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Integrating technology in teaching: beliefs and behaviors of practicing teachers from traditional and online teaching programs

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Integrating technology in teaching: beliefs and behaviors of practicing teachers from traditional and online teaching programs

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Integrating Technology in Teaching: Beliefs and Behaviors of Practicing Teachers from
Traditional and Online Teaching Programs
by

Katherine Whittaker Stopp

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

December 2015

Copyright 2015 Katherine Whittaker Stopp

Copyright Page
ii

Acknowledgements
The best decision I made during this journey was selecting Drs. Helena Seli and
Kimberly Hirabayashi as my advisors. I’m in awe of their intellect and humility. They are wives,
mothers, scholars, and teachers whom I will always hold in the highest regard. They helped me
through with their reasonable timelines, patience, guidance, and their belief in me. I’m especially
obliged to Dr. Seli for her unbelievably timely responses to my communications, detailed
feedback, generous encouragement, and for her easy-going way.
Thank you to my esteemed committee member Dr. Rick Bagley for his willingness to be
part of this process. Dr. Bagley is simply awesome! I’m honored to have him in my corner.
To all my friends and colleagues in the 2012 cohort, I tip my tam! Jenn and Nancy met
me at libraries and coffee shops, talked me through chapters, and encouraged me. One cohort
member stands out from the rest—Susan. She is like a sister to me. We were meant to be on this
journey together. I thank her for our life-long friendship.
I appreciate Dr. Jim Scott for answering his phone at CSULB five years ago. That
conversation marked the beginning of a new chapter of my life, which ultimately led me here.
His belief in me, candid advice, and generosity have made a lasting impact. I also value the
support, flexibility, and patience shown from my colleagues, friends, and leadership in MBUSD
during this journey.
My deepest thanks to my amazing parents, Joe and Tita, who showed me what
selflessness and unconditional love mean and taught me the importance of hard work. I thank my
entire family for the fun times that gave me a break from writing. Finally, I appreciate by
husband, Greg, and my boys, Ky and Zacky, for sticking together while I was at school. I would
not have made it through without their continuous love and support.
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Table of Contents
Acknowledgements ............................................................................................................ ii
List of Tables ...................................................................................................................... v
Abstract ...............................................................................................................................vi
Help
Chapter One…………………………………………………………………………..
Overview of the Study………………………………………………..............
Background of the Problem.………………………………………………….
Statement of the Problem.……………………………………………............
Purpose of the Study.…………………………………………………............
Research Questions.…………………………………………………………..
Significance of the Study.…………………………………………………….
Methodology.…………………………………………………………............
Definition of Terms.………………………………………………………….
Organization of the Study.……………………………………………...........

Chapter Two………………………………………………………………………….
Literature Review.………………………………………………….………...
Technology in Education.…….………………………………………………
21st Century Literacy.………………………………………………..
Expectations for Teachers.…………………………………………...
Technology in Teaching and Teacher Education Programs………………….
Online Learning.……………………………………………………………...
History of Online Learning…………………………………………...
Current Status of Online Learning……………………………………
Benefits of Online Learning………………………………………….
Challenges in Online Learning……………………………………….
Inconsistencies in Research about Online Programs…………………
Motivational Predictors of Success………………………….……………….
Sources of Motivation………………………………………………..
Measuring Motivation………………………………………………..
Self-efficacy…………………………………………………………..
Definition of self-efficacy……………………………………
Sources of self-efficacy............................................................
Importance of self-efficacy…………………………………..
Intrinsic and Extrinsic Motivation……………………………………
Collaborating Through Peer Learning………………………………………..
Definition of Peer Learning…………………………………………..
Importance of Peer Learning..………………………………………..
Benefits of Peer Learning…………………………………………….
Challenges in Research……………………………………………….
Summary……………………………………………………………………...

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Chapter Three………………………………………………………………………...
Methodology………………………………………………….........................
Research Questions.…………………………………………………………..
Research Design……………………………………………………………...
Population and Sample……………………………………………………….
Descriptive Characteristics of Respondents………………………………….
Instrumentation……………………………………………………………….
Demographic Items…………………………………………………...
Self-efficacy..…………………………………………………………
Intrinsic Motivation…………………………………..........................
Voluntary Peer Collaboration………………………………...............
Procedure and Data Collection…………………………………...…………..
Data Analysis…………………………………………………………………

Chapter Four………………………………………………………………………….
Results………………………………………………………………………..
Intercorrelations………………………………………………………………
Analysis of Results…………………………………………………………...
Research Question 1………………………………………………….
Research Question 2………………………………………………….
Research Question 3………………………………………………….
Modes of voluntary peer collaboration……………………...
Summary……………………………………………………………………...

Chapter Five………………………………………………………………………….
Discussion…...………………………………………………………………..
Discussion of Demographic Composition of Practicing Teachers.…………..
Discussion of Self-efficacy and Technology Integration…………………….
Discussion of Intrinsic Motivation and Technology Integration……………..
Discussion of Voluntary Peer collaboration and Technology Integration…...
Implications of Research……….……………..……………………………...
Recommendation for Future Research……………………………………….
Limitations…………………………………………….……………………...
Conclusion….………………………………………………………………...

References….………………………………………………………………...............

Appendices
Appendix A: Demographic Questions .............................................................
Appendix B: Self-efficacy Measure ................................................................
Appendix C: Intrinsic and Extrinsic Motivation Measure……………………
Appendix D: Voluntary Peer Collaboration Measure......................................

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List of Tables

Table 1: Means, Standard Deviations, and Pearson Product Correlations of Measured
Variables…………………………………………………………………………………….
Table 2: Results of between-subjects effects: Self-efficacy by program format…….…..….

Table 3: Results of between-subjects effects: Intrinsic Motivation by program format..…...
Table 4: Results of t-tests and descriptive statistics: Technology Intrinsic Motivation by
program format……………………………………………………………………………...

Table 5: Chi-square test: Modes of voluntary peer collaboration…………………………...

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vi

Abstract
Improved integration of technology into the 21st Century classrooms of today is
imperative. However, after spending millions of dollars equipping school districts with the latest
technology, many educational leaders are finding that new teachers are reluctant to use the
available technology in their practice and instruction. In addition, the meaningful integration of
technology in teacher education programs varies tremendously. For example, the research makes
it clear that the perceptions, attitudes, and skills of pre-service teachers are critical to achieving
or thwarting the use of technology in the classroom (Chen, 2010, 2011; Ertmer, 2005; Teo, 2011;
Oigara & Keengwe, 2013). In this study the literature examined affirms the importance of
teacher education program efforts in preparing preservice teachers for integrating technology
into their pedagogical practices.
In response to the changes in educational technology and expectations regarding its use,
teacher education programs are under reform. This study specifically examined practicing
teachers who completed their Masters in Teaching (MT) at a four-year university on the west
coast. The purpose of this study was to determine the degree to which teacher education
programs, irrespective of the online or face-to-face format, cultivate beliefs and behaviors of
practicing teachers in regards to integrating technology into their pedagogical practices and
collaborating with peers to reach that end. A non-experimental design and a quantitative
approach were employed.
The results of this study indicate no statistically significant difference in the levels of
practicing teachers’ integration of technology into their teaching practices based on program
delivery method. However, program delivery did predict differences in practicing teachers’ level
of intrinsic motivation with online program graduates being more intrinsically motivated to
vii

integrate technology into their teaching practice. Additionally, the study results showed
statistically significant differences in teachers’ beliefs regarding voluntary peer collaboration.
Overall there were very few significant relationships that emerged. The MT online and face-to-
face program formats produce comparable beliefs and behaviors in terms of the degree to which
practicing teachers’ feel self-efficacious and actually practice integrating technology in the work
place. The implications of this study are important for teacher education programs and K-12
school districts, charged with supporting teachers with technology integration.

1

CHAPTER ONE
Overview of the Study
In spite of the transformation in teaching and learning brought about by technological
innovations, practicing teachers in California’s K-12 schools struggle to integrate technology
into pedagogical practices (Becker, 2001; Cuban, Kirkpatrick, & Peck, 2001). The California
Standards for the Teaching Profession (CSTP) require teacher development of knowledge, skills,
and practices to “engage and challenge a diverse student population in a rapidly changing and
increasing technological world” (p. 2). Teachers joining the frontline of reform, whose own
educational experience may show little likeness to that of their K-12 students, are expected to
integrate technology into curriculum (Chen, 2009; Ertmer, Gomez, Sherin, Griesdorn, & Finn,
2008; Lux, 2013; Teo, 2011). Consequently, there is an increasing need for teacher education
programs to focus on effective incorporation of technology to address school and community
expectations, and to meet the needs of K-12 students in the 21st Century.
Research findings (Becker, 2001; Cuban et al., 2001; Teo, 2001, 2008) show that despite
increased spending and access to technology, its use in classrooms remains minimal and teachers
do not utilize the technology effectively. The U.S. institutional market for education software
and digital content alone was estimated at $7.97B in 2012, which was an increase of 2.7% from
the previous year (Richards, 2013). Regrettably, many policy makers, corporate executives, and
parents still assume the simple purchase and distribution of hardware and software will lead to
improved teaching and learning (Cuban et al., 2001). The proportion of beginning teachers hired
in California, with temporary employment status, remained at roughly 24% for the last decade
(Koppich, Humphrey, Bland, Heenan, McCaffery, Ramage, & Stokes, 2013). This is a
significant number of new teachers required to integrate technology. In response to the changes
2

in educational technology and expectations regarding its use, teacher education programs are
under reform.
Using technology is critical to the success of being a teacher in the 21
st
century.
Therefore, additional research is needed to guide teacher educators in support of preservice
teachers. This study examines the degree to which teacher education programs, irrespective of
the online or face-to-face format, cultivate beliefs and behaviors of practicing teachers in regards
to integrating technology into their pedagogical practices and collaborating with peers to reach
that end.
Background of the Problem
There is a growing body of literature informing the reformation of teacher education
concerning preparation of its candidates to teach with technology. Teacher education
restructuring should ensure preservice teachers not only learn to use the new technologies, but
once they are practicing teachers, they must understand how to integrate technology into
instruction (Angeli, 2004; Becker, 2001; Ertmer, 2005; Teo, 2011). Some studies (Angeli 2004;
Chen, 2011; Jang, 2008) focus on technology integration in regards to specific content areas
(e.g., science, math, writing), while others examine the factors influencing teachers’ motivation
to use technology, such as their beliefs and values (Kim, Kim, Lee, Spector, & DeMeester, 2012;
Teo, 2011). Further studies in this area can inform teacher education programs, many of which
are navigating online, of ways to support preservice teachers’ charge of integrating technology
into their pedagogical practices, while in the program and once they graduate.
Technological advancement continues to produce new instructional approaches and
platforms. Online learning is defined as any formal approach to learning, whereby space and
time separate the teacher and the learner (Holmberg, 1989; Kuo, Song, Smith, & Franklin, 2007;
3

Santally, Rajabalee, & Cooshna-Naik, 2012; Shachar & Nuemann, 2003; Urtel, 2008; Walker &
Fraser, 2005). The online learning format, providing convenience and access to the new
demographic of students, results in increased enrollment in online courses (Allen & Seaman,
2014). In spite of the challenges it presents to faculty and students, such as course redesign and
retention, respectfully, online learning, be it synchronous or asynchronous, is earning its position
as a central part of the higher education setting, and is likely to emerge as a key component in
teacher education (Kim & Frick, 2011). Teaching and learning remain central to online learning
in the 21st century.
Comparing the quality and outcomes of online and face-to-face programs in regards to
integrating technology into practice as practicing teachers is difficult in part because of the
confounding factors. For example, researches consider the role of motivation when
contemplating why some individuals flourish in academic and professional settings while others
struggle to gain the knowledge and cognitive resources required to succeed (Pintrich, 2003).
Additionally, there may be differences in academic and professional beliefs and behaviors
critical to practicing teacher success such as self-efficacy and intrinsic and extrinsic motivation,
due to the inherent differences between the two formats. Furthermore, critical academic and
professional behaviors, such as the degree to which they collaborate (Kim, Kim, Lee, Spector, &
DeMeester, 2013) in the learning process may be different. All of these individual differences
have been shown to be predictors of individual success within academic and professional settings
(Boud, Choen, & Sampson, 1999; Clark, 2003; Ryan & Deci, 2000; Pajares, 1997; Pintrich;
2000; Putwain, Sander, & Larkin, 2013; Shen, Choa, Tsai, & Marr, 2013; Schunk et al., 1987;
Yusef, 2011). Comparisons are also difficult because of the disparity within online formats,
particularly those that are synchronous and asynchronous. Although these factors make
4

comparing outcomes from online and face-to-face formats challenging, it is import to determine
to what degree either program succeeds in nurturing practicing teachers’ self-efficacy and
intrinsic motivation in integrating technology into their practice.
Self-efficacy and intrinsic motivation have been identified as two key constructs related
to successful performance in any area in different learning environments. Self-efficacy beliefs
refer to the judgments students hold about their abilities to perform academic tasks and achieve
academic success (Bandura, 1994; 2001; 2012; Caprara, Vecchione, Alessandri, Gerbino &
Barbaranelli, 2011; Usher & Pajares, 2006). These judgments learners hold about their academic
capabilities play a significant role in student learning outcomes (Bandura, 2001) as they impact
the amount of stress an individual experiences as well as persistence when presented with
challenges.
Engaging in tasks for intrinsic versus extrinsic motivation or some combination of both
also significantly impacts behaviors. Intrinsic motivation occurs within individuals when they
feel interest or enjoyment in the task itself (Ormrod, 2010; Pintrich, 2000; Ryan & Deci, 2000;
Wigfield, Guthrie, Tonks, & Perencevich, 2004), whereas extrinsic motivation comes from
outside the individual (Clark 2003; Pintrich & Schunk, 1996). As individuals are often
simultaneously motivated by both intrinsic and extrinsic factors, this study focused on intrinsic
motivation. Thus, when examining practicing teachers’ self-efficacy and intrinsic motivation to
integrate technology, it is important to study the extent to which they are cultivated in on line and
on-ground teacher education programs. Should inherent differences in self-efficacy and intrinsic
motivation be determined to exist between online and traditional on-campus learning settings,
course and program designers must include methods to cultivate these critical motivators.
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There are several academic and professional behaviors that are critical to performance
success with collaborative behavior being one of them (Johnson & Johnson, 1999; Terenzini,
Cabrera, Colbeck, Parente, & Bjorklund, 2001; Cabrera, Crissman, Bernal, Nora, Terenzini, &
Pascarella, 2002). Through collaborative work, individuals show improved achievement,
interpersonal skills, and positive behaviors (Johnson & Johnson, 1999).
Since this study seeks to understand the voluntary nature of collaboration, the term
voluntary peer learning refers to collaborative activities that are neither initiated nor facilitated
by site supervisors or district leadership. This operational definition illuminates the importance
of professional beliefs regarding cooperation, which in turn impact whether they choose to
collaborate and to what extent. Collaborative behaviors such as observation, practice, reflection,
and social cultural support guide and encourage the implementation of newer beliefs. They are
particularly important for practicing teachers as part of effective professional practice to integrate
rapidly changing technology in teaching and learning (Kim, et al., 2013).
The literature affirms teacher education program efforts to prepare preservice teachers for
integrating technology into their pedagogical practices. Furthermore, it shows that online
learning is increasingly more prevalent in higher education. Since individual student differences
in self-efficacy, intrinsic motivation, and peer collaboration behaviors are shown to be predictors
of success within learning environments, the quality and outcomes of online and face-to-face
programs may be examined with regards to these constructs and their influence on practicing
teachers’ instructional practices.
Statement of the Problem
There is a lack of research about beliefs and behaviors regarding the integration of
technology into pedagogical practices among K-12 practicing teachers based on whether they
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completed an on-campus or online teacher education program. While several studies (Angeli,
2005; Chen, Jang, 2008; Ertmer, Lux, 2013; Oigara & Keengwe, 2013) have looked at preservice
teacher education in online and on-campus settings, very few of them have examined the
outcomes of the same program but delivered by different modes, and examined any potential
differences between performance outcomes between the two ways of delivery. Furthermore,
there are especially no studies that look in-depth at motivational predictors of technology
integration such as self-efficacy and intrinsic motivation, and professional behaviors in this
context, such as collaboration.
Purpose of the Study
The purpose of this study was to determine the degree to which synchronous online
versus face-to-face programs cultivate practicing teachers’ beliefs, attitudes, and behaviors
regarding the integrating of technology into instructional practices and collaborating with others
around its use in the K-12 setting. Specifically, the study looked at practicing teachers who
completed their Masters in teaching at a four-year university on the west coast.
Research Questions
The study aimed to answer the following three questions:
1. Is there a difference in practicing teachers’ self-efficacy regarding integration of
technology into instructional practices by program delivery method, controlling for
level of training provided by district?
2. Is there a difference in practicing teachers’ levels of intrinsic motivation regarding
integration of technology into instructional practices by program delivery method,
controlling for level of training provided by district?
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3. Is there a difference in practicing teachers’ beliefs and behaviors related to voluntary
collaboration by program delivery?
Significance of the Study
The research questions posed above are critically relevant to understanding potential
differences in the beliefs and behaviors of practicing teachers regarding the integration of
technology between online and on-campus modes of delivery. By gaining a deeper
understanding of the similarities and differences, recommendations can be offered. Furthermore,
comparisons may be made about the main factors influencing practicing teachers’ decisions
regarding technology integration such as self-efficacy, intrinsic motivation, and peer
collaboration, which are critical for producing desired beliefs and behaviors for all professionals.
These research findings can inform teacher preparation programs, both synchronous online and
on campus, how best to train pre-service teachers to effectively integrate technology and transfer
their knowledge and skills into professional practice. Furthermore, findings can inform schools
and districts how best to support practicing teachers with technology integration.
Methodology
Since the research questions sought to compare learning and motivation based on teacher
preparation programs that use two different modes of delivery, synchronous online and on-
campus, the researcher adopted a quantitative approach. The quantitative approach determined
whether statistical differences or predictive relationships exist. Data was gathered via surveys
that included valid and reliable instruments and demographic questions. Surveys were
administered online. All data was analyzed in SPSS, using statistical tests.

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Definition of Terms
Asynchronous: Learning environment in which students do not participate at the same
time due to constraints of time and place. It is a learning approach, which combines self-study
with peer interaction via use of tools such as emails and discussion boards.
Intrinsic and Extrinsic goal orientation: An individual’s motivation to engage in a task
for the satisfaction of accomplishing the task is intrinsic (Pintrich, Smith, Garcia, & McKeachie,
1991), while an individual’s motivation to engage in a task in order to earn a reward, such as a
good performance evaluation or to avoid penalty is extrinsic (Pintrich, Smith, Garcia, &
McKeachie, 1991).
Self-efficacy: An individual’s confidence in his or her ability to complete a specific task
(Bandura, 1997).
Synchronous: Learning environment in which students participate at the same time
(e.g., lectures or group discussions in traditional face-to-face learning environment; web
conferencing tools such as Adobe Connect and Skype in online learning environment).
Voluntary peer collaboration: Peer-to-peer collaborative activities that are neither
initiated, facilitated, nor required by external others such as site supervisors and district
leadership.
Organization of the Study
Chapter one in this study provides an introduction to the topic of online learning and
preservice education in technology integration, in addition to an overview of the proposed study.
Factors that may influence practicing teachers’ beliefs and behaviors are discussed, as are the
theoretical frameworks that will be analyzed later in this study. This section also discusses the
importance of the study, potential limitations, and provides definitions of relevant terms.
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Chapter two provides a comprehensive overview of the literature regarding technology in
preservice education and online learning, including a brief history and its changing nature. This
chapter also discusses student success related beliefs and behaviors such as self-efficacy,
intrinsic verses extrinsic motivation, and peer learning. Finally, the chapter discusses the
importance of studying both constructs in online and face-to-face contexts.
Chapter three describes the methodology used in this study. This chapter discusses the
sample used, instrumentation, research design, and data collection process. Also described are
the plans for data analysis and as well as this study’s strengths and weaknesses.
Chapter four provides a description of the results from the data analysis. Chapter five is a
discussion of these results, in addition to the limitations of the study and suggestions for future
research.

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CHAPTER TWO
Literature Review
The purpose of this chapter is to provide a comprehensive overview of the literature
regarding technology preparation in preservice teacher education. It begins with a synopsis of
technology in 21
st
century teaching and learning and the critical role teacher education programs
play in preparing teachers to integrate technology into their pedagogical practice. This provides
perspective on the relevance of this study. Next, the chapter offers a brief history of the online
instructional platform, underscoring its changing nature. The current status of online learning
precedes a discussion of the benefits and challenges it presents to educational institutions and
their students. The review then identifies inconsistencies in online learning research regarding its
quality compared with that of traditional, on-campus courses. Since individual differences make
determining the quality of online programs compared with face-to-face programs difficult, self-
efficacy, intrinsic and extrinsic motivation, and collaborative-mindset constructs will be
discussed as predictors of individual success within all academic and professional environments.
Technology in Education
Technology is a leading influence in transforming teaching and learning in the 21st
century. While American educators have long pursued the use of technology in classrooms for
productivity and efficiency (Cuban, Kirkpatrick, & Peck, 2001), the rapid changes in information
and communication technology (ICT) create learning possibilities that barely existed even a
decade ago (Chen, 2009; Teo, 2011). According to the New Media Consortium (NMC) Horizon
Report: 2013 K-12 Edition, new technologies such as cloud computing and mobile learning have
become a pervasive part of everyday life, therefore “students have increasing expectations of
being able to work, play, and learn via cloud-based services and applications across mobile
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devices, whenever they want or wherever they may be” (p. 3). Teachers and educational leaders
are therefore revisiting the potential of modern technology in schools (Chen, 2009; Gomez,
Sherin, Griesdorn, & Finn 2008; Richardson, 2013). Subsequently, teacher education programs
endeavor to prepare preservice teachers to address changing expectations and effectively
integrate technology into their instruction once they are practicing professionals.
21st Century Literacy
The definition of literacy has changed in the 21st century. According to the National
Council of Teachers of English (NCATE, 2013), literacy is defined as a collection of cultural and
communicative practices shared by members of a particular group. Therefore, as society and
technology change, so does the definition of literacy. A literate person today does much more
than simply read and write text. The 21st century demands that a literate person possess at wide
range of abilities and competencies—many literacies (NCATE, 2013). These include, but are not
limited to, developing proficiency and fluency with the tools of technology; building intentional
cross-cultural connections and relationships with others to pose and solve problems
collaboratively; and managing, analyzing, and synthesizing multiple streams of simultaneous
information (NCATE, 2013). These literacies apply not only to students, but also to all members
of a society. Accordingly, it is important for teachers to be proficient and fluent with
technological tools in order to support the development of these kinds of literacies in their
students. The changes in the definition of literacy beget changing expectations for practicing
teachers.
Expectations for Teachers
As technology continues to impact teaching and learning, teachers are expected to
integrate technology into their pedagogical practice. According to National Education
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Technology Standards (NETS-T, 2008), effective integration of technology is achieved when
technology becomes an integral part of how a classroom functions. In other words, technology
use is routine and transparent. Teachers who effectively integrate technology make it “as
accessible as all other classroom tools” with which students are able to obtain, analyze,
synthesize, and present information (NET-T, 2008). Well-integrated use of technology is
expected of teachers in today’s classrooms.
In an effort to communicate new expectations for 21st century teachers, the International
Society for Technology in Education (ISTE) created the National Education Technology
Standards (NETS-T) and Performance Indicators for Teachers. The ISTE asserts that all effective
teachers should model and apply these standards as they “design, implement and assess learning
experiences to engage students and improve learning” (ISTE, 2008). The NETS-T also asks
teachers to enrich professional practice, and be positive models of technology use for their
students, colleagues, and the community (ISTE, 2008).
Another set of standards that call for teacher educators to adapt their pedagogical
practices is the California Standards for the Teaching Profession (CSTP, 2009). The CSTP
require teachers to engage and challenge their students in a rapidly changing and growing
technological world. Therefore the knowledge, skills, and practices of integrating technology
into pedagogy must be addressed during teacher education programs.
Technology in Teaching and Teacher Education Programs
There is a growing body of literature focused on technology in teaching and teacher
education that proves relevant to this study. Several researchers (Chen, 2010, 2011; Ertmer,
2005, Kim et al., 2012; Ma, Anderson, & Streith, 2005) examined the factors impacting an
educator’s decision to incorporate technology into their lessons. According to Chen (2010),
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preservice teacher perceptions of the value of information and communication technology (ICT)
and their own level of self-efficacy with using technology shape their decision to implement, or
not, what they learned in teacher training. Loughran (2006) states that what preservice teachers
see and understand as important to their practice at the time dominates their focus. They are
making decisions based on their perceptions of what is important. This also applies to the
integration of technology into classroom practices.
Other research (Chen, 2011; Ertmer, 2005; Teo, 2011) concurs that there is a relationship
between teachers’ pedagogical beliefs and their technology incorporation practices. In another
empirical study of student teachers’ perceptions of computer technology in relation to their
intention to use it, Ma et al., (2005) found that student teachers’ perceived usefulness of
computer technology has a direct significant effect on their intention to use it. Teacher educators
should examine factors influencing teachers’ motivation to use technology, such as their beliefs
and values, and teachers’ technological skills, which influence technology use in the classroom.
In a study by Oigara and Keengwe (2013), teacher candidates were given pedagogical
support as well as general feedback on technology use. The teacher educators and master
teachers provided specific guidance on technology lesson activities and resources. In addition,
teacher educators and master teachers modeled assignments to use emergent technologies such as
SMART technology, iPads, interactive resources, web quests, blogs, and multimedia. Oigara and
Keengwe’s (2013) research found that an integration of technology course helped preservice
teachers with diverse technological knowledge learn to provide lesson activities that included
interactive technology. Also, it showed that when good modeling was provided for preservice
teachers, their use and desire to learn about instructional technology increased. This increase of
teacher candidate’s pedagogical knowledge improved their confidence in using technologies in
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their lessons (Oigara & Keengwe, 2013). For teacher preparation programs, including courses
focused on integrating technology into pedagogy in teacher education will support preservice
teacher efforts and willingness to integrate technology into their professional practice.
In summary, the literature on technology and teacher education programs points to an
array of factors impacting practicing teachers’ decision to incorporate technology into their
lessons. Some of these include teacher perceptions about the usefulness of technology, their
pedagogical beliefs, and their levels of self-efficacy with technology. While these studies inform
teacher educators, the rise in online teacher education programs highlights a need for further
research comparing the degree to which online and face-to-face formats cultivate the identified
factors of influence on technology integration by practicing teachers.
Online Learning
Recent trends show that online learning is an integral part of the higher education
landscape. This section provides a brief history of the online instructional platform and its
current status, followed by a discussion of the benefits and challenges it presents to students and
institutions. Finally, it will review literature observing the quality of the online compared with
face-to-face educational formats.
History of Online Learning
The history of online learning illustrates its ever-changing landscape. Online learning is a
natural evolution from 20
th
Century methods of distance education, defined as any formal
approach to learning, whereby space and time separate the teacher and the learner (Holmberg,
1989; Kuo, Song, Smith, & Franklin, 2007; Santally, Rajabalee, & Cooshna-Naik, 2012; Shachar
& Nuemann, 2003; Urtel, 2008; Walker & Fraser, 2005). Early distance education expanded
from correspondence instruction to broadcasted radio and television programs in the 1920s and
15

the 1960s respectively (Holmberg, 1989; Shachar & Nuemann, 2003; Walker & Fraser, 2005).
Continued technological advances in the 1990s, such as computer-mediated-communication
(CMC) technologies, two-way interactive video and audio, and Web-based asynchronous
communication, paved the way for new instructional approaches and platforms (Shachar &
Nuemann, 2003). Sophisticated technology from the end of the twentieth and the beginning of
the twenty-first century continued to transform distance education.
The concept of distance education greatly evolved in the digital era of the 21st century. In
fact the term “distance” in distance education grew less significant as a result of extensive use
and access to the Internet and World Wide Web (Santally et al., 2012). Gal-Ezer and Lupo
(2002) submit “distance” became even less pertinent with development of computer-based chats
and audiovisual conferencing, which allowed instructors to facilitate interaction with and
between students synchronously (i.e., in real time). The widespread use of content management
system/learning management system (CMS/LMS) such as Moodle or Blackboard is another
example of technology becoming part of classroom instruction (Graham & Dziuban, 2007). As
information and communication technology models for distance education changed, so did the
terminology used to describe it.
Today’s use of the terms distance e-learning and online learning reflect the evolution in
this field (Berge & Collins, 1995; King, 2001; Santally, et al., 2012). The semantics shift the
conversation from geography and delivery of education within current technologies to a focus on
learning. This implies a change in instructional practice, for the technology offers options for
restructuring content and creating online courses that maximize opportunities and possibilities
for student learning (Berge & Collins, 1995; Shachar & Nuemann, 2003; Santally, et al., 2012;).
Historically, distance education was relative to the technology available and used at the time
16

(Urtel, 2008), however teaching and learning are central to online learning in the 21
st
century.
Current Status of Online Learning
Research shows an overall increase in online learning. Nearly 33% of all college and
university students take at least one online course, in which 80% or more of the content is online
(Allen & Seaman, 2014). The Babson Survey Research Group (2013) reports recent slowing in
growth rates for online enrollments, however, the number still far exceeds that of overall higher
education enrollments. Consequently, 65% of higher education institutions report online
learning as critical to their long-term strategy (Allen & Seaman, 2014; Picciano, Seaman, &
Allen, 2010; Walker & Fraser, 2005). Online learning is an integral part of the higher education
landscape.
The demographic of American students in higher education has changed. According to
the National Center for Education Statistics (NCES) “traditional” U.S. college students are no
longer between 18-22 years old, full-time, on campus residents, nor dependent upon their parents
for financial support. Most college students today are older than 22, attend school part time, and
hold part-time or full-time jobs (Brown, 2012; Chen et al., 2010; Picciano et al., 2010). The
flexibility of online courses helps students who are juggling school, work, and family (Picciano
et al., 2010). For the new demographic of higher education students, online learning appears to
be optimal.
There is upward growth in K-12 online learning. A national study of school district
administrators reported a 47% increase in the number of students enrolled in online or blended
courses (mostly high school) from 2007 to 2009 (Picciano et al., 2010). The National Council of
Online Learning (NCOL) states online learning is growing at 30% annually (Watson et al.,
2009). Based on growth data, NCOL predicts 50% of high schools will offer online courses by
17

2019 (Kim & Frick, 2011). While in its beginning stages, when compared to higher education,
online learning in the K-12 setting shows increasing prevalence.
Benefits of Online Learning
Online learning provides advantages for its unique demographic of students. The format
allows them to maximize the use of their time. Online learners do not need to travel to campus
nor the library because the Internet delivers content and resources to them directly (Brown, 2012;
Shachar & Nuemann, 2003). Open education resources (OER), such as textbooks, streaming
videos, and other tools and materials, support online students’ knowledge attainment from
anywhere at anytime (Atkins et al., 2007). The convenience modern technology provides, in
combination with high fuel prices and busy schedules, makes online or hybrid courses
advantageous for its students (Allen & Seaman, 2014; Campbell, 2008; Picciano et al., 2010).
Students continue to enroll in online courses for access to and the convenience of educational
experiences, opportunities, and resources.
Both higher education and K-12 online students receive access to coursework and
academic experiences that they may not have otherwise. In rural K-12 districts, online learning
provides high school students with access to advanced placement or unavailable college level
courses and previously failed courses (Allan & Seaman, 2014; Picciano et al., 2010). From
virtually anywhere, online learners can interact with instructors, receive feedback, and engage in
conversations with a global community of learners (Robinson & Hullinger, 2008; Shachar &
Neumann, 2003). The ability to access academic opportunities and resources is a benefit to
online learning students and educational institutions.
Educational institutions understand the benefits of online learning programs to their
organizations. Enrollments in online courses are still many times larger than the growth rate of
18

the overall higher education student body (Allen & Seaman, 2013). The platform begets
increased class offerings and helps with overcrowded classrooms, enabling institutions to reach a
larger population of students (Brown, 2012). Roughly 90% of chief academic officers believe
enrollment in online courses will continue to grow (Allen & Seaman, 2014). Therefore, it is
profitable for higher educational institutions to increase their online course offerings.
Challenges in Online Learning
The issue of student retention in online programs is a growing concern for educational
institutions and organizations. Literature suggests significantly greater attrition in online courses
and programs than face-to-face courses (Allen & Seaman, 2013; Brown, 2012; Levy, 2007; Xu
& Jaggars, 2011). Lynch (2001) revealed online learner dropout rates were as high as 35% to
50%, compared to 14% for traditional classes. Park and Choi (2009) found that organizational
support and course design reduce dropout rates. In contrast, Kim and Frick (2011) purport lack
of time and lack of motivation as key factors linked with online-learner attrition. Since online
courses can attract students for whom other obligations preclude them from attending traditional,
on-campus classes, its students are more likely to have time and motivational challenges (Allen
& Seaman, 2013; Picciano et al., 2010; Xu & Jaggars, 2011). The consensus in the literature is
that it is difficult to determine whether high attrition rates are due to the online format or the
nature of the students themselves (Levy, 2007; Xu & Jaggars, 2011). With deference to both
perspectives, educational institutions continue to regard student retention in online courses as a
challenge.
Faculty member attitudes toward online learning present challenges to educational
institutions. Sixty-seven percent of chief academic officers view continued resistance of many
faculty members toward online learning as an important concern (Allen & Seaman, 2013).
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Maximizing opportunities and possibilities for student learning requires faculty to restructure
content and create relevant online courses (Berge & Collins, 1995; Shachar & Nuemann, 2003;
Santally, et al., 2012). However, Castle and McGuire (2010) state many faculty members are
neither prepared nor understand the online environment. Furthermore, the time and effort
necessary to teach and develop online courses is formidable to many instructors (Picciano et al.,
2010). If faculty member acceptance and perceived value of online learning remain low,
transformation in online content and instruction is unlikely (Picciano et al., 2010). As academic
leaders increase online programs, they look to improve faculty beliefs and support behaviors
necessary for positive student outcomes.
Inconsistencies in Research about Online Programs
Unclear definitions within the online learning domain make data collection and analysis
challenging. As the technology evolved over the time, so did its terminology. Researchers used
terms like blended learning, networked learning, hybrid learning, distance education, and partly
online learning with inconsistency (Allen & Seaman, 2010; King et al., 2001; Picciano et al.,
2010). The lack of precise vocabulary made it difficult for online researchers to communicate
concisely and clearly (King et al., 2001). According to Allen and Seaman (2014) the definition of
an online course has remained consistent in their reports for the past eleven years. However, lack
of common, distinct, online learning language makes analysis of the body of data to date
challenging.
There are inconsistencies in research regarding the quality of online learning. This is may
be due in part to a variety of delivery methods, which include synchronous, asynchronous,
hybrid and blended. Evaluation of how the different types of learning environments impact
learning is yet to be fully explored. Lack of shared evaluation methods makes determining its
20

quality difficult (Brown, 2012; Kim & Frick, 2011; Tamin et al., 2011). Research results
describe online learning as inferior to, equal to, or better than FTF depending upon the
conditions being evaluated, such as motivation, attitude, engagement, self-concept, social
presence, and use of technologies (Chen et al., 2010; Picciano et al., 2010; Shachar & Neumann,
2003; Tamin et al., 2011; Zhan & Mei, 2013). Clark, Yates, Yearly, & Moulton (2010) offer the
effectiveness of a course or the increase in learning is not determined by the medium but the by
the instructional design. Hence, “There is a growing need for a systematic and reliable
methodology for synthesizing related [online learning quality] findings” (Tamin et al., 2011, p.
17). Until that time, whether learning outcomes are comparable in online and face-to-face
learning setting remains a point of debate.
In summary, online learning is an integral part of the educational landscape. Emerging
technologies and the non-traditional college student population secured online learning a role in
the future of higher education. While it is a popular learning format primarily because of
convenience, attrition rates among online students remain high, and many faculty members
neither value nor accept online learning as effective. Inconsistencies in online research
emphasize the difficulty in determining the quality of online programs compared with face-to-
face programs. Irrespective of the format, relevant content, teaching, and learning remain central
to the discussion, and it is important to examine the role of motivation in determining student
behaviors in online and face-to-face settings in the 21
st
century.
Motivational Predictors of Success
Educational psychologists understand the critical role motivation plays in teaching and
learning contexts (Pintrich, 2003). Motivation is what “generates the metal effort that drives us to
apply our knowledge and skill” (Clark, 2003, p. 2). Clark (2003) explains, even those most
21

capable fail to act if they lack a lack of motivation. Researches consider the role of motivation
when contemplating why some students flourish in an academic setting while others struggle to
gain the knowledge and cognitive resources required to succeed (Pintrich, 2003).
According to Clark and Estes (2008), a majority of motivation researchers agree there are
three motivational “indexes” or types of motivational processes that come into play: active
choice, persistence, and mental effort (Clark, 2003; Ormond, 2010; Pintrich & Schunk, 1996;
Zimmerman & Bandura, 1992). Active choice is the intention to pursue a goal. Once the student
makes the choice to act, persistence to continue when distractions arise becomes important.
Finally, with a focus on the goal and the drive to endure, individuals determine how much mental
effort to invest toward reaching their desired end (Clark &Estes, 2008; Pintrich, 2003). Pintrich
and Schunk (as cited by Clark & Estes, 2008) add the motivation process, which impacts these
three types of challenges, is regularly encountered in academic settings.
Sources of Motivation
Motivation results from an array of sources. Clark and Estes (2008) state, several
independent research groups agree upon four factors of influence: personal confidence, beliefs
about external barriers, emotions, and individual values for performance goals. A student’s
belief about whether “I have the skills required to succeed at this task” is perhaps the most
important factor of influence on motivation (Clark, 2003). The level of personal confidence in
one’s ability to achieve specific performance goals influences persistence and effort (Clark,
2003; Clark & Estes, 2008; Pintrich & Schunk, 1996). Bandura (1993) agrees, students' beliefs in
their efficacy to master academic activities determine their aspirations, level of motivation, and
academic accomplishments. Conversely, if students do not believe in their ability to accomplish
a task, they may not choose to attempt it at all (Bandura & Locke, 2003; Clark, 2003; Clark &
22

Estes, 2008; Pintrich, 2003). This underscores the importance self-confidence has on a person’s
decision to act.
Another factor of influence on motivation is perceived task value. In motivation research,
task value belief is defined in expectancy-value theory (Pintrich, 2003). This theory makes a
distinction between beliefs about being able to do the task and beliefs about the importance,
value, and desire to do the task, and suggests the combination of the two results in action
(Pintrich & Schunk, 1997; Wigfield & Eccles, 2000). Wigfield and Eccles (2000) state,
“individuals’ choice, persistence, and performance can be explained by their beliefs about how
well they will do on the activity and the extent to which they value the activity” (p. 68).
There are three different types of values people utilize in regards to task evaluation
(Clark, 2003). Eccles (as cited by Pintrich, 1999) proposed the three components as: the
individual's interest in the task, their opinion of task importance, and their perception of its
utility value for future goals. The interest component is closely tied to intrinsic motivation, as
people elect to do what is interesting to them (Clark & Estes, 2008). Interest is assumed to be
individuals' positive attitude toward the task that is somewhat stable over time (Pintrich, 1999).
Wigfield & Eccles (2000) define the importance component of task value, also referred to as
attainment value, as the importance of doing well on a given task. The third component, utility
value, pertains to an individual’s belief in the benefits of finishing the task (Clark, 2003; Clark &
Estes, 2008). Whether a task supports an individual’s plans or goals impacts its perceived
usefulness (Wigfield & Eccles, 2000). Individual perceptions in the each of these task value
types influence motivation.
It is important to consider these different sources of motivation and how they impact
human behavior. For the purpose of this study, self-efficacy and intrinsic and extrinsic
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motivation will be measured as the two key constructs related to individual success in different
academic and professional environments.
Measuring Motivation
The Motivational Strategies for Learning Questionnaire (MSLQ) is often used to measure
motivation. According to Pintrich (1999), the MSLQ is a self-reporting instrument designed to
measure students’ motivation and self-regulated learning in classroom contexts. It is an 81-item
questionnaire that measures different indicators toward a specific course, and can be broken
down into six motivation subscales (Hilpert, Stempien, van der Hoeven Kraft, & Husman, 2013).
The value subscales of the MSLQ, which are based on both achievement goal theory and
expectancy-value theory, include statements about how interesting, important, and useful the
course is to the student, to which they agree or disagree (Hilpert et. al., 2013).
Self-efficacy
Self-efficacy has been shown to impact teachers’ decisions to integrate technology into
their practice (Chen, 2010). This section will review research regarding this critical motivational
factor, self-efficacy.
Definition of self-efficacy. The concept of self-efficacy is central to understanding what
motivates human behavior. Self-efficacy beliefs refer to the judgments individuals hold about
their abilities to perform academic and professional tasks and achieve success (Bandura, 1994;
2001; 2012; Caprara, Vecchione, Alessandri, Gerbino & Barbaranelli, 2011; Usher & Pajares,
2006). According to Pajares and Urdan (2006), these beliefs provide the basis for personal well-
being and accomplishment in all areas of life. Individuals without belief that their actions
produce desirable results are often discouraged and concede in the face of inevitable challenges
(Bandura, 2012; Caprara et al., 2011; Pajares & Urdan, 2006; Usher & Pajares, 2006). These
24

judgments about individual capabilities significantly impact academic and professional
behaviors.
Self-efficacy is a central component in Social Cognitive Theory (SCT). Primarily based
on the work of Albert Bandura, SCT stands in contrast to earlier beliefs that changes in
environmental stimuli cause changes in behavior. Bandura (2012) describes SCT as a model of
learning in which people are agents of change, with some degree of control over personal
performance and the environment. From the social cognitive perspective, human beliefs and
actions are products of interaction between personal, behavioral, and environmental factors
(Bandura, 2001; 2012; Pajares & Urdan, 2006). In this triadic reciprocal causation, change in any
one of the elements results in a ripple effect (Bandura, 2012). Efficacy beliefs regarding
academic success in online or face-to-face formats will influence student behaviors and learning
outcomes.
Sources of self-efficacy. General agreement in the literature indicates student self-
efficacy beliefs are formed by four principal sources. Bandura (2012) identifies these sources as:
mastery experiences, social modeling, social persuasion, and emotional and physiological states.
Mastery experiences refer to successes a learner previously reached performing a task (Hodges
& Murphy, 2009). Prior achievement builds a healthy belief in one's personal efficacy, while
failure weakens it, particularly if failure occurs before efficacy beliefs are established (Bandura,
2001, Hodges & Murphy, 2009). Furthermore, overcoming previous challenges with drive and
resilience promotes mastery experiences, fostering beliefs in one’s capabilities (Bandura, 2012).
Efficacy beliefs are shaped by interpretation of past performances.
Another source of self-efficacy is social modeling. Bandura (as cited by Smith, 2002)
states, through watching credible and similar others, observers raise beliefs in themselves.
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Vicarious experience, through significant role models performing a similar task, in comparison
to ones’ own performance, can either increase or lower self-efficacy (Bandura, 2012; Hodges &
Murphy, 2009; Schunk, Hanson, & Cox, 1987; Usher & Pajares, 2006). Apparent likeness in
competence to social models is an important source of information for one's self-efficacy.
Social persuasion is a third source of self-efficacy. When a capable other tells a person
they have the ability to master a specific task, they are likely to show greater effort (Bandura,
1994; 2012 Usher & Pajares, 2009). Students without accurate self-appraisal skills depend on
others to provide evaluative feedback, judgments, and appraisals about their academic
performance (Usher & Pajares, 2009). When individuals are persuaded and encouraged by
significant models, belief in their capabilities develops.
Finally, physiological and emotional conditions also influence perceptions of self-
efficacy. According to Hodges and Murphy (2009) stress, emotion, mood, pain, and fatigue are
all interpreted when assessing efficacy beliefs. Positive emotional states enhance people’s
perceptions of self-efficacy, while unfavorable states diminish it (Bandura, 2012; Hodges &
Murphy, 2009; Usher & Pajares, 2009). Consequently, reducing anxiety and depression and
building physical strength and stamina improve individuals’ overall well-being and strengthen
self-efficacy.
Should differences in self-efficacy be determined to exist between online and traditional
on-campus learning settings, course and program designers must include methods to cultivate it,
as self-efficacy is a strong indicator of individual success (Sander & Larkin, 2013).
Importance of self-efficacy. The self-efficacy construct receives significant attention in
educational research as a factor in academic and professional achievement. Bandura (2001)
offers, an individual’s knowledge and skills alone are not enough to predict future success, for
26

the beliefs they hold about their capabilities have a powerful influence on their behaviors. For
example, beliefs influence how much effort people spend on an activity and how long they will
persevere when presented with challenges (Bandura, 2001; Schunk, 2001; Zimmerman, Bandura,
& Martinez-Pons, 1992). Beliefs also impact the amount of stress an individual experiences and
their overall level of accomplishment (Bandura, 2001; Pajares, 1997). Those who feel efficacious
for learning or performing a task, compared with those who doubt their abilities, “work harder,
persist longer when they encounter difficulties, and achieve at a higher level” (Pajares & Schunk,
2001, p. 2).
A large body of research identifies self-efficacy as a factor pertaining to individual
achievement (Pajares, 1997; Putwain, Sander, & Larkin, 2013; Shen, Cho, Tsai, & Marr, 2013;
Schunk et al., 1987; Yusef, 2011). Research on students’ beliefs for academic achievement
measure aspects of perceived self-efficacy which respect to: (a) the perceived ability to master
specific academic subjects areas (e.g., mathematics) and to (b) the perceived ability to self-
regulate one’s studying and learning habits (e.g., organize studying times and activities; self-
motivate to complete assignments; prioritize and balance life with academics (Caprara et al.,
201; Pajares & Schunk, 2001). How well individuals acquire knowledge and skills and what they
actually do with them are determined by self-perceptions of ability.
Efficacy beliefs are assessed using different measures. General self-efficacy assessments
provide global scores that transfer into generalized personality traits, while others are domain
specific (Pajares, 1997, Scherbaum, Cohen-Charash, & Kern, 2006). Self-efficacy is context-
specific, therefore Bandura (as cited by Pajares, 1997) argues, accurate judgments about capacity
matched to a specific outcome offer the best explanations and predictions of behavioral
outcomes. Choi (2005) agrees, when measuring self-efficacy with respect to a specific task, the
27

construct yields higher predictive validity.
Academic domain-specific assessments of self-efficacy, common in educational research,
use multiple items to restate different facets of the same academic subject. A Likert scale is a
typical psychometric scale frequently used in questionnaires measuring attitudes. It includes a
five to seven point scale that asks individuals to express to what degree they agree or disagree
with a particular statement. The assessments used to measure self-efficacy vary depending upon
the nature of the question researchers seeks to answer.
In summary, self-efficacy plays a central role in understanding human behavior. Mastery
experiences, social modeling, social persuasion, and emotional and physiological states are
sources of self-efficacy, which help shape beliefs in individual capabilities. Distinct from other
constructs, self-perceptions of efficacy are context specific and relate closely to performance
tasks (Zimmerman & Bandura, 1992). Individuals’ self-beliefs about academic capabilities play
an essential role in achievement, and it is critical to determine whether differences in self-
efficacy development exist between online and face-to-face environments.
Intrinsic and Extrinsic Motivation
In an effort to understand human behavior it is important to consider why people engage
in tasks. Individuals are often simultaneously motivated by both intrinsic and extrinsic factors.
Intrinsic motivation occurs within individuals when they feel interest or enjoyment in the task
itself (Ormrod, 2010; Pintrich, 2000; Ryan & Deci, 2000; Wigfield, Guthrie, Tonks, &
Perencevich, 2004). Individuals who are moved to act because the task is fun or challenging are
intrinsically motivated (Ryan and Deci, 2000). In an academic setting, intrinsically motivated
students feel independent and self-determined, and have high levels of interests (Pintrich, 2000).
They are more likely to join in tasks willingly and practice skills, which increase their
28

capabilities (Ormrod, 2010; Wigfield et al., 2004). Intrinsic motivation is “a critical element in
cognitive, social, and physical development because it is through acting on one’s inherent
interests that one grows in knowledge and skills” (Ryan & Deci, 2000, p. 56). Therefore,
intrinsic motivation has a significant impact on individual behaviors.
In contrast, extrinsic motivation comes from outside the individual (Clark 2003; Pintrich
& Schunk, 1996). A person is extrinsically motivated to perform in order to attain an outcome
such as praise, rewards, or money (Ormrod, 2010; Ryan & Deci, 2000). Clark (2003) offers,
these extrinsic motivators, sometimes referred to as reinforces, incentives, or inducements, can
be motivating to the extent that the individual believes they support efficiency and success.
According to Ryan and Deci (2000), students may perform extrinsically motivated actions with
resentment, resistance, and disinterest or, alternatively, with a willing attitude that shows inner
acceptance. Individuals are often simultaneously motivated by intrinsic and extrinsic factors, so
for the purpose of this study intrinsic motivation to integrate technology is examined.
Self-efficacy and intrinsic motivation are central to understanding human motivation.
Therefore, this study will examine these two key constructs related to practicing teachers’
motivation to integrate technology into their practice.
Collaborating through Peer Learning
Personal self-efficacy is a motivational factor, which manifest motivational behaviors.
Collaboration is one such behavior that is critical to student success (Johnson & Johnson, 1999;
Terenzini, Cabrera, Colbeck, Parente, & Bjorklund, 2001; Cabrera, Crissman, Bernal, Nora,
Terenzini, & Pascarella, 2002). Through collaborative work, individuals show improved
achievement, interpersonal skills, and positive behaviors (Johnson & Johnson, 1999).
29

Consequently, it is critical to study how these behaviors manifest differently in distinct settings
and impact professional behaviors.
Definition of Peer Learning
Collaboration is the act of working in concert with others to achieve a shared
goal. Within the educational context, the catchphrase “collaborative learning” refers to a variety
of approaches involving peer teaching and learning, and their practices (Boud, Cohen, &
Sampson, 1999). It has been described as people helping each other understand tasks,
encouraging each other to show persistence, and working together in search of outcomes that
benefit all (Johnson & Johnson. 2001). According to Boud et al., (1999) peer learning may be
encouraged or monitored by instructional staff, however instructors are neither directly teaching
nor controlling the learning between peers. Peer learning continues in the workplace, and is often
conducted outside of traditional academic environments. Since this study seeks to understand
the voluntary nature of collaboration, the term voluntary peer learning refers to collaborative
activities that are neither initiated, facilitated, nor required by external others such as site
supervisors and district leadership. This operational definition illuminates the importance of
individual beliefs regarding cooperation, which in turn impact whether they choose to
collaborate and to what extent.
Peer learning processes align within sociocultural theory and social constructivist teacher
practices (Fisher & Baird, 2005; Bonk & Cunningham, 1998). Constructivist, such as Dewey,
Vygotsky, and Bruner (as cited by Huang, 2002), view knowledge as constructed by learners
through social interaction with others. Intellectual functioning is naturally situated in social
interactional contexts (Bonk & Cunningham, 2002). Johnson & Johnson (1999) affirm the
collective nature of learning and explain group interaction is central to the peer learning process.
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There are a variety of peer learning models in education. Examples include: student-led
workshops, study groups, team projects, student-to-student learning partnerships, and in-class
peer feedback sessions (Boud et al., 1999). Alongside those more traditional samples of
collaboration live those enabled by technology. The sophisticated technologies of the 21
st

century not only provide for new instructional approaches and platforms (Shachar & Nuemann,
2003), they expand collaboration opportunities through use of e-mail, multi-user, web-based
files, interactive video and audio, cellular phones, and texting. A variety of peer learning
opportunities exist in and out of classroom and in both online and face-to-face environments.
Importance of Peer Learning.
There is an increased focus on peer learning experiences in higher education. According
to Boud et al. (1999), growing financial pressures on universities require instructors to teach
larger classes. Pragmatically, peer-learning strategies may assist instructors’ searching for ways
to cope with increased responsibilities (Boud et al., 1999). Bonk and Cunningham (1998) add,
the traditional teacher-centered model is being replaced by a student-centered, constructivist, and
sociocultural models of instruction, which emphasize the role of individuals as they construct
learning collaboratively.
In addition to meeting the needs of a growing population of students, there is an increased
demand for graduates to develop transferable skills. Boud et al., (1999) define these key
competencies or capabilities as more general learning outcomes or “life-long learning skills” (p.
4-5). These more broad attributes include, but are not limited to: cooperating with a team;
communicating and articulating ideas; being reflective; accepting feedback; and self-regulated
learning in the absence of an authority figure (Bonk & Cunningham, 1998; Boud et al., 1998;
Cabrera et al., 2002; Hernandez et al., 2014; Johnson & Johnson, 1999; Terenzini et al., 2001).
31

Slavin (as cited by Boud et al., 1998) offers, peer-learning approaches foster these transferable
skills. Therefore, it is important to determine to what degree online verses face-to-face settings
foster peer-learning practices, which generate individual attainment of transferable skills.
Benefits of Peer Learning
Peer learning processes positively influence cognitive and affective outcomes. Studies
show peer learning yields higher achievement and greater productivity than working alone
(Johnson & Johnson, 1999; Terenzini et al., 2001; Tinto, 2003). In an educational setting,
learning may be enhanced through active involvement and participation of students on clear and
challenging projects (Johnson & Johnson, 1999; Terenzini et al., 2001). In a study by Tinto
(2003), individuals in a learning community perceived themselves “as making significantly
greater intellectual gains over the course of the semester than did similar students in the
comparison classes” (p. 5). Additional research suggests, peer learning promotes critical
thinking, higher-level reasoning, and greater levels of knowledge generation (Cabrera et al.,
2002; Johnson & Johnson, 1999; Palloff & Pratt, 2005). Through peer learning, individuals show
improved achievement.
The development of interpersonal skills is another benefit of peer learning. According to
Johnson & Johnson (1999), data indicate that peer learning helps develop caring and committed
individuals, whose identity develops when becoming proficient in practices valued by their
learning community. Huang (2002) agrees, through interaction and collaboration with more
capable peers, social skills are improved. In a complex society, learning to cooperate with others
to reach shared goals seems a crucial requirement (Johnson & Johnson, 1999). Rodriguez-Mera
(as cited by Hernandez, Gonzalez, & Munoz, 2014) recognizes the paired result of “collaborating
to learn and learning to collaborate” (p. 2). Through collaboration, individuals learn to
32

compromise, negotiate, break down stereotypes, develop listening skills, and value cooperation
(Cabrera et al., 2002; Johnson & Johnson, 1999; Tinto, 2003). These vital interpersonal skills,
required for effectiveness in the 21
st
century, are honed through peer learning.
The psychological health benefits derived through teaching and learning with peers
impact individual behaviors and outcomes. According to Johnson & Johnson (1999), when
individuals work together to complete assignments, they promote each other's success and form
relationships, which create the foundation for healthy social development. Tinto (2003) reports,
when compared with students in traditional curriculum, students engaged in peer learning
experiences perceived themselves as more academically and socially engaged, resulting in a
significantly higher rate of persistence. Furthermore, Johnson & Johnson (1999) report peer
learning increases an individual’s resilience and ability to cope with adversity and stress. These
behaviors reflect high levels of self-efficacy, which is a strong predictor of success (Bandura,
2001; Pajares & Schunk, 2001). Peer learning promotes healthy social and psychological
development of individuals, which in turn impact their beliefs and behaviors in educational
settings. The positive cognitive and affective outcomes that result from peer-learning experiences
warrant further research regarding its development in different instructional formats.
Challenges in Research
While the literature generally extols the benefits of collaboration in learning, the extent
and specifics of its returns are difficult to generalize. Slavin (as cited by Cabrera et al., 2002),
states that most of the research on the effects of collaboration is based at elementary and
secondary school levels, and that existing empirical studies on peer learning in higher education
have a narrow program or institutional focus. Furthermore, Cabrera et al., (2002) explain,
studies on collaboration among college students are often “correlational and cross sectional in
33

nature” (p. 22). By design, these studies make it difficult to disconnect the effect of other factors
related to student learning and cognitive development, such as academic ability and effort
(Cabrera et al., 2002). Further studies on the benefits of peer learning for graduate students and
practicing teachers, including peer learning within synchronous, asynchronous, hybrid and
blended environments, will provide a more comprehensive picture of its importance in teaching
and learning.
Lack of attention to structuring effective peer-learning experiences impedes its potential
benefits. Johnson & Johnson (1999) state that not all groups are cooperative and that students
participating in pseudo learning groups and traditional classroom learning groups “would
achieve more if they were working alone” (p. 68). Within a pseudo learning group structure,
students are assessed separately, which rears feelings of competition between its members
(Johnson & Johnson, 1999). Similarly, within traditional classroom groups, some students let
their peers do most of the work, who in turn feel resentful and unmotivated (Johnson & John,
1999). Capitalizing benefits of peer learning requires elimination of afore mentioned group work
models and careful reconstruction of course assignments and assessments (Boud et al., 1998).
Although redesigning courses proves complex and intensive for faculty (Terenzini et al., 2001),
it is necessary to ensure academic group work is cooperative.
Measuring collaboration beliefs and comparing findings across studies is difficult. Lack
of consensus among scholars on the definition of collaboration, as stated earlier, contributes to
this challenge (Boud et al., 1998). Furthermore, although literature on “collaborative learning”
typically focuses on instructional design, “it has long been associated with student affairs outside
the classroom” (Cabrera et al., 2002, p. 20). This voluntary type of collaboration stems from
individuals’ beliefs about the value of learning in cooperation with others. The nature of
34

collaboration and the unclear meaning of collaborative terms make its research challenging.
Few collaboration measures exist and those that do are difficult to adapt to specific
contexts and studies (Thomson, Perry, & Miller, 2009). Terenzini et al., (2001) used the
Classroom Activities and Outcomes Survey (CAOS), a multiple questionnaire created by the
Center for the Study of Higher Education at the Pennsylvania State University, to glean
engineering students’ beliefs about the effectiveness of collaborative verses conventional
instructional approaches. Walker and Fraser (2005) developed and validated an instrument called
the Distance Education Learning Environments Survey (DELES). The DELES has 34 items
allocated to six scales, one of which is “student interaction and collaboration” (p. 289). These
questionnaires use a Likert scale to measure student attitudes about collaboration. The
assessments used to measure collaboration vary depending upon the nature of collaboration and
the focus of the study.
In summary, research supports the use of collaborative practices both inside and outside
the classroom. Peer learning experiences, in which individuals learn with and from each other
without mediation (Boud, et al., 1999, p. 2), show positive cognitive and affective outcomes.
Additionally, the transferrable skills gained through these techniques are critical to success in
21st century society. The increased focus on peer learning experiences in higher education and in
the workplace requires restructuring of teacher education programs and the professional working
environment. Wenger (as cited by Palloff & Pratt, 1999) states, whether it be face-to-face or
online, “issues of education should be first and foremost in terms of identities and modes of
belonging” (p. 1). This underscores the importance of studying the degree to which online or
face-to-face teacher education programs cultivate collaborative behaviors that transfer into
practicing teachers’ professional practice.
35

Summary
Technology is pervasive, inevitable, and transformative both in American society (Chen,
2011) and its educational system. Because technology has increased the frequency and intricacy
of scholarly environments, the definition of literacy in the 21st century has moved beyond
reading and writing. The expectation that teachers effectively integrate technology into their
practice to support student development of 21st century literacies has increased. Therefore, there
is a growing need for teacher education programs to focus on effective incorporation of
technology in their preservice courses in order to prepare practicing teachers in their professional
assignments. Research regarding technology in teacher education identifies teacher beliefs and
motivation as factors of influence on individuals’ decision to use technology. As self-efficacy
and intrinsic motivation are high indicators of academic success, and peer learning experiences
show positive cognitive and affective outcomes, it is important to examine to what degree
teacher education programs nurture beliefs and influence behaviors regarding technology use.
Furthermore, as online learning, be it synchronous or asynchronous, has become a vital part of
the higher education landscape, and teacher education programs are navigating online, it is
important to know whether there are differences between online and face-to-face teacher
education programs ability to foster beliefs and behavior regarding incorporation of technology
in pedagogical practice.
36

CHAPTER THREE
Methodology
Teacher preparation programs endeavor to support preservice and thereafter, practicing
teachers’ integration of technology into their pedagogy practice, as it is critical to their success as
21st century educators. Since many teacher preparation programs are moving online, this study
related to this venture informs and perhaps reforms teacher education programs. Little research
exists regarding the effect of online education on teacher preparation; therefore, the purpose of
this study was to examine practicing teachers’ beliefs and behaviors related to technology
integration, and to determine if there are differences between whether a teacher completed an
online or an on-campus teacher education program. The practicing teachers in this study
completed a Masters in teaching program fulfilling state licensure requirements for a teaching
credential. This chapter includes the research questions, and an overview of the research
methodology. The latter describes the sampling procedure and population, instrumentation, and
procedures for data collection and analysis.
Research Questions
The following research questions guided the study:
1. Is there a difference in practicing teachers’ self-efficacy regarding integration of
technology into instructional practices by program delivery method, controlling for
level of training provided by district?
2. Is there a difference in practicing teachers’ levels of intrinsic motivation regarding
integration of technology into instructional practices by program delivery method,
controlling for level of training provided by district?
37

3. Is there a difference in practicing teachers’ beliefs and behaviors related to voluntary
collaboration by program delivery?
Research Design

In order to examine potential differences between beliefs and behaviors based on either
online or on-campus training program, a quantitative design was used. Correlational data
collected from self-reported surveys were analyzed for statistical significance. The independent
variable in this study was the type of program the practicing teacher completed. The dependent
variables were beliefs about self-efficacy and collaboration, the frequency of collaboration, and
the modes of collaboration in which the proposed participants engage.
Population and Sample
The population for this study was practicing teachers who graduated from either the
synchronous online or on-campus formats of a Masters in Teaching (MT) program, at a four-year
University on the West Coast (UWC). The MT was designed to prepare credential candidates for
teaching careers in diverse classroom settings. The specific sample for this study was drawn
from practicing teachers who have completed methodology coursework and the K-12 student
teaching portion of their masters and credentialing program.
Descriptive Characteristics of Respondents
An invitation and survey were sent to 188 MT program graduates. Among the
respondents (N = 43), the majority completed the synchronous online format (n = 32), and the
others completed the face-to-face format (n = 11) of the same program. There was a notable
disparity in sample sizes of the participants who were enrolled in the on-campus cohort and those
who were enrolled in the online cohort. The ages of the respondents ranged from 24 to 54 years
old with a mean of 32 years of age (SD = 7.8). There were 32 female and 10 male participants.
38

The mean teaching experience was three years (SD = 3.9). Practicing teachers in this study self-
reported integrating technology into their instructional practices either 2-3 times a month (2%),
once a week (7.%), 2-3 times a week (39%), or daily (51%).
Instrumentation
A self-reported questionnaire was used for this study. Participants were asked to respond
to a 20-item survey consisting of a number of demographic questions, and three subscales aimed
to measure the constructs of self-efficacy, intrinsic motivation, and voluntary peer learning
(Appendix A).
Demographic items. A number of demographic questions were asked to determine the
characteristics of the sample population. These data included: age, gender, racial/ethnic group,
employment status, relationship status, undergraduate major, previous graduate degree,
volunteer/work experience in schools, and comfort with technology use, additional technology
training offered by their school or district, and what technology is available for use. The
following sections describe the instruments used to measure the three focus constructs.
Self-efficacy. Self-efficacy in regards to technology integration was measured with an
instrument modeled after the Patterns of Adaptive Learning Scales (PALS), developed by
Midgley, Maehr, Hruda, Anderman, Anderman, Freeman, & Urdan (2000). The Cronbach’s
alpha for the original scale was .78. For the purpose of this study, survey items were developed
to address students’ efficacy regarding technology integration during their early teaching
experience. For example, “I’m certain I can master the technology requirements of this school.”
The modified scale consisted of five items rated on a five-point Likert scale, ranging from 1 –
strongly disagree to 5 – strongly agree. The reliability analysis performed for this study reported
an alpha of .91.
39

Intrinsic motivation. In order to measure intrinsic motivation toward technology
integration in early pedagogical practices, this study developed a survey modeled after the
Motivated Strategies for Learning Questionnaire (MSLQ) (Pintrich, Smith, Garcia, &
McKeachie, 1990). The original questionnaire consists of 81 items that measure different
indicators toward a specific course, which can be broken down into six motivation subscales
(Hilpert, Stempien, van der Hoeven Kraft, & Huusman, 2013). For this study an eight-item
survey incorporating a seven-point Likert scale was used to measure intrinsic motivation. The
reliability analysis performed for this study reported the Chronbach’s alpha of .88. Modifications
were made to make the language more applicable to technology integration. One example of an
item used to measure intrinsic motivation was, “In my teaching practice, I prefer to use
technology that arouses my curiosity, even if it is difficult to learn.”
Voluntary peer collaboration. A new instrument was developed to assess the beliefs,
behaviors, and frequency of voluntary collaboration in preservice teachers regarding technology
integration with teaching practices. The scale was developed through collaboration with fellow
doctoral students and faculty chairs. Items for this measure were designed to explore the
voluntary nature of collaboration with regards to technology integration. The measure was
piloted in a graduate level course in education. Cronbach’s Alpha from the pilot test was .93. The
reliability analysis performed for this study reported an alpha of .84 which is lower than the .93
reported in the pilot but greater than the minimum level of acceptability of .70.
Procedure and Data Collection
Upon approval from the Institutional Review Board and the teacher education program,
data collection began. Invitations to participate in the study were sent electronically via e-mail to
practicing teachers who graduated from either the online and face-to-face formats of the MT
40

program. The email also contained a link to the online survey and a brief description of the
study’s purpose. Research participants were informed that completing the roughly 10 minute
survey neither benefitted nor penalized them in any way and that their responses remained
anonymous. The survey was administered through Qualtrics.
Data Analysis
Survey data was downloaded from Qualtrics and preliminary statistical analyses were
conducted using the Statistical Package for the Social Sciences (SPSS) program. The
independent variables in this study were the online and face-to-face program formats – as well as
level of training provided by district as a control variable. The dependent variables were
practicing teachers’ self-efficacy and intrinsic motivation, and voluntary peer collaboration. For
research question one, an analysis of covariance (ANCOVA) was performed to assess mean self-
efficacy regarding integration of technology into instructional practices as predicted by online
versus face-to-face formats and controlling for the level of training provided by the school
district. For the second research question, an ANCOVA was conducted to compare practicing
teachers intrinsic motivation regarding integration of technology into instructional practices as
predicted by online verses face-to-face formats. Again, the procedure controlled for the level of
training provided by the school district. In order to examine which group was more intrinsically
motivated, an independent-samples t-test was conducted to compare intrinsic motivation of
participants who took the program online verses face-to-face. For the third research question,
two independent-samples t-test were conducted to compare teachers’ voluntary collaboration
beliefs and the frequency of collaboration. A Chi-square test was also performed to determine
whether differences existed in voluntary peer collaboration modes of contact according to
41

program delivery method. The table below lists the variables, their levels of measurement and
corresponding statistical tests for each research question.

Research Questions IV(s) Level of
Measurement
DV(s) Level of
Measurem
ent
Statistical
Test
Is there a difference in
practicing teachers’
self-efficacy regarding
integration of
technology into
instructional practices
by program delivery
method, controlling
for level of training
provided by district?

Program
format
Level of
training by
district
Nominal Self-efficacy

Interval
(Likert
Scale)

Interval
ANCOVA
Is there a difference in
practicing teachers’
levels of intrinsic
motivation regarding
integration of
technology into
instructional practices
by program delivery
method, controlling
for level of training
provided by district?

Program
format
Level of
training by
district
Nominal Intrinsic and
extrinsic
motivation

Interval
(Likert
Scale)

Interval
ANCOVA
Is there a difference in
practicing teachers’
beliefs and behaviors
related to voluntary
collaboration by
program delivery?
Program
format
Nominal

Beliefs about
Collaboration
Interval Independent
samples t-test

Frequency of
Collaboration
Interval Independent
samples t-test
Types of
Collaboration
Nominal Chi-square
42

CHAPTER FOUR
Results
The goal of this study was to examine the degree to which online versus face-to-face
programs cultivate practicing teachers’ beliefs, attitudes, and behaviors regarding the integrating
of technology into instructional practices and collaborating with others around its use in the K-12
setting. Specifically, the study looked at practicing teachers who graduated from either the
synchronous online or face-to-face formats of a Masters in Teaching (MT) program at a four-
year University on the West Coast (UWC). This study investigated motivational predictors of
technology integration such as self-efficacy, intrinsic motivation, and professional behaviors in
this context, such as voluntary peer collaboration.
The purpose of this chapter is to report the findings of this study. It begins with the
presentation of descriptive statistics, and then moves to significant findings, which shows the
relationships between variables. The following section discusses the statistical procedures used
to answer each research question and an analysis of results.
Intercorrelations
The means, standard deviations, and correlations of all the measured variables are
presented in Table 1. A Pearson product-moment correlation efficient was computed to assess
the relationships between the variables. Practicing teachers’ perceived degree of intrinsic
motivation and extrinsic motivation were significantly correlated (r = .57, p = .000), indicating
that both types of motivational factors simultaneously affected the participants in this study.
Practicing teacher satisfaction was significantly positively associated with voluntary peer
collaboration (r = .48, p = .000), revealing that practicing teachers who reported being satisfied
with their program format also believe in voluntarily collaborating with their peers. Furthermore,
43

practicing teachers’ reporting beliefs in voluntary peer collaboration were significantly positively
associated with intrinsic (r = .57, p = .000) and extrinsic motivation (r = 1.0, p = .000). This
shows that motivated teachers believe in collaborating voluntarily with peers. Overall, there were
few significant correlations found in the study.
Table 1
Means, Standard Deviations, and Pearson Product Correlations of Measured Variables

Variables M SD 2 3 4 5 6 7 8 9

1. Age 32.18 7.85 -.031 .061 -.197 .052 .034 -.197 -.165 .035
2. Parent education 6.23 2.13 -- -.043 .132 -.025 -.111 .132 .128 -.104
3. Integration of
technology
6.39 .73 -- .114 .110 .258 .114 -.013 -.034
4. Voluntary
collaboration
beliefs
3.05 1.12 -- .130 .574** 1.00** -.042 .484**
5. Self-efficacy 4.57 .551 -- .523 .130 .159 .159
6. Intrinsic motivation 3.80 .821 -- .574** .022 .127
7. Extrinsic
motivation
3.05 1.12 -- -.042 .157
8. Voluntary
collaboration
behaviors
4.13 1.10 -- -.042
9. Student Satisfaction 4.11 .919 --

**p<.01

Analysis of Results
The self-report survey data collected as described in the previous chapter was analyzed in
order to address the research questions posed for the study. The quantitative findings presented
in this chapter will be organized according to the research questions.
Research Questions 1: Is there a difference in practicing teachers’ self-efficacy
regarding integration of technology into instructional practices by program delivery
method, controlling for level of training provided by district? This research question sought
44

to examine potential differences in practicing teachers’ self-efficacy regarding technology
integration between program delivery methods. An analysis of covariance (ANCOVA) was
conducted to investigate practicing teachers’ self-efficacy regarding integration of technology
into instructional practices as predicted by online verses face-to-face formats, with a covariate of
training provided by the school district. The independent variable for the ANCOVA was the
program format and the dependent variable was their self-reported self-efficacy beliefs. After
adjustment by the covariate, there was no significant effect of program delivery on self-efficacy
towards technology integration, F(1, 37) = .153, p < .698. When examining the between-subject
effects, neither the program format variable (p < .074) nor the professional development
provided by the district (p < .112) predicted self-efficacy regarding integrating technology. See
Table 2 below for details regarding analysis of covariance results.
Table 2
Results of between-subjects effects: Self-efficacy by program format

Coefficients
a

Source
Type III Sum
of Squares df
Mean
Square F Sig.
Partial
Eta
Squared
Corrected Model 1.892
a
2 .946 3.438 .043 .160
Intercept 96.393 1 96.393 350.337 .000 .907
DisTrain2 .731 1 .731 2.655 .112 .069
Program .932 1 .932 3.386 .074 .086
Error 9.905 36 .275

Total 820.560 39

Corrected Total 11.797 38
a. Dependent Variable: Mean self-efficacy

The whole equation predicted only 11% of variance in self-efficacy regarding technology
integration, therefore no significant differences in practicing teachers’ self-efficacy to integrate
technology were found based on format delivery.
45

Research Question 2: Is there a difference in practicing teachers’ levels of intrinsic
motivation regarding integration of technology into instructional practices by program
delivery method, controlling for level of training provided by district? This research
question sought to examine potential differences in practicing teachers’ intrinsic motivation
regarding technology integration between program delivery methods. An ANCOVA was
conducted to investigate practicing teachers’ intrinsic motivation to integration technology into
their pedagogical practices as predicted by online verses face-to-face formats, with a covariate of
training provided by the school district. The independent variable for this analysis was the
program format and the dependent variable was their self-reported intrinsic motivation. There
was a significant effect of program delivery method on intrinsic motivation to incorporate
technology, F(1, 36) = 16.1, p < .000, however district training did not emerge as a significant
predictor (p < .233). In essence, 32% of variance in the level of intrinsic motivation was
predicted by whether they graduated from an online or face-to-face program. The relationship
was significant, with program delivery predicting differences in intrinsic motivation. See Table 3
below for details regarding the analysis of covariance.
Table 3
Results of between-subjects effects: Intrinsic Motivation by program format

Coefficients
a

Source
Type III Sum
of Squares df Mean Square F Sig.
Partial Eta
Squared
Corrected Model 8.767
a
2 4.384 9.744 .000 .358
Intercept 59.113 1 59.113 131.397 .000 .790
DisTrain2 .664 1 .664 1.475 .233 .040
Program 7.234 1 7.234 16.080 .000 .315
Error 15.746 35 .450
Total 581.625 38
Corrected Total 24.513 37

a. Dependent Variable: Mean motivation
46

In order to examine which group was more intrinsically motivated to integrate technology
into their pedagogical practice, an independent-samples t-test was conducted to compare intrinsic
motivation of participants who took the program online verses face-to-face. There was a
significant difference in the scores for online (M = 4.08, SD = .704) and face-to-face (M = 3.00,
SD = .559) conditions; t(36) = 4.21, p = .000. Although the two groups were unequal, this
relationship emerged as significant; graduates of the online program were significantly more
intrinsically motivated to integrate technology into their practice. See Table 4 below for details.
Table 4
Results of t-tests and descriptive statistics: Technology Intrinsic Motivation by program format

Group Statistics
Program
Format
N Mean
Std.
Deviation
Std. Error
Mean
Mean
Intrinsic
Motivation
Online 29 4.08 .704 .131
Face-to-face 9 3.00 .559 .186

Independent Samples Test
Levene’s
Test for
Equality of
Variances
t-test for Equality of Means
F Sig t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence
Interval of the
Difference
Lower Upper
.890 .352 4.216 36 .000 1.09 .258 .564 1.61

Research Question 3: Is there a difference in practicing teachers’ beliefs and
behaviors related to voluntary collaboration by program delivery? This research question
sought to examine potential differences is practicing teachers’ voluntary collaboration beliefs
and behaviors between program delivery methods. An independent-samples t-test was
conducted to compare teachers’ voluntary collaboration beliefs in online and face-to-face
47

conditions. A significant difference was found as can be seen in the scores for online (M = 3.28,
SD = 1.11) and on-campus (M = 2.42, SD = .94) delivery methods, on a 1-5 scale; t(37) = 2.12,
p = .041. The results suggest that there are differences in the students’ voluntary peer
collaboration beliefs between the two program delivery methods.
An independent-samples t-test was also conducted to compare students’ voluntary peer
collaboration behaviors as measured by frequency of more than twice a week, 1-2 times per
week, 1-2 times per month, 1-2 times per semester, or not at all. No significant difference was
found as can be seen in the scores for online (M = 4.10, SD = 1.04) and face-to-face (M = 4.22,
SD = 1.39 delivery methods; t(36) = .275, p = .785. The results suggest that there are no
differences in the students’ voluntary peer collaboration behaviors between the two program
delivery methods.
Modes of voluntary peer collaboration. Additional analyses was conducted to determine
if there were differences in the modes of contacts in the teachers’ voluntary peer collaboration
behavior. This research question sought to examine potential differences in practicing teachers’
voluntary peer collaboration behaviors between program delivery methods according to the
different modes of collaboration which included in-person, via phone or text, via email or
discussion board, via social media (e.g., Facebook, Twitter), and via videoconferencing (e.g.,
Skype, Adobe Connect). A chi-square test was performed. See Table 5 below for details.
Table 5
Chi-square test: Modes of voluntary peer collaboration

Chi-Square Tests
Value df Asymp. Sig. (2-sd)
Pearson Chi-Square 38.498
a
36 .357
Likelihood Ratio
N of Valid Cases
43.161
43
36

.192
a. 73 cells (98.6%) have expected count less than 5. The minimum expected count is .26.

48

There was no significance found between course delivery method according to modes of
contact in the teachers’ voluntary peer collaboration behavior, X
2
(36, N = 43) = 3.84, p = .357).
The results suggest that there are no differences in the practicing teachers’ self-reported
voluntary peer collaboration behaviors between the two program delivery methods according to
mode of contact.
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 the intercorrelations to identify any
significant correlations of measured variables. The results suggested that practicing teachers’ in
this study self-reported being motivated overall and believing in collaborating voluntarily with
peers. Also, there was a positive association between self-reported satisfaction with program
format and beliefs about voluntary collaboration with peers. Overall, there were few significant
correlations found in the study.
Each of the research questions was introduced individually, and analysis results were
presented. The research questions asked about potential differences in the constructs of self-
efficacy and intrinsic motivation in regards to integrating technology, and voluntary
collaboration around its use. The first research question asked about potential differences in
practicing teachers’ self-efficacy regarding integration of technology into instructional practices
by program delivery method. The procedure controlled for the level of training provided by the
school district. The relationship between the variables was not significant. There are no clear
differences in terms of how self-efficacious someone is with integrating technology based on
level of training provided by the school district or program format.
49

The second research question asked about the potential differences in practicing teachers’
levels of intrinsic motivation regarding integration of technology into instructional practices by
program delivery method. This procedure also controlled for the level of training provided by the
school district. This relationship emerged as significant. Practicing teachers’ level of intrinsic
motivation toward technology in their teaching was predicted by whether they graduated from an
online or face-to-face program. Furthermore, graduates of the online program were more
intrinsically motivated to integrate technology into their teaching practice.
The final research question asked about potential differences in practicing teachers’
voluntary peer collaboration beliefs and behaviors according to delivery method. As for
practicing teachers’ beliefs regarding voluntarily collaborating with peers, a significant
difference was found between the two formats. The results suggest that practicing teachers who
graduated from an online program have strong beliefs about collaboration. An additional
analysis was conducted to examine potential differences in practicing teachers’ voluntary peer
collaboration behaviors between program delivery methods, according to the different modes of
collaboration. No significant difference was found between course delivery method and teachers’
voluntary peer collaboration behavior according to modes of contact.
In summary, the study results indicate that there is no statistically significant difference in
the levels of practicing teachers’ integration of technology into their teaching practices based on
program delivery method. However, program delivery did predict differences in practicing
teachers’ level of intrinsic motivation with online program graduates being more intrinsically
motivated to integrate technology into their teaching practice. In understanding this finding,
someone who is more intrinsically motivated to integrate technology may be more likely to
enroll in an online teacher education platform. Additionally, the study results showed statistically
50

significant differences in teachers’ beliefs regarding voluntary collaboration with peers. Overall
there were very few significant relationships that emerged. The MT online and face-to-face
program formats produce comparable beliefs and behaviors in terms of the degree to which
practicing teachers’ feel self-efficacious and actually practice integrating technology in the work
place. The implications of these results will be discussed in the next chapter.

51

CHAPTER FIVE
Discussion
This final chapter begins with a brief overview of the study’s purpose. Next it discusses
implications of the research findings. It concludes with consideration of the study limitations and
suggested recommendations for future research.
The purpose of this study was to examine the degree to which teacher education
programs cultivate beliefs and behaviors of practicing teachers in regards to integrating
technology into their pedagogical practices and collaborating with peers to reach that end. Some
studies (Angeli 2005; Chen, 2011; Jang, 2008) focus on technology integration in regards to
specific content areas (e.g., science, math, writing), while others examine the factors influencing
teachers’ motivation to use technology, such as their beliefs and values (Kim, Kim, Lee, Spector,
& DeMeester, 2013; Teo, 2011). As online learning is earning its position as a central part of the
higher education setting, and is likely to emerge as a key component in teacher education (Kim
& Frick, 2011), this study also sought to determine if there were differences in practicing
teachers’ technology integration practices based on whether they completed an online or face-to-
face teacher education program.
The research questions developed for this study were based on well-established
constructs related to academic and professional beliefs and behaviors of practicing teachers in
the K-12 setting. The questions also sought to determine to what degree teacher education
program delivery methods cultivate those critical academic and professional behaviors. An
interpretation of the results from this study is provided in the following sections.
Discussion of Demographic Composition of Practicing Teachers
52

Demographic information was collected to provide the researcher with an overall
participant profile and composition of the individuals surveyed. Furthermore, the information
could help identify potential significant relationships between practicing teacher characteristics
and academic and professional beliefs and behaviors. However, no significant differences
emerged between participants’ age, gender, and ethnicity and their beliefs and behaviors as
identified in this study. There were also no significant relationships that emerged between
practicing teachers’ years of experience, employment status, relationship status, and prior
education in regards to integrating technology into pedagogical practices in the workplace.
The demographic data could also be used to either confirm or disconfirm certain
characteristics about American students in higher education as established in literature. The
respondents in this study averaged 32 years of age and reported working either full or part time,
which supports the literature that most college students today are older than 22, attend school
part-time, and hold part-time or full-time jobs (Brown, 2012; Chen et al., 2010; Picciano et al.,
2010). Additionally, there is a new demographic of higher education students for whom online
learning appears to me optimal, in part because the flexibility of online courses helps students
who are juggling school, work, and family (Picciano et al., 2010). This may explain why the
majority of the research participants graduated from the online format of the MT within the last
five years of conducting this study. Another possible explanation for there being a higher number
of online participants in this study is that there is a much larger body of online students enrolled
in the synchronous online format of the MT program at UWC. Therefore the proportion of online
versus face-to-face participants in this study mirrors the student enrollment in the MT program.

53

Discussion of Self-Efficacy and Technology Integration
The self-efficacy construct receives significant attention in educational research as a
factor in academic and professional achievement. The study results did not find statistically
significant differences in practicing teachers’ level of self-efficacy in regards to integrating
technology based on format delivery of their teacher training. Graduates of both online and face-
to-face MT programs indicated a strong view of agreement on statements regarding their level of
efficacy with integrating technology in the workplace. A possible explanation for this may be
that both online and face-to-face programs provide their students with significant exposure to
technology in the classroom. For example, graduates from both MT formats used the same online
Learning Management System. Furthermore, all MT graduates completed a media class, which
could influence their self-efficacy and beliefs about technology integration in the classroom.
The results also indicated that practicing teachers reporting strong self-efficacy in this
area report integrating it into their teaching practice on a daily basis. These results are consistent
with the literature showing those who feel efficacious for learning or performing a task,
compared to those who have self-doubt about their abilities, are likely to work harder, show
persistence, and achieve at a higher level (Pajares & Schuck, 2001). Moreover, studies have
indicated that self-efficacy has been shown to impact teachers’ decisions to integrate technology
into their practice (Chen, 2010).
Practicing teachers’ level of self-efficacy with incorporating technology was strong with
a mean score of 4.58 on a 1-5 scale for graduates of both online and face-to-face programs. This
indicates that completing an online or face-to-face teaching program made no difference in
someone’s self-efficacy in terms of integrating technology. General agreement in the literature
indicates student self-efficacy beliefs are formed by four principal sources: mastery experiences,
54

social modeling, social persuasion, and emotional and physical states (Bandura, 2012).
Therefore, one explanation for this could be that both programs formats are adequately providing
students with experiences that promote self-efficacy regarding technology integration, reflective
of afore mentioned self-efficacy sources.
Self-efficacy beliefs refer to the judgments students hold about their abilities to perform
academic tasks and achieve academic success (Bandura, 1994; 2001; 2012; Caprara, Vecchione,
Alessandri, Gerbino & Barbaranelli, 2011; Usher & Pajares, 2006). Teo (2011) discussed how
positive experiences and feelings related to technology use may lead to continued use. The
equitable beliefs and behaviors regarding self-efficacy for integrating technology and actual
practice in the work place implies that the MT online and face-to-face programs are providing
preservice teachers with successful experiences, which lead to positive feelings toward
incorporating technology into their pedagogical practice.
Discussion of Intrinsic Motivation and Technology Integration
The study results found a statistically significant difference in practicing teachers’
intrinsic motivation regarding technology integration between program delivery methods.
Specifically, graduates of the synchronous online program were significantly more intrinsically
motivated to integrate technology into their teaching practice.
A possible explanation for this may be that those who are more intrinsically motivated to
integrate technology into pedagogy are also more inclined to enroll in an online teacher
education platform. The interest component is closely tied to intrinsic motivation, as people elect
to do what is interesting to them (Clark & Estes, 2008). Many higher education students choose
the online format because of the convenience modern technology provides (Allen & Seaman,
2014; Campbell et al., 2008; Picciano et al., 2010). For example, the Internet delivers content
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and resources to students directly (Brown, 2012; Shachar & Newman, 2003) and open education
resources (OER), such as textbooks, streaming videos and other tools and materials, support
online learners anywhere at anytime (Atkins et al., 2007). This study’s findings may suggest that
choosing the MT online format requires a certain level of interest and self-efficacy with regards
to technology, which may result in higher intrinsic motivation to incorporate it in the workplace.
Another possible explanation for why online graduates were shown to be more
intrinsically motivated to integrate technology into their teaching practices may be that they
benefitted from synchronous social modeling. Gal-Ezer and Lupo (2002) explain that the
development of computer-based chats and audiovisual conferencing, allows instructors to
facilitate interaction with and between students in real time. Therefore instructors in the
synchronous online program regularly model technology integration for their students. This
aligns with Social Cognitive Theory (SCT), which states that learning is enactive or vicarious
and that people can learn by observing others. Bandura (as cited by Smith, 2002) posits, through
watching credible and similar others, observers raise beliefs in themselves. In the MT online
format, preservice teachers observe their instructor incorporating technology. As they continue
observing effective modeling of technology integration and gain increased experience with its
use in practicum courses, students’ desire to learn about instructional technology and their beliefs
in their ability and confidence about their instructional technology skills increase (DiPietro,
2004; Oigara & Keengwe, 2013). Subsequently, practicing teachers whose beliefs about their
own technology integration skills improved as a result of social modeling may be more
intrinsically motivated to incorporate technology in their classrooms.
Discussion of Voluntary Peer Collaboration and Technology Integration
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The study results found differences in the students’ voluntary peer collaboration beliefs
between the two program delivery methods. The results indicated that practicing teachers who
graduated from an online format have stronger beliefs about collaboration than their on-campus
peers. A possible explanation for this is that technology enables sophisticated collaboration. This
supports the literature stating that e-mail, multi-user, web-based files, interactive video and
audio, cellular phones, and texting expand collaboration opportunities (Shachar & Nuemann,
2003). Although little research discussing the voluntary nature of collaboration exists, this study
seems to suggest that the graduates of the online format may report strong beliefs about
collaborating voluntarily with peers because of the nature of the program and the opportunities
provided by the technology itself. Instructors of the online format may have emphasized the
importance of peer collaboration more so than instructors in the face-to face format.
Consequently, students in the online program may have expressed greater value in collaboration
because they viewed it as important to their success. While the distance made it more difficult
for them to work with their peers, the technology afforded them the opportunity to connect and
collaborate online.
Interestingly, while online graduates reported higher beliefs in voluntary peer
collaboration, there were no significant differences in terms of the frequency of actual
collaboration behaviors. In other words, practicing teachers from both online and face-to-face
formats reported engaging in voluntary peer collaboration with roughly the same frequency.
Implications of Research
This study yields important implications for teacher education programs and K-12 school
districts. In recognizing the increasing need for teacher education programs to focus on effective
incorporation of technology to address school and community expectations, and to meet the
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needs of K-12 students in the 21st Century (Becker, 2000; Cuban, Kirkpatrick, & Peck, 2001;
Chen, 2009; Lux, 2013; Teo, 2011), suggestions are made to further develop the beliefs and
behaviors of practicing teachers in regards to integrating technology into their pedagogical
practices. Moreover, the importance of nurturing intrinsic motivation and collaboration as critical
components of professional training is also taken into consideration. The following
recommendations are made to instructors, course designers, and K-12 district leaders.
In an effort to address this study’s finding that graduates of the online program were
significantly more intrinsically motivated to integrate technology into their teaching practice,
face-to-face programs can create more opportunities for students to observe instructors modeling
technology integration. The nature of the real-time online format allows for instructional
modeling of technology use, however instructors in the face-to-face program must create
opportunities to model technology integration into their pedagogy. Pre-service teacher education
must reflect the educational change brought about by technology (Chen, 2011). Oigara and
Keengwe (2013) found that students’ desire to learn about instructional technology is increased
with effective modeling of its use. This is supported by Social Cognitive Theory (SCT), which
states that learning is enactive or vicarious and that people can learn by observing others. The
vicarious experience can serve both informational and motivational functions (Ertmer, 2005).
Therefore, by modeling technological application and integrating it into the classroom, teacher
educators in face-to-face programs may increase intrinsic motivation of practicing teachers in
regards to incorporating technology into their practice.
Additionally, teacher education programs may focus on refining guided practice
experiences as a way to increase intrinsic motivation toward technology integration. Zeichner
and Tabachnick (1981) argue that teachers are socialized through the internalization of teaching
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models during their undergraduate and graduate courses. For preservice teacher candidates, the
student teaching experience plays an integral role in their socialization. Hence, teacher
candidates may benefit from being placed with supervising teachers who regularly integrate
technology into their pedagogical practice. Lux (2013) found that preservice teachers also benefit
from the chance to “create, design, implement, and even stumble with technology” (p. 87). This
underscores the importance of preservice teachers having authentic field experiences in which
they are working with mentors and peers to build technological pedagogical content knowledge.
The finding that practicing teachers who completed face-to-face MAT programs are less
intrinsically motivated to integrate technology in their pedagogy may be addressed through
technology-rich field experiences.
For practicing teachers, Ertmer (2005) posits that an effective way to start altering teacher
practice using technology is to shift focus from changing their beliefs to showing how
technology applications may improve existing methods of teaching or remedy existing
instructional challenges. According to Zhao and Cziko (2001), many teachers use technology
because it helps them achieve their goals more efficiently than would traditional methods. Ma,
Andersson, and Streith (2005) agree that a viable implementation strategy to improve technology
integration is for teacher educators and school leadership to showcase best practices,
demonstrating how learning goals could more proficiently be achieved through computer
technology. This suggests that highlighting the benefit and the value of technology integration is
one way to advance the intrinsic motivation of graduates of the face-to-face programs.
In order to encourage beliefs and behaviors toward voluntary peer collaboration, teacher
education programs and school districts may consider providing pre-service and in-service
teachers with opportunities to learn within communities that integrate technology as a tool and
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view its integration as certain. Cochran-Smith and Lytle (1999) posit, “there are radically
different conceptions of teacher learning including the way teacher learning is linked to
educational change and the purpose of schooling” (p. 249). According to Gomes, Sherin,
Griesdorn, and Finn (2008), technology presents the potential to create and extend social and
organizational relationships that improve the practice of teaching and impact student learning.
Teachers may generate knowledge of practice related to technology by working within the
context of inquiry communities to theorize and construct their work and connect it to larger
social, cultural, and political issues of the 21st century. Furthermore, school leadership may
improve voluntary peer collaboration beliefs and behaviors by creating regular opportunities for
teachers to observe other practitioners effectively incorporating technology into their
instructional practices, encouraging coaching partnerships, and supporting professional learning
communities.
Teacher leaders and educators can promote changes in teacher beliefs about integrating
technology by encouraging the critical practice of reflection. Teachers are unlikely to integrate
technology unless the practices align with their general beliefs about teaching and learning,
therefore, teacher educators may consider teachers’ existing pedagogical beliefs (Ertmer, 2005).
One concept that is critical in teacher education and examination of pedagogical beliefs is
reflective practice. Loughran (2006) explains that teaching is more than an act of doing, but that
it involves the intellectual methods of thinking and reflecting about teaching. Yost, Sentner, and
Forlenza-Bailey (2000) agree that teacher educators should promote critical reflection with their
teacher candidates in order to prepare them for teaching in the 21
st
century. So, to promote a
change in teacher beliefs regarding the integration and use of technology, teacher educators and
K-12 district leaders should promote opportunities for their preservice and practicing teachers to
60

engage in reflective practice, as it provides them with a life-long way to face their teaching and
their student’s learning.
These recommendations are designed to place an overall emphasis on developing the
beliefs and behaviors of practicing teachers in regards to integrating technology into their
pedagogical practices. Giving consideration to these factors, particularly those related to
nurturing intrinsic motivation and beliefs about collaboration of face-to-face graduates, may help
teacher education programs and K-12 leadership design classes and professional learning
experiences that support teachers working to meet the increasing demands for educational
change brought about by technology.
Recommendation for Future Research
Using technology is critical to the success of being a teacher in the 21st century.
Therefore, additional research is needed to guide teacher educators in support of preservice
teachers and practicing teachers. One recommendation for future research is that a qualitative
component be added. Interviewing the practicing teachers who graduated from both the online
and face-to-face MAT program formats could provide more specifics regarding the amount of
modeling they received, the quality and the nature of their student teaching experience, and their
supervising teachers. In addition, interviewing practicing teachers regarding district factors such
as technology integration expectations, access to hardware and software, and professional
development opportunities would invite more elaborate responses about personal beliefs and
behaviors which are often difficult to obtain through quantitative surveys.
Another recommendation for future studies is to include a longitudinal component.
Beliefs and behaviors of practicing teachers provided with ongoing opportunities to collaborate
with colleagues regarding technology integration may evolve over time. The addition of
61

qualitative and longitudinal components to future research may help validate the findings of this
study and improve its generalizability to other graduate programs and populations.
Limitations
There were several limitations in regards to the study. The most crucial limitation being
the study is correlational; therefore no causal relationship can be determined. The researcher can
only establish that the independent and dependent variables are related, but cannot conclude that
the changes in the dependent variables were a result of the independent variables. Likewise, the
potential differences in practicing teachers’ beliefs and behaviors as a result of the instructional
formats also cannot be concluded because of the design of the study.
Inability to generalize findings is another limitation of this study. The outcomes of this analysis
apply to a single graduate program at one west coast university. Therefore the findings are
delimited to programs that use synchronous live sessions in online programs, as that is the
environment of the MT online program examined in this study. As there may also be significant
differences between the quality and outcomes of online programs that include the synchronous
component and those that do not, it is important to examine face-to-face programs and both
synchronous and asynchronous online formats.
Other limitations include one-time data collection, self-reporting, and small sample size.
Collecting data once, may have impacted the consistency of the practicing teachers’ responses to
survey questions. The self-reporting aspect of the study is both a validity and reliability issue in
that the researcher cannot ensure that participants were being honest, nor eliminate the chance
that they were responding with socially acceptable answers. The small sample size of
respondents may have hindered the generalizability of the study. This may limit interpretation of
findings to specific programs and contexts.
62

Another limitation was that the group of online practicing teacher participants was larger
than the face-to-face sample population. This limitation impacts statistical analysis. While SPSS
is robust enough to account for the unequal groups, the unequal sample sizes may be a limitation
in this study.
One final limitation related to the study design is that while the researcher made efforts to
control for the level of training provided by school districts, the study could not control for the
amount of technology available at the practicing teachers’ worksites. This may have influenced
the practicing teachers beliefs and behaviors related to the self-efficacy, motivation, and
voluntary collaboration constructs. Efforts to overcome these limitations, such as controlling for
the amount of district training received by participants, were made to increase internal and
external validity as well as the reliability of the study.
Conclusion
Teacher education programs must focus on effective incorporation of technology to
address school and community expectations, and to meet the needs of K-12 students in the 21st
Century. The purpose of this study was to determine the degree to which online versus face-to-
face programs cultivate practicing teachers’ beliefs, attitudes, and behaviors regarding the
integrating of technology into instructional practices and collaborating with others around its use
in the K-12 setting. Specifically, the study looked at practicing teachers who completed their
Masters in teaching at a four year university on the west coast. Based on what has been observed
in this study, the MT synchronous online and face-to-face programs are producing equivalent
beliefs and behaviors regarding technology integration. The study identified no significant
relationships in practicing teachers’ self-efficacy or voluntary peer collaboration behaviors
regarding technology integration by program delivery. However, there was a significant
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statistical relationship found between graduates of the online teacher education program and their
intrinsic motivation to integrate technology into their practice as well as in their beliefs about
collaborating with peers regarding its use.
Despite increased spending and accessibility of technology for instructional purposes,
effective incorporation of technology into classroom instruction remains minimal. This
highlights the need for education programs, irrespective of program format, and K-12 school
districts to support practicing teachers in their efforts to address teaching and learning in the 21
st

century.

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Appendix A
Demographic Questions

Please answer the following demographic questions:

1. What is your gender?
a. Male
b. Female
c. Transgender

2. What is your age in years?

3. Please indicate your relationship status
a. Single
b. Married/Domestic Partner
c. Separated/Divorced
d. Widowed

4. What is your current employment status?
a. Not currently working
b. Working part-time
c. Working full-time

5. In what state are you currently teaching?

6. How many years have you practiced as a credentialed teacher?

7. Please choose indicate your ethnicity.
a. Hispanic/Latino
b. American Indian or Alaska Native
c. Asian
d. Black or African American
e. Native Hawaiian or other Pacific Islander
f. White
g. Two or more races
h. Other – text

8. What was your undergraduate major?

9. What is the highest level of education completed by either of your parents and guardians?
a. Primary or less
b. Middle school
c. Some high school
d. High school diploma/GED
e. Some college
f. Associate Degree/Certificate
g. Bachelor’s Degree
76

h. Master’s Degree
i. Doctoral Degree

10. Do you have a previous graduate degree?

11. Did you graduate from the online or face-to-face MAT program?

12. How many online courses did you take when you were enrolled in the MAT program?

13. Did you take a technology/media course as part of your MAT program?
14. Why did you choose to take this format? (check all that apply)
a. Scheduling
b. Instructional considerations (e.g., preferred method of instruction, quality of
instruction, access to instructor)
c. Geographic reasons
d. Family responsibilities
e. Professional responsibilities
f. Other: Text answer

15. Why did you choose to take this format for the MAT program?
16. When did you graduate from the MAT program?
17. How often do you incorporate technology into your teaching?
a. Never
b. Less than Once a Month
c. Once a Month
d. 2-3 Times a Month
e. Once a Week
f. 2-3 Times a Week
g. Daily

18. In what ways to you integrate technology into your teaching?

When answering the following questions, “format” refers to either online or face-to-face.

Likert response categories: Strongly Disagree = 1, Disagree = 2, Uncertain = 3, Agree = 4,
Strongly Agree = 5.

1. I am satisfied with my decision to take the program in this format.
2. If I had an opportunity to take another program in this format, I would do so.
3. I fell that this program format served by needs.
77

4. I will take as many programs in this format as I can.
5. I feel the quality of the program I took was largely enhanced by the format.

78

Appendix B
Self-efficacy Measure
Patterns of Adaptive Learning Scales (PALS)
Please answer the following questions in the context of your experience in your workplace.
Indicate the degree to which you agree or disagree with the following statements.

Likert response categories: Strongly Disagree = 1, Disagree = 2, Uncertain = 3, Agree = 4,
Strongly Agree = 5.

6. I am certain I can master the technology requirements of my workplace.
7. I am certain I can figure out how to complete the most difficult technology requirements
of my workplace.
8. I can incorporate almost all the technology required in my workplace
9. Even if the technology requirements are hard, I can learn them.
10. I can complete event he hardest technology requirement in my workplace if I try.
11. My district requires me to integrate technology into my teaching.

12. My district has provided me with professional development in educational technology.

79

Appendix C
Intrinsic and Extrinsic Motivation Measure
Motivated Strategies for Learning Questionnaire (MLSQ)

Please answer the following questions in the context of your experience in this program.
Indicate the degree to which you agree or disagree with the following statements.

Likert response categories: Strongly Disagree = 1, Disagree = 2, Uncertain = 3, Agree = 4,
Strongly Agree = 5.

Value Component – Intrinsic Motivation

1. In my teaching practice, I prefer technology components that really challenge me, so I
can learn new things.
2. In my teaching practice, I prefer to use technology that arouses my curiosity, even it is
difficult to learn.
3. The most satisfying thing for me in my teaching practice is trying to understand the use
of technology as thoroughly as possible.
4. When I have the opportunity in my teaching practice, I choose to integrate technology
into lessons so I can learn from the experiences even if are not successful the first time.
Value Component – Extrinsic Motivation

1. Getting accolades for using technology in my teaching practice is the most satisfying
thing for me right now.
2. The most important thing for me right now is improving my overall employee evaluation,
so my main reason for integrating technology into my teaching practice is because it’s
part of my evaluation.
3. If I can, I want to get be recognized for integrating technology into my teaching practices
than most of the other teachers.
4. I want to do well integrating technology into my teaching practice because it is important
to show my ability to my colleagues, supervisor, principal, or other.

80

Appendix D

Voluntary Peer Collaboration Measure

Please answer the following questions in the context of your experience in the program.
Indicate the degree to which you agree or disagree with the following statements.

Likert response categories: Strongly Disagree = 1, Disagree = 2, Uncertain = 3, Agree = 4,
Strongly Agree = 5.

1. It is important to collaborate with my teaching colleagues even if it is not required.
2. I believe that teaching and learning from each other is important to succeed in my
professional environment.
3. It is important to both give and receive voluntary peer feedback in my workplace.
For the next two questions, think about your voluntary collaboration with your peers. By
voluntary collaboration, we mean collaborative activities that were NOT initiated or facilitated
by the instructor nor required for the course or course assignments.

On average, how often do you voluntarily collaborate with your peers in the workplace?

1. Not at all
2. 1-2 times per semester
3. 1-2 times per month
4. 1-2 times per week
5. More than twice a week

In what ways do you voluntarily collaborate with other teachers? (check all that apply)
• Not applicable
• In person
• Via Phone or Text
• Via Email or discussion board
• Via Social Media (e.g., Facebook, Twitter, etc.)
• Via Videoconferencing (e.g., Skype, Adobe Connect, etc.)
• Other (please indicate below)
• Text entry

Abstract (if available)

Abstract Improved integration of technology into the 21st Century classrooms of today is imperative. However, after spending millions of dollars equipping school districts with the latest technology, many educational leaders are finding that new teachers are reluctant to use the available technology in their practice and instruction. In addition, the meaningful integration of technology in teacher education programs varies tremendously. For example, the research makes it clear that the perceptions, attitudes, and skills of pre-service teachers are critical to achieving or thwarting the use of technology in the classroom (Chen, 2010, 2011

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Asset Metadata

Creator Stopp, Katherine Whittaker (author)

Core Title Integrating technology in teaching: beliefs and behaviors of practicing teachers from traditional and online teaching programs

School Rossier School of Education

Degree Doctor of Education

Degree Program Education

Publication Date 09/28/2015

Defense Date 06/11/2015

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

Tag intrinsic and extrinsic motivation,OAI-PMH Harvest,online and tradition programs,peer collaboration,self-efficacy,teacher education programs,Technology

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Seli, Helena (committee chair), Bagley, Rick (committee member), Hirabayashi, Kimberly (committee member)

Creator Email stopp@usc.edu,whittkatt@yahoo.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-188477

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Legacy Identifier etd-StoppKathe-3954.pdf

Dmrecord 188477

Document Type Dissertation

Format application/pdf (imt)

Rights Stopp, Katherine Whittaker

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