannot
express
how
much
I
love
you.

iii

Acknowledgements

Over
the
past
few
years,
I
have
received
a
tremendous
amount
of
support
and

encouragement
from
several
individuals,
and
to
each
of
these
people,
I
would
like
to

express
my
most
heartfelt
thanks.

First,
I
would
like
to
thank
my
chair,
Dr.
Kimberly
Hirabayashi,
whose
continued

patience
and
insightful
guidance
has
made
this
a
thoughtful
and
rewarding
journey.
Also,
I

would
like
to
thank
my
dissertation
committee
members,
Dr.
Helena
Seli
and
Dr.
Melora

Sundt,
for
their
support
and
feedback
throughout
my
dissertation
process.

Many
thanks
to

Dr.
Robert
Keim,
as
well,
for
his
valued
assistance
with
all
things
statistic.
I’d
also
like
to

acknowledge
the
faculty
and
staff
of
MAT@USC
,
who
allowed
me
to
distribute
my
survey

for
this
research.

Finally,
my
sincerest
thanks
to
my
friends
and
family
who
supported
me
through
the

hardest
time
in
my
life,
who
encouraged
me
and
never
stopped
believing
in
me,
even
in

moments
when
I
had
trouble
believing
in
myself.
To
Holly
especially,
for
her
persistent
and

much
appreciated
Bandura-‐style
“social
persuasion,”
and
for
expecting
nothing
less
than

the
successful
completion
of
my
dissertation
from
me.

iv

TABLE OF CONTENTS

Dedication……………………………………………………………………………………..

ii
Acknowledgements…………………………………………………………………………...

iii
List of Tables………………………………………………………………………………….

vii
Abstract……....……………………………………………………………………………….. viii

CHAPTER 1: OVERVIEW OF THE STUDY……………………………………………..

1
Introduction………………………………………………………………………….. 1
Background of the Problem……………………………………………………….... 2
Statement of the Problem …………………………………………………………... 5
Purpose of the Study………………………………………………………………… 6
Research Questions………………………………………………………………….. 7
Significance of the Study …………………………………………………………… 7
Methodology…………………………………………………………………………. 8
Assumptions………………………………………………………………………….. 9
Definition of Terms………………………………………………………………….. 10
Organization of the Study.………………………………………………………….. 12

CHAPTER 2: REVIEW OF THE LITERATURE………………………………………...

14
Current Landscape of Postsecondary Distance Education……………………….. 15
Research on Motivation in Distance Education…………………………………… 19
Self-Efficacy in Distance Education through a Social Cognitive Framework…… 22
Self-Efficacy and Distance Education……………………………………… 24
Influences of Goal Orientation on Motivation in Distance Education…………… 27
Goal Orientation and Distance Education………………………………… 31
Connection between Goal Orientation and Self-Efficacy in a Distance
Education……………………………………………………………………..

32
Student Sense of Belonging on Motivation in Distance Education………. 33
Potential Social Cognitive Contributors to Belongingness……………….. 35
Peer Networks……………………………………………………….. 35
Class Size…………………………………………………………….. 35
Belongingness and Distance Education…………………………………………….. 37
Connection between Belongingness and Goal Orientation in a Distance
Education……………………………………………………………………..

39
Connection between Belongingness and Self-Efficacy in a Distance
Education……………………………………………………………………..

40
Student Motivation in Online Pre-service Teacher Education Programs 41
Professional Self-Efficacy Beliefs in Teacher Education…………………..
Sense of Belonging in Teacher Preparation Programs…………………….
42
44
Conclusion…………………………………………………………………………… 45

v

CHAPTER 3: RESEARCH METHODOLOGY…..……………………………………… 47
Research Questions………………………………………………………………….. 48
Research Design……………………………………………………………………... 48
Population and Sample……………………………………………………………… 49
Instrumentation……………………………………………………………………… 51
Demographic Items………………………………………………………….. 51
Information about Participants’ Networking Activities…………………... 52
Teacher Self-Efficacy Scale…………………………………………………. 52
Belongingness Scale………………………………………………………….. 53
Goal Orientation Scale ……………………………………………………… 55
Data Collection………………………………………………………………………. 56
Data Analysis………………………………………………………………………… 56

CHAPTER 4: RESULTS……….…….…….…….…….…….…….…….…….…….……..
Demographic Information…….…….…….…….…….…….…….…….…….…….
Analysis of Results…….…….…….…….…….…….…….…….…….…….…….…
Research Question 1…….…….…….…….…….…….…….…….…….……
Research Question 2…….…….…….…….…….…….…….…….…….……
Belonging as a Predictor of Self-Efficacy…………….…….…….…
Belonging as a Predictor of Learning Goal Orientation…………..
Belonging as a Predictor of Mastery Goal Orientation……………
Belonging as a Predictor of Performance Approach/Performance
Avoidance Goal Orientations…………….…….…….…….…….….
Research Question 3…….…….…….…….…….…….…….…….…….……
Frequency of Social Connection to Peers and Belonging………….
Program “Wall” and Belonging……………………………………..
Campus-Affiliated Groups and Belonging…………….…….……..

CHAPTER 5: DISCUSSION…….…….…….…….…….…….…….…….…….…….……
Demographic Composition of Students by Delivery Method…….…….………….
Discussion of Student Motivation Across Instructional Delivery Platforms……..

58
59
61
61
62
62
63
64

65
65
66
67
68

71
72
74
Self-Efficacy…….…….…….…….…….…….…….…….…….…….………
Goal Orientation…….…….…….…….…….…….…….…….……………..
Sense of Belonging…….…….…….…….…….…….…….…….……………
Contributors to Students’ Feelings of Belonging…….…….…….…….…….…….
Frequency of Out-of-class Social Connection with Peers…….…….……...
Use of program “Wall” to Connect with Peers…….…….…….…….…….
Connection through Campus-Affiliated Groups and Organizations……..
Implications…….…….…….…….…….…….…….…….…….…….…….…………
For Online and Off-site Graduate Education Programs…….…….……....
For Pre-service Teacher Education Programs.…….…….…….…….…….
Limitations…….…….…….…….…….…….…….…….…….…….…….………….
Delimitations………………………………………………………………………….
Recommendations for Future Research…….…….…….…….…….………………
Conclusion…….…….…….…….…….…….…….…….…….…….…….…………..

74
75
77
78
79
80
82
83
84
85
85
88
88
90

vi

REFERENCES…….…….…….…….…….…….…….…….…….…….…….……………..

92
APPENDICES………………………………………………….……………….……………. 101
Appendix A: Demographic Items…………………………………….…………….. 101
Appendix B: Teacher Self-Efficacy Scale…………………………………………... 104
Appendix C: Belongingness Scale…………………………………………………... 105
Appendix D: Goal Orientation Scale……………....……………………………….. 107

vii

LIST OF TABLES

Table 1: Participant demographics: Age……….…….…….…….…….…….……...…….….
Table 2: Participant demographics: Marital status..…….…….…….…….…….…….…….…
Table 3: Participant demographics: Number of Dependents..………….…….…….…….……
Table 4: Participant demographics: Employment status..…….…….…….…….…….…….…
Table 5: MANOVA Results for Research Question 1..…….…….…….…….…….…….……
Table 6: Linear regression of belonging as a predictor of self-efficacy……….…….………..
Table 7: Correlation between belonging and self-efficacy…………………………………….
Table 8: Linear regression of belonging as a predictor of goal orientation (global construct)..
Table 9: Linear regression of belonging as a predictor of mastery goal orientation…………..

Table 10: Linear regression of belonging as a predictor of performance approach goal
orientation….………….…….…….…….…….…….…….…….…….…….…….…….……….

Table 11: Linear regression of belonging as a predictor of performance avoidance goal
orientation……….…….…….…….…….…….…….…….…….…….…….…….…….……..

Table 12: Linear regressions of networking activities as predictors of student feelings of
belonging……….…….…….…….…….…….…….…….…….…….…….…….…….……...

Table 13: Linear regression of social connection as a predictor of feelings..…………….……

Table 14: Linear regression of social connection as a predictor of feelings of belonging.…….

Table 15: Linear regression of campus-related group affiliation as a predictor of feelings of
belonging……….…….…….…….…….…….…….…….…….…….…….…….…….…….…

59

60

60

61

62

63

63

64

64

65

65

66

67

68

68

viii

Abstract

As distance education options expand and increase, online courses have begun to take up
a progressively large portion of the educational sector, specifically in postsecondary institutions
(National Center for Education Statistics [NCES], 2011). Online course offerings have risen in
popularity at the postsecondary level, and well established research literature has been published
which compares learner demographics and learning outcomes in online and in-class settings.
Less established, however, is research on motivation in distance learning settings, specifically in
synchronous online environments that incorporate real-time opportunities for student
collaboration and immediate faculty feedback via visual and audio technology. Studies
comparing latent motivational constructs such as self-efficacy, goal orientation and student sense
of belonging in the online academic setting are limited, especially at the graduate postsecondary
level in the field of pre-service teacher preparation. However, prior research indicates the
importance of motivational elements, namely professional self-efficacy and sense of belonging,
as factors to consider in teacher education programs (Camprara, Barbaranelli, Steca & Malone,
2006; Erdem & Demirel, 2012). A distinct lack of research on these motivational constructs in
online pre-service programs for educators has been paired with a call for further examination of
such topics (Comprara et al., 20016; Kopcha & Alger, 2013). Knowledge around these issues
can help graduate faculty and program course designers to effectively meet a demand in this
growing sector of online education.
The purpose of this study was to examine potential differences in student motivation
between in-class and online methods of instructional delivery in a graduate-level pre-service
teacher training program. A non-experimental, quantitative approach was used to collect data
through a voluntary self-report survey among graduate students enrolled in a traditional in-class

ix

graduate program at a prestigious four-year research university, as well as those enrolled in a
synchronous, online version of this program. Data was collected from 240 participants (N=240);
190 participants attended online classes and 50 participants attended classes in an on-campus
setting. The first purpose of this study was to see if there was a difference in student sense of
belonging, self-efficacy, and goal orientation by program delivery method. The second purpose
was to determine whether or not feelings of belonging predicted self-efficacy or goal orientation
in students, controlling for program delivery method. Finally, the third purpose of the study was
to investigate which self-reported out-of-class networking activities predicted student feelings of
belonging. A quantitative analysis was performed to answer these questions.
This study found that there were no significant differences in student levels of self-
efficacy, goal orientation, and sense of belonging by program delivery method. In essence, the
method of program delivery was not significantly related to students’ motivation in the graduate
level pre-service teacher preparation program examined. Also, student feelings of belonging
were found to be moderately significant predictors of their levels of teacher self-efficacy, and
weakly significant predictors of mastery goal orientation. Furthermore, of the self-reported out
of class networking activities listed on the survey, three in particular were found to be predictive
of student feelings of belonging. These activities were frequent social connection with peers,
self-reported frequency of course discussion “Wall” usage, and student membership in campus-
affiliated groups, such as honors societies and athletic groups.
The results of this study yielded significant insight into motivational factors across
instructional delivery methods, in addition to significant and predictive relationships between the
motivational constructs examined. The implications of this study may provide awareness of
motivational influences on student learning outcomes for program and course developers at the

x

university level. The most relevant observation, that there was no significant difference in
student motivation by instructional delivery method, is one that should encourage program
administrators and course designers.

1

CHAPTER 1
INTRODUCTION
Distance education has begun to take up an increasingly larger proportion of the
educational sector, as roughly two-thirds of all postsecondary institutions and over 70% of all
private four-year institutions offer some form of college-level credit-granting distance education
courses (NCES, 2006-2007; NCES, 2011). Research that evaluates possible differences in
specific motivational variables, such as goal orientation, self-efficacy, and student sense of
belonging brings to light potential differences in student motivation along a social cognitive
framework in such programs. Furthermore, a closer look at potential social psychological
differences inherent in the type of delivery method used in a course, such as influences of peer
network structures and class size on student sense of belonging, are warranted when examining
possible discrepancies in student motivation by program delivery method.
Student motivation and learning are inextricably linked in an educational environment;
students who are highly motivated in the classroom are more likely to engage in scholarly
behavior that will facilitate the learning process (Zimmerman, 2000). Reciprocally, students
who are actively engaged in the learning process will also exhibit high motivation along the
indices defined by Schunk, Pintrich, and Meece (2008), namely those of choice, effort, and
persistence. In short, high student motivation begets successful student learning outcomes. In
accordance to social cognitive theory, developed by Bandura (1986), a triadic reciprocity exists
between the learner’s physical environment, cognitive processes, and learning behavior to create
a dynamic for a motivated learning experience. These theories indicate that a student’s learning
environment plays an important role in the facilitation of both motivation and learning.

2

In contrast, existing research suggests an alternative pattern in practice; that there is no
significant difference in the quality of student learning based solely on the method of content or
concept delivery (Allen, Mabry, Mattrey, Bourhis, Titsworth, & Burrell, 2004; Russell, 1999;
Sitzmann, Kraiger, Stewart and Wisher, 2006). According to these researchers, learning
outcomes should be the same in courses that take place in a traditional face-to-face classroom
setting or in a synchronous or asynchronous distance education setting. This view is in
juxtaposition to the popular assumption that the use of technology in the classroom, in and of
itself, yields positive student learning outcomes (Nagel, 2010; U.S. Department of Education,
2003). A resolution for the apparent discrepancy in beliefs may be evident through a study of
technology use, pertaining particularly to that in a distance education setting, through a social
cognitive lens. The technology in and of itself may not have a direct impact on learning
outcomes, but corollary factors associated with the use of certain types of technology may have
moderating effects on motivational variables such as self-efficacy, goal orientation and student
feelings of belonging on a university-wide or classroom level.
Background of the Problem
A lack of research around the motivational variables along a social cognitive framework
in the area of distance education is evident in many researchers’ suggestions for further study. In
his article on the barriers and limitations of distance education, Galusha (1997) called for future
research on student motivators, specifically in the adult population, that may help inform better
pedagogical and andragogical practices. Allen, Mabry, Mattrey, Bourhis, Titsworth, and Burrell
(2004) presented a meta-analysis that compared the academic performance of students by
distance education and face-to-face delivery methods. While their findings indicated little
distinction between the two delivery methods based solely on academic performance, they

3

addressed neither the pedagogical impact of different forms of distance education nor the
potential differences that may exist in student motivation as a result of self-selection in one type
of delivery method over another. One of their suggestions for further examination was a
comparative study that focused specifically on student motivation by delivery method. At the
conclusion of their comparative study on different delivery methods of an undergraduate course,
Abdous and Yoshimura (2010) suggested that further examination of the varying qualities of
interaction patterns, or social group dynamics, would inform efforts toward a more effective
course design and improve students’ distance learning experiences.
A call for further research coupled with the changing landscape of student learning
environments in the postsecondary sector indicates an overall lack of research on motivational
influences, especially along the social cognitive framework. Clayton, Blumberg and Auld
(2010) found that all of the subjects in their study shared a strong preference for learning
environments conducive to direct interaction between participants. Examining motivation along
the criteria of goal orientation, self-efficacy, and belongingness in both distance education and
traditional class settings will help fill the gap in research that currently exists in this area.
Specifically, Tallent -Runnels, Thomas, Lan Cooper, Ahern, Shaw and Liu (2006) recommended
further research on the theoretical foundations in place around factors such as student motivation
and social interaction in different course environments. They suggested that goal orientation,
self-efficacy and a student’s subjective sense of support from a learning community were
important motivational variables to consider when assessing the conditions of a particularly
effective distance learning or face-to-face academic environment (Tallent-Runnels et al., 2006).
Prior research revealed that students in distance education courses tended to gravitate toward a
performance-based goal orientation while those in face-to-face settings tended to be more

4

mastery oriented (Clayton et al., 2010). Also, the level of students’ goal orientation correlated
closely to student self-efficacy related specifically to beliefs regarding postgraduate professional
performance, which also factored into their levels of motivation (Sahin & Atay, 2010).
The motivational variables of goal orientation and self-efficacy were shown to be key
influences in the education of students in general, including those at the university level (Ames,
1992). However, there was a third emotive variable according to Ford (1992) that played an
equally vital role in the determination of student motivation level. This dissertation study looked
at student feelings of belonging as that third motivational variable. The available body of
research that compared learning outcomes by delivery method provided less insight into the
psychosocial forces that determined the level of student motivation by classroom environment or
climate. A closer examination of motivational dynamics and interplay between goal orientation,
self-efficacy and emotion, narrowed specifically to student feelings of belonging, was examined
to gain a more nuanced understanding of student motivation in both online and face-to-face
settings.
McMillan and Chavis (1986) identified belongingness as a motivational construct but did
not explore the influences of environmental factors on student feelings of belonging by different
types of educational settings. In particular, they emphasized the importance of an emotional
sense of community as a means to obtain mastery through a collective attainment of a group’s
desired level of performance (McMillan, 1996). Mastery experiences, as described by Bandura
(1986), helped to build an individual’s self-efficacy. Belongingness, characterized by McMillan
(1996) as quality community contact, was also connected to a “shared valent event,” a term
which described a group goal orientation (McMillan, 1996, pg. 322). The veracity of this
statement is likely, particularly since belongingness was argued to be the foundation upon which

5

an understanding of goal directed behavior (Ryan & Stiller, 1991) and self-efficacy (Osterman,
2000) were framed. During the course of this dissertation, further insight into these underlying
forces was useful in the formation of a more complete picture of student motivation by delivery
method.
Statement of the Problem
Recent advances in distance education at the university level have allowed for the
development of innovative changes around program delivery. While there are a number of
studies comparing the pedagogical effectiveness of distance education programs to traditional
face-to-face programs, less is known about motivation specifically in synchronous distance
education environments that integrate collaborative peer experiences using real-time visual and
audio technology. Also, there is a deficit in knowledge around the elements that may contribute
to student motivation along a social cognitive or social psychological framework specifically in a
distance education setting, which presents an opportunity for the examination of environmental
characteristics that influence student motivation differently in distance education and in
traditional classroom settings. Social cognitive differences in class environments and climates by
learning environment have the distinct potential to influence student motivational outcomes.
Established literature suggests that goal orientation and self-efficacy contribute to the
level of student motivation (Ames, 1992; Bandalos, Finney, and Geske, 2003; Bandura, 1993;
Ford, 1992; Kaplan & Midgley, 1997; Middleton & Midgley, 1997, Pintridge & Garcia, 1994;
Wolters et al., 1996), but very little is known about the connection between a student’s sense of
belonging and his level of motivation in an online environment. Osterman (2000) found that
classroom environment impacts a student’s sense of belonging, but his findings were not based
upon an examination of a distance education setting. Even less inquiry has been made into the

6

social psychological elements, such as peer network structures and class size, that determine the
level of student belonging by online or face-to-face program or course delivery method at the
university level. Berndt (1999), Ryan (2000), and Nelson and DeBacker (2008) identified the
relationship between belongingness and goal orientation in a face-to-face setting in elementary,
middle school and high school settings. However, not much information exists regarding this
topic in a postsecondary setting.
Feelings of belongingness may differ in a graduate program that has two cohorts running
simultaneously using different delivery methods. Belongingness plays a role in determining the
nature of students’ goal orientation and the level of their self-efficacy (Nelson & DeBacker,
2008), so potential discrepancies in the levels of environmental motivational factors may be
explained by possible differences in feelings of belonging. Further examination of these social
cognitive and social psychological variables may determine differences in motivation by
program delivery method, and possibly explain differences in student motivation if and where
they exist.
Purpose of the Study
The overarching purpose of this study was to examine potential differences in student
motivation in an online and on-campus graduate level teacher preparation program at Western
University, a top-tiered four year research institution. The online version of this program was
unique in that synchronous whole-group and sub-group collaborative opportunities were
available to students and faculty, as well as non-coursework related networking opportunities
built into the program through the custom created software interface. The first purpose was to
determine whether or not there was a difference in student levels of belongingness, self-efficacy,
and goal-orientation for two cohorts of a pre-service teacher education program that were

7

pedagogically equivalent, and where the identifying differentiator between the cohorts was the
delivery method of content. The second purpose of the study was to determine whether
belongingness predicted self-efficacy or goal-orientation, controlling for program delivery
method. The third purpose was to examine whether or not the quality or quantity nonacademic
networking activities predicted student levels of belongingness.
Research Questions
The following research questions guided this study:
1. Is there a difference in student sense of belonging, self-efficacy, and goal orientation by
program delivery method?
2. Controlling for program delivery method, do feelings of belonging predict self-efficacy
or goal orientation?
3. Do out of class networking activities predict student feelings of belonging?
Significance of the Study
A more thorough comparison of the relationships between social cognitive influences and
student motivation by delivery method drew out further understanding of the motivational
nuances in the online and face-to-face learning environment and climate. In future, students who
intend to enroll in distance education programs could use the information from this study to
make a more informed decision about which type of instructional delivery method they prefer,
based on their anticipated use of and level of participation in their learning environment.
Administration and faculty who were in the program that was examined may use the information
gathered through this study to inform future practice. They may process and reflect upon
findings in order to determine whether or not they can or should adjust their pedagogy to
increase levels of student motivation. Finally, universities in general may integrate findings

8

gained from this study into considerations toward next steps in program or curriculum design;
those who already have a distance education program in place may choose to adjust it if findings
indicate benefits from such actions.
Also, this thorough expositional examination regarding the motivational elements at play
elicited a confirmation of sound pedagogical structure in both online and face-to-face learning
environments in this particular pre-service teacher education program, where differences in
several motivational factors were discovered to be statistically insignificant. Possibly, other
institutions that are interested in adopting this type of delivery method for their own graduate
programs may benefit from the findings of the study. Answers to the broader research question in
this dissertation determining the links between goal orientation, self-efficacy, and belongingness
strengthened the existing body of theoretical knowledge by confirming the motivation model
proposed by Ford (1992).
Methodology
This was a quantitative study to address the research questions stated above. A survey
was distributed in order to collect demographic information from voluntary participants who
were enrolled in a graduate level teacher preparation program at Western University. Data
gauging teacher self-efficacy, goal orientation, and belongingness was also collected from these
participants. Details on the instrumentation that was used to measure these latent constructs will
be address in a subsequent chapter of this dissertation. The first research question was analyzed
through the use of a Multivariate Analysis of Variance Assessment, or a MANOVA. A series of
separate single regressions were performed to determine an answer for the second research
question. In this way, the study checked for statistical differences between student sense of
belonging, self-efficacy, and goal orientation that may have possibly immerged from the data

9

collected by program delivery methods. The last question was addressed through a second series
of linear regressions, based in part on students’ demographic responses to the survey regarding
their participation in various co- and extracurricular activities, whether school facilitated or not,
in relation to their sense of belonging to their program.
One of two groups studied in this dissertation was made up of volunteer participants
taken from students who were enrolled in the face-to-face track of a graduate level pre-service
teacher education program at Western University, a prestigious, not-for-profit research university
in Southern California. The other group in this study consisted of volunteer participants taken
from students who were enrolled in the same teacher education program, at the same school and
at the same university, but these students were enrolled in the online version of this program.
Regardless of program delivery methods, both groups had access to the same online interface
that allows students to communicate with each other as well was faculty and staff, and both
groups were at roughly the same trajectory of program completion. Also important to note was
the fact that the program used the same curriculum map, and courses for both groups were often
taught by professors who taught in both the on-campus and online cohorts. Both groups of
student had access to nonsynchronous social components of this learning management system,
namely “the Wall” where students had the option to post questions and comments to their peers,
or to create co- or extracurricular interest groups. The duration of the program was the same for
both groups of students studied, about a year in length.
Assumptions
For the purposes of this study, it was assumed that the two cohorts being examined,
online and face-to-face, were comparable in instructional rigor and caliber. It was also assumed
that a significant majority of survey participants were students who were non-credentialed by

10

any state teaching commissions but who intended to apply for credentials and seek positions in
the field of teaching upon program completion of the program. Since the survey was
administered to students in the second semester of their program, it was assumed that most of the
students did not have had any formal student teaching experience. It was also assumed that any
participants who had student teaching or other related classroom experience were already
credentialed teachers who were in the program for the purpose of obtaining a masters degree.
Finally, it was assumed that all students had an equal opportunity to participate in out-of-class
networking activities as facilitated by their respective delivery method.
Definition of Terms
Belongingness
The term of belongingness, prescribed in this study to the emotive component of Ford’s
(1992) motivation formula, refers to a student’s sense of connectedness, and school or program
membership through the degree of non-curricular peer interaction experienced (Goodenow &
Grady, 1993) while in the pre-service teacher training program.
Delivery Method
Delivery method refers to the dominant media through which lessons were delivered to
students. Two specific types of delivery methods were addressed in this study.
Distance Education
Distance Education (DE) refers to coursework that was completed specifically through an
interface based off of the Moodle platform but specifically adapted to fit the needs of the
graduate level pre-service teacher training program at the university where this study took place.
Each student attended class from his or her computer synchronously with opportunities for visual
and auditory participation.

11

Face-to-Face
Face-to-Face setting, also termed traditional classroom setting, refers to coursework that
required a student’s physical presence on campus at regular intervals throughout a given
semester. Generally, much of the participation occurred in person, and student and faculty all
gathered at a predetermined location to engage in the learning process. Online components of the
class were accessible to students in the face-to-face masters program through Blackboard and the
same interface used by the online cohort of students.
Goal Orientation
The manifestation of the “goal” element in the formula developed by Ford (1992), this
term refers to the standard that is applied by an individual to guide and evaluate his active
progress toward a predetermined end (Schunk et al., 2008). For the purposed of this study, goal
orientation was delineated further into three categories: mastery goal orientation, approach
performance goal orientation, and avoidance performance goal orientation.
Mastery goal orientation. A goal orientation that focuses on the accomplishment of a
task that is in alignment with an individual’s intrinsic interest and personal values
(Dweck, 1999).
Performance approach goal orientation. A goal orientation that focuses on active
measures to exceed performance exhibited by peers to some degree (Elliot, 1997; Elliot
& Harakiewicz, 1996).
Performance avoidance goal orientation. A goal orientation that is characterized by an
individual’s drive to avoid an overt display of underperformance in a group setting
(Elliot, 1997; Elliot & Harakiewicz, 1996).

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Motivation
Motivation, as address along a social cognitive framework, is the direct result of a triadic
relationship between an individual’s learning environment, his behavior as facilitated by his
environment, and his cognition around his environment and behavior (Bandura, 1993).
Self-Efficacy
Broadly defined, self-efficacy refers to an individual’s evaluation of his ability to
successfully complete a task (Bandura, 1993, 1997). As related specifically to this study, self-
efficacy referred to the internal perceptions and beliefs held by participants regarding their
ability to be good teachers upon program completion. This quality aligned to personal agency
beliefs in participants, as addressed by Ford (1992).
Organization of the Study
Chapter one of this study has provided an overview of potential social cognitive elements
that may impact student motivation differently by delivery method. The purpose and significance
of the study were outlined, along with the research questions that were used to guide and
facilitate an understanding of the social cognitive and motivational elements of the programs that
had not yet been examined. A list of terms were provided in order to establish and clarify
terminology that was used in the literature, discussion and analysis portions of this study.
Chapter two will be a review of related literature. The following main topics will be included: 1)
an overview of distance education at the postsecondary level, 2) current research on motivation
in distance education, 3) influences of self-efficacy on motivation in distance education, 4)
influences of goal orientation on motivation in distance education, including the connection
between self-efficacy and goal orientation in this setting, 5) influences of student sense of

13

belonging on motivation in distance education, including potential environmental influences
such as class size and peer network structure, and 6) existing research on the significance of
motivational constructs in online pre-service teacher education programs. These topics build
upon each other to support a justification for the need for further research, guided by the
researched questions provided in this chapter. Chapter three will provide information on the
methodology, which will summarize the design of the research that will be conducted, give
further details regarding the population in the study, provide information on instrument
reliability and validity, outline data collection procedures and the statistical analyses performed
with the data collected. Chapter four will give a detailed report of the results from the
administration of the survey. Chapter five will discuss the findings, and provide a reflection on
implications for future research and practice.

14

CHAPTER 2
LITERATURE REVIEW
Distance education continues to advance and grow in popularity, and presents academic
opportunities to more students by providing them with a viable option for access via a delivery
method that is alternative to the traditional face-to-face variety (Jennings & Bayless, 2003).
However, given the limited understanding of the social cognitive effects on motivation in a
synchronous online setting, further research is warranted to evaluate the motivational outcomes
of students. Despite the rise in distance education programs over the years, there is limited
research on the topic, particularly with regards to motivation along the social cognitive
framework.
Bandura (1986) identified a triadic reciprocity that exists between a learner’s cognitive
processes, the behavior associated with a learning task, and the environment in which the task
and individual are situated. Ford (1992) indicated a multiplicative relationship between key
motivational variables- goals, agency beliefs, and emotions. Looking at these motivational
elements, concern arises from a distinct potential for disparities between learning environments.
Considering the motivations of students in both traditional face-to-face and synchronous online
environments, this chapter examines several studies and meta-analyses of these three essential
components of motivation in a range of academic environments.
The first purpose of this review is to provide the general context for a discussion on
recent pedagogical opportunities afforded via distance education. Built upon this discussion will
be a synopsis of significant findings around student motivation in a distance education setting.
Then, this chapter aims to provide an overview of constructs that will be measured; specifically
self-efficacy, goal orientation, and belongingness; and to look at studies regarding the

15

motivational influences on goal orientation, self-efficacy, and belongingness as is relevant to
their roles in student motivational profiles. The final purpose of this chapter, after an overview of
findings related to the motivational and social cognitive factors around distance education, is to
provide a brief review of potential congruities and incongruities that may exist in these profiles
by face-to-face and distance education settings. This final point is the basis upon which to build
a case for the relevance of further study in this area.
Current Landscape of Postsecondary Distance Education
Distance education programs offered at the undergraduate and graduate level have
increased and expanded in recent years. A survey conducted by the National Center for
Education Statistics indicated that 65% of coursework offered at postsecondary institutions in the
United States was conducted via some form of distance education (NCES, 2006-2007; Parsad &
Lewis, 2008). Corresponding figures for distance education coursework related specifically to
the graduate level were smaller but still noteworthy at 36%. This review will focus on the
characteristics of distance education in graduate programs, as these will be most relevant to the
study at hand.
The reason for growth in the area of distance education is twofold. First, universities are
striving to meet student demands for greater convenience and flexibility in their academic
programming (Donavant, 2009). Second, these institutions are increasing their financial net
potential by increasing student enrollment while limiting the overhead costs that would have
been incurred through such expansion in a strictly on-campus environment (Abdous &
Yoshimura, 2010). To this end, distance education has become a plausible alternative for both
students and universities to the traditional in-class mode of teaching and learning in order to fill
present needs.

16

There has been a movement in teaching media from a purely on-campus, in-class setting
to a context where students’ peer and faculty contact takes place, at least partially, in either an
asynchronous or synchronous distance education setting. This is a shift that is very likely to gain
momentum in the coming years, as technology advances to allow for a more seamlessly
interactive distance education experience. Defining the parameters of distance and face-to-face
education before engaging in a discussion around these elements provides a more detailed
perspective of delivery method options available to student in the postsecondary setting. The
traditional face-to-face environment at the university level is easy to understand and envision,
since it is closely akin to the typical instructional setting option throughout primary and
secondary educational settings. Instructor and students engage in learning through interaction
that takes place in a physical setting; everyone is typically situated in one room at a
predetermined designated schedule.
In contrast, distance education is a broad term under which several distinct learning
platforms are subsumed. It is defined as being an instructional scenario that incorporates the
internet and communication technologies and includes but is not limited to hybrid or blended
courses, computer-based training, distance learning, satellite broadcast, video-streamed
instruction, or various synchronous of asynchronous web platform-based instruction (Abdous &
Yoshimura, 2010; Tallent-Runnels et al., 2006). This is in juxtaposition to traditional learning
environments, which have been defined by Clayton et al. (2010) as a learning space in which
communication and moderation between teacher and students take place in person and is not
facilitated by technology.
A key advantage to the increasingly ubiquitous nature of distance education programs is
the additional access it provides students who may need the flexibility of time and location it can

17

provide (Donavant, 2009). Recent advances in technology are moving online distance education
away from the satellite broadcasting format of instruction or other asynchronous formats of
delivery to one that is conducive to real-time collaboration between learners. This setting allows
students to engage in interactive and collaborative learning (Bernard, Abrami, Lou, Borokhovski,
Wade, Wosney, et al., 2004; Burbules & Lambeir, 2003). Learning in a dynamic synchronous
collaborative engagement, previously available only in the classroom through face-to-face
interaction, is now an increasingly viable option in the distance education scenario.
Distance education programs have been found to be a good fit for many university
students. These program options provide a learning process in an environment complementary to
many students’ lifestyles and learner-instruction preferences (Clayton et al., 2010; Galusha,
1997). Students who experience success with this particular method of program delivery also
express a degree of familiarity with an online learning platform, usually because they have
positive prior experience in a distance education setting (Clayton et al., 2010). These benefits
allow students the flexibility to take coursework or even complete entire programs in a distance
education setting.
Technological advances have enabled the facilitation of online education in the
undergraduate and graduate school level (Abdous & Yoshimura, 2010; Allen et al., 2004).
Recent leaps in the form of expanding collaborative technologies have allowed student learning
in the online environment to take place on a more meaningful and collaborative level (Abdous &
Yoshimura, 2010). Students are able to view and communicate with their peers and professors in
a manner conducive to collaborative learning, thus addressing the main shortcoming connected
to distance education (Bernard et al., 2004).

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While many universities have adopted distance education in an attempt to accommodate
student needs and demands, it has not been largely implement on a whole-program level. Rather,
a proportion of coursework that fulfills program completion requirements may typically be
offered in a distance education setting and a majority of the courses are taught in a face-to-face
setting. Numbers for the National Center for Education Statistics (2006-2007) indicated that
52% of public universities and 21% of private not-for-profit universities offer at least one
program at the graduate level designed to be completed exclusively through a distance education
format. There is no indication as to the specific type of distance education media that qualifies a
program in this statistic. The program could be delivered any number of ways, from a
synchronous video and audio platform that integrates class and group collaboration and
discussion of course materials, to an asynchronous forum-type setting where there may be
varying degrees of student or faculty interaction, to reading assignments posted online and
summative multiple choice assessments after each “lesson” that require very little or no peer or
instructor contact.
The type of technology utilized in instructional delivery also plays a role in defining the
quality of a given program. Only 12% of all coursework, graduate or undergraduate, utilizes
synchronous educational technology in a distance education setting (NCES, 2006-2007). No
figures are available for statistics regarding synchronous technology as applied to graduate
distance education courses or graduate distance education programs. This lack of data indicates
that not very many of these kinds of courses, let alone programs, exist. Research in the area of
distance education, especially in these new and innovative media, could inform program and
curriculum design in postsecondary education, if the overarching intent of public and private not-
for-profit universities is to expand and improve their distance education options for students.

19

In summary, the emergent developments around distance education at the university level
present an opportunity for studies around innovative programs that have recently become viable
options for current and prospective students. The notion of distance education in an online
setting has been around for over two decades, and there is a substantial body of knowledge
cultivated around this topic. However, less research has been conducted on motivation in
synchronous distance education environments that apply collaborative classroom experiences
using real-time visual and audio technology on a program level. In large part, this particular gap
in knowledge is due to a general lack of prior access in this particular type of learning context.
With recent innovations, graduate degree programs can be administered wholly via cooperative
and collaborative synchronous online technology, where students can log into a virtual classroom
and engage in an authentically cooperative and collaborative learning process with peers and
faculty via an innovative new technology platform. This study seeks to compare an online pre-
service teacher training program with these characteristics to an equivalent traditional face-to-
face program delivered on campus to determine differences in student motivation and at may
exist, as well as potential relationships between motivational constructs by learning environment.
Research on Motivation in Distance Education
A look at findings via prior studies brings to light the scarcity of information regarding
key motivational factors, specifically social cognitive elements, related to a distance education
learning environment. Many studies examine distance education under a pedagogical lens of
academic evaluation (Bullock & Ory, 2000; Clark, 1983; Galusha, 1997; Kekkonen-Moneta &
Moneta; 2002, Kozma, 1994; Ledman, 2008; Russell, 1999). However, few reveal findings about
the motivational implications of courses taught online versus face-to-face in a traditional campus
setting (Clayton et al., 2010, Sahin & Atay, 2010). An overview of the rise in distance education

20

as a valid method of instructional delivery brings to light a deficit that exists regarding
knowledge around motivational variables as they affect academic environments that are
exclusively online.
Distance education and face-to-face environments both possess elements that may
contribute to or detract from the level of student motivation for a particular program or course.
In the face of recent advances in distance education, many studies have been conducted on the
effects of different methods of content delivery on student learning outcomes. The results of
most of these studies come to the same conclusion, which is that varied delivery methods do not
elicit different learning outcomes. Clark (1983) stated that the use of media, including
technology that facilitates distance education, did not improve learning outcomes of students in
and of itself. Skylar, Higgins, Boone and Jones (2005) and Kekkonen-Moneta and Moneta
(2002) made similar observations when they said that student outcomes are not affected solely by
a change in method of delivery. These studies indicate that effective pedagogy, with or without
the integration of educational technology, influences learning outcomes.
However, another large body of literature indicates that in addition to effective pedagogy,
motivational beliefs influence the degree to which students succeed in an academic setting
(Pintrich, Wolters & Bacter, 2000; Schunk & Pajares, 2002; Zimmerman, 1989.) Wolters (1999)
established through his studies that in traditional, in-class settings, high student motivation was
related to learning and performance. Zimmerman (1989) elaborated upon this idea by stating that
successful learners were able to modify their learning strategies in the face of a challenge in
order to achieve their learning goals. Student motivation, specifically the active involvement and
persistence in their academic environment is an important piece to consider when evaluating the
extent to which learning outcomes have been achieved (Clayton et al., 2010).

21

Evidence has been established to support the argument that there is a strong correlation
between the level of a student's self-efficacy and his observed ability as well as course
satisfaction in a traditional in-class setting (Bandura, 1997). According to Bandura (1997) self-
efficacy, or a student's internalized perceptions of his or her abilities related to a specific task, is
a motivational construct that influences student success in an academic setting. The reason for
this connection between self-efficacy and academic outcomes is that students with high self-
efficacy are open to implementing a wide array of learning strategies as opposed to their peers
with low self-efficacy (Bandura, 1997). However, there is less evaluation on the effects of
motivational factors on student performance in a distance-educational setting versus an on-
campus environment. It is important to take student motivational factors, in addition to the
caliber of pedagogy, into account for effective instructional design (Tallent-Runnels et al., 2006).
Galusha (1997) also expressed the importance of educators acknowledging and taking into
account the different motivators for students in a distance education setting, as opposed to an in-
class setting, in order to drive pedagogical strategy.
Clayton et al. (2010) conducted a study that explored the possibility that the differences
observed in motivational levels of online versus on-campus students could be explained by the
self-selecting nature of enrollment in a given program by delivery method. The results indicated
that the students in this particular study who chose to learn in a traditional learning environment
were more mastery oriented than their online counterparts. Also, those who chose to enroll in
the online program reported greater technological self-efficacy in an academic setting. In their
study, Clayton et al. (2010) identified a pattern between goal orientation and preferred delivery
method. There was also an association established between self-efficacy and academic
environment. However, there is a current lack of findings on any connections between the type

22

of delivery method and emotional influences, the third motivational element identified by Ford
(1992).
In summary, there is limited research that sheds light upon motivational influences in
distance education settings despite the evidence of its role in the degree of success and
satisfaction that a student may experience in these types of programs. A contributing factor to
this lack of research may be the absence of distance education settings that would facilitate
positive social cognitive elements that would impact motivational outcomes. For instance, an
online course that required very little interaction between the student and his instructor or peers
would yield different results in a study than one that incorporated synchronous visual and audio
interaction conducive to class discussions and small group collaboration. This review strives to
examine distance education settings that align to the latter scenario over the former. Clayton et
al. (2010) found that all learners in their study, irrespective of delivery method, stated a strong
preference for learning environments that were engaging and that enabled an element of direct
interaction between students and faculty. Some elements of this type of environment are
immediate feedback, spontaneity, and relationship-building or networking opportunities with
faculty and classmates. Examining motivation and learning in distance education and traditional
class settings along these criteria, through a social cognitive construct, will allow for the
informed design of effective learning environments, both online and in class.
Self-Efficacy in Distance Education through a Social Cognitive Framework
Social cognitive theory, as identified and defined by Bandura (1986), is grounded upon
the triadic relationship between a learner’s cognitive processes, his learning environment, and his
behavior related to a given learning task. These aspects of social cognitive theory guide the
current discussion on motivation in distance education. The relationship between the learning

23

environment and aspects of student cognition is particularly relevant to the discussion at hand,
since this study takes a comparative look at student motivation in online and face-to-face
settings. The relationship between self-efficacy beliefs and learning environment in regards to
student motivation to engage in learning behaviors is a particularly important aspect due to its
predictive value on future academic performance.
Self-efficacy as a social cognitive construct can be described as the mind’s “symbolic
representation” of the cognitive product of behavioral and environmental factors (Bandura, 1986,
p.51). The relevance of self-efficacy to this study lies in its strength as a predictor of
achievement in an academic setting. According to Bandura (1986), there are four main sources
of self-efficacy: mastery experience, vicarious experience, social persuasion, and physiological
factors. Mastery experience is the way an individual actively learns through the direct
accomplishment of a learning task. Bandura (1986) also states that successful actions increase a
person’s efficacious believes around his ability to perform that specific task, and leads him to
feel confident about being able to repeat the undertaking with confidence. This method of
raising self-efficacy is lies in the successful balance of behavioral, environmental, and cognitive
factors in a given situation (Bandura, 1986). When an individual observes and internalizes the
successes of peers in his environment, his own self-efficacy for the particular task he witnessed
grows through vicarious experience. Even in the case of passive observation, self-efficacy can
be increased. Whether an activity is deliberately modeled by an instructor or unknowingly
modeled by a fellow learner, vicarious experiences lead to a rise in self-efficacy beliefs.
According to Bandura (1986), social persuasion is another environmental influence that can lead
to more self-efficacy in a student. When peers or instructors verbally or physically indicate
confidence in a student’s ability, that student will become more efficacious about that particular

24

task (Bandura, 1986). The final factor identified by Bandura (1986) is the physiological factor,
which is the least thoroughly explained in his work. However, there are definite connections
between an individual’s cognitive state, as impacted by his physiological state and environmental
context, and his efficacy regarding the quality of his successful task performance.
Co-varying with a student’s sense of self-efficacy is his sense of mastery over a skill or
concept (Ames, 1992). Self-efficacy, defined as internalized perceptions of an individual’s
abilities, is a motivational construct that influences student success in an academic setting
(Bandura, 1986). A student's level of self-efficacy, which is largely determined by prior
experience, can also support, predict, or determine his level of motivation along the indices of
active choice and effort (Tzeng, 2009). The impact of efficacious beliefs is evident in the types
of academic tasks set by students, according to Tzeng (2009) - those with higher self-efficacy
usually undertake more challenging tasks, which increase motivation and commitment toward
the accomplishment of them. In essence, self-efficacy molds subsequent behaviors and beliefs.
Self-efficacy has also been shown to predict the level of persistence and the degree of success
within an academic major at the undergraduate level (Wang & Newlin, 2000). Self-efficacy, as a
construct, will be used in this dissertation to determine the level of student motivation in both
face-to-face and distance education settings by indicating the personal agency beliefs held by
students (Ford, 1992).
Self-Efficacy and Distance Education
Evidence has been established to support the argument that there is a strong correlation
between the level of a student's self-efficacy and his observed ability as well as course
satisfaction in a traditional in-class setting (Bandura, 1997). Self-efficacy also plays a role in a
student's selection of one type of course delivery method over another (Clayton et al., 2010). For

25

example, students who registered for an online delivery method of coursework held highly
efficacious beliefs regarding their ability to be academically successful in that particular course
environment. Efficacy beliefs held by students regarding their postgraduate, professional
performance have proven to be significant indicators of motivation as well (Sahin & Atay, 2010).
In their study, Sahin and Atay (2010) indicated that levels of self-efficacy affected motivation
during a course. They also posited that efficacious beliefs held by students would likely have
longer reaching effects on career-oriented motivation (Sahin & Atay, 2010).
Social cognitive theory states that learning occurs through a dynamic of triadic
reciprocity, whereupon cognition, environment, and a given task all contribute to an individual’s
learning experience (Bandura, 1986). Over the years, studies have taken the idea that classroom
environments influence student learning a step further by positing that social cognitive influences
in educational situations specifically affect learning by influencing student motivation (Ames,
1992; Nelson & DeBacker, 2008). There are a number of cognitive characteristics that play
varying roles in the formation of motivation, including goal orientation and self-efficacy beliefs
in students. Though there is a relatively well-established body of research comparing learning
outcomes by traditional and media-based delivery methods (Bullock & Ory, 2000; Clark, 1983;
Galusha, 1997; Kekkonen-Moneta & Moneta; 2002, Kozma, 1994; Russell, 1999), there is less
information on the influences of classroom, or environmental, characteristics on motivational
outcomes in similar educational settings (Abdous & Yoshimura, 2010; Nelson & DeBacker,
2008). A general lack of studies to date measuring the effects of this key social cognitive factor
on motivational outcomes, specifically in distance education settings at the post secondary
masters level, brings to light the potential for further relevant study in this area.

26

Sahin and Atay (2010) examined students' efficacy scores for their profession before and
after they completed the student teaching portion of a teaching credential program. Findings
indicated that self-efficacy, specifically for the profession, rose as a result of the enactive
mastery experiences and vicarious experiences related to the student teaching experience. Self-
efficacy is a largely internal construct that stems from external events, such as mastery
experiences via the successful accomplishment of performance approach and mastery oriented
goals. However, there are external factors in an academic setting, such as classroom climate and
environment, which can influence students’ level of self-efficacy. These potential differences in
classroom environment can possibly result from differing methods of delivery, such as
traditional in-class settings or online distance education settings.
The four sources of self-efficacy are mastery experience, vicarious experience, social
persuasion and psychological responses; the most effective source is mastery experience
(Bandura, 1993). Both performance approach and mastery oriented goals should, in theory, elicit
mastery experiences when successfully executed. While the motivational focus may differ by
goal orientation, the successful attainment of both types of goals would produce similar
observable results, such as a good grade in a class or positive peer or instructor feedback.
Therefore, it can be deduced that students whose motivations are grounded in either mastery
orientation or performance approach orientation are more likely to gain self-efficacy, via mastery
experience, in a task related to their identified goal. According to the literature that was
addressed in this section, a positive correlation should exist between self-efficacy and
performance approach or mastery goal orientations.
In summary, an overview of prior findings on the environmental, cognitive and physical
influences brings to light the interconnected nature of motivational elements, as detailed in this

27

section. Also apparent through the description of self-efficacy as a variable is the potential that
social cognitive differences by learning environment, distance education or face-to-face, may
influence student motivational outcomes. A thorough initial exploration of this variable provides
a foundation upon which to present the argument for further related research in face-to-face and
distance education settings. Distinctions in classroom environments and classroom climates may
be present in different delivery methods, which may in turn influence student motivation.
Specifically, examining the effects of goal orientation and self-efficacy on student motivation
could shed light on motivational inconsistencies that may exist within a program that is taught
via different methods of delivery.
Influences of Goal Orientation on Motivation in Distance Education
Level of motivation can be determined, in part, by studying the type of goal orientation
held by an individual (Ford, 1992). To clarify this point further, Ford (1992) designed a formula
that states that goals, emotions, and personal agency beliefs combine multiplicatively to
determine an individual’s motivation:
MOTIVATION = GOALS X EMOTIONS X PERSONAL AGENCY BELIEFS
This model suggests that the presence and quality of goals set by students in an academic setting
play an important role in the formation of motivation. Ames (1992) asserts through her study that
different goals set by students elicit different motivational outcomes. Therefore, both the
presence and type of goal orientation influence motivation. An overview of goal orientation as a
construct helps to justify its use as one of three key measures of student motivation in both face-
to-face and distance education settings.
Goal orientation can be understood as the standard by which an individual defines,
approaches and evaluates success (Schunk et al., 2008). Schunk et al. (2008) and Ames (1992)

28

separated goal orientation into two distinct types: mastery goal orientation and performance goal
orientation. Dweck (1999) also termed them dichotomously as task and learning goals, and
Nicholls identified them as task orientation and ego orientation. The terms aren't completely
interchangeable, but the theoretical frameworks behind all terms overlaped enough that they
could be discussed as one construct.
The two main goal orientation definitions, as addressed by Schunk et al. (2008), are
mastery goal orientation and performance goal orientation. Mastery orientation involves a
learner's perception of success as a result of effort toward an aspiration of intrinsic value,
measured by self-set, ego-centered standards (Dweck & Leggett, 1988; Nicholls,1984; Schunk et
al., 2008). In contrast, performance orientation is defined by a focus on the demonstration of
skill and knowledge (Ames, 1992; Dweck & Legett, 1988). To a student guided by this type of
goal orientation, success is defined by standards that are extrinsic in nature, such as scoring
above the class curve or receiving public recognition of excellence by the instructor or another
source of authority in the learning environment.
Subsequent studies of the two definitions yielded a three-factor model of the goal
orientation construct, where performance goals were divided further into performance approach
goals and performance avoidance goals (Elliot, 1997; Elliot & Harakiewicz, 1996; Vandewalle,
1997). The former is an orientation focused on success by the means of taking proactive
measures to perform certain tasks better than their peers, while the latter defines success by
avoiding the display of underperformance. Also, both kinds of performance goal orientations are
usually exhibited by students who actively work to preserve their sense of self-worth (Covington,
1992), by either taking actions to prove one’s ability to perform above the norm or avoiding
action to prevent peers from discovering a lack of ability or knowledge.

29

Lee, McInerney, Liem and Ortiga (2010) recently looked more closely at mastery
oriented goals and performance approach goals. Specifically, they examined the relationship
between future goals (FG), related to endeavors regarding long-term career, societal and family,
and achievement goal orientations (AGO), related to aspirations around fame and wealth. Their
study measured the differences in student motivation using the intrinsic and extrinsic
motivational constructs, and determined that the intrinsically valued FGs are strongly correlated
to mastery goal orientation while extrinsically valued AGOs are less strongly associated with
mastery oriented goals and more strongly related to performance approach goal orientation.
These findings suggested that, despite prior research that indicated a similarity between
performance approach goals and mastery goals (Pekrun, Elliot & Maier, 2009), the two goal
orientations bear distinct differences when viewed through the value orientation lens. This study
further validated prior research findings (Elliot, 1997; Elliot & Harakiewicz, 1996; and
Vandewalle, 1997) that there were at least three separate factors within the construct of goal
orientation.
Elliot and McGregor (2001) provided a detailed description and analysis of mastery goal
orientation as a construct that could be further subdivided into two separate valences, mastery
approach and mastery avoidance goal orientation. In their study, Elliot and McGregor (2001)
described both mastery approach and mastery avoidance goal orientations as being held by
individuals who sought personal mastery of an academic task. They stated that mastery approach
orientation was defined by an individual’s drive to continually further their mastery attempts
around a given task, while master avoidance orientation was described as an individual’s
avoidance of performing a task with less mastery than was exhibited in a previous attempt by
that same individual (Elliot & McGregor, 2001). They introduced a four-factor model of goal

30

orientation: performance avoidance, performance approach, mastery avoidance, and mastery
approach. Elliot (1999) described mastery avoidance goals in the context of aging athletes who
adopted a mastery avoidance goal orientation to circumvent the potential situation of not
completely a task to the self-set standard they used to be able to attain in the peak of their
careers. However, for the purposes of this dissertation, mastery goal orientation will be examined
amongst novice teachers, for the most part, and so it would be less relevant to split mastery goal
orientation into distinct valences. In the interest of structuring the survey for this dissertation
study with a degree of parsimony, the three-factor model of goal orientation is the one that will
be used.
Goal orientation is influenced in part by whether value orientations placed on either
particular content or a given course are intrinsic or extrinsic (Schunk et al., 2008). Intrinsic
motivation, cognitively based, is characterized by an individual’s innate preference in a topic or
engagement in an activity. In contrast, extrinsic motivation is evident when an individual
engages in a particular activity as a result of influences other than personal enjoyment. A student
can be intrinsically or extrinsically invested in a goal that is performance oriented in nature.
Mastery goals, which are self-set, are very much in alignment with an intrinsic value orientation.
These types of goals increase competence in an area and elicit a behavior pattern that seeks a
challenge (Schunk et al., 2008). Motivationally, people with mastery oriented goals exhibit high
persistence (Dweck, 1999).
The explanation of goal orientation via value orientation clarifies the connection between
mastery goals with intrinsic motivational factors. The relationship between performance goals
and various extrinsic motivational factors are also detailed in this review. Looking at both goal
and value orientations as theoretical undercurrents that flow through motivation helps to clarify

31

the reciprocal natures of these two elements. In relation to this study, the correlation between
different value orientations and goal orientations may manifest itself by delivery method.
Further, the differing environmental factors in face-to-face and distance education settings may
be dependent on extrinsic and intrinsically guided goal orientations.
Goal Orientation and Distance Education
Prior research on this topic indicates the benefits of a closer look at student goal
orientations in a program with cohorts set in two distinct teaching environments, with delivery
methods being either traditional face-to-face or in a synchronous online setting. The conclusion
of a study conducted by Klein, Noe and Wang (2006) on the influences of goal orientation by
delivery method and student perceptions indicated that the nature of domain-specific goal
orientation influenced students' level of motivation. Clayton et al. (2010) also found significant
differences in the learning strategies implemented and motivational beliefs held among students
who opted for the traditional in-class setting as opposed to those who chose to take classes
delivered nontraditionally. For example, they found that students who took classes online
showed performance-oriented goals while those who preferred a face-to-face delivery method
were mastery goal oriented (Clayton et al., 2010). Ames (1992) argued that different learning
environments result in either performance or mastery oriented goals, which in turn affected
motivational patterns. Findings from studies revealed that mastery and performance approach
goal orientations brought about high motivation in learning environments (Clayton et al., 2010;
Elliot & Harackiewicz, 1996). These kinds of goal orientations facilitated academic success by
giving students a means to establish motivational patterns that would lead toward the realization
of content mastery.

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Connection between Goal Orientation and Self-Efficacy in Distance Education
Research indicated that mastery and performance approach goal orientations were both
affiliated with a student’s intrinsic value of a given learning task (Ames, 1992; Wolters, 1996).
Several studies indicated that mastery goals cultivated intrinsic motivation by instilling an
activity with purpose through challenge and choice, providing a sense of task ownership, and
facilitating self-determination (Deci & Ryan, 1991; Nicholls, 1989). Elliot and Harackiewicz
(1996) added to these findings by noting that students with performance approach orientations
exhibited intrinsic motivation at levels equal to those who held mastery-oriented goals. People
in this orientation set defined success by outperforming or avoiding the display of
underperformance compared to peers (Elliot & Church, 1997; Middleton & Midgley, 1997). In
motivational terms, people with performance-oriented goals differed based on their level of self-
efficacy (Dweck & Leggett, 1988). In this way, a correlation between goal orientation and self-
efficacy is further evident, making apparent the relationship between goal orientation theory and
social cognitive theory.
Achievement goal orientations, as addressed previously, moderate the level of student
motivation with the frequency and nature of student mastery experiences, a characteristic that
directly impacts student self-efficacy. From the outset of goal orientation theory, there has been
a link between goal orientation and self-efficacy. Dweck and Leggett (1988) established in a
laboratory setting that students who are mastery goal oriented also exhibit high self-efficacy
beliefs. There has been much researched that has followed that supports this initial assertion
(Ames, 1992; Bandalos et al., 2003; Kaplan & Midgley, 1997; Middleton & Midgley, 1997,
Pintridge & Garcia, 1991; Wolters et al., 1996). Performance goals are less endorsed universally
as a means through which to build self-efficacy; several studies indicate that performance goals

33

elicit high self-efficacy only when they are based on performance approach and not performance
avoidance. (Anderman & Midgley, 1997; Middleton & Midgley, 1997; Skaalvik, 1997; Wolters
et al., 1996).
In summary, student motivational factors have recently been taken into more
consideration when planning effective instructional design (Tallent-Runnels et al., 2006).
Literature addressed in this section suggested that goal orientation, self-efficacy and a student’s
subjective sense of support from a learning community were important motivational variables to
consider when assessing the conditions of a particularly effective distance learning or face-to-
face academic environment. This dissertation study seeks to examine the relationship between
goal orientations and self-efficacy in students within a given program to see if patterns between
certain goal orientations and levels of efficacy manifest themselves by delivery method.
Student Sense of Belonging on Motivation in Distance Education
Among the motivational factors addressed at present, the greatest deficit in research
exists within the degree to which belongingness influences motivation in a distance education
setting. The need to belong has been found to be a fundamental human motivation because it
elicits goal-oriented behavior (Baumeister & Leary, 1995). Belongingness is an internalized
construct that is largely dependent upon internal perceptions of external, environmental forces,
such as group influences upon individuals and a purveying sense of community in group
interactions (McMillan & Chavis, 1986). Ryan (2000) used the term “relatedness” to describe
student sense of belonging, and echoed the argument set by Maslow (1954) that belongingness is
a basic human need that must be actualized in order fulfill higher order functions. However, prior
studies have not addressed belongingness, and the impact of the presence or absence thereof, to
motivation within different methods of delivery. This section will present the available research,

34

however limited, on the effects of student feelings of belonging on the motivational constructs
discussed in previous sections, namely goal orientation and self-efficacy.
Self-efficacy and goal orientation are two of three major components that factor into a
student’s motivational profile (Ford, 1992). Without the third, emotion-based, component, an
integral piece of students’ motivational profile would be missing and any analyses conducted
would be limited in scope. Hui, Hu, Clark, Tam and Milton (2008) posited a positive correlation
between a student’s perceived level of peer- and faculty-based academic support and his learning
effectiveness. Hui et al. (2008) also found that there were some essential antecedents of student
learning satisfaction in a course delivery method that integrated distance education technology,
such as perceived support from a learning community, content learnability, and course
effectiveness.
The state of relatedness (internalized feelings of group affiliation) and interaction (regular
involvement with peers) were two essential characteristics of belongingness as identified by
Baumeister and Leary (1995). A study conducted by Hoyle and Crawford (1994) found that
student sense of belonging to a given university setting was strongly linked to social involvement
in university activities. This furthered the point made by Baumeister and Leary (1995) that
regular interaction was a necessary component to feelings of belonging. In a distance education
setting, evidence of relatedness and involvement would look different than in a traditional face-
to-face setting. Two social psychological factors addressed in this review and examined in this
dissertation, peer network structures and class size, may contribute to a disparity in students’
levels of relatedness by the two delivery methods described.

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Potential Social Cognitive Contributors to Belongingness
Peer Networks. One social psychological influence to consider is that of peer networks.
Nelson and DeBacker (2008) posit that student peer groupings should be taken into account
when examining student motivation because of its impact on a student’s social learning
environment. Peer networking studies indicate that peers motivate each other by guiding the
active choices made (Berndt, 1999) and also by reinforcing values and beliefs which impact
motivation along the index of engagement, or mental effort (Ryan, 2000). Given these findings,
there is potential for a difference in peer networking structures according to delivery method,
distance education or face-to-face, that may influence student motivation along the indices of
choice and effort in a post-secondary setting.
At this time, very little research is available on the connection between peer networks and
learning environments at the university level. However, Nelson and DeBacker (2008) examined
the connection between peer-related variables, namely friendship dynamics, goal orientation, and
self-efficacy, in a secondary education setting. Their significant findings include the positive
correlation of belongingness to mastery and performance goal orientation, and also to self-
efficacy. They also discovered a positive correlation between networking variables and
belongingness. Research on this topic in a post-secondary setting might promote a better
understanding of the relationship between peer networking to various motivational constructs
such as self-efficacy, goal orientation, and belongingness.
Class Size. Studies have shown that class size negatively correlates with student
academic performance from a pedagogical standpoint, both in the face-to-face class setting
(Johnson, 2010; Kokkelenberg, Dillon & Kristy, 2008) and distance education learning
environment (Hewitt & Brett, 2007). Smaller classes are associated with higher student grades

36

and more scholarly behavior, while larger class sizes are associated with lower student grades
and less scholarly behavior. However, less is known about the connection between class size
and student motivation, particularly in distance education. There is limited information on the
topic of class size and student perception of connectedness; the findings of such a study may
potentially suggest a social cognitive mediation between student motivation and classroom
environment.
Bolander (1973) conducted an early study on the relationship between class size and
individual and intragroup student motivation at the postsecondary level. His findings indicated a
significant negative correlation between class size and student motivation- the smaller the class
size and greater the motivation. However, his study did not take into account distance education
options, most likely because such options were severely limited at the time he conducted his
study. Burris (2008) looked at students in a nursing program that was conducted completely
online to see if there was a correlation between class size and educational practices, including the
level peer interaction and outcomes, including perceived connectedness. At the graduate level,
the study indicated that students who were in smaller distance education classes of twenty
students or less experienced greater connectedness than those who were in larger distance
education classes of over twenty students. This information sheds light upon a new perspective
when comparing the effects of student connectedness, or belonging, by delivery methods in a
graduate program where the distance education courses limit their enrollment to smaller classes
of less than twenty students, and face-to-face courses have larger enrollments of twenty students
or greater.
A social psychological perspective accounts for the motivational differences that may
exist as a result of the factors at play in an online environment versus a face-to-face environment.

37

Peer networks and class size, in particular, are factors along the social cognitive framework that
aid in the cultivation of a student’s sense of belonging to a given academic environment (Schunk
et al., 2008). Class size and peer networks are factors that influence group cohesion and can be
considered when examining social influences on students as they consider whether or not to drop
out of a course or program conducted online. Eisenberg (2007) suggested that groups in which
students possess a common trait are likely to be more cohesive. Students in online environments
are less likely to possess these commonalities (Galusha, 1997; Klein et al., 2006). There are
likely to be a broader demographic range of students in an online class than in a traditional on-
campus locale, in terms of age, geography, and professional status. Students in a given course
could be located around the world and freshly out of high school or looking into their second
career. According to Galusha (1997), some could be full time students, while other may be
taking a course while maintaining a fulltime job. Moreover, since MAT online programs have a
higher overall capacity, more students may be admitted into an online program than a traditional
brick-and-mortar program, which would be limited by physical space (J. Springer, EDUC 524
discussion, September 2009). Higher admission rates as a result of higher capacity may decrease
a program’s perception of academic exclusivity and prestige. This influx of admits could
potentially be interpreted by some students as a dilution or compromise of academic rigor,
further dissolving a sense of group cohesiveness. In this way, a student’s cognition during a
learning task likely impacts and is impacted by his learning context, whether it is in a traditional
classroom or a distance education setting.
Belongingness and Distance Education
Inherent differences may exist by delivery method in opportunities for students to
experience belongingness. Feelings of belonging have also been shown to be largely dependent

38

on learning environment. Osterman (2000) indicated that a student's classroom and school
environment influence his sense of belonging, and in turn affect his motivation and achievement.
Weiner (1990) argued that older motivational frameworks, such as belongingness, needed to be
considered in the evaluation of social cognitive factors that influenced student motivation. While
the internal aspect of belongingness manifested itself in the individual's feelings of affiliation,
literature indicated that a student's learning environment, namely the degree to which he interacts
with his peers and instructors, plays an integral role as well (Baumeister & Leary, 1995). The
succinct statement that “motivation cannot be divorced from the social fabric in which it is
embedded" (Weiner, 1990, p. 621) described the interconnected nature between the internal and
external influences on motivation. In other words, there was an irrevocable connection between a
student’s drive to learn and his learning environment.
Clayton et al. (2010) stated that students preferred learning environments that foster
interaction and community connection in both on-campus and online settings. She added to her
study by positing that self-selection into a group by interest or comfort levels, specifically
comfort with technology, would promote feelings of belongingness and thus affect motivation.
However, her assertion that student motivation differed as a result of student self-selection into a
cohort by delivery method was in direct opposition with findings related to belongingness by
Locksley, Ortiz and Hepburn (1980) that self-selection into a group did not elicit statistically
significant differences in feelings of inferred similarity than when subjects were placed in
randomly assigned groups. In other words, feelings of belonging should not necessarily increase
for students simply because they were able to choose a face-to-face or on-campus program
delivery method by preference than for groups where students may not have had a say in cohort
placement. Locksley et al. (1980) posited that student self-selection into a cohort for any reason,

39

including by delivery method, should have no bearing, in and of itself, on levels of
belongingness in students.
The value of belongingness as a motivational construct has been established in a
traditional face-to-face academic setting but has not been explored in nontraditional academic
settings. Prior research indicated that classroom environment impacted a student’s sense of
belonging (Osterman, 2000). Belongingness as a construct is a foundation upon which an
understanding of goal directed behavior and self-efficacy are framed. Therefore, gaining a
clearer picture of belongingness, and social psychological sources of belongingness as a
motivational variable in traditional and distance educational settings will provide a fuller picture
of student motivation.
Connection between Belongingness and Goal Orientation in Distance Education
Prior research has established that feelings of belonging can influence student motivation
and academic behaviors. A student's need to belong prompts behavior that is goal-directed in
nature. This is an indicator of motivation according the to definition provided by Schunk et al.
(2008). Ryan and Stiller (1991) established a link through a study between belongingness and
goal orientation. They surveyed a population of seventh grade students from an urban middle
school to measure the level of peer group motivation and achievement characteristics, as well as
level of individual motivation and achievement. They used items developed by Eccles (1983) to
gauge student expectancy, via goal orientation, and value, specifically intrinsic and utility values.
Their study supported the assertion that group affiliation and interaction was essential to
promoting motivation through mastery or performance approach goal orientation.
Mastery oriented goals can also foster a more positive "global perception of self" (Ames,
1992, p.263) and thus allow a learner to experience an emotive connection to the classroom

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environment and climate. In other words, mastery goals expand the degree to which a student
can perceive academic success as a result of effectively participating in a learning process in his
learning environment. This was described as an "integration of self with task and others" that
was less likely to happen when students were focused on preserving their sense of self-worth and
defining success through comparing their ability against those of their peers (Ames, 1992, p.
263). Nelson and DeBacker (2008) found that feelings of belongingness significantly influenced
both learner self-efficacy and learner’s orientation toward mastery and performance approach
goals. This sense of belonging, termed “belongingness” (Hoyle & Crawford, 1994; Weiner,
1990) in the field of educational psychology, has roots in Maslow’s hierarchy of needs (Maslow,
1954).
According to this theory, basic human needs, or deficiency needs, have to be fulfilled
before higher needs, or growth needs, can be met. Students struggling to preserve their self-
worth would not be in a position to fulfill needs for belongingness. Similarly, students who do
not possess feelings of belonging, through social acceptance and identity by group affiliation,
would not be able to actualize performance approach goals, termed on the hierarchy as esteem
needs, or mastery goals, represented as self-actualization needs, by Maslow (1954) as the
pinnacle of his hierarchy (Schunk et al., 2008).
Connection between Belongingness and Self-Efficacy in Distance Education
Research has also established that the absence of belongingness in a student’s
motivational profile negatively impacts academic self-efficacy and persistence (Osterman, 2000).
Osterman (2000) provided a comprehensive review of studies that identified belongingness as a
motivational construct related to goal orientation and self-efficacy, to support the formula set
forth by Ford (1992) which indicated that all three are multiplicative influences in determining

41

the level of student motivation. However, there was no information about how this motivational
scenario would differ by delivery method.
In summary, several motivational variables direct an understanding of motivational
influences on students at the university level. While there is a body of research that addresses
pedagogical outcomes by delivery method, less inquiry has been made into the social
psychological forces that determine the level of student motivation by classroom environment or
climate. The motivational variables addressed in this study will be goal orientation, self-efficacy
and belongingness as defined and discussed in this section. Potential relevant social
psychological influences may include belongingness, as defined by peer network structures and
class size. The dissertation study at hand will examine to what extent these elements will
potentially differ by learning environments, either by distance education or face-to-face settings,
and whether or not differences will influence student levels of self-efficacy and goal orientation.
Student Motivation in Online Pre-service Teacher Education Programs
Prior research on the topic of motivation and online pre-service teacher education
programs is limited. However, the ever increasing accessibility of technology has recently
allowed prospective teachers viable options in pre-service distance education that were
previously unavailable to them. Whether online or in-person, teacher credential candidates who
completed their respective programs equipped with a high sense of self-efficacy for their chosen
profession and strong sense of belonging and connection to fellow teachers were more likely to
be successful teachers on several levels. Some of the motivational variables discussed in
previous sections, namely self-efficacy and student sense of belonging, were identified as
important elements to consider in teacher education programs.

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Professional Self-Efficacy Beliefs in Teacher Education
Much of the literature that exists on the topic of motivational influences on pre-service
teacher education programs centers around the topic of teacher self-efficacy in a traditional, in-
person learning environment. The few articles around self-efficacy and teacher education in an
online setting focus on technological self-efficacy of pre-service teachers in online credentialing
programs (Worch, Li, and & Herman, 2012). Therefore, the literature review on self-efficacy
beliefs in teacher education programs will be limited to programs in a traditional, in-person
setting.
Erdem and Demirel (2007) developed and administered a survey studying teacher self-
efficacy among 346 student teachers at a university in Ankara, Turkey in the spring term of the
2003-2004 academic year. Their study built upon the previous findings of Woolfolk (1998)
which stated that teachers with a high sense of self efficacy displayed motivation on the index of
persistence when they encountered difficulty in the classroom, due in part because these teachers
were confident in their abilities as well as those of their students. In their study, Erdem and
Demiral (2007) incorporated the Teachers’ Sense of Self-Efficacy Scale, an instrument
developed by Hoy and Woolfolk (1990) to measure the level of self-efficacy in teachers along
three factors: efficacy for student engagement, efficacy for instructional strategies, and efficacy
for classroom management. While their study proved to be a valid and reliable indicator of self-
efficacy levels in pre-service teachers, it was limited in that it did not provide longitudinal data
that indicated whether or not the level of efficacy developed in the program carried into their first
year as novice teachers.
However, there were studies that addressed the impact that teachers with high self-
efficacy had on their students’ academic achievement and motivation. Caprara, Barbaranelli,

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Steca and Malone (2006) administered a self-report survey to 75 high school teachers in Italy to
assess their self-efficacy levels. They also collected the students’ average final grades. Results
from their study revealed that teachers’ levels of self-efficacy affected their job satisfaction as
well as their students’ performance. The importance of the purposeful cultivation of a high sense
of teacher self-efficacy was made apparent through this study, because it identified self-efficacy
as the latent independent variable that was able to predict a teacher’s success in the classroom. It
also corroborated previous well-established studies around teacher self-efficacy, which stated
that teacher efficacy beliefs were associated with teachers’ willingness to devote more time to
academic instruction and persist in efforts to educate students with learning difficulties (Dembo
& Gibson, 1985).
Oh (2014) recently published a quantitative study that identified sources of pre-service
teachers’ self-efficacy during reading and writing lessons. Her focus was in the area of teacher
efficacy that had been conducted on pre-service teachers. She administered the Teachers’ Sense
of Self-Efficacy Scale, developed by Hoy and Woolfok (1990) as a pre- and post-test. While 43
participants responded to the pre-test, only 14 participants responded to the post-test. Her
results, in the form of a multiple regression analysis, indicated that personality characteristics,
enactive mastery experiences with social/verbal persuasion, and motivation were significant
predictors of high efficacy for classroom management. These findings corroborate with earlier
research along a similar vein conducted by Poulou (2007), who indicated in-service teachers’
personality characteristics, capabilities, and motivation as potential sources of teacher self-
efficacy.

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Prior research indicates that teacher burnout is less likely to occur in teachers who exhibit
higher levels of self-efficacy. This may be due in part to higher levels of job satisfaction
experienced by these particular individuals (Caprara, Barbaranelli, Steca & Malone, 2006).
Sense of Belonging in Teacher Preparation Programs
Kopcha and Alger (2013), along with the National Council for Accreditation of Teacher
Education’s Blue Ribbon Panel (Blue Ribbon Panel, 2010), provided the recommendation that
teacher preparation programs should include opportunities for pre-service credential candidates
to connect with a larger community of learners through the use of technology. Their suggestion,
in conjunction with the research on the importance of student sense of belonging in an
educational setting (Baumeister & Leary, 1995; Hoyle & Crawford, 1994; Ryan & Stiller, 1991;
Weiner, 1990), indicated that a teacher candidate’s sense of connectedness and support was an
important motivational factor to consider in evaluating the effectiveness of the teacher education
program as a whole.
Hong (2010) made an observation in his study of new teachers as they completed their
pre-service program and entered their classrooms; he noted the importance of teacher community
and collaboration in their experience. In his mixed methods study of 84 pre-service participants,
several individuals were interviewed as they entered their first year of teaching. He found that
most of the novice teachers who decided to leave the teaching profession did so due to
“emotional burnout,” and one participant stated in her interview that “the relationship with
[other] teachers actually kept [her] there longer than [she] wanted to stay, but it make [her] stay
longer (Hong, 2010, p. 1530). Other individuals indicated that they felt it was important to
cultivate strong relationships with co-teachers, for guidance as well as a means to reflect upon
the formation of their own professional identity.

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In summary, research that investigates motivational constructs, such as self-efficacy and
student sense of belonging, in online pre-service teacher education is extremely limited.
However, this topic is increasingly relevant as distance education has become more accessible
through recent technological advances.
Conclusion
Goal orientation, learner self-efficacy, and belongingness are three constructs that
indicate the level of student motivation. The varying presence of these elements may account for
differences in levels of motivation between two different delivery methods of a graduate level
program – face-to-face delivery and online delivery. Of the three variables, the least amount of
literature exists on the affects of belongingness. Berndt (1999), Ryan (2000), and Nelson and
DeBacker (2008) provided some insight regarding the relationship between belongingness and
goal orientation in a face-to face setting. However, these studies were around a primary and
secondary school population, and limited data currently exists regarding this topic in a university
setting.
Feelings of belongingness may predict the level of student self-efficacy for future
profession and academic goal orientation in a graduate level pre-service teacher preparation
program that has two cohorts running simultaneously using different delivery methods -- online
and face-to-face. Belongingness has been shown to influence goal orientation and self-efficacy
(Nelson & DeBacker, 2008), so possible differences in student self-efficacy and goal orientation
may be associated with the differences in feelings of belongingness by delivery method. Goal
orientation and self-efficacy have been proven to impact student motivation. There are obvious
environmental differences inherent in the face-to-face and distance education delivery of a
graduate level pre-service teacher preparation program, such as peer network structures and class

46

size, and these differences may be found to correlate to differences in student motivational
outcomes by delivery methods. Further examination of these motivational variables may
potentially determine possible discrepancies in student motivation if and where they exist.

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CHAPTER 3
METHODOLOGY
The purpose of this study was to conduct an investigation of social cognitive influences
on motivational variables in a graduate program with comparable course work conducted via
both an online and a physical classroom environment in a parallel fashion. These variables were
examined at Western University, a top tier not-for-profit research university, in a graduate level
pre-service teacher preparation program. This program was facilitated in partnership with a for-
profit online development company, where the course curricular design and sequence were
deliberately constructed to be the same regardless of program delivery method. The initial area
of focus of this study was to determine whether student levels of belongingness, self-efficacy,
and goal orientation in a traditional face-to-face classroom setting were statistically different than
in an online, distance education environment. The second focus of this study was to examine
whether or not a connection could be made between the level of a student’s self-efficacy or goal
orientation based upon a measurement of the individual’s feelings of belonging; to ascertain
whether or not a relationship existed between belongingness and motivation, evidenced by the
presence of self-efficacy and the nature of goal orientation; and also to assess the strength of this
relationship where it existed. The final area of focus was to determine the degree to which social
psychological factors such as class networking activities and class size played moderating roles
in student feelings of belonging. The goal of this research was to provide insights into potential
differences in student levels of motivation along a social cognitive framework.
This chapter will present the research questions that will lend focus to this investigation
and also provide details regarding the specific methodology implemented. Details addressed will
include sampling procedures and specific population characteristics, as well as an overview of

48

data collection instruments and data collection procedures. Also included will be a
comprehensive description and rationale for statistical analyses applied in this study.
Research Questions
The following research questions guided this study:
1. Is there a difference in student sense of belonging, self-efficacy, and goal orientation by
program delivery method?
2. Controlling for program delivery method, do feelings of belonging predict self-efficacy
or goal orientation?
3. Do out of class networking activities predict student feelings of belonging?
Research Design
For this study, a non-experimental design and quantitative approach were applied to
assess correlational relationships between student sense of belonging, self-efficacy and goal
orientation to distance education and face-to-face educational settings. A survey was
administered to students during the second semester of the masters program in teaching that was
examined. There were a total of 55 items, composed of 13 demographic items, 12 items
measuring self-efficacy, 16 items measuring belongingness, and 14 items measuring goal
orientation. The scales integrated into the survey will be described in greater detail in a
subsequent section of this chapter.
The same survey was offered to all participants, regardless of their matriculation in a
face-to-face or distance education setting. With instructor consent, paper surveys were
administered in person by the researcher for willing participants in the classroom setting, in
foundational courses that were required for program completion. In the distance education
setting, instructors of the same requisite course were asked to administer the survey to their

49

students via the Qualtrics survey interface. Survey administration took place during the first or
last ten minutes of class, and surveys were distributed by the course professor or the student
researcher, who informed students that participation was not compulsory but completely
voluntary and anonymous.
Population and Sample
A total of 240 participants from both the online delivery method group and the face-to-
face delivery method group completed the survey that was administered for this study. This
sample was obtained from a graduate program at a not-for-profit research university located in
the Los Angeles, California. One group examined was composed of students who were enrolled
in a traditional face-to-face version of the graduate level pre-service teacher preparation program
at Western University. The other group consisted of students who were enrolled in the same
program, and engaged in the same coursework, but via a synchronous distance education
medium that incorporated visual, audio, verbal and collaborative components. Class sizes in the
brick-and-mortar classroom setting were approximately 30 students per instructor, while class
sizes in the latter, online setting were limited to 20 students per instructor.
The total number of participants (N= 240) far exceeded the researcher’s expectations.
Participation was strictly voluntary, and students had already self-selected into groups by
delivery method at the time of the study. Of the 240 participants, 50 participants were enrolled
in the on-campus cohort (n=50) and 190 participants were in the online cohort (n=190).
Participant demographic information, such as sex, age, marital status, and employment status,
was collected in the metric administered and will be described at length in the following chapter
of this dissertation.

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There was a notable disparity in sample sizes of the participants who were enrolled in the
on-campus cohort and those who were enrolled in the online cohort of the program examine in
this study; 140 more online students completed survey responses than did students who
undertook coursework in a traditional brick-and-mortar learning space. The reason for such
disproportionate group sizes was due primarily to the skewed enrollment numbers in the online
cohort, compared to the on-campus cohort. During the time that this study was conducted, there
were approximately 1,200 students enrolled in the graduate level teacher preparation program
and of those candidates, roughly 70 students were online. Of approximately 1,130 potential
participants from the online cohort, 190 completed the survey, and the survey response rate was
roughly 17% of the online cohort population. In comparison, 50 of approximately 70 students
from the face-to-face cohort completed the survey, or roughly 71% of the traditional on-campus
cohort population.
Ideally, most research designs would examine groups with an even number of subjects in
each sample. However, participant response rates in differing groups are highly likely to vary in
practice, especially in instances where group populations are naturally skewed as in the case of
this particular dissertation study. Before technological advances in statistical analysis, the
traditional approach to minimizing the issue of unequal group sizes would have been to
randomly select 50 survey results to examine from the 190 responses from the online cohort, thus
creating even sample groups. However, the use predictive analytics software such as SPSS
automatically accounts for and adjusts for unequal sample sizes in its statistical analyses (French,
Macedo, Poulsen, Waterson & Yu, 2006; Grace-Martin, 2014; Keppel & Wickens, 2004).
Therefore, the researcher in this study was able to incorporate survey feedback received from all
240 participants with the use of SPSS software.

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The proposed research questions were answered by taking a look at responses to a survey
distributed to students enrolled in the graduate level pre-service teacher preparation program.
Students were sampled from two distinct campus settings. The smaller sample was obtained from
a campus-based site centrally located in a large urban city in Southern California (n=50).
Students in this particular sample were enrolled in the traditional face-to-face program, where
they were able to meet directly with their professors and classmates with regularity. Data from
this face-to-face program was compared to data collected from students enrolled in the distance
education program (n=190), an online, synchronous master of arts in teaching program with a
comparable course of instruction to the traditional brick-and-mortar program option. The same
courses were offered to these students with many of the same professors leading these courses as
in the on-campus course options.
Instrumentation
Following is a detailed description of the development and statistical support for each of
the instruments that were used, as well as the demographic information that was requested of
each participant.
Demographic Information
At the beginning of the survey, participants were asked to complete 13 demographic
items, which were provided in multiple-choice or fill-in-the-blank format. They were asked to
give information on their previous experience with online education programs, their employment
status at the time the survey was administered, and their city and state of residence at the time the
survey was administered. Also, they were asked for information regarding their gender, ethnicity,
and the type of program delivery method they chose (distance education or face-to-face). See
Appendix A for more details.

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Information about Participants’ Networking Activities
Several questions in the survey were included to gain a sense of the types of networking
activities in which participants engaged, as well as the frequency with which they participated in
these activities. The questions inquired about students’ campus group affiliations, frequency of
networking as facilitated by class environment (such as “Wall” access for online students and
lunch time or pre/post-class social and networking activities for traditional on-campus students),
and the different social or academic network media used to connect to others in the program.
See Appendix A for more details.
Ohio State Teacher Efficacy Scale
This scale was developed by Tschannen-Moran, Woolfolk Hoy, Fox, Gaskill, Lee,
Maurer, Patel, Raymond, Risko and Yu, two professors and eight graduate students at Ohio State
University (Tschannen-Moran & Woolfok Hoy, 2001). The group who developed the Ohio State
Teacher Efficacy Scale individually and collectively selected items from the teacher efficacy
scale developed by Bandura (undated) and generated items that they felt were not adequately
reflected on the scale. A 9-point Likert format was used for each of the items generated. In a
study with a sample of 410 participants, the Cronbach’s alphas for all three factors indicated high
reliability for the long form of this scale (α=0.91, α=0.90, and α=0.87 respectively). The
abridged version of this survey also yielded reliable results with strong Cronbach’s alphas (α=86,
α=.86, and α=.81).
The abridged version of the Ohio State Teacher Efficacy Scale consisted of 12 items and
measured the level of teacher efficacy along three distinct factors: efficacy for instructional
strategies, efficacy for classroom management, and efficacy for student engagement. Sample
items taken from the abridged version of the OSTES metric used for this dissertation study

53

included questions such as, “To what extent do you use a variety of assessment strategies?” to
measure efficacy for instructional strategies; “How much can you do to control disruptive
behavior in the classroom?” to measure efficacy for classroom management, and “How much
can you do to get students to believe they can do well in schoolwork?” to measure efficacy for
student engagement. See Appendix B for more details.
In this dissertation study, the abridged version of the OSTES was used to measure
participants’ levels of self-efficacy for their intended profession as educators in a classroom
setting. The Cronbach’s alpha for the sub-factor of efficacy for instructional strategies was high
(α=.86), the Cronbach’s alpha for the sub-factor of efficacy for classroom management was also
high (α=.86), and the Cronbach’s alpha for the sub-factor of efficacy for student engagement was
similarly respectable (α=.81), according to the original study conducted by Tschannen-Moran &
Woolfok Hoy (2001). The Cronbach’s alphas yielded for this dissertation study for the 240
participants were high in self-efficacy for instructional strategies (α=.81), high in self-efficacy
for classroom management (α=.85), and also high in self efficacy for student engagement
(α=.84). Consistent with previous studies, all three factors indicated high reliability for
participants’ levels of self-efficacy for teaching along the three sub-factors outlined.
Belongingness Scale
The measurement of student sense of belonging was taken by using items from the first
two factors of the Confirmatory Four-Factor Model, developed by Summers, Beretvas, Svinicki
and Gorin (2005). A 6-point Likert format was used for each of the items generated in their
Belongingness Scale. In a study with a sample of 207 participants (N=207), the two factors
included in this study were found to be highly reliable (Summers et al., 2005). The first factor,
Social Connectedness, had 15 items, and a Cronbach’s alpha of 0.90 (α=.91). It was used to

54

measure a student’s perceived feelings of belonging to the university. The second factor,
Classroom Community, was composed of 4 items with a Cronbach’s alpha of 0.82 (α=.82) and it
was used to measure a student’s sense of belonging within a specific classroom dynamic.
The distinct factor loading by university and classroom sense of community most
contributed to the decision to include this scale in this particular dissertation study over other
current belongingness scales, such as the Psychological Sense of School Membership developed
by Goodenow (1993) and adapted by Freeman, Anderman and Jensen (2007). Since the
theoretical discussion of this construct in previous chapters’ identified group as a potential link to
motivation, a more nuanced examination of classroom versus campus-affiliated feelings of
belonging was conducted to compare the two program delivery types. Sample statements that
gauged participants’ sense of belonging to the university included sentences like “There are
people on campus with whom I feel a close bond,” and also some sentences that had to be
reverse coded after the data collection process like, “I feel disconnected from campus life.” Items
that measured participants’ sense of belonging to their classroom community included statements
like “I feel connected to people in this class.” See Appendix C for more details.
In this dissertation study, the Cronbach’s alpha for participants’ perceived feelings of
belonging to the university, the first factor measured indicated a strong level of internal
consistency for this scale with this specific sample (α=.91). The Cronbach’s alpha for these
same participants’ perceived feelings of belonging to their classroom community, whether it was
in a traditional brick-and-mortar classroom or an online distance learning classroom, also
indicated that this sub-factor was highly reliable (α=.83). Both of these results were in alignment
with the original study, in that both alphas indicate a high level of internal consistency among the
sample participants.

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Goal Orientation Scale
In this dissertation study, goal orientation was measured using the Patterns of Adaptive
Learning Scale (Midgley, Maehr, Hruda, Anderman, Anderman, Freeman, Gheen, Kaplan,
Kumar, Middleton, Nelson, Roeser, & Urdan, 2000) to determine participants’ personal
achievement goal orientations in their respective academic settings. This scale was used in its
abridged version of 14 items, as compared to the original full length version of 17 items designed
by Midgley, Kaplan, Middleton, Maehr, Urdan, Anderman, Anderman and Roeser (1998). The
rationale behind this choice was to prevent testing fatigue in participants in order to potentially
yield more surveys that were complete in order to collect valid data.
On the short version of the PALS conducted in the original study, the Cronbach’s alpha
for 5 items on mastery goal orientation was high (α=0.85). The 5 items yielded a high
Cronbach’s alpha for performance-approach goal orientation (α=.89). Finally, the Cronbach’s
alpha for the 4 items that make up performance-avoidance goal orientation was high (α= 0.74).
There were 14 items in total for this construct. The mastery goal orientation portion of this scale
included statements like, “It’s important to me that I learn a lot of new concepts this year,” and
students were asked to agree of disagree with this statement in a Likert scale response. Samples
for performance approach goal orientation included statements like, “It’s important to me that
other students in my class think I am good at my classwork. Samples for performance avoidance
goal orientation included statements like, “It’s important that I don’t look stupid in class.” See
Appendix D for more details.
In this dissertation study, the Cronbach’s alpha for the sub-factor that measured mastery
goal orientation was high (α=.85). The Cronbach’s alpha for the sub-factor that measured
performance approach goal orientation was also high (α=.89). The Cronbach’s alpha for the sub-

56

factor that measured performance avoidance goal orientation was high as well (α=.82). As in the
original study, the alphas for all three types of goal orientation measured were high, indicating a
strong level of reliability.
Data Collection
Before collecting data for analysis, the researcher received Institutional Review Board
Approval from Western University for the Protection of Research Subjects. Data collection took
place in the second semester of participants’ enrollment in the graduate level pre-service teacher
preparation program, regardless of whether coursework was completed online or on campus.
The data collection process happened within a two-week timeframe beginning within the month
of October. Surveys were distributed in EDUC 501, a required methodology course on
instructional practices for teaching English as a new language to linguistic minority students, for
both online and face-to-face cohorts, because it was one of the few classes required by all
students regardless of credential track. In this way the sample of data collected was not skewed
toward a particular credential track—single subject, multiple subject, elective, or non-credential.
Distance Education students were provided a Qualtrics link in order to complete the survey, and
students who were engaged in a face-to-face delivery method were asked to fill out a paper copy
of the survey. All survey responses were voluntary and anonymous.
Data Analysis
All data was quantitative, coded, and prepared for computerized analysis via the
Statistical Package for Social Sciences (SPSS) program. The Cronbach’s alphas were computed
for each of the construct scales and sub-factors to confirm reliability of each measure for this
study. Basic descriptive statistics were determined for all nominal and ordinal demographic
data, such as gender, age, ethnicity and employment status. Then factor analyses were

57

performed in each of the construct scales described above in the section entitled,
“Instrumentation.”
This was a quantitative study to address the research questions stated in a prior section of
this chapter. The first question was analyzed through the use of a MANOVA. Then a series of
separate single regressions were performed to determine an answer for the second research
question. The last question was addressed through linear regressions to determine if out-of-class
networking activities, such as those suggested in survey items 9-13, predict student feelings of
belonging.
For the first research question, the independent variable was the method of instructional
delivery at the program level. Instructional delivery was facilitated through either an online
interface that was conducive to learning in a synchronous collaborative environment of up to
twenty students to a faculty member or a traditional face-to-face environment where students
come to campus to attend classes of approximately thirty students to one instructor. The
dependent variables for the first question were the motivational indicators in the form of
belongingness, self-efficacy, and goal orientation. For the second research question, the
independent variable was the level of belongingness experienced by students and the dependent
variables were self-efficacy and goal orientation. For the third research question, the
independent variables were the classroom environmental factors such as class size, campus
group affiliation, the specific type(s) of peer network media used, and degree to which students
engaged in out of class networking activities as facilitated by their respective coursework setting.
The dependent variable was the level of belongingness experienced by students.

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CHAPTER FOUR
RESULTS
This study examined social cognitive factors related to motivation variables, to the extent
that they were present in two distinct delivery methods in a pre-service teacher education
program. The research questions developed were based on three specific constructs: student
feelings of belonging to the program, career self-efficacy, and goal orientation. Specifically, this
study was designed to answer the following research questions:
1. Is there a difference in student sense of belonging, self-efficacy, or goal orientation by
method of program delivery?
2. Controlling for program delivery method, do feelings of belonging predict self-efficacy
or goal-orientation?
3. Do out of class networking activities predict student feelings of belonging?
These research questions were answered by a 55-item metric composed of three survey
instruments and several demographic questions. The survey instruments were used to measure
the constructs of teacher self-efficacy (OSTES, Tschannen-Moran & Woolfok Hoy, 2001), sense
of belonging to campus and classroom communities (adapted Social Connectedness &
Classroom Community Scales, Summers et al., 2005), and goal orientation (PALS, Midgley et
al., 2000). Surveys were distributed in hard copy to on-campus Masters candidates of a teacher
education program at Western University, and also on a proprietary online platform that
facilitated a Distance Education virtual campus. Respondents totaled N=240, with 190
participants from the online program (n=190) and 50 from the traditional in-class learning
environment (n=50).
This chapter will provide information regarding the findings of this study. The first section

59

will outline the descriptive characteristics of survey participants, including demographic data,
information regarding their experience in their chosen program environment, and length of time
in the program. The section that follows will contain various statistical analyses of these results
organized by research question.
Demographic Information
Several nonparametric analyses were performed on the demographic information that was
gathered in this survey. Pearson’s Chi-square test revealed significant results with regard the type
of program, traditional or online, as cross-tabulated with participant age, marital status,
employment status, and number of dependents. The majority of participants enrolled in the
graduate level pre-service teacher preparation program, regardless of program delivery method,
reported being in the 24-30 age range. However, significant differences in participant age were
reported between groups. A larger percentage of participants age 41-60 were enrolled in the
online setting (n= 51, 27%) than in the face-to- face program (n=1, .02%). Conversely, a larger
percentage of students ages 18-23 were enrolled in the face-to-face program (n=15, 31%) as
compared to students in the online program (n=22, 12%). See Table 1 for information on
participant demographics by age.
Table 1
Participant demographics: Age
Instructional Delivery Method
Age Online % Traditional % Total %
18-23 22 12 15 31.3 37 15.5
24-30 70 37 24 50 94 39.4
31-35 23 12 5 10.4 28 11.7
36-40 21 11 3 6.2 24 10
41-50 33 17 0 0 33 14
51-60 18 10 1 2 19 8
61+ 3 1 0 0 3 1
Total 190 48 238

Other demographic differences came to light through Pearson’s Chi-square analysis with

60

regard to marital status. Fifty percent (n=95) of students enrolled in the online program were
married or in a domestic partnership at the time that they took this survey as compared to 14%
(n=7) enrolled in the traditional face-to-face program. See Table 2 for information on participant
demographics by marital status.
Table 2
Participant demographics: Marital status
Instructional Delivery Method
Marital Status Online % Traditional % Total %
Single
(never married)
79 41.5 43 86 122 50.8
Married

95 50 7 14 102 42.5
Divorced

12 6.3 0 0 12 5
Separated

3 1.5 0 0 3 1.25
Widowed 1 .5 0 0 1 .4
Total 190 50 240

Another reported difference between students in the online versus the traditional face-to-
face programs was related to the number of dependents claimed. The percentage of students in
the online program who claimed one or more dependents was 43% (n=82). In contrast, only 12%
(n=6) of students enrolled in the face-to-face program claimed one or more dependents. See
Table 3 for information on participant demographics by number of dependents in household.
Table 3
Participant demographics: Number of Dependents
Instructional Delivery Method
No. Dependents Online % Traditional % Total %
0 108 56.8 44 88 152 63
1 27 14.2 4 8 31 13
2 32 16.8 2 4 34 14
3 10 5.2 0 0 10 4.2
4+ 13 6.8 0 0 13 5.4
Total 190 50 240

The Chi-square test also showed significance with regard to employment status. Twenty-

61

seven percent (n=52) of students surveyed in the online program reported working full time,
compared to 6% (n=3) of those enrolled in the traditional face-to-face program. See Table 4 for
information on participant demographics by employment status.
Table 4
Participant demographics: Employment status
Instructional Delivery Method
Employment
Status

Online

%

Traditional

%

Total

%
Full Time
(30+ hrs/wk)
52 27.4 3 6 55 23
Part Time
(<30 hrs/wk)
54 28.4 12 24 66 27.5
Unemployed 84 44.2 35 70 119 49.6
Total 190 50 240

In summary, this survey revealed that participants enrolled in the online version of the
graduate level pre-service teacher preparation program were significantly more likely to be older,
employed, married, and with one or more dependents in their households compared to those
enrolled in the on-campus program.
Analysis of Results
Survey data collected via the self-report instruments described at length in the previous
chapter was analyzed in order to address the research questions posed at the outset of this study.
The quantitative findings presented in this chapter will be organized by these three research
questions.
Research Question 1: Is there a difference in feelings of belonging, self-efficacy, or
goal orientation by method of program delivery? This research question sought to investigate
potential differences in select social cognitive factors between program delivery methods. The
two independent variables, namely the online and face-to-face program delivery methods, were
compared across three latent constructs: sense of belonging, self-efficacy and goal orientation.

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See Table 5 below for details regarding the MANOVA results research question 1.
Table 5
MANOVA Results for Research Question 1
Main Effect Wilks’ Lambda F df P Eta2
Method of Program Delivery .980 .776 6.0 .589* .020
*p<.05

No significant differences were found between groups in face-to-face and online program
delivery methods in student sense of belonging, student sense of efficacy for the profession of
teaching, and student’s goal orientation while in the program. These results suggest that there are
no differences in the three identified social cognitive factors for this study between on-campus
and distance education learning contexts. The method of program delivery did not significantly
impact students’ sense of belonging, efficacy for the profession, or goal orientation in the
graduate level pre-service teacher preparation program examined at Western University.
Research Question 2: Controlling for program delivery method, do feelings of
belonging predict self-efficacy or goal orientation? The second research question posed in this
study investigated the existence of a predictive relationship between constructs. Specifically, the
question attempted to determine whether student feelings of belonging to the program and to the
class predicted student self-efficacy for the teaching profession and goal orientation while in the
program.
Belonging as a predictor of self-efficacy. The first linear regression performed
determined that the degree to which students sensed they belonged in a program was a
significant predictor of student self-efficacy for his or her profession. Specifically, 12.8% of the
variance in participants’ self-efficacy could be explained by the level of that individual’s feelings
of belonging. In other words, feelings of belonging partially predicted the level of a student’s
professional self-efficacy in this graduate program. This result supports the relevance of
belonging as a construct with regard to its predictive influence on other motivational factors at

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play in a student’s success in a graduate level pre-service teacher preparation program. See Table
6 for more specific details regarding the linear regression of belonging as a predictor of self-
efficacy.
Table 6
Linear regression of belonging as a predictor of self-efficacy

Model Unstandardized
Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
1
(Constant) 3.547 .130 27.214 .000
Belonging Mean .173 .029 .358 5.913 .000

a. Dependent Variable: Self Efficacy Mean

ˆ A Pearson’s correlation analysis was performed to further determine the relationship
between participants’ sense of belonging to the program and their self-efficacy for the teaching
profession. There was a moderate, positive correlation between belonging and self-efficacy
(r=.358, N=240, p<.01). See Table 7 for details regarding the correlation between belonging and
self-efficacy.
Table 7
Correlation between belonging and self-efficacy
BEL_MEAN SE_MEAN
BEL_MEAN Pearson Correlation 1 .358
**

Sig. (1-tailed) .000
N 240 240
SE_MEAN Pearson Correlation .358
**
1
Sig. (1-tailed) .000

N 240 240
**. Correlation is significant at the p< 0.01 level (1-tailed).

Belonging as a predictor of learning goal orientation. An initial linear regression conducted in
order to examine the predictive value of student feelings of belonging on their learning goal
orientation as a global construct did not reveal statistically significant results. See Table 8 below

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for details regarding the linear regression of belonging as a predictor of goal orientation, as a
global construct.
Table 8
Linear regression of belonging as a predictor of goal orientation (global construct)
Model Unstandardized
Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
1
(Constant) 2.817 .202 13.943 .000
Belonging Mean .021 .045 .030 .458 .647

a. Dependent Variable: Goal Orientation Mean

While student sense of belonging seemed to have no predictive value on the goal
orientation, further observation of the sub-factors within the larger construct of goal orientation,
namely that of mastery goal orientation, offered a more nuanced perspective with statistically
significant findings.
Belonging as a predictor of mastery goal orientation. A second linear regression was performed
in order to determine whether feelings of belonging predicted mastery goal orientation. This
exercise yielded limited statistical significance in the analysis of this relationship. Feelings of
belonging were found to be weakly predictive; an individual’s sense of belonging 5.4% of the
variance in the level of his or her mastery goal orientation. See Table 9 for details regarding the
linear regression of belonging as a predictor of mastery goal orientation.
Table 9
Linear regression of belonging as a predictor of mastery goal orientation

Unstandardized
Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
(Constant) 1.920 .159 12.103 .000
Belonging Mean -.131 .036 -.232 -3.674 .000
a. Dependent Variable: Mastery Orientation Mean

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Belonging as a predictor of performance approach and performance avoidance goal
orientations. Subsequent linear regressions were conducted to determine whether feelings of
belonging predicted either performance approach or performance avoidance goal orientations.
No significance was found for these relationships. In other words, there was no evidence from
this study to support the idea that feelings of belonging to an academic community were able to
predict student performance approach goal orientation, nor was it predictive of performance
avoidance perspectives or behaviors. See Tables 10 and 11 for more details regarding the linear
regressions of belonging as a predictor of performance approach goal orientation and as a
predictor of performance avoidance goal orientation.
Table 10
Linear regression of belonging as a predictor of performance approach goal orientation

Unstandardized
Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
(Constant) 3.539 .307 11.514 .000
Belonging Mean .083 .069 .078 1.201 .231
a. Dependent Variable: Performance Approach
Orientation Mean

Table 11
Linear regression of belonging as a predictor of performance avoidance goal orientation

Unstandardized
Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
(Constant) 3.012 .329 9.167 .000
Belonging Mean .136 .074 .119 1.845 .066
a. Dependent Variable: Performance Avoidance
Orientation Mean

Research Question 3: Do self-reported out of class networking activities predict
student feelings of belonging? The final research question examined a series of potential
predictors influencing feelings of belonging. Specifically, this study investigated the predictive

66

value of a student’s perceived frequency of specific class networking activities on his feelings of
belonging. In this study, students were asked to self-report the frequency with which they felt
they were able to participate in class networking activities. Also, participants were asked about
which specific types of formal and casual networking vehicles they utilized, such as professional
or social online networking sites like LinkedIn or Facebook or campus affiliated clubs and
societies, to study prospective predictors of student sense of belonging. See Table 12 for more
details regarding the various linear regressions of networking activities as predictors of student
feelings of belonging in the program.
Table 12
Linear regressions of networking activities as predictors of student feelings of belonging

Unstandardized
Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
(Constant) 3.169 .203 15.596 .000
No. of affinity group
memberships
.070 .045 .104 1.537 .126
Freq. of “Wall” access
(online students only)
-.061 .092 -.075 -.659 .511
Freq. of social
connection w/ peers
.205 .077 .270 2.660 .009
Network: Facebook
.043 .029 .105 1.464 .145
Network: LinkedIn
-.171 .091 -.126 -1.879 .062
Network: Google+
-.061 .041 -.103 -1.480 .141
Network: In-Person
before/after class
-.038 .052 -.089 -.725 .469
Network: In-Person
during breaks
.072 .048 .166 1.511 .133
Network: “Wall”
.129 .043 .254 2.989 .003
Network: Student
facilitated activities
.018 .041 .032 .436 .663
Network: Campus
affiliated groups/clubs
.181 .092 .140 1.973 .050
a. Dependent Variable: Belonging Mean

Frequency of social connection to peers predicted feelings of belonging. Students’ involvement
in their graduate level pre-service teacher preparation program was measured by their perceived
frequency of out-of-class social connections with peers, whether these connections took place in

67

person or online. A linear regression analysis of the frequency of general out-of-class student
connection and a student sense of belonging yielded a statistically significant relationship
between the two factors. In other words, students who felt connected to their academic peers in
this graduate program interacted with more frequency in general using both online and face-to-
face networking methods. See Table 13 for more details regarding the linear regression of social
connection as a predictor of feelings of belonging.
Table 13
Linear regression of social connection as a predictor of feelings of belonging

Unstandardized
Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
(Constant) 3.169 .203 15.596 .000
Frequency of
Social/Peer
Connection
.205 .077 .270 2.660 .009
a. Dependent Variable: Belonging Mean

Use of program “Wall” predicted feelings of belonging. All students in the graduate level pre-
service teacher preparation program were able to use the “Wall,” an online social networking
space exclusively for students enrolled in the program to connect with their peers via messages,
status posts, and notifications. Additionally, students were able to self-organize common-interest
user groups by course, cohort, region, or other characteristics. The results of this study found a
significant positive correlation between the self-reported frequency of “Wall” usage and student
sense of belonging. In other words, students who perceived that they often used the “Wall” as a
vehicle to initiate and maintain contact with their peers felt a higher sense of belonging to the
program. See Table 14 for details regarding the linear regression of social connection as a
predictor of feelings of belonging.

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Table 14
Linear regression of social connection as a predictor of feelings of belonging

Unstandardized
Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
(Constant) 3.169 .203 15.596 .000
Frequency of
“Wall” Access
.181 .092 .254 2.660 .009
a. Dependent Variable: Belonging Mean

Use of campus-affiliated groups and organizations predicted feelings of belonging. The third
statistically significant connection found within the series of regressions performed to answer
this research question was that between student participation in campus affiliated groups and
organization and student feelings of belonging. The number of a given student’s involvement in
campus-affiliated clubs was found to be predictive of that individual’s sense of belonging. In
other words, students who sought out and obtained membership in university- or school-related
organizations, such as honors societies, sports teams, or the school band, felt a higher sense of
connectedness and belonging. See Table 15 for details regarding the linear regression of campus-
related group affiliation as a predictor of feelings of belonging.
Table 15
Linear regression of campus-related group affiliation as a predictor of feelings of
belonging

Unstandardized
Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
(Constant) 3.169 .203 15.596 .000
Campus-
affiliated
Groups/Clubs
.129 .043 .140 1.973 .050
a. Dependent Variable: Belonging Mean

In summary, this chapter identified and described the quantitative statistical findings from
the analysis of data collected in order to answer the research questions of this study. A brief

69

review of the research questions delineated in previous chapters was provided, preceded by an
outline of the procedure implemented to collect data from participants. The initial analysis
conducted and presented in this chapter was a demographic sketch of participants, which was
consistent with information presented in current research literature regarding distance education.
Predictably, students enrolled in the online program were older, married, employed, and with one
or more dependents in their household.
The research questions were then introduced individually, and quantitative analyses of the
results were presented. The first research objective was to determine potential differences in the
two instructional delivery methods examined across the constructs of self-efficacy, feelings of
belonging to the academic community, and goal orientation. No significant differences were
found in the three motivational constructs between the two delivery methods, whether students
were enrolled in the traditional in-class program or the online program. The next research
objective was to determine whether the participant’s self-reported levels of belonging correlated
to their levels of professional self-efficacy or goal orientation. In this sample, there was a
moderate correlational relationship between feelings of belonging and self-efficacy, and there
was a weaker relationship between feelings of belonging and mastery goal orientation. These
findings helped to further clarify the relationship between feelings of belonging and motivational
constructs that facilitate academically successful students in the program and professionally
competent teachers in the classroom. Belonging had some predictive ability on student self-
efficacy for his profession, and to lesser degree predicted mastery goal orientation. The last
research objective was developed to provide practical insight into the program structure by
identifying which specific out-of-class social networking vehicles might enhance student sense
of belonging. The results of this analysis showed a relationship for three factors: frequency of

70

out-of-class contact, use of the “Wall,” and affiliation with campus affinity groups.
Significant results were found for all or part of each research question.
The implications of these results will be discussed in the next chapter. Sections in the
Chapter 5 will carry the statistical observations from this chapter into a discussion about how the
correlational dynamics may inform current program design and possibly guide further studies on
the interplay within and between these motivational constructs and their implied affects on
learner behavior.

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CHAPTER FIVE
DISCUSSION
This section will begin with a brief overview of findings in order to provide context to the
reflective discourse on the nature of student motivation in distance and traditional learning
environments. Differences and similarities in the learners and learning environments for both
online and traditional masters programs will be examined through social cognitive and social
psychological perspectives. This chapter will conclude with implications, limitations, and
potential pathways for future research.
A reasonable prediction at the outset of this study would have been that data would yield
an outcome that revealed some differences in motivational factors along a social cognitive and
social psychological framework by program delivery method, specifically in students’ sense of
belonging and connectedness to instructors, peers, and their respectively different learning
environments (Osterman, 2000). The quality of connectedness perceived by students based on
method of instructional delivery had not been widely investigated. There may have been an
assumption in a disparity between the motivation levels of students who had regular physical
contact with faculty, peers, and campus environments and those who interacted virtually, through
a synchronous learning management system. However, the lack of evidence to support a
statistically significant difference in levels of motivation between students of an online learning
environment and those in a face-to-face setting indicates a need for further analytical
consideration.
While there was no significant difference in student motivation between the two programs
examined, this study did yield statistically significant predictors of motivation for learners in
both programs when student were examined in aggregate. For instance, students’ feelings of

72

belonging to their campus and their program were revealed to be positive predictors of their
sense of teacher self-efficacy. There was also a weak positive correlation between Mastery Goal
Orientation and self-efficacy for the teaching profession.
The research questions developed for this study were built upon well-established
motivational constructs aligned to student persistence in a given educational setting throughout a
program. In order to answer the research questions outlined in this study, quantitative data was
collected from students in both on-campus and distance education environments. The following
sections will provide a discussion based on the results of this study.
Discussion of Demographic Composition of Students by Delivery Method
This study examined certain stable characteristics of its participants to determine any
differences in learner characteristics that may have been present by instructional delivery
method. The rationale behind collecting this kind of data was to further examine the influence of
these factors on student motivation by delivery method, should a statistically significant
demographic disparity between the two groups of learners exist.
Well-established literature on the topic of learner demographics in traditional face-to-face
programs versus distance education programs states that there are demographic distinctions
between students who elect to enroll in distance learning programs and those who choose the
traditional, brick-and-mortar programs, specifically at the undergraduate and graduate levels
(Abdous & Yoshimura, 2010; Clayton, Blumberg & Auld, 2010; Clayton et al., 2010). This
study examined demographic factors such as age, employment, marital status, and number of
dependents in household, and found significant differences between students who were in the
distance education program and those who attended face-to-face courses. In alignment with the
most recent statistical brief published by the U.S. Department of Education, National Center for

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Education Statistics (2011) students in this study who were enrolled in the online program were
generally more likely to be older, employed, and married with dependents.
The consistent disparity in demographic data between onsite and online learners have been
identified by previous researchers (Parsad & Lewis, 2008; NCES, 2011), who state that students
who possess these particular learner characteristics are likely attracted to distance learning
opportunities over traditional face-to-face coursework. Students who are in the process of
developing and maintaining families and careers are more likely to choose distance learning
options over campus-based programs because of the greater likelihood that they will be able to
fit coursework delivered in this medium into their already busy schedules (Donavant, 2009;
NCES 2011).
With the ever-increasing opportunity for such students to enroll in online programs and
courses is also the concern around the student attrition rate in these programs. The specific
demographic learner characteristics, such as relationship status, career status, or number of
dependents, that may lead a student to choose the online option over one that is facilitated onsite
also puts him at risk of dropping out without completing his educational objective (Donavant,
2009; Tallent-Runnels et al., 2006). Increased responsibilities in different areas of their lives,
paired with the finding that online students were less mastery goal oriented than their in-class
learner counterparts (Clayton et al., 2010), may explain the higher rate of attrition in distance
learners (Allen et al., 2004; Bernard et al., 2004; Clayton et al., 2010; Lou et al., 2006; Parsad &
Lewis, 2008). Findings from this dissertation study align to those from previous studies about
demographic characteristics (NCES, 2001; Tinto, 1993); factors such as age, marital status, and
employment status, have again been proven to be significantly correlated to a lack of academic
persistence in students, independent of instructional delivery method. A closer look at the

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motivation of students in this study may provide insight into specific motivational scaffolds that
are built into the existing curricular structure of this program, derived from social cognitive
learning theory, that support and cultivate student learning and motivation in a distance
education setting.
Discussion of Student Motivation Across Instructional Delivery Platforms
The examination of differences in student motivation across delivery methods in this study
was conducted with a social cognitive focus. According to Bandura (1986), motivational
components that impact the learning experience, regardless of instructional delivery method,
interact within a dynamic of triadic reciprocity between a learner’s cognitive processes, his
behavior within the learning task, and his learning environment. Ford (1992) further posited a
three-way multiplicative relationship in a learner’s social cognitive process – between goals,
agency beliefs, and emotions. The three social cognitive constructs identified in this study were
self-efficacy, goal orientation, and student sense of belonging.
Self-Efficacy. The results of this study yielded no significant differences in students’
perceptions of self-efficacy for the teaching profession across instructional delivery platforms,
which is consistent of findings in previous studies (Clayton et al., 2010; Russell, 2001; Zhao et
al., 2005). In these studies, participants’ motivational beliefs, including those of self-efficacy,
were found to correlate on a weak level in both traditional in-class settings and online learning
settings. Literature on this topic largely indicated that motivational beliefs and learning strategies
influenced the degree to which students were successful in their programs, irrespective of
delivery methods. In other words, students’ self-efficacy beliefs around their ability to teach
students effectively upon obtaining their degree and credential were not affected by the delivery
method of their pre-service teacher education program. Regardless of whether they participated

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in an online setting or learned in an in-person environment with their peers and instructors,
students felt the same level of efficacy for the program content.
Goal Orientation. This study found no significant differences in goal orientation by
program delivery method. This result was inconsistent with findings from a previous study that
stated that students who opted to take courses online exhibited performance-oriented goals while
those who preferred onsite programs and coursework were mastery goal oriented (Clayton et. al.,
2010). Discussion around the results of the study conducted by Clayton et al. (2010) took into
account that the difference in student goal orientation may likely be explained by the
participants’ ability to self-select into a program by delivery method. Their study indicated that
the students who self-selected into in-person, face-to-face learning environments were more
likely to be mastery goal oriented than their peers who chose to take classes in an online setting.
Both goal orientation and professional efficacy beliefs among students in this dissertation
study were the same across both instructional delivery methods. These results were consistent
with pervious studies on self-efficacy in distance education (Clayton et al., 2010; Russell, 2001;
Zhao et al., 2005) but different from a previous study conducted on students’ goal orientation in
distance education (Clayton et al., 2010). This outcome was one that was desirable to the
developers of this particular program, and one explanation may be found in the connection
between goal orientation and domain-specific self-efficacy in a distance education setting.
Dweck and Leggett (1988) established that students who were mastery goal oriented also
exhibited high self-efficacy beliefs. The results of this study may be explained in this
connection. If student efficacy beliefs were not affected by instructional delivery method, and
efficacy is correlated to goal orientation, then a student’s level of goal orientation should be
similarly independent of instructional delivery method.

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Another possibility is one posited by Klein, Noe and Wang (2006) that the nature of a
domain specific goal orientation in the graduate degree program examined in this dissertation
study influenced student levels of motivation. Participants in this particular graduate program
were admitted because they had already undergone an admissions process, which qualified them
as academically successful candidates. Further, they entered into this program with a specific
academic and career goals in mind, specifically the objectives to obtain a master’s degree in
teaching and a teaching credential through the designated state agency, and to become highly
qualified teachers. Also, this study was conducted relatively early in the program, while students
were in the process of completing their foundational coursework and before they had started the
student teaching assignments. Prior studies on the development of teacher self-efficacy in pre-
service teachers found that students in graduate teacher credentialing programs had high
professional efficacy during methodology coursework, then lower self-efficacy at the beginning
stages of the student-teaching process, and finally higher level of efficacy after successful
completion of the student-teaching portion of their coursework (Erdem & Demirel, 2007; Hong,
2010; Kopcha & Alger, 2013). There was the possibility that participants’ sense of efficacy for
teaching was high at the time the survey was distributed, because they took the survey before
encountering practical challenges faced by teachers while actually in the classroom. Perhaps this
common trait, the fact that none had student-teaching experience, among this population of
students was an undetected demographic learner characteristic that mitigated some of the
discrepancies identified in previous studies around goal orientation (Clayton et al., 2010) in
distance education that contradict the findings in this study.
Furthermore, prior studies (Hewitt & Brett, 2007; Klein, Noe & Wang, 2006) examined
learner characteristics and motivation in course environment settings, rather than whole program

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settings. Perhaps the learner characteristics of a cohort of students who were selected to progress
together through a two-year program cultivated domain-specific self-efficacy and academic or
career specific mastery goal orientation.
Sense of Belonging. No significant differences in students’ perception of belonging were
reported across delivery methods in this study. Students enrolled in the online program reported
a sense of belonging that was equivalent to students who were enrolled in the face-to-face
version of the same program. This outcome was clearly desirable, and an explanation for this
result may lie in a deeper understanding of some of the structural elements of the learning
management system, used by both onsite and online students, that encourage and facilitate
community, networking, and relationship-building opportunities among students. The online
communication platform was accessible to students who attended classes in a traditional in-class
setting, as well as those who took their classes online. Students had the option of initiating
discussions and creating interest groups in an autonomous fashion; in this sense, personal and
networking connections with peers were authentically made. Though there were two distinct
methods by which curriculum was delivered, the dominant forum for the program’s student body
was not segregated into these distinct groups. Therefore students’ sense of allegiance and
belonging was to the program as a whole and not to their classroom environment, whether it was
brick-and-mortar or online.
Findings from this study aligned to the long-standing argument posited by Locksley, Ortiz
and Hepburn (1980), that a student’s feelings of belonging, referred to in their study as “inferred
similarity,” (p. 782) should not necessarily be influenced by instructional delivery method. The
definition of distance education as it was used at the time of their particular study did not include
online learning through management systems that included synchronous opportunities for

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collaboration. Large scale synchronous distance education was not a practical option for most
students during the time of their study, as technology related to hardware, software and
connectivity were yet to be developed and far less ubiquitous. However, the results of this study,
which examined students’ sense of belonging to a synchronous online master’s degree program,
are consistent with the idea that Locksley, Ortiz and Hepburn proposed over 30 years ago, in the
sense that no significant difference was found in students’ sense of belonging to a program by
the instructional delivery method of courses within that program.
In summary, levels of student motivation did not vary in students who were enrolled in
face-to-face learning environments and those who participated in online learning environments.
The results of this dissertation study echoed the outcomes of previous studies around potential
differences in student motivation by instructional delivery method, which is that varied delivery
methods do not in and of themselves elicit different learning outcomes. Clark (1983) stated that
the use of media, including technology that facilitated distance education, did not necessarily
improve learning in students. Skylar, Higgins, Boone and Jones (2005) and Kekkonen-Moneta
and Moneta (2002) also found through their respective studies that student outcomes are not
affected solely by a change in method of delivery. The conclusion of this study, which is in
alignment with similar well-established studies that came before it, is that effective pedagogy,
with or without the integration of educational technology, influences learning outcomes.
Contributors to Students’ Feelings of Belonging
No significant differences were found between cohorts that conducted their coursework
online and those that took classes in a more traditional brick-and-mortar classroom setting. Data
was collected from students along several class networking activities, and three factors stood out
as statistically significant to a positive sense of belonging to the student body and program.

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Namely, these three elements are the self-reported frequency of out-of-class social connection
with peers, students’ self-reported use of the graduate program’s “Wall” features, and students’
perceived connection with each other through campus-affiliated groups and organizations, and
each of these distinct contributors to students’ feelings of belonging will be discussed in this
section.
Frequency of Out-of-class Social Connection with Peers. In this study, the self-reported
frequency of out-of-class social connection with fellow students in the graduate level pre-service
teacher preparation program promoted a high sense of belonging to the program itself. These
findings make sense, given previous findings that students are able to motivate each other
effectively along the motivational indices of active choice and mental effort (Berndt, 1999;
Ryan, 2000). In other words, students who felt a sense of connection with their peers in an
extracurricular setting may have been able to participate in a dialogue that ultimately enabled
them to purposefully begin the process of fulfilling course and program objectives and exercise
the mental effort to engage with the program to successfully see it through to completion.
A series of specific questions aimed at examining the various potential vehicles through
which this out-of-class connection took place yielded unsubstantial results. Specifically,
students’ self-reported membership and use of several of the more prevalent and well-established
online professional and social networking sites such as Facebook and LinkedIn produced
insignificant connections to student sense of belonging to their peers and connectedness to the
program. Also, in-person contact with fellow students in the form of pre-, mid-, or post- class
socialization or other student facilitated social activities returned statistically insignificant results
as predictors of feelings of belonging among participant of this study.
Two opportunities for out-of-class social connection among the student body were

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available to students and will be discussed below. Specifically, use of the “Wall” feature of the
graduate level pre-service teacher preparation program and membership to campus-affiliated
groups and organizations were identified and statistically significant predictors of student
feelings of belonging to the overall student body and the specific masters program in which they
were enrolled.
Use of program “Wall” to Connect with Peers. Students in this study used their “Wall,”
an online social structure in which members were able to connect to other members through
status posts, private messages, and common-interest groups, to foster a significant sense of
belonging to other students in the program. This space was exclusively for students who were
currently enrolled in the program, and the ability to use the “Wall” was limited to those who
were given a username and password through the program and the university. Within the features
of the “Wall” was a member’s ability to autonomously create common-interest user groups. For
example, students could form groups by course, cohort, or region. They could also could self-
organize into groups around specific subject matter, such as English, math, or history, or around
a credential type, such as single subject of multiple subject credentials. In this way, students were
able to facilitate and participate in discussions around specific topics at their discretion.
Students from both online and in-class cohorts of the masters program had access to the
“Wall,” which served as a vehicle for significant but not in-person discussions and opportunities
to connect personally, academically, and professionally with others in the program. Throughout
the program, students had the option to lead and facilitate conversations in this digital space. The
“Wall” provided students with an opportunity for personal contact, which they could have used
to connect with fellow candidates in the masters program, even when there was not necessarily
an opportunity for direct in-person interactions. This opportunity for peer connection may have

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facilitated a sense of belonging among a perceived rather than physically present student body.
The concept of a “Wall” was not a novel one at the inception of this program. In fact, an
almost identical feature already existed on several widely established online networking sites,
including Facebook and LinkedIn. Hypothetically, there was nothing to stop a student from
creating similar common-interest groups and initiating the same types of discussions through
these vehicles. However, results of this study indicate no relationship between student feelings of
belonging via these sites, but a definite connection between student feelings of belonging via the
“Wall.”
An explanation for this observation may be found in examining the situation through the
social psychological lens of peer networks, specifically in applying the findings of Brewer
(1979) around in-group bias. Results from her experimental research suggested that the
enhancement of in-group bias was related more to in-group favoritism than hostility towards the
out-group. In the context of the setting and circumstances presented in this particular study,
access to the “Wall” was limited to those students who were in the masters program, thus
creating an in-group to all those who had been accepted by the university and the school into the
program. The differentiating factor between the “Wall” and other similar opportunities to
connect with peers was the relative exclusivity of this resource, which was limited to those
admitted into the same program. Similar to the study conduct by Brewer (1979), there was no
distinct out-group identified, rather the automatic program subscription to an group dynamic of
individuals in the masters program with the same goal and interests facilitated an in-group
favoritism that enhanced a sense of belonging to those in in the program.
The asynchronous aspect of the “Wall” might have also allowed a more cohesive sense of
student body and belonging in the program. Students in the online version of this teacher

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preparation program did not have many opportunities for traditional in-person peer contact, due
to the fact that the student body was spread out across the state, sometimes in different countries,
and therefore in several different time zones. The fact that students were able to check into their
“Wall” at their convenience likely afforded many individuals, particularly those enrolled in the
online program, with personal and professional networking opportunities that allowed them to
feel more connected to the program. Especially considering the demographic features of the
online cohort, who were largely married, with careers, and with dependents, real-time contact
would have been difficult to manage and maintain. Allowing students to connect with each other
at their own time and in their own space likely allowed greater participation that included a more
sizeable and inclusive group of the students in this program.
Connection Through Campus-Affiliated Groups and Organizations. Campus affinity
groups and organizations were also found to be statistically significant predictors of student
belonging, and similar observations were made to the membership opportunities of these groups.
Namely, students were permitted to join specific campus affiliated groups, such as university
clubs, school-related honors societies, sports teams and marching bands, were limited to those
who were admitted to and enrolled as students at the university.
Astin (1999) wrote extensively about the various affinity groups that cultivate a higher
education students’ sense of belonging to a program or campus environment. He specifically
named honors programs, student-faculty interaction, athletic programs, and involvement in
student government as effective vehicles with which to cultivate a stronger sense of belonging in
undergraduate students. His work was extended by Weidman, Twale and Stein (2001) to a
graduate student population, and their study corroborated his findings on the importance of
certain campus affinity groups to students’ sense of belonging with peers in graduate programs,

83

as well. The dissertation study yielded results that further supported previous findings on the
connection between student sense of belonging and student participation in campus-affiliated
groups.
In this study, physical presence in a university setting had less to do with a given student’s
sense of belonging to the university and program community than that student’s ability to
connect with the university and program through these relatively exclusive groups and
organizations. As in the case with “Wall” usage, affinity to an in-group dynamic had less to do
with hostility toward an identified out-group, and more to do with in-group favoritism fostered
through opportunities to self-identify with peers. In this way, student sense of belonging was
enhanced.
Implications
Implications to this study can inform program and course developers at the university level,
as well as methodology and practice at primary and secondary levels of education. The most
relevant observation, that there was no significant difference in student motivation by
instructional delivery method, is one that should reassure and encourage program administrators
and course designers. Students enrolled in the graduate level pre-service teacher training
program at the institution that was studied were receiving the same caliber of education, whether
they chose to take courses online or in class.
The moderate positive correlation found between sense of belonging and professional self-
efficacy in the graduate program studied holds implications regarding importance of cultivating a
sense of connectedness in the program student body. In particular, providing networking
opportunities to students that include both online and campus-based learners has the potential to
raise level of student self-efficacy for the teaching profession, according to the survey result of

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this dissertation study. While the non-experimental nature of this study was unable to establish a
causal link between the two constructs, there was a statistically significant correlational
connection between them.
For online or off-site graduate education programs. At the university of program level,
the outcomes of this study have the potential to lend some insight to administrators and program
developers of similar online graduate-level learning environments. Findings may shed some
light on which resources should be made available to students, both campus-related and online,
that may have a positive correlational link to student motivation. Specifically, the value of
developing a strong sense of student belonging among program participants was shown to
correlate to high student motivation, especially along the construct of student self-efficacy. Also,
the moderate correlational relationship between student sense of belonging and professional self-
efficacy among students could provide administrators with a reason to dedicate time and effort
into developing resources for students that would help them cultivate a stronger sense of
belonging to their university and their program. If the correlational relationship established in
this study holds true, then student levels of self-efficacy could in turn be predicted to rise as well.
Other colleges and universities implementing similar off-site graduate programs could
easily foster student sense of belonging using by encouraging its members to join student affinity
groups, and to initiate or contribute to student-led discussions in an online forum. Namely,
masters programs that are conducted exclusively online or at a satellite site may foster
connectedness by creating a student “Wall” in their existing learning management structure and
encouraging student participation in campus or school related affinity groups at the graduate
school level. In this way, programs can facilitated a stronger feeling of connectedness among
peers within a given graduate program, thus boosting professional self-efficacy and mastery or

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performance approach goal orientation.
For pre-service teacher education programs. The connection between the findings in
this study and other pre-service teacher education programs is particularly strong. Distance
education programs are increasing in the market share of reputable programs, and implications
identified in the findings of this study to graduate programs in general are even more strongly
associated to graduate programs in the field of teacher education.
In particular, the insignificant differences of online programs and in-class programs along
the motivational constructs of professional self-efficacy, goal orientation and student sense of
belonging were encouraging. This essentially means that the online cohort of the teacher
education program at this university is proving to be effective and should continue to exist if not
expand. These results could also encourage other universities who do not have distance learning
options in teacher education to consider adding one to their repertoire of course and program
options for prospective students.
Limitations
The complexity of the latent motivational constructs studied, as well as the current
structure of the masters program, created some limitations for this study. First, the participants
in this study were graduate students at a large private university with a culture for a strong sense
of school pride and in-group belonging. However, none of the participants were direct members
of this campus culture, since classes were conducted either at a campus-affiliated building
approximately 10 minutes driving distance from the main campus or exclusively online. There
was not a “true” on-campus cohort, by which to measure the motivational difference of the two
non-campus cohorts.

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Another major challenge in this study was the difficulty that was experienced in
obtaining enough completed survey responses in the distance education method of delivery. The
strength of findings, specifically in the second research question that called for a series of
regressions, rested in the number of participants. Since there were three variables being
measured, there needed to be a minimum of 90 participants for a series of single regressions.
The best case scenario would have been a number of participants well over the minimum
required so that multiple regressions could be performed, with an opportunity to further tease out
the nuances of goal orientation, self-efficacy and the degree to which they are influenced by
feelings of belonging. Fortunately, the sample size of participants was 240 students, well over
the 90 student minimum required to report findings with reasonable statistical strength and
confidence.
Also, the students in this study were enrolled in a pre-service graduate program specific to
the field of Education. In this respect, the sample of participants was narrow in scope, and
therefore the results discussion, and conclusions of this study may not necessarily be
generalizable to other educational settings, fields of study, or demographics. Furthermore, due to
the timing of this study, all survey submissions reflected levels of student motivation at a specific
phase of their professional development, specifically a time prior to student teaching experiences
and after methodology coursework. Therefore, results of this dissertation cannot necessarily be
generalized to characterize students at other stages of their graduate level pre-service teacher
education, such as at the beginning of their coursework or immediately prior to program
completion.
Additionally, the replication of results from this study may be difficult due to the unique
components of the online platform utilized. Specific adaptations developed by the service

87

provider partner allowed students the opportunities for live interactivity, both during class
sessions and outside of designated instructional time. These new collaborative functions
imbedded into the platform broke the stereotypes heretofore established around the structure of
forum-based online distance education programs. Student and faculty experiences were distinctly
innovative because coursework was facilitated in a uniquely asynchronous manner. The distance
education teacher preparation program at Western University took place in a distinctive setting
where faculty and students experienced whole class “face-to-face” encounters, utilizing
technology similar to video-calling telecommunication programs such as Skype or FaceTime.
Since a majority of the distance education programs available to students at present do not
incorporate such features, generalizability would be difficult to determine without further study.
The nature of the structure of the study at hand may have also pose some limitations. The
study was non-experimental, so there was not a pre-survey which measure participant’s levels of
professional self-efficacy, goal orientation, or sense of belonging at the outset of the program.
Instead, the survey instrument was administered later in the program. As mentioned in previous
sections, there was no constant group by which to measure the motivational variables examined
in participants who received instruction through non-traditional, off- campus means. Even
though some students received traditional face-to-face instruction, both groups identified in the
study were technically off-campus cohorts, one was strictly online and one was off-site. Also,
because this study was non-experimental the findings of this study were limited to correlational
connection and not causal connections between the constructs studied.
Furthermore, this study was not longitudinal in nature. Factors such as pre-service teacher
burnout, and attrition to the program and later on to the profession were not identified or
affirmed in this study. Potential qualities that may define success in the field of teaching, such as

88

job attainment and retention, performance evaluations, anecdotal evidence, or achievement of
tenure, were not identified and tracked as teachers completed the program and entered the
classroom.
Delimitations
Only full-time students in the second semester of their first year of the graduate level pre-
service teacher preparation program were included in the study in order to control for potential
varying levels of motivational factors at different points in the program. Motivational variables
examined in this study were limited to the three that align with the elements detailed in the
Ford’s (1992) formula, which stated that self agency beliefs, goals, and emotions interact
multiplicatively to form an individual’s level of motivation. Personal agency beliefs were
measured through self-efficacy, goals through goal orientation, and emotions were interpreted
through levels of belongingness perceived by students in the program.
Recommendations for Future Research
Information from this study may shed light on motivational variables in pre-service teacher
professional development, or similar masters programs that are facilitated through a distance
education medium. Recommendations for further research include a closer look at the
motivational constructs as they affect similar programs in another demographic, such as a public
university or a non-research university. These types of studies would help to validate the
findings of this study, and also allow for the generalization of the result to other programs or
populations.
Similar studies examining other graduate-level programs that incorporate asynchronous
distance education elements might allow for further insight regarding the comparison of online
versus on-campus teacher education programs along latent motivational constructs such as self-

89

efficacy, goal orientation, and student feelings of belonging. Results from further studies at
different universities with less synchronous online platforms or course content may serve to
further substantiate the findings from the first research question posed in this study, that there
was no statistical difference in student motivation by program delivery method. Conversely,
differing results to this research question when applied to an asynchronous online program may
lend a more nuanced analysis and examination of results. The distinct possibility exists that a
lack of statistically significant differences between program delivery methods as indicated in this
dissertation may have been due to the unique delivery platform of the specific distance education
program at Western University, which incorporated many unique “face-to-face” features even
though coursework was facilitated 100% online.
A longitudinal study would expand upon the metaphorical snapshot provided in this non-
experimental quantitative study by delivering a more comprehensive picture of motivational
changes in students. An examination of motivational constructs in students as they begin their
program, complete their coursework, complete their fieldwork or student teaching, and enter
their chosen profession may be insightful. Specific to the field of teaching, a causal effect may
be established between success in the profession and one of more of the motivational factors at
play in pre-service teacher education programs. Potential causal relationships may be identified
between student sense of belonging and self-efficacy.
Also, a qualitative examination of these constructs may shed light on the correlational
interplay between the constructs in this study. Participants would then have the ability to expand
upon their responses by providing anecdotal experiences to provide insight that may be lacking
through a quantitative study. There are some studies that incorporate feedback provided through
course evaluations in an online pre-service teacher education setting (Gonzalez-Espada, 2009).

90

However, the nature of the feedback was limited to students’ perceived satisfaction specifically
related to an online science methodology course. A future study dedicated specifically to inquiry
around student sense of belonging to the program and self-efficacy for the teaching profession
would shed light on a broader level to potential benefits and drawback of pre-service teacher
education in an online environment. For instance, qualitative anecdotal feedback regarding
specific elements of “Wall” interactions could lend insight into the types of discussion or
proposed networking activities that cultivate a sense of connectedness in the student body.
Perhaps a detailed examination of the various types discussion threads initiated and facilitated by
students would help administrators, practitioners, and developers identify common topics that aid
in peer bonding and sense of belonging.
Conclusion
The shifting landscape of education in recent years, as a result of the increased integration
of technology in the learning process, has brought to light the deficit in research around the
influence of these changes on student motivation and the learning process. In particular, further
research in non-traditional educational settings on the latent constructs of goal orientation,
learner self-efficacy, and learner sense of belonging to his environment has been recommended
by several researchers over the years (Berndt, 1999; Nelson & DeBacker, 2008; Ryan, 2000).
More specifically, there has been a deficit of research related to these constructs and the
implementation of instructional technology in a distance educational setting with a graduate
student population. This study addressed this gap in research through a qualitative survey that
confirmed previously identified differences in learner characteristics between on-site and on-line
learners, but more importantly identified significant correlational connections between sense of
belonging, teacher self-efficacy, and goal orientation in a graduate teacher education program at

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a prestigious not-for-profit research university. The implications of these outcomes may be used
for further study of the complex interplay of latent motivational constructs and learner
characteristics, so that program and curriculum developers and educational practitioners alike
may acknowledge and account for these critical factors in an effective learning environment.

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APPENDICES
Appendix A: Demographic Items
The following survey items will be collected in order to obtain descriptive data for all
participants. Questions 1-8 will request descriptive data, and questions 9-13 will be used to
answer Research Question 3 in this Dissertation. The wording for the questions and possible
responses are below:

Please answer the following demographic questions:

1. What is your gender?
• Male
• Female
• Transgender
2. Please choose the option that best describes you:
• American Indian / Native American
• Asian
• Black / African American
• Hispanic / Latino
• White / Caucasian
• Pacific Islander
• Other
3. What is your current employment status?
• Full time
• Part time
• Unemployed, but seeking employment
• Full time student
4. What program type are you currently enrolled in?
• Online
• On campus
5. When does your Foundational/Methodology class meet this semester?
[Options
to
be
determined]

102

Appendix
A:
Demographic
Items
Continued

6. How far do you live from the main campus?
• 0-10 miles
• 11-30 miles
• 31-50 miles
• 51-100 miles
• In-state, 100 + miles
• Out-of-state
• Out of country
7. How many online COURSES have you completed, including those taken in this current
program? (Do no include this current course)
• 0
• 1
• 2
• 3
• 4+
8. How many online PROGRAMS have you completed, NOT including this current program?
• 0
• 1
• 2
• 3
• 4+
9. How many on-campus groups are you affiliated with? Please include sports teams, clubs,
honor societies, Kappa Delta Pi, online groups/clubs, etc.
• 0
• 1
• 2
• 3
• 4+
10. If you are enrolled in the online program, how often per week do you access your “Wall” to
connect with peers and faculty?
• 0
• 1-2
• 3-5
• 5-10
• 10+

103

Appendix
A:
Demographic
Items
Continued

11. If you are enrolled in the on-campus program, how often per week do you connect with
peers socially (in-person or online)?
• 0
• 1-2
• 3-5
• 5-10
• 10

12. How do you connect with your program cohort peers and instructors outside of course-
required activities? Choose all that apply:
• Facebook
• LinkedIn
• Google+
• In Person – before class
• In person – during instructor facilitated breaks in instruction (lunch, dinner, etc.)
• In Person – after class
• “Wall”
• Student facilitated activities outside of class
• Campus affiliated groups/organizations
• Others ____________________________________________________________

13. On average, how often do you connect with peers and faculty in a non-academic context
using the networking media/methods you selected above?
• Never
• 1 hour or less per week
• 2-3 hours per week
• 4-5 hours per week
• 6 or more hours per week

104

Appendix B: Teacher Self-Efficacy Scale
The following survey items will be used to measure participants’ efficacy for their
profession during the second semester of their respective programs. Responses will be along a 9-
point Likert scale from strongly disagree to strongly agree. This construct will be subdivided
into three factors, efficacy for instructional strategies, efficacy for classroom management, and
efficacy for student engagement. These items will be taken from the short version of the Ohio
State Teacher Efficacy Scale (Tschannen-Moran & Woolfolk Hoy, 2001).
Factor 1: Efficacy for Instructional Strategies (α=0.86)
1. To what extent can you use a variety of assessment strategies?
2. To what extent can you provide an alternative explanation or example when students are
confused?
3. To what extent can you craft good questions for your students?
4. How well can you implement alternative strategies in your classroom?
Factor 2: Efficacy for Classroom Management (α=0.86)
5. How much can you do to control disruptive behavior in the classroom?
6. How much can you do to get children to follow classroom rules?
7. How much can you do to calm a student who is disruptive or noisy?
8. How well can you establish a classroom management system with each group of students?
Factor 3: Efficacy for Student Engagement (α=0.81)
9. How much can you do to get students to believe they can do well in schoolwork?
10. How much can you do to help your students value learning?
11. How much can you do to motivate students who show low interest in schoolwork?
12. How much can you assist families in helping their children do well in school?

105

Appendix C: Belongingness Scale
The following survey items will be used to measure participants’ sense of belonging.
They will be taken from the first two factors of the Confirmatory Four-Factor Model, developed
by Summers, Beretvas, Svinicki and Gorin (2005). This is a scale that is modified from Lee and
Robbins’ (1995) Social Connectedness Scale to reflect the sense of belonging felt by students in
a college setting. Responses will be along a 6-point Likert scale from strongly disagree to
strongly agree. The last two factors in the Confirmatory Four-Factor Model, Group Processing
(Evaluation) and Group Processing (Effect on Individual) will not be used because they do not
relate directly to the research questions outlined in this dissertation. The two subscales that will
be included in the survey are detailed below, along with their respective Cronbach’s alphas.
Factor 1: Social Connectedness (α=0.90)
1. I feel disconnected from campus life.*
2.
There
are
people
on
campus
with
whom
I
feel
a
close
bond.

3.
I
don’t
feel
that
I
really
belong
around
the
people
that
I
know.*

4.
I
feel
that
I
can
share
personal
concerns
with
other
students.

5.
I
feel
so
distant
from
the
other
students.*

6.
I
have
no
sense
of
togetherness
with
my
peers.*

7.
I
catch
myself
losing
all
sense
of
connectedness
with
college
life.*

8.
I
feel
that
I
fit
right
in
on
campus.

9.

There
is
no
sense
of
brotherhood/sisterhood
with
my
college
friends.*

10.

I
don’t
feel
related
to
anyone
on
campus.*

11.

Other
students
make
me
feel
at
home
on
campus.

12.

I
don’t
feel
I
participate
with
anyone
or
any
group.*

106

Factor 2: Classroom Community (α=0.82)
13. I feel connected to people in this class.
14. I’ve made friends in this class.
15. I feel I fit into this class.
16. I know other people well in this class.

* Items are reverse coded.

107

Appendix D: Goal Orientation Scale
The following survey items will be used to measure participants’ learning goal
orientations during the second semester of their respective programs. Responses will be along a
5-point Likert scale from not at all true to very true. This construct will be subdivided into three
factors: mastery goal orientation, performance approach goal orientation, and performance avoid
goal orientation. These 14 items will be taken from abridged version of the Patterns of Adaptive
Learning Scales (Midgley, et al., 2000); the original was designed by Midgley et al. (1998).
Factor 1: Mastery Goal Orientation (α=0.85)
1. It’s important to me that I learn a lot of new concepts this year.
2. One of my goals in class is to learn as much as I can.
3. One of my goals is to master a lot of new skills this year.
4. It’s important to me that I thoroughly understand my class work.
5. It’s important to me that I improve my skills this year.
Factor 2: Performance Approach Goal Orientation (α=0.89)
6. It’s important to me that other students in my class think I am good at my class work.
7. One of my goals is to show others that I’m good at my class work.
8. One of my goals is to show others that class work is easy for me.
9. One of my goals is to look smart in comparison to the other students in my class.
10. It’s important to me that I look smart compared to others in my class.
Factor 3: Performance Avoid Goal Orientation (α=0.74)
11. It’s important to me that I don’t look stupid in class.
12. One of my goals is to keep others from thinking I’m not smart in class.
13. It’s important to me that my teacher doesn’t think that I know less than others in class.

108

14. One of my goals in class is to avoid looking like I have trouble doing the work.

Abstract (if available)

Abstract As distance education options expand and increase, online courses have begun to take up a progressively large portion of the educational sector, specifically in postsecondary institutions (National Center for Education Statistics [NCES], 2011). Online course offerings have risen in popularity at the postsecondary level, and well established research literature has been published which compares learner demographics and learning outcomes in online and in-class settings. Less established, however, is research on motivation in distance learning settings, specifically in synchronous online environments that incorporate real-time opportunities for student collaboration and immediate faculty feedback via visual and audio technology. Studies comparing latent motivational constructs such as self-efficacy, goal orientation and student sense of belonging in the online academic setting are limited, especially at the graduate postsecondary level in the field of pre-service teacher preparation. However, prior research indicates the importance of motivational elements, namely professional self-efficacy and sense of belonging, as factors to consider in teacher education programs (Camprara, Barbaranelli, Steca & Malone, 2006

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Asset Metadata

Creator Chao, Soomin (author)

Core Title A comparison of student motivation by program delivery method: self-efficacy, goal orientation, and belongingness in a synchronous online and traditional face-to-face environment

School Rossier School of Education

Degree Doctor of Education

Degree Program Education

Publication Date 02/10/2015

Defense Date 12/17/2014

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

Tag belongingness,Distance education,goal orientation,Higher education,Motivation,OAI-PMH Harvest,online learning,preservice teacher education,self-efficacy

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Hirabayashi, Kimberly (committee chair), Seli, Helena (committee member), Sundt, Melora A. (committee member)

Creator Email soominchao@gmail.com,szee@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-531524

Unique identifier UC11298673

Identifier etd-ChaoSoomin-3180.pdf (filename),usctheses-c3-531524 (legacy record id)

Legacy Identifier etd-ChaoSoomin-3180.pdf

Dmrecord 531524

Document Type Dissertation

Format application/pdf (imt)

Rights Chao, Soomin

Type texts

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

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

Repository Name University of Southern California Digital Library

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