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Instructional delivery as more than just a vehicle: A comparison of social, cognitive, and affective constructs across traditional oncampus and synchronous online social work graduate programs.
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Instructional delivery as more than just a vehicle: A comparison of social, cognitive, and affective constructs across traditional oncampus and synchronous online social work graduate programs.
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Content
Instructional delivery as more than just a vehicle: A comparison of social, cognitive, and
affective constructs across traditional oncampus and synchronous online social work
graduate programs.
by
Sara Behani Zaker
A Dissertation Presented to the
FACULTY OF THE USC ROSSIER SCHOOL OF EDUCATION
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF EDUCATION
December 2012
Copyright 2012 Sara Behani Zaker
ii
Acknowledgements
My sincerest thanks to my advisor, Dr. Kimberly Hirabayashi for her guidance
over the years. My decision to continue my education to the doctoral level was a result of
her influence, and I am truly appreciative of her encouragement and support.
Thank you to my esteemed committee members Dr. Ron Astor and Dr. Helena
Seli for their advice and direction throughout my dissertation process. Their help in this
process was invaluable. I would also like to acknowledge the faculty and staff of the USC
School of Social Work for their enthusiasm and willingness to participate in my study. In
particular, I am ever grateful for the help of Dr. Doni Whitsett, without whom my sample
size and motivation may have suffered.
I would also like to thank to my friends and colleagues Dr. Erik Schott and Dr.
Edmund Young for making the process easier. Lastly, I would like to thank my parents
and my husband for their continuous patience, kindness, and support.
iii
Table of Contents
Acknowledgements
...................................................................................................................................
ii
List of Tables ...................................................................................................................... v
List of Figures.....................................................................................................................vi
Abstract ............................................................................................................................. vii
Chapter
One:
Introduction
......................................................................................................................
1
Background of the Problem ............................................................................................ 3
Statement of the Problem ................................................................................................ 6
Purpose of the Study ....................................................................................................... 6
Research Questions ......................................................................................................... 7
Significance of the Study ................................................................................................ 7
Limitations ...................................................................................................................... 8
Definition of Terms ......................................................................................................... 9
Organization of the Study ............................................................................................. 11
Chapter
Two:
Literature
Review
......................................................................................................
13
Social Cognitive Theory & Distance Education ........................................................... 13
Model of Triadic Reciprocality ............................................................................. 14
Learning Context .......................................................................................................... 18
Types of Distance Education ................................................................................ 19
Differing Characteristics Across Instructional Delivery Method ......................... 23
Learner Characteristics ................................................................................................. 30
Student Demographics in Distance Education ...................................................... 30
Goal Orientation .................................................................................................... 31
State Learner Characteristics ........................................................................................ 33
Self-Efficacy ......................................................................................................... 34
The Affect of Belonging ....................................................................................... 37
Learner Behaviors ......................................................................................................... 42
Student Involvement ............................................................................................. 42
Conclusion .................................................................................................................... 46
Chapter
Three:
Methods
.......................................................................................................................
48
Research Questions ....................................................................................................... 48
Research Design ............................................................................................................ 49
Population & Sample .................................................................................................... 50
Instrumentation ............................................................................................................. 51
Demographic Questions ........................................................................................ 51
Adapted Social Connectedness & Academic Classroom Community Scales ...... 51
iv
Patterns of Adapted Learning Scales .................................................................... 52
Ehrenkranz School of Social Work Scale ............................................................. 53
Procedure & Data Collection ........................................................................................ 54
Data Analysis ................................................................................................................ 55
Chapter
Four:
Results
............................................................................................................................
57
Descriptive Characteristics of Respondents .................................................................. 58
Demographic Information ..................................................................................... 58
Analysis of Results ....................................................................................................... 61
Research Question #1 ........................................................................................... 61
Research Question #2 ........................................................................................... 66
Research Question #3 ........................................................................................... 69
Chapter
Five:
Discussion
......................................................................................................................
73
Learner Characteristics and the Learning Context ........................................................ 73
State Learner Characteristics and the Learning Context ............................................... 75
Feelings of Belonging as a Predictor of Constructs ............................................. 77
Student Involvement as a Predictor of Feelings of Belonging ............................. 78
Socio-Affective Constructs and the Problem of Student Retention...............................79
Implications ................................................................................................................... 80
Limitations .................................................................................................................... 82
Recommendations for Future Research ........................................................................ 84
Conclusion .................................................................................................................... 85
References
..................................................................................................................................................
87
Appendices
..............................................................................................................................................
102
Appendix A: Demographic Questions ........................................................................ 102
Appendix B: Measures of Belonging .......................................................................... 105
Appendix C: Social Work Self-Efficacy Scale ........................................................... 106
Appendix D: Patterns of Adaptive Learning Scale ..................................................... 108
Appendix E: Factor Analysis of Variables ................................................................. 109
v
List of Tables
Table 1: Participant Demographics: Age………………………………………………...59
Table 2: Participant Demographics: Marital Status……………………………………...60
Table 3: Participant Demographics: Dependents………………………………………...60
Table 4: One way ANOVA measuring belonging across program delivery…….....……62
Table 5: Scheffe post-hoc belonging across program delivery……….…………….....…63
Table 6: One way ANOVA measuring self-efficacy across program delivery…….……63
Table 7: One way ANOVA measuring mastery orientation across program delivery..…64
Table 8: One way ANOVA measuring performance avoidance orientation across program
delivery..........................................................................................................................…65
Table 9: Scheffe post-hoc performance avoidance across program delivery……........…65
Table 10: One way ANOVA measuring performance approach orientation across
program delivery............................................................................................................…66
Table 11: Scheffe post-hoc performance approach across program delivery……........…66
Table 12: Linear regression of belonging as a predictor of self-efficacy..........................67
Table 13: Linear regression of belonging as a predictor of mastery orientation...............68
Table 14: Linear regression of belonging as a predictor of performance approach
orientation..........................................................................................................................68
Table 15: Linear regression of belonging as a predictor of performance avoidance
orientation..........................................................................................................................69
Table 16: Linear regression of DE student involvement as a predictor of feelings of
belonging...........................................................................................................................70
Table 17: Linear regression of oncampus student involvement as a predictor of feelings
of belonging.......................................................................................................................71
vi
List of Figures
Figure 1: Bandura’s Model of Triadic Reciprocality…………………………………….15
Figure 2: Adapted Model of Triadic Reciprocality………………………………………16
Figure 3: Types of Communication and Participation…………………………………...22
vii
Abstract
Much of the research literature on learning technologies and distance education
has concentrated on achievement, with little to no emphasis on factors pertaining to
motivation. This lack of research is a concern given the high enrollment and low
retention rates in distance education programs. The focus of this comparative study was
to investigate potential differences between socio-affective and cognitive factors related
to motivation across methods of instructional delivery. The social cognitive model of
reciprocal determinism was adapted and applied to frame the discussion of factors
including feelings of belonging to the academic community, domain-specific self-
efficacy, and goal orientation. These variables were compared between students in a
traditional face-to-face Masters of Social Work program and those enrolled in an online,
synchronous version of this program. Both programs examined in this study have
comparable course work and they are offered at the same top-tier, not-for-profit, private
university. This study employed a non-experimental design and quantitative approach to
assess correlational relationships between the aforementioned social cognitive variables.
The results of this study report significant findings with regard to differences in
constructs across instructional delivery, in addition to significant and predictive
relationships between constructs. The implications of this study are important for the
field of education as it provides a new perspective for the transformation of educational
institutions.
1
Chapter One
Introduction
Over the past two decades advances in technology have contributed to the
evolution of American higher education (Dede, 1996). These technologies are
facilitating the shift from traditional brick-and-mortar university systems to the emerging
distance learning paradigm (Lucas, 1996; McGorry, 2003). Distance education (DE) is
defined as instruction that occurs with a temporal and/or spatial discrepancy between
learner and instructor (U.S. Department of Education, 2011). Historically, DE has been
used for centuries. The first reported application of distance learning was developed to
teach “short hand” through mail correspondence in the mid-18
th
century (Holmberg,
1995). Since then, distance education programs have utilized a variety of media to
communicate instruction such as written correspondence, radio, TV, CD/DVD, podcasts,
and a number of other platforms (U.S. Department of Education, 2011). Although
distance learning has been available for some time, improvements in technology have
increased the efficiency, effectiveness, and availability of these programs in higher
education.
Distance education is a broad term that encompasses many forms of instruction
and media platforms. However, as a result of increased access to the Internet the
overwhelming majority of distance education programs are now offered online. These
programs can be completely online, or blended/hybrid programs, incorporating
characteristics of both face-to-face and distance education. Online distance education, or
e-learning, can be classified into distinct groupings based on the percentage of instruction
2
delivered online. These groups range from traditional classrooms that have no online
instruction, to fully web-based courses (Allen & Seaman, 2006). In addition to variability
in the method of instructional delivery, a number of instructional approaches also exist.
For example, distance education can be synchronous, with designated login times for live
instruction and discussion, asynchronous where login is at the students’ discretion, or a
combination of both synchronous and asynchronous elements.
The diversity and flexibility of distance education programs has led to the
enrollment of a large non-traditional demographic of students as compared to oncampus
programs (Merriam & Caffarella, 1999). Compared to students in face-to-face programs,
students in DE are more likely to be older, married with dependents, and employed
(Forster & Washington, 2000; Freddolino & Sutherland, 2000; Haga & Heitkamp, 2000).
These students choose to continue or complete their education often while working full
time and taking on the responsibilities of a family (Merriam & Caffarella, 1999). This
new student demographic and their demand for accessible, flexible learning has sparked
an increase in both non- and for-profit distance education solutions.
MacDonald and colleagues (2001) suggest that the increased development of
programs exemplifies the emerging demand driven approach to providing quality
education to a large and ever-expanding consumer base. Demand for accessible
education is present, and so organizations strive to fulfill this need. Critics of DE argue
that academic institutions are rushing to compete in this financially driven market,
without an adequate understanding of the consequences of moving beyond the physical
and temporal boundaries of the traditional classroom (Barbera, 2004).
3
To date, the majority of research on the topic of distance education is
performance-focused. Comparative studies overwhelmingly report that there is no
difference in learning and academic performance between students in DE and traditional
face-to-face courses. This research is commonly referred to as the “No Significant
Difference” literature (Russell, 2001). This literature, compiled by Russell (2001),
provides insight into the expanding research genre of distance education and emphasizes
the reoccurring finding that there is no significant difference in learning and performance
across instructional delivery methods. Unfortunately, the no significant difference in the
literature begins and ends with learning and performance. A number of comparative
studies found significant differences between instructional delivery platforms with regard
to socio-affective factors, such as feelings of isolation and connectedness (Valenta et al.,
2001; Eastmond, 1995; Kerka, 1996, Besser & Donahue, 1996; Twigg, 1997; Ashar &
Skeenes, 1993; Bessemer & Donahue, 1996). Within many models of learner motivation,
deficiencies in the fulfillment of certain socio-affective needs are related to the
development of a number of maladaptive learner behaviors.
Background of the Problem
Growth rates in distance education programs are rising at an exponentially higher
rate than on-campus programs. According to a report by the Sloane Consortium (2009),
enrollment in distance education programs has an average growth rate of 17% annually.
This is a considerable difference from the 1.2% annual growth rate of traditional on-
campus programs. In addition to a high enrollment rate, DE programs also report high
attrition rates compared to traditional face-to-face programs (Carr, 2000; Kreijns,
4
Kirschner & Jochems, 2003; Moody, 2004). Lack of student persistence is a significant
concern for the administrators of DE programs (Clark, 2003). Schools invest
considerable funds in student recruitment, and program development and delivery.
Therefore, high attrition rates often result in significant financial loss for the institution
(Moody, 2004). In addition to financial loss, high attrition directly and negatively
impacts the perception of instructional quality (Thompson, 1999). Negative perceptions
of instructional quality are detrimental to both the institution and the graduates of these
programs.
Identifying and understanding potential causes of student attrition in distance
education contexts is critical for the success of these programs (Angelino et al., 2007).
Unfortunately, there is a profound lack of research on cognitive and socio-affective
factors pertaining to persistence in distance education contexts (Chen & Jang, 2010;
Jones & Issroff, 2005). To address this gap in the literature, this study examines the
constructs of feelings of belonging to an academic community, career self-efficacy, and
goal orientation with regard to instructional delivery method. These constructs have all
been correlated with attrition in traditional academic settings. Comparing these factors
across instructional delivery platforms allows for an increasingly holistic view of distance
education.
Specifically, this study examined potential differences across traditional and DE
learning contexts in a Masters in Social Work (MSW) program. Over the past two
decades there has been a large increase in the number of distance MSW programs
worldwide (Guri-Rosenblit, 1999; Petracchi & Patchner, 2001). Like many other
5
distance education programs, distance MSW graduate schools have been met with some
trepidation. Many critics have voiced concern over placements and the quality of
supervision, as placement agencies may be a considerable distance from the university,
and therefore difficult to assess (Oliaro
& Trotter, 2010). These critics have also
discussed the limitations of the instruction and practice of necessary Social Work skills in
DE contexts. According to Oliaro
and Trotter (2010), students enrolled in DE programs
that require fieldwork may develop feelings of isolation from the academic community,
and instead begin relating to their placement community. However, due to a lack of
research on the topic, it is still unclear whether there are differences in socio-affective
and cognitive factors, such as self-efficacy, feelings of belonging, and goal-orientation
across program delivery method in the field of Social Work.
In this study a social cognitive approach will be utilized to better understand the
relationship of the aforementioned constructs with regard to instructional delivery
method, and related student behaviors. Social Cognitive Theory (SCT) provides a
framework to understand, examine, and predict human behavior and motivation
(Bandura, 1977). This theory emphasizes the importance of recognizing cognition and
affect in the study of human behavior, a notion that was lacking in preceding behaviorist
theories. The model of triadic reciprocality is a central component of SCT. This model
defines reciprocal relationships between behaviors, environment, and learner
characteristics. An adapted version of this model is utilized in this study to organize the
constructs studied. According to this model, a number of affective and cognitive
constructs present in distance education contexts may influence behaviors such as
6
persistence in educational contexts (Bandura, 1996). Many social cognitive constructs
have been examined, but rarely with an explicit focus on program delivery (Chen & Jang,
2010). Investigating these factors as they relate to instructional delivery may provide
insight into potential differences in motivational components across delivery platform.
Statement of the Problem
Many distance education programs are faced with high rates of student attrition
(Miltiadou & Savenye, 2003; Chen & Jang, 2010). Student attrition can impact the
institution in a number of ways. Financially, recruiting new students to online programs
is a significantly higher cost than maintaining students in the online programs (Chiu &
Wang, 2008). Furthermore, high attrition rates negatively influence the ranking of the
programs relative to comparable schools, and influence the perception of instructional
quality (Allen & Seaman, 2006). Drops in standing can be detrimental for DE programs
as the legitimacy and value of these degrees is already debated in many institutions of
higher education (Allen & Seaman, 2006).
To better understand student attrition in distance education settings it is critical to
examine factors typically associated with attrition in traditional oncampus settings. For
this purpose, research that focuses on socio-affective and cognitive constructs such as
belonging, self-efficacy, and goal orientation were compared across DE and face-to-face
academic contexts.
Purpose of the Study
The purpose of this study was to determine whether there is a difference in the
effects of belonging, career self-efficacy, and goal orientation by program delivery. This
7
study specifically examined both online and oncampus versions of a Masters of Social
Work program at a large, urban, private research university. This study also investigates
whether feelings of belonging predict self-efficacy or goal orientation. Lastly, this study
examined whether student extracurricular involvement predicts feelings of belonging to
the academic community.
Research Questions
Within this study, the following research questions were examined and answered:
1. Is there a difference in feelings of belonging, career self-efficacy, or goal
orientation by method of program delivery?
2. Do feelings of belonging predict self-efficacy or goal-orientation?
3. Does involvement in out of class activity, such as frequency of peer
interactions, predict student feelings of belonging?
Significance of the Study
The significant increase in distance education programs, particularly in graduate
and professional programs, as well as the high attrition rate, (Allen & Seaman, 2006;
Parsad & Lewis, 2008) underscores the need for comparative studies examining social
cognitive factors pertaining to persistence across method of program delivery. The
research questions of this study give insight into the potential impact of socio-affective
and cognitive factors on student attrition. This understanding allows for online educators
to ensure that these components are accounted for in the web-based learning process.
As the educational paradigm shifts and distance education increases in relevance,
it becomes the responsibility of educators and educational researchers to ensure that
8
students are provided all the benefits of a quality education, including socio-affective
factors, regardless of the method of program delivery. Research comparing cognitive and
socio-affective factors across instructional delivery begins to close the significant gap in
the literature.
Limitations
The design of this study has inherent limitations that must be addressed. First,
participants in this study are enrolled at a large, private research university. The culture
of this particular institution emphasizes the development of campus community. As a
result, the conclusions of this study may not be generalizable to other demographically
similar populations. Another limitation is that this study is not longitudinal. It is
hypothesized that over time, the reported socio-affective factors will change, effecting
learner behaviors within the program. Unfortunately, the design of this study limits this
analysis of change.
Although significant differences were found, the quantitative nature of this study
limits the depth of analysis and insight into these results. A final limitation is that the
differences found may not be attributable to program delivery method. The study
performed is not experimental, and therefore, no direct causal relationship can be
inferred.
9
Definition of Terms
Belonging (affect). A sense of relatedness to others within a community. This
includes the importance of the group’s influence on the individual, the fulfillment of
needs, and an emotional bond shared by all members of the community.
Content Management System (CMS). Software that tracks all information on a
certain site. This information may include text, images, videos, or documents.
Distance education (DE). A course, or a program of courses, that is delivered to
students while maintaining a gap in space, time, or both (Perraton, 1988)
Goal Orientation Theory. The belief that an individual’s behaviors, such as
active choice, persistence, and mental effort are a result of their reasons or rationale for
engagement.
Mastery Orientation. An individual’s goal is to successfully understand and
complete a task. Mastery orientation has less to do with demonstrating competence, but
more with actually being successful at a task.
Performance approach Orientation. An individual’s goal is to demonstrate
competency in a subject or task.
Performance avoidance Orientation. An individual’s goal is to avoid
demonstrating a lack of competency in a subject or a task.
Hybrid/blended education. Courses designed to incorporate both face-to-face
and distance learning components.
10
Learning Management System (LMS). Software that delivers, tracks, and
reports on a training system. An LMS has the ability to store content, track progress, and
deliver customizable instruction based on user factors.
Program/platform delivery. The structure and framework in which instruction
is provided to leaners.
Scaffolding. Providing support to learners, in order for them to achieve at their
highest potential. Scaffolding is related to the Zone of Proximal Development (ZPD). In
order for students to achieve at the highest level of their ZPD, they must have adequate
support from more knowledgeable others.
Self-efficacy. An individual’s belief about their own ability to successfully
perform a task under specific conditions. This differs from self-concept and self-esteem,
which are more global constructs.
Social Cognitive Theory (SCT). A theory that aims to understand human
behavior, and the relationship between these behaviors, personal factors, and the
environment. Personal factors refer to cognitive, affective, and biological events. A key
factor of this theory is the concept of self-efficacy.
Traditional learning/face-to-face/oncampus. Contexts in which instructional
material is delivered in person, without temporal or geographic gaps between instructors
and students.
Web-based learning (WBL)/e-learning. The process of educational content
being delivered through a technological medium (i.e. Internet). WBL can be
11
synchronous, with scheduled online meetings with instructors, or asynchronous, where
learners control time and pace of instruction.
Web-facilitated course. A course of instruction that utilizes multimedia with the
classroom environment. Examples of this are online lectures (e.g., Khan Academy),
animated visual aids, and videos to exemplify a concept, process, or procedure (Relan &
Gillani, 1997). In this capacity, technology is seen as a supplement to traditional
instruction.
Organization of the Study
Chapter one in this study provides an introduction to the topic of distance
education and issues with regard to student attrition, in addition to an overview of the
proposed study. Specific factors that may influence motivation are discussed, as is the
theoretical framework that will be used in this study. This section also discusses the
importance of the study, potential limitations, and gives definitions of relevant terms.
Chapter two begins with an in-depth look at the theoretical framework used to
organize this chapter. Within this framework, features of the learning context are
described. A typology of distance education is provided, and features that differentiate it
from traditional on-campus contexts are articulated. These features include opportunity
for interactions, feedback, and modeling. Furthermore, this chapter compares
components that have been found to influence student attrition in traditional educational
contexts, such as feelings of belonging, career self-efficacy, goal orientation, and student
involvement.
12
Chapter three describes the methodology used in this study. This chapter
discusses the sample, instrumentation, research design, and data collection process. Also
described are the plans for data analysis and as well as the strengths and weaknesses of
this study.
Chapter four is a description of the results from the data analysis. Chapter five
provides a discussion of these results and implications for practice, in addition to the
limitations of the study and suggestions for future research.
13
Chapter Two
Literature Review
There has been a significant growth in distance education programs within the
American higher education system. Although forms of distance education have been
utilized for centuries, the digital revolution of the past few decades and the proliferation
of the Internet have caused a surge in high-quality and accessible education at a distance.
This relatively quick evolution from the traditional educational paradigm to the vast and
relatively uncharted world of DE has emphasized the need for research on the topic.
Currently, most research on DE has focused on achievement, leaving a sizable gap in the
literature regarding socio-affective and cognitive factors relating to motivation.
This chapter will begin with an overview of social cognitive theory and the adapted
model of reciprocality as an organizational framework for this literature review, followed
by descriptions of socio-affective, cognitive, and behavioral constructs relating to the
framework utilized. This chapter is organized by the four components of the adapted
model: the learning context, learner characteristics, state learner characteristics, and
learner behaviors.
Social Cognitive Theory and Distance Education
Social Cognitive Theory (SCT) provides the organizational groundwork for the
design of this study. This theory is founded on a model of reciprocal causation that
incorporates an individual’s cognitions and affect, behaviors, and environment (Bandura,
1989). This perspective differed from earlier behaviorist thought, which placed emphasis
on the environment as the sole predictor in determining behavior (Bandura, 1986). Within
14
academic contexts, social cognitive theory provides a framework to examine potential
contributors to learner motivation and behavior (Bandura, 1986, 1996).
Model of triadic reciprocality. Social Cognitive Theory describes the
relationship between an individual’s environment, behaviors, and personal factors
through the model of triadic reciprocality, or reciprocal determinism (Bandura, 2002).
Prior to the development of the model of triadic reciprocality, most models of human
behavior were unidirectional in nature (Bandura, 1989). Early psychological theories
described behavior as a result of stimulants within the environment, or as a result of
internal factors such as cognition. The model of triadic reciprocality was the first to
recognize the impact of both environment and internal learner characteristics on behavior.
Furthermore, Bandura’s (1989) model stated that the three factors of determinism have a
reciprocal relationship with one another. This groundbreaking concept moved motivation
and learning theory away from the unidirectional models preceding it. This model states
that changes in any of these three factors may influence changes in the others (see Figure
1). However, these changes may not be immediate. According to Bandura (1989) “it
takes time for a causal factor to exert its influence and activate reciprocal influences,”
(Bandura, 1989, p. 3).
15
Behaviors
Environment Personal Factors
Figure 1. Bandura’s model of triadic reciprocality
This study uses an adapted version of the model of triadic reciprocality (Figure 2).
This adapted version includes typical factors such as Learning Context (environment) and
Learner Behaviors. However, Learner Characteristics are divided into a sub-category of
State Learner Characteristics. This sub-category differentiates between variable learner
characteristics such as affect, and other stable characteristics such as cognitive
components and demographic factors. The adapted version adds an additional component
to the model, which, in essence, makes it no longer triadic. Therefore this study will refer
to this version of the model simply as the adapted model of reciprocality.
16
Figure 2. Adapted model of triadic reciprocality
Learning Context within the Adapted Model of Triadic Reciprocality. The
learning context in the model of triadic reciprocality refers to any possible stimulant
within the academic environment and the environment itself (Bandura, 2004).
Environments are constantly evolving as a result of human agents and other factors
within the environment, and in turn, the changing environment influences the learner and
learner behaviors (Bandura, 1989). As a result, individuals are both “products and
producers of their environment” (p. 4). Potential stimulants within the learning context
include modeling, opportunity for student interactions, and feedback (Bandura,
1977,1986). These factors have been researched extensively, and are elaborated upon in
the following sections. However, within this study the learning context specifically refers
to the method of program delivery. Program delivery has been studied with regard to
Learner Characteristics
Age Marital status Number of
dependents Goal-orientation
Learning Context
Method of instructional delivery
State Learner Characteristics
Feelings of Belonging Career self-
efficacy
Learner Behaviors
Out-of-class activity Degree of Persistence
17
achievement, but rarely in terms of socio-affective and cognitive factors relating to
student attrition. Comparing these factors across learning contexts using the adapted
model of reciprocality provides a progressive view of distance education.
Learner Behaviors within the Adapted Model of Triadic Reciprocality. Learner
behavior refers to the actions learners engage in as a result of their “expectations, beliefs,
self- perceptions, goals and intentions” in addition to factors inherent in the learning
context (Bandura, 1989, p. 3). An individual’s behaviors influence and are influenced by
their potential environment, which then becomes their actual environment. An example
of this relationship is the existence of social groups on a college campus. These groups
may be in an individual’s potential environment by result of spatial proximity. However,
the student must put forth effort to join one of these groups. Once this effort is exerted,
the group becomes part of the individual’s actual environment (p. 4). According to
Bandura (1989), “hot stove tops do not burn unless they are touched,” but when it is
touched, this action will create natural reactions within the environment and changes to
internal learner characteristics.
Learner Characteristics within the Adapted Model of Triadic Reciprocality.
Learner characteristics refer to an individual’s physical features, such as age, sex, gender,
racial-ethnic affiliation, attractiveness, social roles, and relatively stable cognitions such
as goal orientation. Others within a potential environment are likely to react differently to
an individual based on these characteristics. Within academic contexts perceived social
roles are often the largest determinant of in-group acceptance. For example, a student
perceived by others to be an outcast or a loner may not be invited to social events, which
18
in turn, may influence cognitive characteristics such as general self-perception and self-
confidence.
State Learner Characteristics within the Adapted Model of Triadic
Reciprocality. Within the adapted model, affective and other variable factors are
categorized as state learner characteristics. These factors are context-specific and are
more likely to evolve than learner characteristics. In this study examples of state learner
characteristics include feelings of belonging and Social Work career self-efficacy. Both
of these characteristics are context specific and may change depending on environmental
and behavioral input. Self-efficacy is defined as a context-specific construct and will
change depending on feedback from sources of self-efficacy judgments. For example, a
student with low Social Work self-efficacy may adjust this internal construct as a result
of feedback from a more knowledgeable other.
In summary, the adapted model of reciprocality describes the bidirectional nature
of the human experience. As shown in Figure 2, the learning context, learner behaviors,
learner characteristics, and state learner characteristics influence and are influenced by
one other. A change in one of these factors may have reciprocal consequences for the
others. These consequences may not occur immediately or be equal in strength to the
original change. The rest of this chapter will be organized by the factors presented in this
figure.
Learning Context
In our recent history higher education was often inaccessible for many that
deviated from the traditional college student demographic. Students were expected to
19
live close to a university that offers the degree they want to work towards, and to be
available for classes when they were offered. The increase in distance education and
web-based learning (WBL) programs has removed these requirements (Hui et al., 2007;
Hwang & Arbaugh, 2009). Distance education is defined as a course, or a program of
courses, that is delivered to students while maintaining a gap in space, time, or both
(Perraton, 1988). Inherent within this definition is the idea that higher education has the
potential to be a flexible commodity accessible by a greater number of students.
The lack of research on socio-affective and cognitive factors with regard to
instructional platform leaves a sizeable gap in the literature pertaining to distance
education. This review will examine these factors, in addition to a description of distance
education, and effected demographics.
Types of distance education. Distance education refers to instructional
methodologies that use varying levels of remote instruction. These formats are on a
spectrum ranging from completely in-class to completely online with a range of options
between the two (Bonk et al., 2000). For the purpose of this study, only traditional,
oncampus and online courses are examined.
Traditional course. The schema of the traditional classroom is becoming less
relevant in the modern view of education, particularly in higher education settings (Relan
& Gillani, 1997; Reigeluth, 1994). Within traditional classrooms instruction is oriented
towards whole group learning, and content is mostly delivered by classroom lecture. In
traditional classrooms technology may be used to expand upon a particular concept, but
not to deliver instruction (Relan & Gillani, 1997).
20
Web-facilitated course. Advances to technology allow for instructors to easily
integrate multimedia into their classrooms. Instructors are now able to present online
lectures (i.e., Khan Academy), animated visual aids, and videos to exemplify a concept,
process, or procedure (Relan & Gillani, 1997). In this capacity, technology is seen as a
supplement to traditional instruction, ranging from 1-29% of instructional delivery.
Blended course. Courses designed to incorporate both face-to-face and distance
learning components are referred to as blended learning contexts. In these courses
students are required to meet in traditional or web-facilitated environments, in addition to
attending a significant portion of class (between 30-79%) through instructional media
(Hui et al., 2007). Blended settings allow for students to engage both in-person and
online with instructors and classmates. This provides opportunity for students to build
relationships in person, and then build upon them in DE contexts.
Online or distance education. Distance learning is an instructional format that
delivers content through a gap in space or time from learners (Keegan, 1986; Jonassen,
1982). Courses where instruction is delivered at least 80% via a technological medium
are considered to be online or distance education. Distance learning can be synchronous,
with scheduled online meetings with instructors, or asynchronous, where learners control
time and pace of instruction. Distance education courses are often delivered on Learning
Management Systems or Content Management Systems (CMS) (Griffin & Rankine,
2010). These systems allow a wide variety of features to be embedded into instruction,
such as user profiles, content databases, and discussion forums. Distance education
21
programs allow for access to instruction and content from remote locations, including
mobile devices.
Synchronous vs. asynchronous. Distance learning may be synchronous,
asynchronous, or a hybrid of the two. Synchronous DE contexts require same-time
interaction. Often, this means students must be logged in at a certain time to engage with
faculty and other students. Examples of synchronous interactions are video-enabled chat,
and instant messaging capabilities. Asynchronous DE refers to programs with no
designated login time. Learners access materials anytime, anywhere, and communicate
with faculty and other students via discussion boards and email. According to Hrastinski
(2008), both synchronous and asynchronous programs have a number of benefits and
limitations. These programs types, and their distinguishing features, will be further
examined in this section.
Synchronous distance learning. According to Kock (2005), synchronous
communication increases psychological arousal. This is attributed to the similarity of
synchronous DE learning to “natural media,” such as face-to-face interactions. Research
on e-learners perception of synchronous communication shows that these interactions are
often seen as more similar to “talking” with classmates, compared to asynchronous
communication (Hrastinski, 2008). Learners enrolled in synchronous programs are more
likely to discuss topics outside of course content and engage in social support.
Furthermore, students engaged in synchronous communication report higher levels of
arousal and motivation than those in asynchronous programs (Kock, 2005; Robert &
22
Dennis, 2005). These characteristics of synchronous communication encourage the
development of learning communities and collaboration.
A limitation of synchronous communication is the emphasis on quantity of
interaction rather than quality. Synchronous interactions tend to have a higher sentence
count and less complexity than asynchronous communication (Hrastinski, 2008). This
may be a result of not wanting to disrupt the flow of conversation, and the expectation to
provide an immediate response. According to Hrastinski (2008), these characteristics
induce a high level of personal participation (see Figure 3). Personal participation is
associated with collaboration, arousal, and development of learning communities.
Figure 3. Types of communication and participation (Hrastinski, 2008).
Asynchronous distance learning. Distance learning contexts with anytime access
to instructional materials, lectures, and communicative functions are considered
asynchronous DE learning environments. With no expectation for immediate response,
learners in asynchronous environments have time to process information, reflect, and
respond thoughtfully. As a result, asynchronous environments are rated high in cognitive
participation (see Figure 3) (Hrastinski, 2008).
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The potentially asynchronous nature of distance education is highly appealing for
many potential students. However, a major limitation of asynchronous communication in
DE settings is the lack of personal participation that accompanies it. A study by
Hrastinski (2008) showed that the majority of interactions in asynchronous settings were
content-related. This focus on course content within interpersonal interactions detracts
from the development of personal relationships and learning communities.
In summary, there is a significant variation in types and features of distance
education. There are countless combinations of instructional delivery structures, and
each may have a unique influence on student motivation. Instructional delivery has been
found to be inconsequential with regard to achievement (Coe and Elliot, 1999), but may
have a significant influence on affective factors pertaining to motivation (Clark, 2005).
Although both synchronous and asynchronous DE contexts have learning benefits for the
learner, in terms of student persistence, synchronous contexts may be desirable over
asynchronous delivery methods.
Differing characteristics across instructional delivery method. Within the
scope of educational environments, there are a number of factors that may influence
student persistence and motivation (Bandura, 1986). These include opportunities for
modeling, feedback, student-student, and student-teacher interactions (Bandura, 2002;
Tinto, 1993; Clark, Yates, Early, & Moulton, 2010). Within the literature, these factors
have been correlated to student retention (Lepper & Cordova, 1992), feelings of
belonging (Astin, 1992), self-efficacy (Bandura, 2002), and course satisfaction (Fujita-
Starck & Thompson, 1994). These factors are often present, or easily incorporated into
24
face-to-face learning environments, but are often overlooked in DE settings. According
to the adapted model of reciprocality, these factors within the learning environment have
a bidirectional relationship with learner characteristics, state learner characteristics, and
learner behavior. Although these factors are not explicitly researched in this study, an
analysis of the literature on these topics was conducted. The goal of this literature
analysis is to examine these specific characteristics of the learning context in light of the
adapted model of reciprocality.
Opportunity for interactions. Student interaction with peers and faculty has been
linked to a number of positive academic behaviors, including retention (Tinto, 1993;
Astin, 1992). Meaningful student–student and student-teacher interactions may occur in
class or during extracurricular activities such as participation in sports, clubs, and student
government (Tinto, 1993). Interactions outside of the classroom allow for the
opportunity to further discuss academic subject matter with peers and faculty, and to
develop relationships with significant others.
Student-teacher interactions. Fulfilling relationships with faculty in students’
learning environments are positively correlated with student persistence (Chickering &
Gamson 1987; Johnson & Johnson, 1998; Tinto, 1993; Wentzel, 1997). In on-campus
settings faculty are often lecturers, mentors, and scaffolds for students. In the context of
distance education, the role of teachers has expanded to also include course developer
and facilitator (Gaudioso, Hernandez-del-Olmo, & Montero, 2009). This change in
teacher roles and responsibility has created a new standard for effective teaching.
25
The quality and frequency of student-teacher interactions is positively correlated
with student involvement behaviors in campus and extracurricular activities (Tinto,
1993). According to Astin (1993), student involvement is related to internal learner
characteristics and a decrease in student attrition in higher education contexts (Gaudioso
et al., 2009; Tinto, 1993). Perceived teacher support is also linked to course satisfaction
(Chou & Liu, 2005; Liaw, 2004). On-campus, face-to-face educational settings allow
students frequent accessibility to instructors. Instructors in these settings have the
advantage of continuous non-verbal student feedback to gauge learning, and are able to
use this feedback as a formative evaluation of their teaching (Gaudioso, Hernandez-del-
Olmo, & Montero, 2009). This feedback is also valuable for students; non-verbal
feedback from the instructor facilitates self-regulatory and metacognitive skills.
Although learning performance is comparable in on-campus and distance education
settings (Russell, 2001; Piccoli et al., 2001), students in DE contexts report a significantly
higher anxiety level. Newby (2002), among others, attribute this anxiety to the perceived
lack of accessibility to instructors, and limited and delayed feedback. Positive
interactions with instructors are essential for students’ to fulfill their academic, social,
and affective potentials.
Another type of student-teacher interaction is purposeful help-seeking behavior.
Hwang and colleagues (2002) describe help-seeking behavior as interactions aimed at
gauging or acquiring knowledge. These behaviors are critical aspects of the learning
process, and can be used by both students and instructors to gauge and remedy deficits in
knowledge (Hwang & Arbaugh, 2008). Many DE contexts provide on-demand help
26
systems, which replicate instructor feedback channels; however, literature on the topic
suggests that many students are not effectively utilizing these functions (Aleven et al.,
2003). Student-teacher feedback is discussed in greater detail later within this chapter.
Student-student interactions. Academic interactions with peers play a significant
role in promoting student persistence. Unlike face-to-face learning contexts, student-
student interaction in distance education is less likely to be directed by the instructor, but
rather facilitated via instructional technologies such as board postings, breakout groups
and student chat boards (Roach & Lemasters, 2006). This change in instructional media
requires students to be proactive in order to communicate with peers for both social and
academic purposes (Roach & Lemasters, 2006).
Instructors can encourage content-based interactions through their applied
instructional approach. Team-based learning (TBL) is a process that is based on
structuring instruction around group interactions (Gomez, Wu, & Passerini, 2010). This
highly constructivist approach is based on the dissemination of information through
learner interactions with team members. Team-based learning is traditionally conducted
in face-to-face contexts, however in a study of computer-supported TBL, Gomez et al.
(2010) found that having an asynchronous web-based learning environment allows for
greater depth and quality of student-student interactions. This is attributed to the removal
of stringent time restrictions that are enforced in face-to-face or synchronous settings
(Gomez et al., 2010).
Feedback. When constructive, and given in a timely manner, feedback can greatly
influence student motivation (Clark, Yates, Early, & Moulton, 2010). Students use
27
instructor feedback to assess their own learning and self-regulation (Hwang & Arbaugh,
2008). Hatzipanagos and Warburton (2009) describe feedback as the “’bridge’” between
identifying gaps in knowledge in the learning process. Hwang and colleagues (2002),
have recognized three main feedback channels: asking professors questions in class,
asking questions outside of class, and asking peers outside of class. All three are
important components in developing student metacognition. According to Clark et al.
(2010), feedback is a necessary part of effective instruction. In a study by Tuckman and
Sexton (1991) half of the participants in a study received positive feedback from their
instructor, while the other half received no feedback. The group with the feedback scored
significantly higher on a self-efficacy survey, and did better in the class as measured by
overall test scores.
Effectiveness of feedback may vary depending on the content being taught (Clark
et al., 2001). Therefore, the type of feedback given should be structured in accordance to
content and audience. When learning complex tasks, students should be provided
synchronous, corrective feedback (Clark et al., 2010). During more simple tasks,
feedback can be given in asynchronous contexts. Feedback is often given through
varying channels, such as informally during formative assessments. Oncampus academic
programs allow for the opportunity for teachers to engage in formative assessments
(Hatzipanagos & Warburton, 2009). These assessments give occasion for students to
receive synchronous feedback, and aide in correcting students’ erroneous beliefs or ideas.
In the distance education context, where immediate dialogue and formative assessments
28
may not be possible, or difficult to implement, creating ample opportunity for feedback is
crucial (Hatzipanagos & Warburton, 2009).
Distance education settings often provide a self-regulated environment where
feedback may be utilized at the discretion of the student (Wallace et al., 2006). Students
with less developed metacognitive skills may find it difficult to correctly apply feedback
for knowledge gains in DE settings. In order to ensure feedback is effective for less
autonomous learners, Wallace et al. (2006) suggest high levels of instructor-student
dialogue in online or distance contexts. Furthermore, the degree of structure and
transactional time in developing feedback opportunities into instruction may vary. In DE
contexts, highly structured practice and feedback, in addition to a decrease in transaction
time for feedback, is beneficial for many learners (Wallace et al., 2006). This feedback
allows for student evaluation of their progress, and gives opportunity for revision of
ideas, if necessary.
Modeling. Vicarious experience is a major component of learning and motivation
within Social Cognitive Theory (Bandura & Adams, 1977). When learners are not able
to perform an action themselves, they may rely on models to relay critical information, in
addition to providing valuable information for developing self-efficacy judgments
(Bandura, 2002). According to Schunk et al. (2008), modeling is multi-faceted in that it
can be either informative, motivational, or both. Modeling can serve as a social prompt,
inhibiting and disinhibiting behaviors, in addition to teaching procedures and processes.
Modeling is a valuable source of information for all learners, however the extent
that modeling is possible may vary by the method of program delivery. Students in
29
traditional learning environments have constant access to more knowledgeable others in
the form of the instructor as well as peers. Students in online programs tend to be more
isolated and therefore may benefit less from vicarious experiences. Some DE programs
attempt to mitigate this problem by providing learners virtual examples and non-
examples of appropriate behavior (E. Schott, July 18, 2011, Personal Communication).
Providing ample opportunity for vicarious experience regardless of program delivery
method is an instructional approach that may influence learning and motivation for
students.
In summary, factors within the learning context such as, interactions with faculty
and peers, feedback, and modeling influence learner behaviors such as persistence, and
state learner characteristics like feelings of belonging to a campus community and elf-
efficacy. However, the implementation of these factors may differ by program delivery.
Interactions, feedback, and modeling are often inherent in face-to-face communication.
Applying these factors in DE contexts requires forethought, resources, and active
participation, in addition to the appropriate software tools. Social software tools may
serve as an alternate for face-to-face contact with peers. These tools by definition are
comprised of user driven content, allow for social feedback, conversations, and social
networks (Hatzipanagos & Warburton, 2009). Social software provides opportunities for
learners to support one another throughout the learning process (Anderson, 2006).
Distance education programs that are equipped with supportive social opportunities allow
students to actively participate in their learning through active dialogue, and feedback
opportunities.
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Learner Characteristics
According to the adapted model of reciprocality learner characteristics play a role
in developing learner motivation (Bandura, 1989). Learner characteristics include
physical traits and demographic information in addition to stable cognitive
characteristics. Within the adapted model of reciprocality, affective characteristics are
broken out into a component referred to as state learner characteristics. This breakdown
emphasizes the contextualized nature and variability of affective components such as
self-efficacy and belonging. In this analysis, the learner characteristics examined include
demographic characteristics and goal-orientation.
Student demographics in distance education. Distance education programs
have contributed to a transformation of the American higher education system. Students
no longer have to live close to universities they hope to attend, and are allowed
considerable flexibility in choosing class times (Lorenzetti, 2005). These differences
have allowed access to higher education contexts for an increasingly diverse learner
population. Students that choose to enroll in DE courses are often “non-traditional” as
compared to the demographic data of the average college student (Oliaro & Trotter,
2010). In a study on student demographics, Haga and Heitkamp (2000) found the
majority of students enrolled in a DE social work program were significantly older than
students in the on-campus version of the program. According to Cercone (2008), an
older student demographic comes with different responsibilities that must be accounted
for:
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Adult learners are different from traditional college students. Many adult learners
have responsibilities (e.g., families and jobs) and situations that can interfere with
the learning process. Most adults enter educational programs voluntarily and
manage their classes around work and family responsibilities. Additionally, most
adult learners are highly motivated and task-oriented (p. 139).
Expanding upon this quote, these students are more likely to be married with dependents,
employed, live a considerable distance from campus, have more experience in their field
of study, and financially more secure than their on-campus peers (Freddolino &
Sutherland, 2000; Forster & Washington, 2000; Merriam & Caffarella, 1999). Physical
and social demographic learner characteristics influence components within the adapted
model of reciprocality. These learner characteristics play a role in determining learner
behaviors, such as choice of learning context. Furthermore, demographic and social
characteristics influence environmental stimuli within the learning context.
Goal orientation. According to Bandura (1989), goal setting “is first and
foremost a discrepancy-creating process.” Setting a goal and determining the necessary
effort and resources needed to achieve that goal helps learners to develop an academic
focus and reason for engagement (Clayton et al., 2010). According to Dweck (1986,
1989) goal setting behavior is moderated by the relatively stable characteristic of goal
orientation. Dweck describes two types of goal orientation, mastery and performance.
Individuals with a mastery goal orientation are characterized by an intrinsic desire to
learn and increase their competence at a task. Conversely, individuals with a
performance goal orientation are extrinsically motivated to engage in learning behaviors.
Continued research on the topic suggests that performance goal orientation can be
separated into two distinct categories- performance approach and performance avoidance
32
(Elliot & Church, 1997). Performance approach goal orientation is described as the
active pursuit of potential positive academic outcomes. Performance avoidance goal
orientation is defined as self-regulation in response to potential negative outcomes.
Whether an individual has mastery, performance approach or performance
avoidance goal orientation impacts various decisions made with regard to academic
contexts. Specifically, an individual’s goal orientation has been associated with choice of
enrollment in traditional and distance education courses. A number of studies have found
that students enrolled in distance education are more likely to be performance goal
oriented than their peers in traditional classroom settings (Ames, 1992).
Mastery goals. Mastery or learning goals are those that are developed from an
individual’s desire to learn something new, or increase their competence in a subject
(Dweck, 1986). Individuals with this goal orientation are more likely to persist in
achieving their goals, even in the face of challenges. According to Ames (1992),
individuals with mastery goal orientation can be seen as being intrinsically motivated.
Satisfaction for mastery-oriented individuals is derived from increases in efficiency or
perseverance towards goals. Mastery goal orientation is often associated with increases
in cognitive engagement by the learner (Ames & Archer, 1988; Pintrich & Garcia, 1994).
Performance approach goals. Individuals with performance approach goal
orientation are primarily motivated by external factors to work towards goals (Dweck,
1986). Learners with a performance goal orientation engage in learning activities with
the goal of performing well at a subject compared to their peers (Clayton et al., 2010).
Students with a performance approach goal orientation gain satisfaction from extrinsic
33
rewards, such as grades, praise, and recognition (Clayton et al., 2010). These students
tend to have high levels of self-efficacy, however they may see learning as less important,
or secondary to the attainment of outside reinforcements (Dweck, 1986).
Performance avoidance goals. Performance avoidance goal orientation is
regarded as the most maladaptive of all goal orientations (Dweck, 1986). This type of
goal orientation is the result of a desire to avoid potential failure, which results in
helplessness of behavior (Elliot & Church, 1997). Students with performance avoidance
orientations have lower levels of self-efficacy. These individuals are also most likely to
give up in the face of challenges, have feelings of anxiety, and to engage in negative
thought patterns when confronted with challenges (Dweck, 1986).
In conclusion, an individual’s degree and type of goal orientation has been related
to their choice of enrollment in traditional or distance education programs (Ames, 1992).
In a study by Ames (1992), a pattern emerged that showed students who chose
traditional, face-to-face, instructional contexts as more mastery goal orientated compared
to students who chose to take online courses. Within the adapted model of reciprocality,
a the learner characteristic of goal orientation may influence choice of enrollment in
traditional or DE learning contexts.
State Learner Characteristics
Within the adapted model of reciprocality state learner characteristics refer to
variable and context-specific affective characteristics such as domain-dependent self-
efficacy and feelings of belonging. By separating these specific learner characteristics
from other more stable factors, the malleability of these characteristics is emphasized.
34
Self-efficacy. Self-efficacy is defined in Social Cognitive Theory as an
individual’s beliefs about their personal capability for success at a specific task at a
particular point in time. Specifically, self-efficacy refers to “beliefs in one’s capabilities
to organize and execute the courses of action required to produce given attainments”
(Bandura, 1997, p. 3). Self-efficacy has been shown to be a strong predictor of learner
behavior at a task (Bandura, 1997; Zimmerman, 2000). To determine one’s level of self-
efficacy, an internal process of reflection and self-regulation is necessary. According to
the adapted model of reciprocality, self-efficacy influences and is influenced by learner
characteristics, learning context, and learner behaviors. Specifically, self-efficacy
judgments are developed through engagement in domain behaviors and interactions with
the learning context. These sources of self-efficacy judgments are described in further
detail below.
Sources of self-efficacy judgments. In order to determine their feelings of self-
efficacy towards a specific task, an individual will evaluate their own knowledge and
skills, in addition to any possible challenges or barriers to the achievement of the task
(Clark, 1999). The more efficacious an individual is the more likely they are to persist at
a task. Because of this, self-efficacy is seen as a crucial element of motivation (Young &
Kline, 1996). According to Bandura (1977), individuals determine their self-efficacy of a
task through a number of different channels. Specifically these channels include prior
experiences, vicarious experiences/modeling, and verbal persuasion. In developing self-
efficacy judgments, these channels are evaluated by the individual to extract relevant
information (Bandura, 1986). The strongest source of self-efficacy judgments is actual
35
experience, or past history of success or failure. By actually performing a task an
individual gets the most concrete feedback of success or failure. Individuals may also
infer their abilities by recalling previous performance on similar tasks.
Vicarious experience is another source of self-efficacy judgments (Bandura,
1986). When an individual has little experience with a task, observing a model
performing the task may be enough to develop a sense of self-efficacy. As discussed
previously, creating vicarious experiences for students in DE contexts may prove more
difficult than those enrolled in face-to-face programs.
Verbal persuasion and feedback, although not as strong as actual or vicarious
experience, is an important source of perceived self-efficacy judgments, particularly
when the persuader is a credible and genuine source (Bandura, 1986). Verbal feedback
provides information to learners regarding some aspect of their success or failure at a
task, and can be highly effective at improving future performance if provided in a
constructive manner (Young & Kline, 1996, p. 44). According to Locke and Latham
(1990), self-efficacy in career and academic achievement is highly influenced by verbal
feedback. Constructive verbal feedback from faculty and peers helps students gauge their
self-efficacy at specific tasks. Within DE contexts verbal persuasion may be limited,
particularly in asynchronous programs where real-time interaction with the instructor is
unlikely to occur.
In summary, self-efficacy has been shown to be a factor pertaining to learner
motivation to engage in achievement behaviors. According to Ford (1992), our initial
concept of efficacy is one based on superficial beliefs and prior experiences. As we
36
continue on a task, our ability beliefs may be adjusted, which in turn, may result in
changes in learner behaviors and state learner characteristics (Clark, 1999). According to
the adapted model of reciprocality, the problem of student attrition in DE learning
environments has a reciprocal relationship with state learner characteristics such as
learner self-efficacy. Currently, there is no research on self-efficacy comparing across
traditional and distance education environments in Social Work graduate programs.
Social Work self-efficacy. Bandura (1997) describes self-efficacy as a highly-
context dependent construct. Unlike self-concept, which is a global concept, an
individual’s self-efficacy may be high in some domains and low in others. Therefore, to
accurately explain or predict behavior, the self-efficacy measures must be domain-
specific. The study focuses on self-efficacy in the domain of Social Work (SW).
Social Work as a discipline has the mission of working towards both individual
adjustment and institutional justice (Han & Chow, 2009). Social Workers are employed
in a variety of fields, and work with diverse populations. The wide scope of this field
requires Social Work education programs to correspond with a number of specificities.
Effective Social Work education should be relevant, relational, and practical (Galambos,
2008; Witkin & Saleebey, 2007). These qualities of effective SW education facilitate the
abstract application of concepts to real world settings. Accredited Social Work programs
require students to complete a number of supervised fieldwork hours, and many programs
teach theory in case study contexts. These experiences help to develop the knowledge,
skills, attitudes and other relevant characteristics (KSAOs) to apply SW theories, and
may influence student career self-efficacy.
37
An individual’s self-efficacy of career related behaviors has a positive
relationship to motivational factors in higher education (Hackett & Betz, 1995).
Individuals with high levels of career self-efficacy are more likely to show resilience in
the face of challenges, tend to be more organized, and have a greater inclination to seek
out improved methods to meet the needs of the task (Clark, 2005; Allinder, 1994).
Furthermore, these individuals often show greater motivation through enthusiasm, and
commitment to the field (Allinder, 1994). These benefits of self-efficacy are highly
valuable, and are often first conceived through education programs (Holden et al., 2002).
In summary, measures of domain-specific self-efficacy are imperative to ensuring
the validation of predictive and explanatory efforts based on the construct. With regard
to self-efficacy, the more specific the domain, the more effective the measure of
behavioral variability (Bandura, 1997). In this study, Social Work self-efficacy is
compared across traditional and DE learning contexts. The examination of self-efficacy
across these contexts allows current and future research endeavors to derive an
understanding of potential differences in related learner behaviors, such as student
involvement and persistence.
The affect of belonging. The affective construct of belonging is an important
focus of psychological research (Aronson, 2008; Osterman, 2000; Nohria, Lawrence and
Wilson, 2001; Ryan & Deci, 2000; Thompson, Grace and Cohen, 2001). According to
Osterman (2000) this need for belonging is defined as a sense of relatedness to others
within a community. In further defining a sense of community McMillan and Chavis
(1986) discuss the importance of the group’s influence on the individual, the fulfillment
38
of needs, and an emotional bond shared by members of the community. Furman (1998)
adds that a sense of belonging can only be present if members have feelings of trust and
safety within the community. Often the affect of belonging is assessed through the
influence of group norms on the individual and the emotional bonds created within this
group dynamic (McMillan & Chavis, 1986).
Although feelings of belonging have been long understood to be part of the
human experience, early psychological literature lacked a critical emphasis on this socio-
affective characteristic (Ryan, 1995). Over time it was realized that the need to belong
might actually play a far more critical role in the physiological and psychological
wellbeing of most individuals than originally thought (Ryan, 1995). Within a number of
motivation models and theories, feelings of belonging are seen as an essential component
for human development, growth, autonomy, and competence (Deci et al., 1991; Maslow,
1943; Ryan, 1995). Many theorists have gone even further, and describe feelings of
belonging to a community as a human need which must be satisfied in order to develop
higher order psychological functioning (Maslow, 1943; Ryan, 1995). Basic human
needs, as defined by Baumeister and Leary (1995), apply to everyone, in every setting,
and influence cognitive and affective patterns, both positively and negatively.
Literature on feelings of belonging spans the domains of medicine, psychology,
and sociology. Research on belonging shows it to be one of the few interpersonal
processes that directly influences health (Hagerty et al., 2004). Individuals who report
feelings of belonging to one or more communities are less likely to be less stressed,
emotionally distressed, and anxious (Resnick et al., 1997). Resnick and colleagues also
39
reported that individuals who lack a feeling of belonging or community membership are
more likely to engage in suicidal ideation and suicide attempts, substance abuse, and
premature sexual activity.
In summary, the adapted model of reciprocality explains the affect of belonging in
terms of its impact on other factors of the human experience. The development of
feelings of belonging to a community is a human need that if goes unfulfilled, may result
in a number of “pathological and long-lasting consequences” (Osterman, 2000, p. 327).
In the next section, the current literature on this construct is examined with regard to the
learning context.
Feelings of belonging to the academic community. As a construct, belonging is
of particular relevance to examine in the context of education. The significance of this
construct is apparent in the work of Vygotsky and Dewey. Social constructivist
approaches and experiential learning concepts focus on the development of learning
communities through collaborative interactivities (Dewey, 1958; Vygotsky, 1977). These
approaches and concepts emphasize the direct relationship between feelings of belonging
and quality of education (Dewey, 1958).
A sense of belonging to an individual’s campus community is essential for
academic success for the majority of students (Osterman, 2000). Students that lack
feelings of school belonging may be more likely to develop problems with motivation.
According to Tinto (1993), in the context of higher education, there is a positive
correlation between students’ feelings of involvement and degree completion, while
feelings of incongruence or isolation within an academic setting are associated with
40
student attrition. This idea is echoed by a number of other educational researchers.
Within academic contexts, feelings of belonging and a sense of community are
associated with positive emotions such as happiness, satisfaction, and tranquility
(Osterman, 2000). Conversely, a lack of relatedness has been associated with anxiety,
depression (Battistich & Hom, 1997), and a lack of persistence. In order to avoid these
issues, and develop higher order functioning, the need for relatedness to others within a
community must be satisfied (Ryan, 1995).
Dewey (1958) described the institution’s role in developing this sense of
belonging to the academic community. Feelings of belonging are often developed
through student involvement in campus activities. Involvement in a campus community
can be through participation in extracurricular activities such as sports, clubs, and student
government. It can also be having positive relationships and interactions with peers,
faculty, and campus staff. Student involvement in extracurricular activities has been
linked to a number of positive academic traits such as continued involvement, and a
decrease in student attrition (Tinto, 1993). This may be due to the development of
meaningful relationships with significant others on campus (Osterman, 2000), in addition
to allowing greater opportunity to discuss academic subject matters outside the classroom
with peers and teachers (Astin, 1993). According to Tinto (1993) these factors contribute
to persistence in academics.
The state learner characteristic of feeling a sense of belonging to an academic
community is often developed through social relationships within the academic
institution and involvement in extracurricular campus activities. The development of
41
feelings of belonging through campus involvement may pose a potential problem when
students are separated from the campus community spatially or temporally. Students
enrolled in distance education programs may have less opportunity to be involved in on-
campus activities which may influence their feelings of belonging to the academic
community. This lack of opportunity for interaction has been considered by many to be
an inherent characteristic of distance education, a price to be paid in return for the
convenience of anytime, anywhere learning (Shin, 2002). Unfortunately, the
repercussions of this burden may be far more pervasive than simply “missing out” on
peer interactions.
In her work on the theory of community building in distance education, Brown
(2001) detailed a three-stage phenomenon that moved students from classmates to
camaraderie. The first stage of this theory of relationship building in DE contexts is the
most fundamentally different from relationship development in face-to-face contexts. In
this case students must proactively seek others to interact with. According to Brown, this
gravitation would often occur as a result of a shared characteristics or interests. In
traditional oncampus settings, learners may engage in peer interactions and develop
relationships simply as a result of spatial proximity to one another; students enrolled in
DE contexts do not have the same luxury. This initial barrier to “meeting” others may
hinder many students from non-required peer interactions. However, Brown’s research
shows that once peer interactions in DE contexts develop into personal relationships, they
may be more sustainable since the medium of development and maintenance is one in the
same.
42
In summary, a sense of belonging to an academic community influences a number
of student behaviors. Scholars in the field consistently connect feelings of belonging to
positive academic outcomes and persistence (Haythornthwaite et al., 2001; Tinto, 1993).
Conversely, absence of the perception of belonging may have academically detrimental
consequences. Educational theorists have recognized the importance of having a strong
sense belonging to an academic community and have researched this construct
extensively (Deci et al., 1991; Maslow, 1943; McMillan & Chavis, 1986; Osterman,
2000; Ryan, 1995). Through applying the adapted model of reciprocality to this review
of the literature, it can be inferred that a state learner characteristic such as belonging,
may influence and be influenced by the method of instructional delivery and learner
behavior such as proactive student involvement.
Learner Behaviors
Learner behaviors refer to the actions that a learner performs or engages in as a
result of internal stable or state characteristics, and the learning context. Learner
behaviors include achievement behavior, and behavior related to the motivational index
of persistence (Clark, 2005). The focus of this review will be on the learner behavior of
student involvement in academic settings.
Student involvement. Astin (1999) defines student involvement as the “quantity
and quality of physical and psychological energy the student invests in the academic
experience,” (p. 518). This definition includes behavioral engagement in activities such
as studying, interactions with faculty and peers, and participation in campus activities or
groups. Student involvement has been linked to a number of positive academic traits,
43
most notably, student persistence (Tinto, 1993). According to Astin (1999), the
constructs of student involvement and student persistence are closely related to one
another:
The persister-dropout phenomenon provides an ideal paradigm for studying
student involvement. Thus, if we conceive of involvement as occurring along a
continuum, the act of dropping out can be viewed as the ultimate form of
noninvolvement, and dropping out anchors the involvement continuum at the
lowest end (Astin, 1984, 1999).
This view of involvement underscores the importance of research designed to determine
how involvement influences other learner behaviors such as persistence. Astin’s (1984,
1999) theory of student involvement describes several types of involvement within the
academic community. These categories of involvement may be objective-oriented or
socially-oriented. There are two main objective-oriented types of involvement: academic
involvement and athletic involvement.
Academic involvement is characterized as the extent to which students devote
time and efforts to course studies (Astin, 1999). Interestingly, students who are deeply
involved in academic studies often report high satisfaction in college life, but low
feelings of belonging to the campus community. Astin (1999) purports that this may be a
result of the long hours spent studying in isolation. Another category of objective-
oriented involvement is athletic involvement. Similar to those with high academic
involvement, students who are highly involved in athletics report high college
satisfaction, but low feelings of belonging to the campus community in general. This
may be due to long hours required for practice, and time away from campus for games
(Astin, 1999). Socially-oriented involvement includes participation behavior such as
44
involvement in student government, campus organizations, honor societies, and informal
faculty and peer interactions. For the purpose of this study, peer interactions will be
examined.
Peer interactions play a critical role in the development of student involvement
behavior. These interactions expose students to social groups and are the foundational
components to creating meaningful peer relationships. Peer interactions often originate
through opportunity for classroom collaboration (Astin, 1993; Summers et al., 2005). In
class, peer groups allow for the exchange of ideas and perspectives, which help to create
feelings of connectedness within the classroom (Slavin, 1995). Constructivist principles
state that learning is best facilitated as a group process where peers have opportunity to
learn from and scaffold one another (Vygotsky, 1978). Learning through collaborative
group work is often associated with greater enjoyment and increased understanding of
instructional content (Cooper, 1999). Through content-based interactions, peer
relationships develop and often extend outside the boundaries of the classroom.
According to Tinto (1993) extracurricular involvement with peers gives students
opportunity to discuss academic subject matters outside the classroom. This behavior has
been related to gains in learning, and negatively correlated with student attrition.
In discussing student-student interactions, it is helpful to examine the effect of
peer groups within the adapted model of reciprocality. Homophily, or the tendency for
individuals to choose social groups with similar values or beliefs is often prevalent in
academic settings (Ryan, 2001). Students often choose to join organizations they have a
vested interest in, and within these organizations there is a reciprocal socialization factor
45
that influences students. Peer influence is often transmitted through direct student-
student interactions, and other times via more subtle socio-cultural factors such as
modeling (Ryan, 2001). Interactions with socially-based and school-affiliated peer
groups have the potential to influence student engagement in academic achievement
behaviors.
The pervasiveness of peer group influence is dependent on the ability of the
learning context to facilitate peer interactions and group development. For decades, both
critics and proponents of distance education believed that high-quality peer interactions
would be impossible to fully emulate in distance education contexts (Walther, 2001).
However, more recent applications of synchronous DE environments and social software
have bridged the obvious deficits of asynchronous DE contexts. Students enrolled in
synchronous or part-synchronous DE courses have the opportunity for real-time
interaction with peers via chat messaging and video conferencing technologies. Real-
time peer interactions move students away from the content-driven conversations of
asynchronous contexts, and encourage social, rapport-building behavior (Gustafson &
Gibbs, 2000). In real-time contexts within American institutions, it is customary and
appropriate to engage socially with peers prior to discussing content-related topics. This
cultural norm is critical to the development of real and meaningful peer relationships.
Brown’s research and theory development discussed in the preceding section
(2001) emphasizes the importance of peer interactions in the development of
relationships in distance education. Within distance education contexts, initial peer
interactions are often a result of a shared characteristic between learners. Over time,
46
these interactions are likely to increase to the point where students reported feeling that
they were a part of a community of learners. Each progressive stage of Brown’s theory
of community development describes peer interactions as increasingly frequent and
intense. Brown acknowledges peer interactions as a necessary instigator for the socio-
affective state learner characteristics described in this study.
In summary, student involvement through peer interactions is a learner behavior
with long-reaching implications. Applied to the adapted model of reciprocality, student
involvement behavior has been found to have a direct relationship with state learner
characteristics, such as feelings of belonging to a campus community (Brown, 2001), and
the development of domain- and task-specific self-efficacy through constructive feedback
and modeling (Bandura, 2002; Tinto, 1993). In turn, involvement influences and is
influenced by the learning context. Regardless of instructional delivery method,
opportunity for student involvement and peer interaction is critical to the American
educational experience.
Conclusion
Advances in instructional technologies over the past few decades have led to a
dramatic increase in the number of distance education programs, particularly within
higher education (Allen & Seaman, 2009). Despite rapid increases in distance education
enrollment rates and adoption of DE programs, there is still a sizeable gap in the literature
on this topic (Chen & Jang, 2010). Most of the literature on the subject of distance
education is performance-focused, and compares levels of achievement across program
delivery method (Russell, 2001). However, few studies on the topic have examined the
47
influence of instructional delivery on socio-affective and cognitive factors critical for
success (Chen & Jang, 2010). This study begins to close this gap in the literature by
examining elements such as domain-specific self-efficacy, feelings of belonging to the
academic community, and goal orientation across learning context.
This chapter organized the aforementioned constructs within a framework based
on the four components of the adapted model of triadic reciprocality: learning context,
learner characteristics, state learner characteristics, and learner behaviors. This chapter
looked at each of these components both individually and as they relate within the
context of education.
48
Chapter Three
Methods
The surging growth of distance education programs in higher education has
preceded much evidence-based research on this topic. A significant number of studies on
the topic of DE have focused on comparing achievement between on campus and online
programs, however few studies have examined cognitive and socio-affective factors
pertaining to persistence into their analyses. By examining the relationship between
psychological constructs such as feelings of belonging, self-efficacy, and goal orientation
across program delivery platform, the current study provides insight into this gap in the
literature. This chapter will discuss the research questions, sample and population,
methodology, procedures and data collection and analysis.
Research Questions
The literature examined in chapter two underscores the effect of the constructs of
belonging, self-efficacy, and goal orientation within the scope of student motivation.
Social cognitive theory stresses the interrelationship between a learner’s environment,
behavior, and personal factors such as cognition and affect. Using this framework, a
number of constructs will be examined with relation to instructional contexts. Each of
these constructs is a significant force in educational and psychological research; however,
there is little to no research on the relationship of instructional delivery method to these
social psychological constructs, and their relationship to one another. The following
research questions in this study aim to begin closing this gap in the literature:
49
1. Is there a difference in student sense of belonging, self-efficacy, and goal orientation
by program delivery method?
2. Do feelings of belonging predict self-efficacy or goal orientation?
3. Does involvement in out of class activity, such as frequency of peer interactions,
predict student feelings of belonging?
Research Design
The primary purpose of this study is to examine potential differences in an
individual’s feelings of belonging to their academic community, career self-efficacy, and
goal-orientation across instructional delivery methods. This information is highly
relevant, however it is not enough to have practical application in higher education. To
delve deeper into the topic, the secondary purpose of this study is to determine whether
there is a predictive relationship between feelings of belonging and self-efficacy or goal-
orientation. Lastly, this study will examine the relationship between out-of-class activity
and feelings of belonging to an academic institution. One common critique of distance
education is that these contexts are not conducive to high-quality student-teacher and
student-student interactions (Barbera, 2004). According to the adapted model of
reciprocality (Bandura, 2002), these interactions, or lack thereof, may then impact an
individual’s cognitive and affective factors linked to motivation.
50
In order to analyze potential relationships between these constructs a quantitative
design was implemented. The independent variables in this study were 1) program
delivery method, which will be determined by questionnaire, 2) feelings of belonging,
which will be measured using the two subscales of the Adapted Social Connectedness
Scale, and 3) frequency of involvement in out-of-class activities as measured by self-
report.
Population and Sample
The population for this study were graduate students in both online and on-campus
versions of a Master’s in Social Work program at a large, private research university
located within an urban community in Southern California (N=491). The oncampus
cohorts include students enrolled on the main campus (n=131) and on a satellite campus
(n=67). The satellite campus was included in this study to act as a control for the
potential effect of simply being on the main campus, rather than differences due to
instructional delivery. The specific sample for this study is drawn from students enrolled
in their first year of Social Work classes. The DE sample (n=293) was approached to
participate in the study via electronic mail, sent by faculty and staff at the school of social
work. Through this email students were provided a link to the survey housed on
Qualtrics so as to be accessed anonymously. Students enrolled in the on-campus cohorts
were approached in class by either the principal investigator with instructor permission,
or directly by the instructor. Students in the on-campus program were provided a paper
version of the survey.
51
Instrumentation
Participants were asked to complete a self-report questionnaire consisting of a
number of demographic questions, and three subscales aimed to measure the constructs
of belonging, career self-efficacy, and goal-orientation (Appendix A). Self-report
measures were also used to ask students about their frequency of out-of-class activity.
Demographic Questions. A number of demographic questions were asked in
order to determine possible socio-cultural influences on the psychological constructs
being measured. These questions asked learners to provide information regarding their
gender, racial-ethnic group, marital status, number of dependents, program type they are
enrolled in, and employment status. Additionally, participants were asked about prior
experiences with online courses/programs and residence proximity from campus.
Finally, students were asked about their frequency of participatory involvement. This
question was asked differently for students enrolled in face-to-face and online programs.
For students in the DE program, frequency was measured by number of out-of-class
interactions per month on their “wall,” a Facebook-like social forum within the course
structure. Students enrolled in the traditional oncampus program were asked to report the
number of times they would socialize with peers per month. These questions allow for
comparisons of demographic differences between programs, and are relevant for the
results of this study.
Adapted Social Connectedness & Academic Classroom Community Scales.
The belonging scale that will be used in this study is the Adapted Social Connectedness
Scale (ASCD) derived from Lee and Robbins’ (1995) original Social Connectedness
52
Scale (Summers et al., 2005). The original scale focused on measuring feelings of
connectedness to peers in higher education contexts. The adapted Social Connectedness
Scale measures feelings of connectedness specific to the campus context. This scale
consists of 12 questions that are to be answered on a six-point Likert scale (ranging from
1=strongly disagree to 6= strongly agree). The original Social Connectedness Scale
reported an alpha of .92 (Lee & Robbins, 1995), which is similar to the adapted version,
where reliability ranged from .90 to .92 (Summers et al., 2005). A reliability analysis
conducted for this study found an alpha of .91 for the adapted Social Connectedness
Scale. Feelings of belonging to the academic program were assessed using an adapted
version of the Academic Classroom Community Scale (ACCS) (Summers et al., 2005).
This measure was developed based on evidence-based constructs related to the
development of feelings of belonging at the micro-classroom level, but was modified for
this study to measure feelings of belonging to the Masters in Social Work program rather
than to a specific classroom. The ACCS is a four-item measure with a reported alpha of
.82 (Summers et al., 2005). The reliability analysis for this study reported an alpha of .80
for the adapted Classroom Community scale.
Patterns of Adapted Learning Scale (PALS). Goal orientation theory describes
a goal as a potential motivation to learn. Goal orientation can be either performance or
mastery oriented, and within performance goals, an individual may have performance
approach or performance avoidance goal orientation. Students with mastery goal
orientation are more likely to engage in adaptive learning behaviors than students with
performance approach or avoidance goal orientations (Eliot & Church, 1997; Midgley et
53
al., 2000). Midgley and colleagues (2000) developed the Patterns of Adaptive Learning
Scales (PALS) to examine student motivation, affect, and goal-oriented behavior within a
learning context. Specifically, PALS includes a number of scales that examine student
and teacher goals and perceptions of their academic environment. In this study a
subsection of the PALS scales were used. This subsection examines student personal
achievement goal orientations with scales focused on mastery goal orientation,
performance approach goal orientation, and performance avoidance goal orientation. A
reliability analysis was performed and all subsections had a reported alpha greater than
.70, the minimum level of acceptability used in this study. The mastery goal orientation
scale was found to have an alpha of .79, as compared to the alpha of .85 reported in the
literature. An alpha of .86 was reported for the performance approach scale in this study,
which is comparable to the alpha of .89 reported in the literature (Midgley et al., 2000).
The reliability analysis performed for this study reported an alpha of .81 for the
performance avoidance goal orientation scale. This reliability coefficient is higher than
the alpha of .74 stated in the literature.
The revised mastery orientation and performance approach scales consist of five
questions while the revised performance avoidance scale is comprised of four questions.
The individual PALS scales are scored on a 1-5 Likert scale ranging from 1, “Not at all
true” to 5, “Very true.” The revised PALS measures allow a greater depth of insight into
adaptive and maladaptive learning behaviors.
Ehrenkranz School of Social Work Scale (ESSW). Self-efficacy, as a construct
is highly context specific. An individual’s self-efficacy may vary by environment and
54
task, and must therefore be measured as such (Bandura, 2006). Due to these validity
constraints, many professions have developed specific self-efficacy measures. Within the
field of Social Work, there are a number of self-efficacy scales, each with a focus on a
specific context (Betz & Hackett, 1981; Cuzzi et al., 1996). However, there is only one
scale that aims to measure students’ social work career self-efficacy. This instrument, the
Social Work Self-Efficacy Scale, is comprised of two subscales; one of which is the
Ehrenkranz School of Social Work (ESSW) scale. The ESSW is a set of 19 questions
developed from suggestions provided by chairpersons across five emphases of Social
Work (practice, HBSE, field, policy and research) from the Ehrenkranz School of Social
Work, NYU (Holden et al., 2002). These questions reflect the skills often associated with
success as a Social Worker, ranging from practice ethics to research design. In the
original use of the entire SWSE scale, students were asked to answer the questions within
the current context, as well as to answer them in retrospect. This form of retrospective
pre-test allowed the researchers to determine changes in self-efficacy over the course of
their social work education. As the participants in the current study are in their first year
of the program, this retrospective analysis is not necessary. For this study, instructions
will be for students to complete the questions as they feel now. This scale consists of 19
questions, with a reported alpha of .94 (Holden et al., 2002). This is similar to the alpha
of .96 found from the reliability analysis performed in this study.
Procedure and Data Collection
All data was collected via self-report questionnaires that were distributed either in
person or online. Survey delivery varied depending on whether students were enrolled in
55
the online program, or face-to-face programs. Students enrolled in the online program
were sent an email from a faculty or staff member from the institution’s school of social
work. This email provided students a link to the online survey, and a brief description of
the study. This email also explained that participation in the study is voluntary and
anonymous. The survey was administered online using the survey creator site Qualtrics
For students enrolled in face-to-face programs on the main campus and the satellite
campus the principal investigator and faculty members distributed surveys and provided a
brief explanation of the purpose of the study. Students were informed verbally that
responses are anonymous and participation is voluntary. The expected time to complete
either the online or paper version of the survey was originally estimated at approximately
20 minutes, however, in actuality, students took only between five and ten minutes to
complete the survey. In applicable cases the researcher left the room while respondents
completed the survey to ensure anonymity was maintained. In these cases, participants
were asked to leave their surveys on a desk placed in the back of the room. All
participants were informed that there is no immediate benefit for them to participate in
the study and they will not be penalized for not participating.
Data Analysis
The independent variables in this study are 1) program delivery method, 2) feelings
of belonging, and 3) frequency of involvement in out of class activities. The
corresponding dependent variable are 1a) student sense of belonging, 1b) self-efficacy,
1c) goal orientation, 2a) self-efficacy, 2b) goal orientation and 3) feelings of belonging.
56
Data collected to examine the independent and dependent variables was coded,
cleaned, and input into the Statistical Package for the Social Sciences (SPSS) 15.0
program. Descriptive statistics were performed to analyze demographic information,
while three separate one way ANOVAs were conducted to answer the first research
question. For the second and third research questions, a series of linear regressions were
used to analyze the data. A reliability analysis for the scales using Cronbach’s alpha was
also conducted. The results and implications of these analyses will be discussed in
chapters four and five.
57
Chapter Four
Results
The goal of this study was to investigate social cognitive factors pertaining to
motivation across instructional delivery methods. The research questions developed were
based on constructs such as feelings of belonging, career self-efficacy, and goal
orientation. Specifically, this study was designed to answer the following research
questions:
1) Is there a difference in feelings of belonging, self-efficacy, or goal orientation
by method of program delivery?
2) Do feelings of belonging predict self-efficacy or goal-orientation?
3) Does involvement in out of class activity, such as frequency of peer
interactions, predict student feelings of belonging?
These research questions were answered by the use of three survey instruments
and demographic questions totaling 69 questions. The survey instruments were used to
measure the constructs of goal orientation (PALS, Midgley et al., 2000), feelings of
belonging to a campus community (adapted Social Connectedness & Classroom
Community Scales, Summers et al., 2005), and Social Work Self-Efficacy (ESSW,
Holden et al., 2002). Surveys were distributed to students enrolled a the Masters of
Social Work program at the Main Campus (MC) of a private research university, the
Satellite Campus (SC) located about 130 miles from the main campus, and Distance
Education campus (DE). Respondents totaled n=491, with 293 participants from the DE,
131 from MC, and 67 from SC.
58
The purpose of this chapter is to report the findings of this study. The first section
will provide an overview of the descriptive characteristics of respondents. This includes
information regarding campus of enrollment, length of time in the program, and
demographic data. This is followed by an analysis of results organized by research
question. Statistical analyses performed are described and outcomes are provided.
Descriptive Characteristics of Respondents
In order to answer the proposed research questions surveys were distributed to
students enrolled in a Masters of Social Work program. Students were sampled from a
variety of campuses. The larger oncampus sample was from the main campus located in
the urban hub of a large coastal city in Southern California (n=131). Students enrolled in
face-to-face programs were also sampled from the satellite campus (n=67); this campus
offers the same courses with many of the same professors as the main campus MSW
program. Examining data from the SC helps to determine whether potential differences
in feelings of belonging to a campus community are due to a difference between distance
and face-to-face instruction, or if there is a difference due to being enrolled on the main
campus. Data from these face-to-face programs was compared to data collected from
students enrolled in the distance education program (n=293), an online, synchronous
MSW program with a comparable course of instruction to the oncampus programs.
Demographic information. Demographic information was collected about these
groups and nonparametric analysis was conducted on the information gathered.
Pearson’s Chi-square test showed significant results with regard to campus of enrollment
as cross-tabulated with participant age, marital status, employment status, and number of
59
dependents. The majority of participants enrolled in the MSW 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 aged 41-60 are enrolled in the DE (n= 51, 17%) than in either of the face-to-
face programs (MC, n= 6, 5%; SC, n=9, 13%). Conversely, a larger percentage of
students aged 18-23 are enrolled in the face-to-face program delivery contexts (MC,
n=42, 32%; SC, n=15, 22%) as compared to students in the online program (DE n=24,
8%).
Table 1
Participant demographics: Age
Campus
Age
Total
DE MC SC
18-23 24 42 15 81
24-30 124 70 30 224
31-35 57 8 10 75
36-40 37 5 3 45
41-50 43 4 8 55
51-60 8 2 1 11
Total 293 131 67 491
Significance was also found using Pearson’s Chi-square analysis with regard to
marital status. Forty-five percent (n=128) of students enrolled in the online program are
married or in a domestic partnership as compared to 20% (n=26) enrolled at MC and 30%
(n=20) at SC. This outcome is reflected in the difference in the number of
dependents between students in the DE and those in the face-to-face programs. The
percentage of students in the online program that report one or more dependents is 47%
60
(n=136). By contrast, only 15% (n=19) of students enrolled in the MSW program at the
MC, and 23% (n=15) enrolled at the SC claim one or more dependents. The Chi-square
test also showed significance with regard to employment status. Thirty-five percent
(n=101) of participants in the DE reported working full time, compared to 13% (n=17)
of those enrolled at the MC and 21% (n=14) at the SC.
Table 2
Participant demographics: Marital status
Marital Status
Campus
Total DE MC SC
Married/Domestic
partnership
128 26 20 174
Single 128 103 39 270
Divorced 29 1 7 37
Separated 4 1 0 5
Widowed 3 0 0 3
Total 292 131 66 489
Table 3
Participant demographics: Number of dependents
Campus
Number of
Dependents
Total DE MC SC
0 156 112 51 319
1 52 13 5 70
2 50 3 5 58
3 20 2 3 25
4+ 14 1 2 17
Total 292 131 66 489
61
In summary, participants enrolled in the DE program are significantly more likely
to be older, married, employed, and have dependents compared to those enrolled in either
of the oncampus programs. These results are consistent with research conducted on the
topic of demographics in distance education (Forster & Washington, 2000; Freddolino &
Sutherland, 2000; Haga & Heitkamp, 2000; Oliaro & Trotter, 2010).
Analysis of Results
Data was collected using self-report instruments and analyzed in order to answer
the research questions proposed in Chapter One. Each research question was analyzed
and answered separately; this results presented in this chapter are organized as such.
Research question: Is there a difference in feelings of belonging, self-efficacy,
or goal orientation by method of program delivery? This research question seeks to
investigate potential differences between program delivery methods with regard to select
socio-affective and cognitive factors. Each of these constructs was analyzed separately in
this study.
Belonging across program delivery. To determine whether there is a difference
in feelings of belonging to the campus community across program delivery methods
students enrolled in both the face-to-face programs and the online program were asked to
complete the Adapted Social Connectedness and the modified Academic Classroom
Community Scales (Summers et al., 2005). The data collected from this survey was then
analyzed using an one way ANOVA (Table 4). The independent variable of program
delivery method was compared across three groups: students enrolled at the main campus
MSW program (MC), those enrolled at the satellite campus (SC), and the distance
62
education (DE) program to determine levels of belonging to the academic community. A
concern in the design of this research was that the effect of being on the main campus
would overshadow the feature of the learning context intended for study, the difference
between face-to-face and distance education. To mediate this concern, the satellite
campus was used as a control for the possible socio-affective effects of being on the main
campus.
Table 4
One way ANOVA Measuring Belonging Across Program Delivery
Sum of
Squares df Mean Square F Sig.
Between
Groups
19.903 2 9.952 13.753 .000*
Within Groups 353.114 488 0.724
Total 373.017 490
*p < .05
Due to unequal sample sizes across campuses, a Scheffe post-hoc was performed
(Table 5). Significance differences in feelings of belonging were found between groups
in face-to-face and online program delivery methods. Pair-wise differences were
demonstrated between the DE and MC cohorts, as well as between the DE and the SC
groups. No significant differences were reported between the MC and SC groups. These
results suggest that there are differences in belonging between oncampus and DE learning
contexts, but not across groups within the same instructional medium. Regardless of
being on the main campus, or the satellite campus over 100 miles away from the main
campus, students in face-to-face programs had a higher sense of belonging to the
academic community than their DE counterparts.
63
Table 5
Scheffe Post-Hoc Belonging across program delivery method
Campus
Campus
Mean
Difference
Std.
Error
Sig.
95% Confidence Interval
Lower
Bound
Upper
Bound
DE
MC -.42002
*
0.0894 .000 -0.6395 -0.2005
SC -.39050
*
0.11519 .003 -0.6733 -0.1077
MC
DE .42002
*
0.0894 .000 0.2005 0.6395
SC 0.02952 0.12776 .974 -0.2842 0.3432
SC
DE .39050
*
0.11519 .003 0.1077 0.6733
MC -0.02952 0.12776 .974 -0.3432 0.2842
*p < .05
Self-efficacy across program delivery. A one way ANOVA was conducted in
order to compare student level of self-efficacy across method of program delivery (See
Table 6). No significant differences were found between groups. In other words, there is
no difference in students’ Social Work self-efficacy whether they are enrolled in
oncampus or DE programs. This result echoes findings from much of the No Significant
Difference literature (Russell, 2001).
Table 6
One way ANOVA Measuring Self-Efficacy Across Program Delivery
Sum of
Squares
df
Mean
Square
F Sig.
Between
Groups
0.514 2 0.257 1.164 .313
Within Groups 107.721 488 0.221
Total 108.235 490
*p < .05
64
Goal orientation across program delivery. The construct of goal-orientation is
comprised of three distinct and measureable subsections: mastery goal orientation,
performance avoidance goal orientation, and performance approach goal orientation. For
the purpose of this study, each of these components was analyzed separately.
Mastery orientation across program delivery. In order to determine potential
differences in mastery goal orientation between methods of program delivery a one way
ANOVA was conducted (Table 7). Results of this analysis showed no significant
differences between groups. That is to say, there is no difference in the extent of mastery
orientation between students in oncampus and DE programs.
Table 7
One way ANOVA Measuring Mastery Orientation Across Program Delivery
Sum of
Squares
df
Mean
Square
F Sig.
Between
Groups
0.514 2 0.257 1.164 .313
Within Groups 107.721 488 0.221
Total 108.235 490
*p < .05
Performance avoidance across program delivery. Table eight presents the results
of an one way ANOVA performed to analyze differences in performance-avoidance goal
orientation between program delivery methods. Significance was found between groups.
Results from a Scheffe post-hoc analysis showed pair-wise differences in performance-
avoidance goal orientation between the DE and SC groups (See Table 9). No differences
were found between MC and either group. That is, students enrolled in DE contexts
reported significantly higher levels of performance avoidance goal orientation than
65
students enrolled in the satellite campus. Since there was no difference between students
enrolled in the main campus program and either other group, it can be assumed that the
method of instructional delivery was not a factor in this difference.
Table 8
One way ANOVA Measuring Performance Avoidance Across Program Delivery
Sum of Squares df Mean Square F Sig.
Between
Groups 10.139 2 5.069 5.573 .004*
Within Groups 443.933 488 0.91
Total 454.072 490
*p < .05
Table 9
Scheffe Post-Hoc Performance Avoidance Across Program Delivery
Campus Campus
Mean
Difference
Std.
Error
Sig.
95% Confidence Interval
Lower
Bound
Upper
Bound
DE
MC 0.22287 0.10024 .086 -0.0233 0.469
SC .38114
*
0.12916 .013 0.064 0.6983
MC
DE -0.22287 0.10024 .086 -0.469 0.0233
SC 0.15826 0.14325 .544 -0.1935 0.51
SC
DE -.38114
*
0.12916 .013 -0.6983 -0.064
MC -0.15826 0.14325 .544 -0.51 0.1935
* p < .05
Performance approach across program delivery. Similar to other constructs
analyzed in this study, a one way ANOVA was conducted to determine significant
differences across program delivery methods with regard to performance approach goal
orientation (Table 10). Overall significance was reported between groups, but no pair-
wise differences were found in the Scheffe post-hoc analysis. Differences between the
DE and SC groups were reported to be approaching significance (Table 11). This implies
66
that as a construct there were consistent trends in performance approach goal orientation,
but not enough to be significant between specific groups.
Table 10
One way ANOVA Measuring Performance Approach Across Program Delivery
Sum of Squares df
Mean
Square
F Sig.
Between Groups 6.296 2 3.148 3.672 .026*
Within Groups 418.355 488 0.857
Total 424.651 490
*p < .05
Table 11
Scheffe Post-Hoc Performance Approach Across Program Delivery
Campus Campus
Mean
Difference
Std. Error Sig.
95% Confidence
Interval
Lower
Bound
Upper
Bound
DE
MC 0.18314 0.09731 0.171 -0.0558 0.4221
SC 0.29393 0.12538 0.065 -0.0139 0.6018
MC
DE -0.18314 0.09731 0.171 -0.4221 0.0558
SC 0.11079 0.13907 0.728 -0.2307 0.4522
SC
DE -0.29393 0.12538 0.065 -0.6018 0.0139
MC -0.11079 0.13907 0.728 -0.4522 0.2307
*p < .05
Research question: Do feelings of belonging predict self-efficacy or goal
orientation? The second research question posed in this study investigates the existence
of a predictive relationship between constructs. Specifically, whether feelings of
belonging predict student self-efficacy and goal orientation. This question is relevant as
it helps to describe the relationship between feelings of belonging and state and stable
learner characteristics.
67
Belonging as a predictor of self-efficacy. A linear regression was performed to
determine whether feelings of belonging predict students’ Social Work self-efficacy.
Feelings of belonging were found to be significantly predictive of student self-efficacy at
6.5%. In other words, feelings of belonging are reported to at least partially predict a
student’s Social Work self-efficacy. This result highlights the relevance of belonging as
a construct with regard to other adaptive learner characteristics.
Table 12
Linear regression of belonging as a predictor of self-efficacy
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 3.44 0.24 14.318 .000
BelongMean 0.322 0.055 0.255 5.796 .000
a. Dependent Variable: SelfEfficacyMean
Belonging as a predictor of mastery goal orientation. In order to determine
whether feelings of belonging predict mastery goal orientation a linear regression was
performed. Significance was reported in the analysis of this relationship; however,
feelings of belonging were determined to be only weakly predictive at 1.4%. This means
that feelings of belonging explain a small percentage of an individual’s mastery goal
orientation.
68
Table 13
Linear regression of belonging as a predictor of mastery goal orientation
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 4.368 0.105 41.732 .000
BelongMean 0.064 0.024 0.118 2.626 .009
a. Dependent Variable:MasteryMean
Belonging as a predictor of performance approach goal orientation. A linear
regression was conducted to determine whether feelings of belonging predict
performance approach goal orientation. Significance was found for this relationship at
1.1% of variance in performance approach goal orientation. In other words, feelings of
belonging to an academic community will explain a small percentage of an individual’s
performance approach goal orientation.
Table 14
Linear regression of belonging as a predictor of performance approach goal orientation
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 2.99 0.208 14.405 .000
BelongMean -0.113 0.048 -0.106 -2.365 .018
a. Dependent Variable: PerformApproachMean
69
Belonging as a predictor of performance avoidance goal orientation. The
relationship between feelings of belonging as a predictor of performance avoidance goal
orientation was analyzed using a linear regression. This predictive relationship is
reported to be significant at 3.8% of variance in performance avoidance goal orientation.
That is, 3.8% of an individual’s performance avoidance goal orientation is explained
through feelings of belonging to the academic community.
Table 15
Linear regression of belonging as a predictor of performance avoidance goal
orientation
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B
Std.
Error
Beta
1
(Constant) 3.727 0.212 17.597 .000
BelongMean -0.214 0.049 -0.194 -4.373 .000
a. Dependent Variable: PerformAvoidMean
Research question: Does involvement in out of class activity, such as
frequency of peer interactions, predict student feelings of belonging? The third
research question examines a potential predictor influencing feelings of belonging.
Specifically, this study asks whether the frequency of out-of-class student involvement
predicts students’ feelings of belonging to the campus community. This research
question was examined separately for students enrolled in the face-to-face program and
those in the online MSW program.
Online student involvement predicts feelings of belonging. Student involvement
in the online MSW program is measured in terms of the frequency of out-of-class social
interactions on the individual students’ “wall.” A student’s “wall” is a virtual location on
70
the program dashboard that can be accessed to engage socially and academically with
peers. In order to determine whether frequency of student involvement in the online
program predicts feelings of belonging a linear regression was conducted. The results of
this analysis were found to be significant, but weakly predictive at 1.9%. Worded
another way, a very small percentage of students’ feeling of belonging to the academic
community can be directly related to their frequency of interactions with peers on their
“wall.”
Table 16
Linear regression of DE student involvement as a predictor of feelings of belonging
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B
Std.
Error
Beta
1
(Constant) 3.889 0.096 40.505 .000
Online:
Frequency
of social
interactions
0.078 0.033 0.136 2.365 .019
a. Dependent Variable: BelongMean
Oncampus student involvement predicts feelings of belonging. In this study,
oncampus student involvement is defined as the frequency of out-of-class interactions a
student has with his or her peers in the program. A linear regression was performed to
determine whether frequency of student involvement in the oncampus program predicts
feelings of belonging. Significance for this relationship was reported and 9.5% of
variance in feelings of belonging was predicted. This means that the frequency of student
involvement for students enrolled in the oncampus MSW program predicts approximately
9.5% of students’ feelings of belonging. In summary, student involvement is a much
71
stronger predictor of feelings of belonging for students enrolled in the oncampus program
than those in the DE program.
Table 17
Linear regression for oncampus student involvement as a predictor of feelings of
belonging
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B
Std.
Error
Beta
1
(Constant) 3.826 0.084 45.784 .000
Oncampus:
Frequency
of social
interactions
0.284 0.046 0.309 6.24 .000
a. Dependent Variable: BelongMean
In summary, this chapter reported the results of the statistical analyses performed
to answer the research questions of this study. An overview of the research questions and
methodology was provided. The first analysis presented in this chapter was a description
of participants, which was consistent with the research literature in the field. As expected,
students enrolled in DE programs were more likely to be older, married, have dependents
and be employed. The research questions were then introduced individually, and analysis
results were presented. The first research question asked about potential differences in
the constructs of self-efficacy, feelings of belonging to the academic community, and
goal orientation across instructional delivery method. No significant differences were
found in reported self-efficacy or mastery goal orientation across contexts. However,
significant differences were found with regard to performance approach and avoidance
orientations and feelings of belonging to the academic community.
72
The adapted model of triadic reciprocality emphasizes components pertaining to
the learner and learning environment, in addition to the relationships between these
constructs. To better understand these relationships, the construct of belonging was
examined with regard to Social Work self-efficacy and goal orientation, and significant
predicative relationships were found. Belonging predicted Social Work self-efficacy, and
weakly, but significantly predicted both mastery and performance goal orientations. The
last research question was developed to provide insight into practice. Specifically, this
question looked at student involvement, a potential predictor of feelings of belonging.
The results of this analysis showed a significant and predictive relationship for students
enrolled in both oncampus and DE programs. Significant results were found for all or
part of each research question. The implications of these results will be discussed in the
next chapter.
73
Chapter Five
Discussion
This chapter begins with an overview and discussion of the results from a social
cognitive perspective. Specifically, the adapted model of reciprocality is used to frame
the discussion of learner characteristics, state learner characteristics, and learner
behaviors with regard to learning context. This chapter concludes with a discussion of
implications for affected parties, study limitations, and directions for future research.
This study examined a number of socio-affective and cognitive constructs
pertaining to motivation across instructional delivery methods. These include feelings of
belonging to an academic community, career self-efficacy, and goal orientation. Each of
these constructs has been correlated with motivation in academic contexts; however,
research on these factors has been limited to on-campus, traditional education settings.
The purpose of this study was to examine these constructs across both oncampus and
distance education contexts.
The research questions developed for this study were based on evidence-based
constructs related to persistence in oncampus educational contexts. These questions were
examined across both face-to-face and DE academic environments. The following
section provides a discussion of the results from this study.
Learner Characteristics and the Learning Context
Within this study, certain learner characteristics were examined with regard to the
learning context; specifically, the relatively stable characteristics of learner demographics
and goal orientation were compared across oncampus and distance learning programs.
74
According to the adapted model of triadic reciprocality learner characteristics influence
and are influenced by factors of the learning environment, state learner characteristics,
and learner behaviors.
The literature comparing learner demographics in oncampus and distance
education contexts shows significant differences between the two populations (Oliaro &
Trotter, 2010). This study examined age, marital status, employment status, and number
of dependents, and as expected, significant results were found similar to the demographic
information reported in the literature. In particular, students enrolled in distance learning
programs were older, more likely to be married, have dependents, and be employed.
These factors often impose outside demands on students’ time and resources making the
flexibility and convenience of anytime, anywhere learning within distance education
programs a desirable option for a non-traditional learner population (Lorenzetti, 2005).
These characteristics have been correlated with a lack of student persistence in academic
environments agnostic to methods of instructional delivery (Astin, 1991; Tinto, 1993).
However, this outcome may be compounded by socio-affective factors related to the
learning environment. The application of principles derived from adult learning theory in
DE contexts may better mitigate student attrition and help to ensure that learning needs
pertaining to persistence and motivation are met for a non-traditional population.
The results of this study reported that the method of instructional delivery, a
feature of the learning context, is related to the learner characteristic of goal orientation.
In a study by Ames (1992), patterns of goal orientation were found across enrollment in
DE and oncampus programs, with greater performance goal orientations reported in DE
75
programs. In this study significant differences were found between learners in distance
education contexts and those enrolled in the satellite campus; however, no significant
differences were found between the main campus sample and either group. This lack of
significance implies that, with regard to goal orientation, between group differences are
more likely related to factors other than method of instructional delivery.
In summary, differences in learner characteristics such as age, marital status,
number of dependents, and employment status may play a significant role in student
attrition in DE environments. Distance learning programs attract non-traditional learners,
but may not be able to retain them effectively. Accounting for learner characteristics in
the design and implementation of DE courses is an important implication of this research.
This implication, among others, will be discussed in greater depth later in this chapter.
State Learner Characteristics and the Learning Context
The adapted model of triadic reciprocality emphasizes the importance of socio-
affective and cognitive characteristics by separating out state learner characteristics from
the construct of learner characteristics. State learner characteristics are highly context-
dependent, variable, and play an important role in the motivation literature (Bandura,
2001; Osterman, 2001). In this study, the state learner characteristics of Social Work self-
efficacy and feelings of belonging to the academic community were examined.
The results of this study found that there is no significant difference in Social
Work self-efficacy across program delivery platforms. This outcome is consistent with
findings reported in the literature (Russell, 2001). Although this result is desirable, it begs
76
the question that if learners have similar levels of career self-efficacy what are other
factors influencing persistence in DE contexts?
In this study, significant differences in perception of belonging were reported
across methods of program delivery. Students enrolled in both the main campus and the
satellite campus reported higher perceptions of belonging to the academic community
than students enrolled in the distance education program. This between-group difference
suggests that, compared to the face-to-face MSW program, the DE learning context may
not be as conducive to community and relationship building. Information derived from
this construct analysis is valuable to all parties involved in academia: students, educators,
administrators, and instructional designers. The impact of perceived belonging reported
in this study echoes the sentiment that feelings of belonging to an academic community is
what that ultimately “attracts and retains learners” (Rovai, 2002, p. 199). Designing
engaging courses and collaborative activities with roots in constructivist learning theory
provides students the opportunity to develop feelings of belonging in oncampus
programs, but as the delivery method changes, this is often not enough. Web-based
learning is the new frontier in education and the majority of faculty teaching in these
programs are relatively new users of this instructional medium, although within the
traditional classroom they may be veteran instructors. Unfortunately, simply porting
instructional techniques used in face-to-face settings to a new instructional medium often
results in ineffective instructional approaches. Even so, faculty training in using DE as
an instructional medium is rare and often occurs informally (NCES, 2009). Training
faculty on how to effectively build rapport and meaningful relationships with their
77
students in distance courses may be the key to developing a learning community and
reducing high student attrition rates in these programs.
In summary, educational scholars contend that research designed to simply find
differences between traditional oncampus and DE contexts is simply not enough (Weigel,
2000; MacDonald, 2002); what is needed is innovative expressions of theory in practice.
This study attempts to provide insight into application by examining not the problem
behavior of student attrition, but rather, constructs such as state learner characteristics
that relate to the problem.
Feelings of Belonging as a Predictor of Constructs. In addition to examining
potential differences in individual constructs, this study addresses the relationships
between these constructs. Specifically, feelings of belonging were examined in relation to
the other socio-affective and cognitive constructs addressed in this study. Although there
is some research that explicitly focuses on feelings of belonging in academic contexts
(Osterman 2000), the quantity of this research is sparse compared to research on the
constructs of self-efficacy and goal orientation. Therefore, the importance of belonging
was measured as it relates to the well-researched topics of domain-specific self-efficacy
and goal orientation. A linear regression was performed to determine whether a
predictive relationship between the constructs exists. The results of this analysis found
that feelings of belonging are a significant predictor of self-efficacy. Feelings of
belonging were also found to be a significant, but weak predictor of mastery,
performance approach, and performance avoidance goal orientations. These findings
indicate that feelings of belonging are indeed a critical component relating to the
78
constructs of Social Work self-efficacy and goal orientation, and should be accounted for
in the design and implementation of DE programs. Although significance differences
were found between the oncampus and DE samples, demographic variables such as age
were not controlled for in this study. Therefore it is possible that variables pertaining to
belonging may differ for adult learners as compared to traditional student demographics.
The implications of this study account for these potential differences.
Student Involvement as a Predictor of Feelings of Belonging. The purpose of
the final research question proposed in this study is to determine whether there is a
predictive relationship between student involvement and feelings of belonging to the
academic community. To answer this question, participants were asked about their
frequency of out-of-class engagement with their peers in the program. Out-of-class
activity was found to be significantly predictive for both on-campus and DE students.
The importance of student involvement in academic settings is widely accepted (Astin
1992; Tinto, 1993), but for years opportunity for involvement has often been seen as a
luxury of face-to-face, traditional education. However, as synchronous programs and
synchronous features within asynchronous programs gain popularity, there is increased
opportunity to use shared spaces in the virtual environment (Jamieson, 2003). Similar to
social spaces on university campuses, these spaces in the online environment are places
where learners can gather for academic or social discussion. The creation of virtual
social spaces is not enough to ensure their use.
There are a number of ways to encourage group work within the virtual classroom
context. Within the traditional classroom, Aronson (2008) suggests a jigsaw method of
79
group formation. This method encourages collaboration of students with diverse
backgrounds and ability levels. Jigsaw groups help scaffold lower-achieving students,
while all students benefit from the divergent perspectives introduced in the group.
However within DE contexts, students tend to build and sustain meaningful relationships
with classmates with whom they share a commonality (Brown, 2001). This shared
characteristic may be geographic, demographic, or a shared goal or motivation to engage
in the course. The implications of this study in light of prior research suggest that
students in DE contexts may benefit from collaborative group work with similar others,
an anti-jigsaw. One way for instructors to apply this finding is to group students into peer
groups by interest. For example, students interested in Military Social Work can work
together to write a paper on the topic. An illustration of this using the adapted model of
reciprocality is that a learner’s behavior is focused on in-class group collaboration, this
behavior may positively affect state learner characteristics such as self-efficacy and
belonging. Changes in these state learner characteristics will have a reciprocal effect
back on learner behavior, resulting in an increase of peer interactions. In theory, the
involvement-belonging cycle is an infinite relationship of reciprocality.
In summary, the results of this study found that student involvement behavior is a
predictor of feelings of belonging to an academic community. The answer to this
research question is of value to educators and administrators alike.
Socio-Affective Constructs and the Problem of Student Retention
A lack of student retention in distance education programs is a cause for concern
for educators and administrators of these programs (Sloane Consortium, 2009). Students
80
drop-out of DE programs at a significantly higher rate than students enrolled in oncampus
programs, which underscores the need for comparative research on factors pertaining to
motivation. This current study found significant differences in student demographics and
in the socio-affective factor of belonging. Both of which are correlated to student
retention (Cercone, 2008; Tinto, 1993).
Perceived belonging in higher education contexts is positively correlated to
student persistence (Astin, 1992; Tinto, 1993). A sense of community is crucial for
retention for traditional college students. However, this construct has not been well-
researched for non-traditional student populations. Adult learners tend to have higher
student attrition rates than traditional full-time student populations (Cercone, 2008). This
discrepancy may be a indication of learner needs, both personal and academic, that are
not traditionally accounted for in higher education contexts. Whether feelings of
belonging to the academic institution are related to persistence in adult student
populations should be considered when examining the relevance of this study for student
retention. Although this current study did not control for significant differences
demographics, the needs of adult learners are represented in the implications below.
Implications
An escalating number of distance education programs, high attrition rates, and a
gap in the corresponding research make studies such as this highly relevant to the field.
This section details the potential implications of these findings for educators,
administrators, and other professionals working with instructional technologies.
81
Significant differences were found between oncampus and distance education
contexts with regards to student demographics, feelings of belonging, and student
involvement as a predictor of belonging. Interestingly, with regard to belonging, there
were no significant differences between the main campus and satellite campus groups.
This suggests that spending time in the same physical space with classmates may be
beneficial to learners. Also, differences in learner characteristics, such as student
demographics, suggest that a different approach may be warranted for this learning
audience. The implications of these results are relevant for professionals in higher
education, and potentially beyond. In light of these outcomes, the researcher makes the
following recommendations to educators, administrators, and instructional designers in
distance education learning contexts:
1. Implement faculty training in the art of effective instruction in distance
education contexts. This includes instructing faculty on the application of
principles from adult learning theory (see Knowles, 1980), when
appropriate. Such training may increase retention of an adult student
population with specific learning needs.
2. Encourage early faculty interactions with students.
3. Emphasize the importance of peer interactions through in-class
collaborative group work and required discussions.
82
4. Provide means for students to sustain relationships with significant others
within the educational institution after their required coursework is
complete.
5. Incorporate course features inline with the needs of adult learners.
6. Require a certain percentage of in-class meetings, either on the main
campus or on satellite campuses if possible.
These recommendations are designed to increase student involvement and
feelings of belonging to an academic community. Accounting for these factors in the
design of a program may facilitate the development of a community of learners.
Limitations
The study of socio-affective and cognitive components pertaining to persistence
across methods of program delivery is a complicated endeavor. As a result of the
complexity of this task, there are a number of limitations to this study. The participants
in this study were enrolled in programs at a large private university located within the
urban hub of a large coastal city in the United States. This institution is known for its
emphasis on campus community. Additionally, students participating in this program
were enrolled in a graduate program in Social Work. Due to the specific participant
sample used in this study, these results and conclusions may not be generalizable to other
schools, programs, or demographics.
With regard to learner behaviors, student involvement is measured by the
frequency of out-of-class interactions with peers. These interactions manifest themselves
differently depending on the learning context. In this study, “out-of-class activity” for
83
students enrolled in the DE MSW program is defined as the number of times a student
accesses their “wall” for social use. For students enrolled in the oncampus program,
student involvement is defined in a more traditional sense, simply through frequency of
out-of class interactions. These specific definitions and designed measures lack the
ability to analyze the breadth and depth of possible student involvement. The questions
asked of students in this study only analyzed the frequency of peer interactions. Without
an understanding of the quality and nature of these interactions, the conclusions we are
able to draw about these interactions are sparse.
There were a few potential limitations resulting from the study design. One such
limitation was that the study was not longitudinal. Over time, it is likely that participants
will undergo changes in state learner characteristics and learner behaviors. According to
the adapted model of reciprocality, changes in these components have reciprocal
influences on learner characteristics and factors within the learning context. Without a
longitudinal perspective, the effects of these potential changes remain unexamined.
Furthermore, this study only examined factors pertaining to motivation, but not
motivational behaviors such as attrition. Another limitation of the study design was that
qualitative data was not collected. Qualitative research provides rich insight into the
results of quantitative analysis. Finally, the differences found in this analysis may not be
attributable to program delivery method. The study performed is not experimental, and
therefore, no direct causal relationship can be inferred.
84
Recommendations for Future Research
The significant increase in distance education programs, particularly in graduate
and professional programs (Allen & Seaman, 2006; Parsad & Lewis, 2008), underscores
the need for comparative studies examining the factors influencing student behavior
across method of program delivery. Future research that expands upon the constructs
examined in this study is essential to a greater understanding of motivation across
instructional delivery contexts. It is suggested that future studies expand on these
constructs by including qualitative and longitudinal research. Qualitative data has the
potential for participants to expand upon their answers, and provide meaningful insights
that are difficult to obtain through quantitative means. As emphasized earlier in this
study, state learner characteristics are highly variable and may change over time. This
may be of particular relevance to this study’s population given that the MSW program is
a two year program that includes onsite fieldwork. Longitudinal research will be better
adept at capturing changes overtime, and will help researchers to identify times within the
program where intervention is most effective.
Another suggestion for further study is to replicate this work across different
learner populations and program types. This future research could validate these findings,
and make the results of this study more generalizable outside of the current population.
Future studies should also consider other measures of student involvement, such as
interactions with faculty and detailed logging data collected from the Learning
Management System (LMS).
85
Another recommendation for future studies is to ask whether there is a difference
in variables pertaining to the development of feelings of belonging by learner
demographic. The assumption that all learner needs are the same may result in poor
program and course design. Future studies may partially attain this information by
controlling for learner characteristics such as demographics when examining differences
between socio-affective and cognitive variables. Understanding constructs such as
feelings of belonging by demographic may help determine relevant design principles that
influence student retention.
Finally, although research on all features of DE is crucial, there is a particular
need for applied research. Studies focused on bringing theory into practice will serve a
developing need for faculty, administrators, and instructional designers tasked with the
challenge of creating a successful DE environment.
Conclusion
Despite the staggering attrition rates reported in distance education contexts, most
of the current DE literature focuses on learning and performance, with little emphasis on
factors pertaining to motivation. This study aimed to remedy this gap in the literature by
examining potential contributors to student attrition in terms of socio-affective and
cognitive constructs traditionally related to motivation in academic settings. Significant
differences were found in feelings of belonging across instructional delivery methods,
and between belonging and other learner and state learner characteristics. The
implications of these outcomes are highly relevant to the field of distance learning and
may play a principal role in the development of learning communities. It is the hope of
86
this researcher that this study continues the conversation aimed to realize the complexity
and nuances of learner needs, and the critical role of these needs in the development of
effective distance education.
87
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Appendix A: Demographic Questions
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/Asian American/Pacific Islander
• Black / African American
• Hispanic / Latino
• White / Caucasian
• Other
3. Please indicate your age:
• 18-23
• 24-30
• 31-35
• 36-40
• 40-50
• 50-60
• >60
4. Please indicate your marital status?
• Married
• Single
• Divorced
• Separated
• Widowed
103
5. Please indicate the number of dependents you have:
• 0
• 1
• 2
• 3
• 4+
6. What is your current employment status?
• Full time (over 30 hours a week)
• Part time (less than 30 hours a week)
• Unemployed, but seeking employment
• Full time student
7. What program type are you currently enrolled in?
• Online
• On campus
8. Prior to this program, were you ever employed or a volunteer at a government agency,
non-profit, or community organization that worked with children, youth, and/or families?
• Yes
• No
9. If so, please indicate the length of time at the organization(s):
• >1 year
• 1-2 years
• 3-5 years
• 5+ years
10. How far do you live from USC?
• 0-10 miles
• 11-30 miles
• 31-50 miles
• 51-100 miles
• 100 + miles
104
11. How many online courses (including those currently enrolled in) have you previously
completed?
• 0
• 1
• 2
• 3
• 4+
12. How many on-campus groups are you affiliated with? Please include sports teams,
clubs, honor societies, etc.
• 0
• 1
• 2
• 3
• 4+
13. If you are enrolled in the online program:
How often per month do you access your “wall” for social use?
• 0
• 1-2
• 3-5
• 5-10
• 10+
14. If you are enrolled in the on campus program:
How often per month do you meet with peers in social settings?
• 0
• 1-2
• 3-5
• 5-10
• 10+
105
Appendix B: Measures of Belonging
Adapted Social Connectedness Scale
1. I feel disconnected from university life.
2. There are people at this university with whom I feel a close bond.
3. I don’t feel that I really belong around the people that I know.
5. I feel that I can share personal concerns with other students.
7. I feel so distant from the other students.
8. I have no sense of togetherness with my peers.
9. I catch myself losing all sense of connectedness with university life.
10. I feel that I fit right in at this university.
11. There is no sense of brotherhood/sisterhood with my university friends.
13. I don’t feel related to anyone at this university.
14. Other students make me feel at home at this university.
15. I don’t feel I participate with anyone or any group.
Program Community
4. I feel connected to people in this program.
6. I’ve made friends in this program.
12. I feel I fit into this program.
16. I know other people well in this program.
106
Appendix C: Social Work Self-Efficacy Scale
How confident are you that you can. . . .
1. initiate and sustain empathic, culturally sensitive, non-judgmental, disciplined
relationships with clients?
2. elicit and utilize knowledge about historical, cognitive, behavioral, affective,
interpersonal, and socioeconomic data and the range of factors impacting upon client to
develop biopsychosocial assessments and plans for intervention?
3. apply developmental, behavioral science and social theories in your work with
individuals, groups and families?
4. understand the dialectic of internal conflict and social forces in a particular case?
5. intervene effectively with individuals?
6. intervene effectively with families?
7. intervene effectively with groups?
8. work with various systems to obtain services for clients (e.g., public assistance,
housing, Medicaid, etc.)?
9. assume the social work role of change agent / advocate by identifying and working to
realistically address gaps in services to clients?
10. function effectively as a member of a service team within the agency and service
delivery system, consistently fulfilling organizational and client-related responsibilities?
11. maintain self-awareness in practice, recognizing your own personal values and biases,
and preventing or resolving their intrusion into practice?
12. critically evaluate your own practice, seeking guidance appropriately and pursuing
ongoing professional development?
13. practice in accordance with the ethics and values of the profession?
14. analyze a critical piece of welfare legislation?
15. define the impact of a major social policy on vulnerable client populations (e.g., the
Welfare Reform Act)?
16. use library and on-line resources to retrieve published articles and reports from the
empirical research literature?
17. critically review and understand the scholarly literature?
107
18. evaluate your own practice using an appropriate research method (e.g., single system
designs, brief measures such as scales, indexes, or checklists)?
19. participate in using research methods to address problems encountered in practice and
agency based settings?
108
Appendix D: Patterns of Adaptive Learning Scale
Mastery Goal-orientation revised
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.
Performance approach goal-orientation revised
1. It’s important to me that other students in my class think I am good at my class work.
2. One of my goals is to show others that I’m good at my class work.
3. One of my goals is to show others that class work is easy for me.
4. One of my goals is to look smart in comparison to the other
students in my class.
5. It’s important to me that I look smart compared to others in my
Performance-avoid goal orientation
1. It’s important to me that I don’t look stupid in class.
2. One of my goals is to keep others from thinking I’m not smart in
class.
3. It’s important to me that my teacher doesn’t think that I know less
than others in class.
4. One of my goals in class is to avoid looking like I have trouble
doing the work.
109
Appendix E: Factor Analysis of Variables
110
111
112
Abstract (if available)
Abstract
Much of the research literature on learning technologies and distance education has concentrated on achievement, with little to no emphasis on factors pertaining to motivation. This lack of research is a concern given the high enrollment and low retention rates in distance education programs. The focus of this comparative study was to investigate potential differences between socio-affective and cognitive factors related to motivation across methods of instructional delivery. The social cognitive model of reciprocal determinism was adapted and applied to frame the discussion of factors including feelings of belonging to the academic community, domain-specific self-efficacy, and goal orientation. These variables were compared between students in a traditional face-to-face Masters of Social Work program and those enrolled in an online, synchronous version of this program. Both programs examined in this study have comparable course work and they are offered at the same top-tier, not-for-profit, private university. This study employed a non-experimental design and quantitative approach to assess correlational relationships between the aforementioned social cognitive variables. The results of this study report significant findings with regard to differences in constructs across instructional delivery, in addition to significant and predictive relationships between constructs. The implications of this study are important for the field of education as it provides a new perspective for the transformation of educational institutions.
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Asset Metadata
Creator
Zaker, Sara Behani
(author)
Core Title
Instructional delivery as more than just a vehicle: A comparison of social, cognitive, and affective constructs across traditional oncampus and synchronous online social work graduate programs.
School
Rossier School of Education
Degree
Doctor of Education
Degree Program
Education (Leadership)
Publication Date
09/12/2012
Defense Date
07/25/2012
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
belonging,blended learning,distance learning,goal orientation,Higher education,OAI-PMH Harvest,self-efficacy
Language
English
Contributor
Electronically uploaded by the author
(provenance)
Advisor
Hirabayashi, Kimberly (
committee chair
), Astor, Ron Avi (
committee member
), Seli, Helena (
committee member
)
Creator Email
sbehbeha@usc.edu,sbzaker@gmail.com
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-c3-95739
Unique identifier
UC11289423
Identifier
usctheses-c3-95739 (legacy record id)
Legacy Identifier
etd-ZakerSaraB-1191.pdf
Dmrecord
95739
Document Type
Dissertation
Rights
Zaker, Sara Behani
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
Tags
belonging
blended learning
distance learning
goal orientation
self-efficacy