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SECONDARY TEACHER SELF-EFFICACY AND TECHNOLOGY INTEGRATION
by
James Lee Hale
Ed.S., The University of West Florida, 2011
M.Ed., Jones International University, 2004
B.S., Samford University, 1993
A dissertation submitted to the Department of Instructional and Performance Technology
College of Professional Studies
The University of West Florida
In partial fulfillment of the requirements for the degree of
Doctor of Education
2011
© 2012 James Lee Hale
The dissertation of James Lee Hale is approved:
______________________________________________ __________________
Holly H. Ellis, Ph.D., Committee Member Date
______________________________________________ __________________
Nancy B. Hastings, Ph.D., Committee Member Date
______________________________________________ __________________
Byron C. Havard, Ph.D., Committee Chair Date
Accepted for the Department/Division:
______________________________________________ __________________
Byron C. Havard, Ph.D., Chair Date
Accepted for the University:
______________________________________________ __________________
Richard S. Podemski, Ph.D., Dean, Graduate School Date
iv
ACKNOWLEDGMENTS
As is nearly always the case, no monumental task can be completed alone. Only with the
assistance, patience, and love of those closest to me and to this process could this dissertation
project have been completed. To those who have remained beside me through this journey, I say
―thank you‖ from the bottom of my heart.
I would like to extend a special acknowledgment to my dissertation committee: Dr.
Byron Havard (Chair), Dr. Nancy Hastings, and Dr. Holly Ellis. To you I offer my heartfelt
gratitude for your patience and encouragement. You have been a ―can do‖ group from the
beginning and that is exactly what I needed.
To the Emerald Coast Cohort I would like to extend my thanks and my well wishes. You
kept me accountable and energized. We have created a lifelong network of support and
collegiality and for that I am eternally grateful.
I would like to acknowledge my admiration of, and sincerest appreciation to, my parents,
Jim and Nancy Hale, for being mentors, role models, and my biggest fans. Your support and
belief in me through these many years of arduous education have been an encouragement and
motivator to me.
Finally, I would like to express my deepest love and gratitude to my wife, Gretchen, and
my children, Brett and Eve, for the sacrifices they have made and the support they have shown
me through this journey. I am blessed to have such an understanding and accepting family. I
love you very much.
v
TABLE OF CONTENTS
ACKNOWLEDGMENTS ............................................................................................................. iv
LIST OF TABLES ........................................................................................................................ vii
ABSTRACT ................................................................................................................................. viii
CHAPTER I. INTRODUCTION .............................................................................................1
A. Technology Integration ...............................................................................2
B. Teacher Self-Efficacy .................................................................................4
C. Overview of Theoretical Framework ..........................................................6
D. Statement of the Problem .............................................................................8
E. Purpose of the Study ....................................................................................9
F. Significance of the Study .............................................................................9
G. Research Questions ....................................................................................11
H. Operational Definitions of Terms ..............................................................11
I. Chapter Summary ......................................................................................12
CHAPTER II. LITERATURE REVIEW ................................................................................14
A. Conceptual Framework ..............................................................................14
B. Technology‘s Impact upon Education .......................................................18
C. Global Educational Climate .......................................................................21
D. Self-efficacy Theory ..................................................................................23
E. Self-efficacy‘s Impact upon Technology Integration ................................25
F. Chapter Summary ......................................................................................27
CHAPTER III. METHODOLOGY ..........................................................................................29
A. Research Question and Hypotheses ...........................................................29
B. Research Design.........................................................................................30
C. Chapter Summary ......................................................................................40
CHAPTER IV. RESULTS ........................................................................................................41
A. Descriptive Statistics ..................................................................................41
B. Data Analysis .............................................................................................44
C. Chapter Summary ......................................................................................49
CHAPTER V. DISCUSSION ..................................................................................................50
A. Summary of the Study ...............................................................................50
B. Implications of the Study ..........................................................................52
C. Limitations ................................................................................................59
D. Recommendations for Further Study ........................................................60
E. Chapter Summary ......................................................................................62
REFERENCES ..............................................................................................................................64
vi
APPENDIXES ...............................................................................................................................76
A. Electronic Informed Consent .....................................................................77
B. Demographic Survey .................................................................................79
C. Sources of Self-efficacy Instrument ...........................................................81
D. LoTi Digital Age Survey for Teachers ......................................................90
vii
LIST OF TABLES
1. Gender, Education Level, Age, Longevity of Participants, and Level of Innovation ............43
2. Correlation of Independent Variables and Dependent Variable .............................................46
3. Regression Output ..................................................................................................................47
4. Regression Model Summary...................................................................................................49
viii
ABSTRACT
SECONDARY TEACHER SELF-EFFICACY
AND TECHNOLOGY INTEGRATION
James Lee Hale
This dissertation is based on a conceptual framework founded in the plight of the United
States in the critical areas of science, technology, engineering, and mathematics, such as student
performance, global economy, job opportunities, and technological innovation. Subpar
performance can be traced to, among other things, education and specifically a lack of student
engagement due to non-innovative teaching and technological self-efficacy issues among
teachers. This study suggests a multiple regression analysis of the sources of self-efficacy as
noted by Albert Bandura (1997): enactive mastery experiences, vicarious experiences, verbal
persuasion, and physiological and affective states and their predictive capability with regard to
technology integration in the classrooms of today as measured by the Levels of Teaching
Innovation Digital Age Survey measure constructed by Christopher Moersch (2009).
1
CHAPTER I
INTRODUCTION
There are many factors that negatively influence teachers‘ decisions to use technology in
the classroom (Eyadat & Alodiedat, 2010). Among these are organizational, administrative,
pedagogical, or personal constraints (Leh, 2005) as well as anxiety and productivity difficulties
(Eyadat & Alodiedat, 2010). Also among the barriers are lack of vision, lack of leadership, lack
of funding, and lack of time (Norris & Soloway, 2011). Another barrier is avoidance (Eyadat &
Alodiedat, 2010).
Instructors manifesting the avoidance factor often indicate that they are intimidated by
the prospect of technology and its use (Okojie & Olinzock, 2006; Okojie, Olinzock, & Okojie-
Boulder, 2006). These instructors refuse to deploy new technologies in their classroom not
because they are incapable, but because they are unwilling. Their unwillingness does not appear
to be as much a result of stubbornness as diffidence.
According to Abbitt (2011), even though knowledge of technology is necessary for this
type of technological deployment in the classroom, it is simply not enough. It is necessary to
examine not only knowledge, but also beliefs. Despite demonstrating proficiency in even the
most mundane tasks, many are unwilling to accept technology as necessary or assistive (Albion,
1999). Teachers either claim to not know how to perform a technological task despite having
demonstrated the ability many times or claim to not be comfortable enough to use the particular
tool or technique regularly in their classroom instruction or management. Even as many pieces
of technology have commonalities with other more frequently used technologies, teachers with
low self-efficacy levels avoid them. They do not believe in their own capabilities.
2
What are the contributing factors to these low self-efficacy levels? Are there any
common threads? Why is it that instructors with proven track records of proficiency remain
dubious? Is there any way to build the self-efficacy levels of these instructors? Is it even
important?
In the continuing climate of the science, technology, engineering, and math (STEM)
initiative, it is of utmost importance that our students are able to compete in the global economy.
As the United States continues to trail many other countries of the world in these STEM areas
(―Influence Of,‖ 2010; Kuenzi, Matthews, & Mangan, 2006; President‘s Council of Advisors on
Science and Technology [PCAST], 2011), it becomes the responsibility of educators to close the
gap in student performance. In order to do this, student engagement, a major factor upon student
learning outcomes (DeWitt & Horn, 2005), must be high in the classic studies while more
rigorous coursework in the STEM specific areas must be more widely available for all students.
While several factors play a part in the current state of affairs in this country, at the most basic
level, technological integration in the classroom, that integration that makes technology an
accepted and expected part of the student classroom experience, that integration that facilitates
student achievement and critical thinking through enhancement and motivation (Kopcha &
Sullivan, 2008), is lagging.
Technology Integration
The 21st century is a time of dynamic and constant technological change (McLeod,
Bathon, & Richardson, 2011; Moore-Hayes, 2011). As the world evolves, the needs of the
global market change. Career opportunities and job prospects are similarly subject to the stated
ebb and flow. Science, technology, engineering, and mathematics fields are the stalwarts of the
global economy and as such, are the areas of critical need in terms of employment as well as
3
educational support. There is no question that the United States trails a large number of
countries in student performance related to these STEM areas (―Influence Of,‖ 2010). As the
demand for productive, technologically savvy, workers increases, the United States is poised to
fall further behind unless change occurs. Students are now being asked to be team oriented,
lifelong learning oriented, and technologically literate (Gaytan, 2002). It becomes the
responsibility of schools to provide the education that ultimately fulfills the needs of industry,
assisting in the production of a worthy and equipped workforce as ―every societal and economic
sector that revolves around information is being radically transformed by digital technologies,
online services, and social media‖ (McLeod, Bathon, & Richardson, 2011, p. 292).
In order for schools to provide an education that supports technology and in turn produce
capable employment candidates, there must be an increase in technology integration in
classrooms. Students must not only have the opportunity to learn about new technologies and
learn the skills that will enable them to effectively deploy current technologies as well as future
and emerging technologies, but also to experience a robust technological environment in core or
traditional subject areas.
Student engagement is a major factor in learning outcomes (Bakker, Demerouti, & ten
Brummelhuis, 2012; Danielson, 2007; Park, Holloway, Arendtsz, Bempechat, & Li, 2012;
Svanum & Bigatti, 2009; Van Ryzin, 2011). Students process information today much
differently than previous generations (DeWitt & Horn, 2005) and so it becomes necessary to
embrace this idea and provide a technology rich environment. Students are now digital natives
(Prensky, 2005) and learning can no longer be merely supplemented by technology. Technology
use in the classroom has become an expectation of students. Başer, Mutlu, Şendurur, and
Şendurur (2012) state that as these students ―are growing within these environments surrounded
4
by technological developments, their perceptions on technology have started to become a vital
element to provide them with healthy educational environments‖ (p. 592). Jackson, Helms,
Jackson, and Gum (2011) indicate that students of the current generation have become so
accustomed to various technologies that comprehension has become dependent upon the
deployment of said technologies. However, according to a survey conducted by Purcell et al.
(2012), many teachers claim that a lack of engagement in the traditional classroom and drops in
student performance are associated with this dependency. Technology, according to this survey
of the opinions of upper level secondary teachers, has become a distraction, despite its
acknowledged positive impact. This appears to be, within the context of the study, a dogmatic
position unsupported by real data. Interest levels and information retention are shaped by the
delivery models to which students have become acclimated (Jackson et al., 2011). This
phenomenon, combined with the global STEM emphasis, provides further evidence of the need
for technology integration in classrooms.
Teacher Self-Efficacy
Student learning has historically been viewed as critical, but in the current competitive
global market as described above, its importance has never been more evident. Engagement is a
major contributing factor in student learning (Bakker et al., 2012; Danielson, 2007; Park et al.,
2012; Svanum & Bigatti, 2009; Van Ryzin, 2011). Students are more apt to retain information
or skills when interest levels or perceived practical application levels are high. For today‘s
students who have never known a world without computers or the Internet, instruction must be
conducted in accordance with the times in which they live. In short, a technologically rich
school environment seems imperative. Technology must not only be used as a tool in the
classroom, it must be integrated into the classroom in terms of instruction and production as well
5
as in assumption and expectation. This is easier said than done. While technology integration
proves to be essential in providing a complete and worthy educational experience, one that will
allow schools to more effectively respond to the challenges of diverse and changing student
populations (Gaytan, 2002; Kukulska-Hulme, 2009), the degree to which technology is
incorporated in the classroom paradigm depends upon many factors. One such factor is the self-
efficacy level of the individual instructors.
Self-efficacy is defined as the ―belief in one‘s capabilities to organize and execute the
courses of action required to produce given attainments‖ (Bandura, 1997, p. 3), and is classified
as either high or low. Fullan (2011) indicates that self-efficacy is further defined based on a
perception of personal control. There are many contributing factors to whether or not technology
is actually integrated into a classroom many of which are imposed upon an instructor rather than
left up to choice (Hamann, 2007; Leh, 2005). Self-efficacy may be the deciding factor as it is
personalized to each individual, occurring after other contributing factors have leveled
opportunity. The importance of self-efficacy emerges when teachers are given freedom to
choose their level of technology integration. Pajares (1992) states that ―beliefs are instrumental
in defining tasks and selecting the cognitive tools with which to interpret, plan, and make
decisions regarding tasks‖ (p. 325). Instructors develop their own rationales for which and
whether technology is to be integrated into their lesson plans (Albion, 2001; Okojie, Olinzock, &
Okojie-Boulder, 2006), and self-efficacy factors largely in that determination. Ultimately, how
much and which technologies come into the normal classroom experience for each teacher is a
function of their belief in their ability once all other contributing factors have been accounted for
(Barnyak & McNelly, 2009). Instructors with low self-efficacy often use technological tools
only when mandated and then very hesitantly. Those with high self-efficacy are much more
6
likely to integrate. Bandura (1977) explains, ―people fear and avoid threatening situations they
believe themselves unable to handle, whereas they behave affirmatively when they judge
themselves capable of handling successful situations that would otherwise intimidate them‖ (pp.
79-80).
Overview of Theoretical Framework
Bandura’s self-efficacy. Within his social cognitive theory, Bandura (1977) describes
self-efficacy as one‘s belief in his or her ability to perform a task. This is the construct around
which this study centers. According to Barnyak and McNelly (2009), educator self-efficacy
levels significantly influence planning, development, and implementation of classroom activity
and educational programs. Whether or not a teacher has the ability to perform a task is not in
question. It is the personal belief in ability upon which Bandura‘s work focuses. If one has a
high level of self-efficacy, he or she has a high level of belief in his or her ability and will work
diligently to overcome difficulties, ultimately attaining the goal. Goal attainment provides the
foundation for further self-efficacy development. If self-efficacy is low, belief in one‘s ability is
low and avoidance ensues. Self-efficacy in this case is further reduced.
The concept of self-efficacy is important when considering the instructional choices of
teachers, especially with regard to technology. As evidenced by the ever growing and ubiquitous
nature of emerging technologies, devices, and online social platforms, technology is currently an
area of educational and pedagogical concern (Watson, 2012). Teachers have traditionally lagged
behind in integrating technology as instructional or administrative tools (Turner, 1989).
According to Brinkerhoff (2006), there are many barriers that prevent instructors from deploying
technologies in the classroom. Among these barriers is the instructors‘ level of self-efficacy.
While some instructors are highly efficacious in terms of technology integration, many are not.
7
As teachers continue to determine the makeup of their classroom environments as well as the
tools they will utilize for instruction and administrative tasks, the self-efficacy levels with regard
to technology integration are of paramount concern. According to Kopcha and Sullivan (2008),
other barriers include time, beliefs, access, professional development, and culture. It is also
important to note that Bandura‘s (1983) theory of triadic reciprocal causation, or reciprocal
determinism (Bandura, 1977), points to the fact that self-efficacy is not only affected by
cognitive factors but also by many of the environmental and behavioral influences suggested by
Kopcha and Sullivan. Collectively, Bandura (1997) notes four specific sources of self-efficacy:
enactive mastery experience, vicarious experience, verbal persuasion, and physiological and
affective states.
Technology integration in the classroom. ―The incorporation of new technological
resources into the process of pedagogical design may lead to two distinctive types of change:
effectiveness and efficiency‖ (Salaberry, 2000, p. 29). Technology integration increases the
efficiency of the educational process and promotes student learning (King-Sears & Evmenova,
2007). This type of change is altogether favorable, inevitable, and necessary. In fact, Brown and
Warschauer (2006) claim that effective technology integration has become a ―national
imperative‖ (p. 599). As such, technology integration is an obligatory component in a complete
and current curriculum, and it is the result of many factors (Gorder, 2008). Integration is much
more than simply using a technological device or technique in a detached or limited way. For
technology integration to have occurred, technology must be a welcome and necessary
component in the classroom environment. It must be a part of the students‘ and teachers‘
expectations. It must be a necessary arm in the instructional and administrative model.
Technology integration, according to Dockstader (1999), is a complex, threefold process that
8
begins with learning a tool or technique, is followed by using the tool or technique as an
instructional device in the classroom, and culminates with integration into the classroom in order
to provide students with supplemental enrichment. This integration is considered particularly
valuable to real learning as it supports the proven pedagogical and student-centered principles of
active learning, mediation, collaboration, and interactivity (Gorder, 2008). There is additional
research that suggests that technology enhances curriculum, motivates students to learn, and
improves student learning of subject-specific content (Kopcha & Sullivan 2008).
Statement of the Problem
As previously indicated, the technological integration deficiency in the United States is a
symptom of the larger problem in a global sense. This problem is complex—seemingly built
from many inputs. One such foundational component of the integration problem is identified as
the self-efficacy levels of the nation‘s instructors (Albion, 1999, 2001). Self-efficacy, as defined
by Bandura (1977), is one‘s belief in his or her ability to complete a task. Self-efficacy is
contextual (Bandura, 1997), and it is the self-efficacy related specifically to technology
integration in the classroom that informs this work. According to Albion (1999), knowledge
about, and availability of, computers does not necessarily translate into a high level of
technology integration in the classroom. This indicates ―it is at least arguable that there are
factors other than technical knowledge and skill which contribute to teachers‘ success at
technology integration in their teaching‖ (Albion, 1999, p. 2). It is not uncommon for teachers to
be equipped to integrate technology into their normal classroom paradigms, demonstrate
proficiency, and yet still demonstrate reluctance. Hardware and software may or may not be in
place. Foundational knowledge with regard to usage may or may not be in place. Support may
or may not be available. The research demonstrating the positive effects of technological
9
integration is certainly abundant, yet many capable instructors, no matter the circumstance,
continue to resist. Albion (1999, 2001) indicates that this is largely the result of low self-efficacy
and that beliefs are a stronger behavioral determinant than actual knowledge. Teachers may
know what to do and how to do it, but their comfort and efficacy levels, according to Albion
(1999, 2001), restrict them.
Purpose of the Study
With self-efficacy playing so important a role in the integration of technology into
individual classrooms, and with the importance of integration higher than it has ever been, the
topic of factors influencing the self-efficacy levels of teachers becomes a critical area of
research. In order to raise self-efficacy levels, a given necessity when focused upon the need for
improved STEM support, factors influencing self-efficacy must be identified so they can be
individually addressed. It is important to know what factors contribute to high self-efficacy
levels and low self-efficacy levels in order to promote those things that contribute to high self-
efficacy and offset those that contribute to low self-efficacy. The purpose of this study is to gain
insight into factors contributing to the technological self-efficacy levels of teachers so as to
provide a foundation for professional development designers concerned with improvement of
instruction through technological integration. Identifying those contributing factors that can be
influenced either environmentally or cognitively, will, in concert with Bandura‘s (1977) concept
of reciprocal determinism, allow a restructuring of self-efficacy and consequently, behavior, and
most notably in this study, the integration of technology in the classroom.
Significance of the Study
More than ever before, classroom instruction must be technology laden. Students of
today have never known a world without computers or the Internet and they process differently
10
than generations past (Prensky, 2005). Student learning is facilitated by engagement, and
technology integration plays a very important role. Technology integration can only occur when
highly efficacious teachers are in place.
In order to effectively raise self-efficacy levels among teachers, especially those that
instruct in the critical STEM areas, understanding about the factors upon teacher self-efficacy is
needed. This is critically important and different than self-efficacy issues related to other
disciplines.
It is not enough to conduct studies that further illustrate the unavoidable fact that those
with low technological self-efficacy are less likely to integrate technology in the classroom than
those with a high self-efficacy. There is no applied or practical reason to investigate self-
efficacy level as an independent variable upon anything if there is no research regarding the
influences upon that self-efficacy level. The sources of self-efficacy must be explored in this
specific context so as to provide an empirical foundation for relevant professional development
addressing those things that prevent teachers from using the tools, ideas, and strategies they
know will benefit their students. In this area there is a significant gap in the literature.
There is a wealth of information pertaining to self-efficacy levels and various resultants
such as technology integration but very little with regard to individual influences upon the self-
efficacy levels themselves. Individual influences are significant information for professional
development design in schools due to the fact that schools may accommodate those strategies
that facilitate raising self-efficacy while moving to eliminate factors that contribute to low self-
efficacy.
The results of this study may include meaningful data and if so, be significant in as much
as we may begin to develop more highly efficacious instructors who will in turn facilitate a more
11
technologically integrated classroom, thus supporting the United States in its critical STEM
initiative as well as more fully engaging the learners of today. In order to affect change, one
must not only know what to change but how the change can be made.
Research Questions
As the scope of this dissertation was to determine the relative strengths of the
acknowledged sources of self-efficacy as they relate to teachers‘ classroom technology
integration, there is but one research question: Are Bandura‘s (1997) four noted sources of self-
efficacy (mastery experience, vicarious experience, verbal persuasion, and physiological and
affective state) significant predictors of teachers‘ classroom technology integration?
Operational Definitions of Terms
Change agent. Change agents are those who assist, nurture, encourage, persuade, and
push people to change, adopt an innovation, and then incorporate the change or innovation into
their lives (Harada & Hughes-Hassell, 2007).
Developing countries. This term refers to the 60 member countries of the Organization
for Economic Cooperation and Development who participated in the 2009 Programme for
International Student Assessment.
Digital immigrants. Digital immigrants are those who were not born into the digital
world yet have adopted some or many aspects of technology (Prensky, 2005). Prensky
associates immigrant learning of technology to that of someone learning a new language,
complete with confusing accents caused by a root in the past.
Digital natives. Digital natives are today‘s students, fluent in the digital language of
computers, video games, the Internet, and other emerging technologies (Prensky, 2005).
12
Professional development. Professional Development is a collective term for teacher
training opportunities based on grounded theory or best practices. It is most often developed,
produced, and offered by educational leaders based on the needs of his or her school.
Self-efficacy. Self-efficacy is the belief one has in his or her own ability to execute a
course of action in order to complete a task (Bandura, 1977, 1997). It is not related to what one
can or cannot do; it is related to what one believes he or she can or cannot do.
STEM. STEM is an acronym for the educational and industrial fields of science,
technology, engineering, and mathematics.
Technology. Technology is any tool or strategy used to accomplish a task. For the
purposes of this study, technology refers primarily to electronic digital tools such as computers,
tablets, smart phones, and similar devices along with their accessories or complimentary
equipment and software.
Technology integration. The term technology integration does not refer to using
technology for specific tasks. It refers to an archetype where technology is naturally occurring
and expected. For the purposes of this study, the phrase Level of Innovation is synonymous with
level of technological integration.
Chapter Summary
Technology integration is a major influence upon student engagement, which has a
powerful effect upon student learning. Its importance is further highlighted by the increased
focus on STEM related fields and student performance. For these reasons, classroom instruction
must be replete with technology. Despite the documented advantages of a technologically rich
classroom environment, many teachers resist. This work aims to acknowledge and address one
specific contributing factor to this phenomenon.
13
Self-efficacy plays a major role in the choices that teachers make with regard to how
much and which technologies are employed. The purpose of this study is to determine what
factors influence self-efficacy among teachers, specifically when dealing with technology.
This chapter includes a description of the background of the study and an overview of the
conceptual framework as well as brief narratives for each of its constructs. The research
question and descriptions of the purpose and significance of the study are included. The chapter
concludes with a list of operational terms critical to the understanding of the study.
14
CHAPTER II
LITERATURE REVIEW
This chapter includes the conceptual framework for this study and a review of the
pertinent related literature. Technology‘s impact upon educative practices is addressed with
supporting literature related to global student assessment measures as well as student expectation
and technological literacy. The chapter also includes an overview of the current educational
climate in the United States, specifically with regard to science, technology, engineering, and
mathematics (STEM) related concerns, and a review of Bandura‘s (1977) social cognitive theory,
specifically with regard to reciprocal determinism and the four sources of self-efficacy. The
chapter concludes with a description of the impact teachers‘ self-efficacy has upon their
technology integration.
Conceptual Framework
As noted by the President‘s Council of Advisors on Science and Technology [PCAST]
(2011), ―the success of the United States in the 21st century will depend on the ideas and skills
of its population,‖ (p. 33), and the quality of STEM education can and will ultimately determine
that success. Conceptually, the foundation for this dissertation lies in the premise that the United
States trails the world when considering STEM preparation and its eventual outcomes
(Fleischman, Hopstock, Pelczar, & Shelley, 2010; Kuenzi, Matthews, & Mangan, 2006; PCAST,
2011). There is an abundance of literature detailing the plight of the United States,
comparatively, in areas of economic development, trade, job opportunities, and most notably,
student performance in critical STEM areas (Etter, 2011; Fleischman et al., 2010; Merrill &
Daugherty, 2010; PCAST, 2011; Salek, 2011; Voeller, 2010). In 2009 alone, 15-year-old
students in the United States scored 11th among developing nations in reading, 25th in
15
mathematics, and 17th in science on the Programme for International Student Assessment
(PISA) despite showing moderate gains from 2003 in each category (Fleischman et al., 2010).
While there are a multitude of reasons for the United States‘ deficiencies, including teacher
education programs, economic climate change, average and retirement ages of the teaching
force, geographic preference and quality teacher availability, non-native speaking issues, and the
nature of education and its demands, among others (Levine, 2010; Newmann, 1992), Merrill and
Daugherty (2010) assert that the problem can be traced back to student engagement and then
ultimately, to individual teacher and school leader beliefs and choices. Salek (2011) indicates
that the curricula in which students participate are genuinely affected by engagement and that
STEM area coursework can be supported through processes that involve students, their interests,
and their abilities.
―Technology is rapidly changing how we teach and how we learn‖ (Dilworth et al., 2012,
p. 11). Despite the relative scarcity of literature supporting specific research tying technology
integration to improved student performance, save populations such as English Language
Learners and students with disabilities (Billings & Mathison, 2012), there is abundant literature
tying technological innovation to student engagement (Brunvand & Byrd, 2011; Casey & Jones,
2011; Jackson et al. 2011; Terrion & Aceti, 2012) and then engagement to improved student
performance (Bakker et al., 2012; Danielson, 2007; Park et al., 2012; Svanum & Bigatti, 2009;
Van Ryzin, 2011). If the mathematical transitive property of equality (If a = b and b = c, then a
= c.) may be asserted as a given, then the nature of those relationships provide evidential
justification for the assumption that technological innovation and integration into the classrooms
of today do indeed assist in the improvement of student performance. Even so, according to Brill
and Park (2008), due to its relatively unknown effect, technology as a tool for engagement must
16
be coupled with sound pedagogical theory. A multitude of research (Chen & Looi, 2011;
Danielson, 2007; Kukulska-Hulme, 2009; Reid & Solomonides, 2007; Stuber, 2007; Willis,
2011) indicates that engaged learners are more apt to demonstrate learning gains, and many
indicate that students are less apt to be disruptive. Increased classroom engagement yields
increased motivation to participate (Willis, 2011).
There is no question that engagement plays a critical role in the learning and performance
of today‘s students; however, the performance component, according to Newmann (1992), is
secondary to the concept of learning in light of inconsistent standards and assessment
perpetuated by policymakers. Newmann further emphasizes the point by stating that
disengagement is an indication of a lack of students‘ psychological investment, or what
Danielson (2007) refers to as intellectual involvement, and that this is a more critical concern to
teachers than student performance. Disengagement, contrary to popular opinion, is not merely
manifested in misbehavior, although this is certainly an indicator (Danielson, 2007); it is also
manifested through a lack of excitement or commitment to whatever is being taught (Newmann,
1992). Students cannot be expected to learn when they cannot concentrate or invest themselves
in the work. In order to maximize student engagement, the design of instruction should ensure
extrinsic reward, cultivate intrinsic interest, allow for student ownership, reflect applied and
useful concepts, and even specifically involve enjoyment (Newmann, 1992).
―It is through active engagement that students learn complex content‖ (Danielson, 2007,
p. 82). Engagement indicators are predictors for academic and behavioral outcomes (Appleton
& Lawrenz, 2011). Correspondingly, according to Errey and Wood (2011), the higher a
student‘s level of engagement, the higher the performance outcome that can be expected. In
order for students to learn most efficiently and most thoroughly, they must be engaged, and it
17
becomes the teachers‘ responsibility to provide this enhancement. It is their duty to take the
steps necessary to learn how to create investment and engage students (Newmann, 1992).
Generally speaking, teachers have carte blanche when determining delivery methods for
instruction. They are free to determine how content will be delivered and how students will
interact with it (Barnyak & McNelly, 2009; Eyadat & Alodiedat, 2010). They are free to
determine their entire classroom paradigm from routines and procedures to instructional delivery
methods and physical space. But students, in order to be engaged, must also own the instruction
and corresponding activities, strategies, or methods (Newmann, 1992). In order to create an
engaging environment, student investment and self-regulation are required (Appleton &
Lawrenz, 2011). Today‘s students, the digital natives, or those fluent in the digital language of
computers, video games, the Internet, and other emerging technologies as described by Prensky
(2005), are invested in techniques, strategies, and tools associated with 21st century culture and
would benefit most from participating in the development, execution, and evaluation of the
various classroom components (Newmann, 1992).
Technological advances allow for a paradigm shift of thought. What once was an idea of
technology being merely a means of disseminating information can now become an expectation
that it be an environment capable of ―fostering the adaptation of student-centered pedagogy
(Wang & Reeves, 2004, p. 50). Brunvand and Byrd (2011) state that innovative technological
tools and ideas can be used to promote motivation through student engagement with today‘s
learners, and the result is a more enhanced learning environment that provides for more
differentiated and individualized instruction. With this in mind, it seems quite curious that many
teachers continue to demonstrate resistance to saturating a classroom with technology. This
resistance is attributed to, among other things, low technological self-efficacy (Brinkerhoff,
18
2006). Barnyak and McNelly (2009) further substantiate this claim in their assertion that
educator self-efficacy levels significantly influence planning, development, and implementation
of classroom activity and educational programs.
Technologically speaking, perceived self-efficacy with respect to computer technology
has been found to be an important factor in the decisions made about using it (Hill, Smith, &
Mann, 1987), and increased performance with computer related tasks was found to be
significantly related to higher levels of teacher self-efficacy (Harrison, Rainer, Hochwarter, &
Thompson, 1997). An instrument including several subscales for self-efficacy in relation to
particular aspects of computer use has been developed and validated with students studying
business, nursing, and education (Kinzie, Delcourt, & Powers, 1994). A more recent study
confirmed the reliability of the instrument and found that the most significant predictor of self-
efficacy for computer use among teacher education students was frequency of computer use
(Albion & Ertmer, 2002).
Technology’s Impact upon Education
PISA. In 2000, 2003, 2006, and 2009, the Organisation for Economic Cooperation and
Development (OECD) conducted the PISA, which was a measurement of 15-year-old students‘
math, reading, and science strength (Fleischman et al., 2010). It included questions regarding
students‘ home and school computer usage, background information, and school characteristics
(Bielefeldt, 2005). Initial analysis of the study determined that there is a significant positive
relationship between student performance and computer access, but these findings were
challenged soon after. The results were later held to a much higher degree of scrutiny by Fuchs
and Woessmann (2004) who looked upon the findings with a much more narrow and specific
focus. While they acknowledge a statistically significant correlation between computer
19
availability at school and student performance, and students who attend schools where computers
are lacking perform much worse than those who attend schools where computers are abundant,
they also assert that this positive pattern with regard to computer availability is but one feature of
a school with other positive, and performance-contributory, characteristics (Fuchs &
Woessmann, 2004). Students with home access to computers, educational software, and the
Internet categorically and statistically outperform those without. Much like Lum (2005) who
states that some new, commonly available technologies such as cell phones create a
democratizing and equalizing effect among students, Fuchs and Woessmann discount the
possibility that the performance disparity is a result of more able students gaining access.
Rather, they contend, ―the results may suggest that using computers for productive purposes at
home indeed furthers students‘ educational performance‖ (Fuchs & Woessmann, 2004, p. 15).
One can conclude that available technology, improperly or infrequently used, has no viable use
for student improvement, yet proper, contextually relevant usage supports performance and
learning. According to Smolin and Lawless (2011), one way to move forward and attain this
contextual relevance is through technology integration in schools. As evident in Fuchs and
Woessmann‘s findings, technology has the potential to take learning and as a result, student
performance, beyond the boundaries of the traditional and expected educational paradigm
(Smolin & Lawless, 2011). Considering the position of the United States with respect to the rest
of the world and with regard to this discouraging performance data, it is immeasurably troubling
that these assessments are conducted near the end of the compulsory school attendance
timeframe, an indicator of educational deficiency (Appleton & Lawrenz, 2011).
Students as digital natives. Students of today are digital natives (Prensky, 2005), never
having known a world without computers, the Internet, DVD‘s, cell phones, or color television.
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To them, technology is, and has always been, ubiquitous and omnipresent, integrated into their
lives (Leh, 2005; Levine, 2010; Lusk, 2010; Yakel, Conway, Hedstrom, & Wallace, 2011). This
reality ensures that today‘s learners are more accustomed to digital technology and more
adaptable to technological change (Leh, 2005). Functioning in a world such as this, students
possess technologically founded critical-reasoning skills enabling them to constantly and actively
engage information (Berson & Berson, 2006). Consequently, the traditional paradigm of
schooling is now passé (Levine, 2010). While acknowledging technological innovation as a
cultural and social phenomenon, Kukulska-Hulme (2009) state that ―widespread ownership of
mobile phones and the increasing availability of other portable and wireless devices have been
changing the landscape of technology-supported learning‖ (p. 157). This is not to say that old
methods should be eradicated. Rather, it is to say that old and new methods should be aligned
with strategic educational goals, and technological strategies tailored for today‘s learners should
be accepted and, in fact, embraced (Kukulska-Hulme, 2009). Technological enhancements to
traditional pedagogical practices are no longer considered niceties, but necessities (Jackson et al.,
2011). With the noted influence of emerging technology in today‘s world, logic dictates that
these advances must influence the ways in which people learn (Beetham & Sharpe, as cited in
Kukulska-Hulme, 2009). ―The changing nature of how we receive and distribute information
suggests that educators need new strategies and tools for teaching and learning‖ (Moore-Hayes,
2011, p. 3). Students are already, without the benefit of formal instruction, performing many of
the tasks long held up as critical by curriculum developers. Content standards for
communicating, sharing, buying and selling, exchanging, creating, meeting, collecting,
coordinating, evaluating, searching, reporting, programming, socializing, and learning are all
21
being addressed through emerging technologies that natives understand to be a part of their
archetype (Prensky, 2005).
Global Educational Climate
For many years, education in the United States was sufficient. Providing children with
analytical, critical, and communication skills was enough to ensure their place and standing in
the global community. Now students must have a firm grasp of the disciplines that comprise
STEM. Only these disciplines provide students with the tools to take what is learned and apply it
practically (Jamison, 2008). It is these tools that are necessarily required for students to ensure
their place and affect change in the global market (Jamison, 2008). Through technology
integration, teachers can ―transform the teaching and learning context in a way that will position
their students for future opportunities in the global context‖ (Smolin & Lawless, 2011, p. 92). It
has never been more critical. As technology continues to evolve and improve and global
communication and access become more equitable universally, job markets and economic
markets become increasingly competitive (Friedman, 2006).
As corroborated by Merrill and Daugherty (2010) and Appleton and Lawrenz (2011), the
United States‘ performance in STEM area disciplines has placed it at risk of losing its
competitive edge in the global marketplace. In a world where the United States economy once
was the vanguard, it is now positioned as reactionary. In fact, there is no question that the
United States trails a growing number of countries in student performance related to STEM areas
(―Influence Of,‖ 2010). Since as far back as the late 1990‘s, American students have fared
poorly, specifically in the areas of reading, science, and math, when compared to students in
other developing Western countries (Hansen, 2011; Newmann, 1992). With regard to
mathematics specifically, the President‘s National Mathematics Advisory Panel, according to
22
Brown (2008), says, ―American student achievement in math is ‗at a mediocre level‘ compared
with peer nations‖ (p. 9). Science, engineering and technology are similarly lagging. In 2006,
PISA assessments once again showed that United States students were categorically
outperformed by a significant number of countries, ranking between 18th and 26th among the 30
participating countries in each scientific and mathematic area (Appleton & Lawrenz, 2011).
While poor student achievement results have drawn attention and spurned governmental policy
shifts (most notably the Bush administration‘s No Child Left Behind Act of 2001, the Obama
administration‘s Race to the Top, and STEM programs which reward student achievement in the
fields of science, technology, engineering, and math) deficiencies remain (Hansen, 2011). As the
demand for productive, technologically savvy workers increases, the United States is poised to
fall further behind unless change occurs. ―Students lacking in STEM skills will not have the
ability or skills to enter in the professions of science and engineering or areas requiring
mathematics, science, and technology literacy‖ (Merrill & Daugherty, 2010, p. 21). According
to Jamison (2008) 32.4% of American undergraduates, compared to 60% of German and
Japanese undergraduates and 56% of Chinese undergraduates, graduate with a science or
engineering degree. This indicates a lack of exposure. Curiously though, Brown (2009) finds to
the contrary and states that the United States university system provides a large number of
graduates each year in critical STEM fields. In fact, Brown (2009) claims that there are three
times as many qualified STEM area graduates as there are highly innovative jobs available.
Additionally, Brown illustrates that since the early nineties the overwhelming majority of the
best and brightest graduates either does not enter a critical STEM field job or are not retained
through a ten-year span. This would seem to indicate that critical STEM area jobs are not only
scarce, they are also less attractive than other jobs (―Best Tech,‖ 2010). Brown (2009)
23
speculates this is due to a variety of possibilities including better wages, incentives, and career
opportunities.
No matter the cause for deficiency, a lack of highly innovative and technical job
candidates or a lack of highly innovative and technical job opportunities, there is little doubt that
the systems of today‘s United States were built for a very different paradigm than the one in
which they function today. The educational system was built for a pre-digital, industrial
economy, and the model, according to Levine (2010) appears broken and in need of a retrofit.
As the world changes, so must the systems supporting this technological innovation—
specifically educational systems. The focus of educational system processes must be not only on
the indices of the problem, but also on the root problem of disengagement (Newmann, 1992).
Innovative programs are needed and will certainly ―play a major role in strengthening America‘s
competitive position and in ensuring that . . . young people are fully functioning citizens in the
21st century‖ (Jamison, 2008, p. 39). But educators must be the change agents. According to
Harada and Hughes-Hassell (2007), change agents are those that ―support, assist, nurture,
encourage, persuade, and push people to change, to adopt an innovation‖ (p. 8). At the most
basic and foundational level, educative experiences form tendencies, and as gatekeepers,
educators bear the responsibility of transformation.
Self-efficacy Theory
Bandura (1977, 1997), within his social cognitive theory, defines self-efficacy as the
belief one has in his or her own capability to execute a course of action in order to complete a
task. It is based upon the tenet that the behavior of humans is not only influenced by external
factors but also by internal factors. In effect, the term refers not to what someone can do; rather,
it refers to what someone believes he or she can do, a belief influenced by behavioral,
24
environmental, and internal or affective events (Bandura, 1997). Each of these event types
interacts with and influences the others in their affect upon behavioral choices. This is a causal
phenomenon known as reciprocal determinism, or triadic reciprocal causation (Bandura, 1977,
1997).
Reciprocal determinism. Bandura (1977), in his social cognitive theory notes that
humans have the opportunity to influence their destinies as well as their self-direction limits. It
is the process of reciprocal determinism (Bandura, 1977), or triadic reciprocal causation
(Bandura, 1997), that explains human behavior in terms of a perpetual interaction between
cognitive, behavioral, and environmental influences. The theory is such that each of these
determinants has functional dependence on the other, and each influences the other
bidirectionally (Bandura, 1997). This is not to say that each influences the others equally. The
strength of the influence is largely related to circumstance.
Enactive mastery experience. Enactive mastery experience refers to those events
previously experienced with either positive or negative result outcomes and is noted by Bandura
(1997) as being the single greatest influence upon self-efficacy. Successful outcomes assist in
the construction of strong beliefs in ability while unsuccessful outcomes contribute to lesser
beliefs.
Vicarious experience. Self-efficacy is affected as a result of modeled actions by, or the
attainments of, others (Bandura, 1997). It is not necessary for someone to actually experience
something themselves in order to formulate a mastery opinion. These modeled experiences are
known as vicarious experiences. People appraise their capabilities based on the demonstrated
capabilities of others.
25
Verbal persuasion. According to Bandura (1997), it is much easier to sustain a sense of
efficacy when encouraged by others. There is no question that difficulties arise no matter what
degree of expertise or mastery exists in a given situation. According to Bandura (1997), those
who are ―persuaded verbally that they possess the capabilities to master given tasks are likely to
mobilize greater effort and sustain it than if they harbor self-doubts and dwell on personal
deficiencies when difficulties arise‖ (p. 101).
Physiological and affective state. Certainly, one major determinant on capability beliefs
lies with physiological and affective, or emotional, states. Somatic, or bodily, conditions are
especially influencing in the realms of physical accomplishment and health or mental functioning
(Bandura, 1997). Emotions play a large part of this source of self-efficacy because emotional
reactions to stressful situations often cause aversive thoughts and dysfunction (Bandura, 1997).
It is often these emotions that can be treated in order to raise self-efficacy. That is not to say that
the only affects in this category are mental or emotional. Physical conditions of the body can
influence beliefs as well. Normal body alert systems such as fatigue or aches and pains are often
interpreted as indicants of physical inefficacy (Bandura, 1997). Additionally, moods, like
emotions, affect ability judgments.
Self-efficacy’s Impact upon Technology Integration
According to Bandura (1997), self-efficacy of an individual is contextual, or domain
specific. Due to the continuous change in the field of technology, self-efficacy has been noted as
the most useful standard used in determining outcomes of technology influence (Beas &
Salanova, 2006). Irrespective of whether or not teachers choose to create a technologically rich
environment, opting for adoption of new tools and strategies aligned with contemporary societal
norms and expectations, student learners will use available technologies to support their learning
26
(Kukulska-Hulme, 2009). While teachers will continue to be the pedagogical experts (Kukulska-
Hulme, 2009), making decisions with regard to content, delivery, environment, and structure, it
behooves them, due to the social and cultural context created by emerging technology, to
integrate. As stated by Prensky (2005), teachers must ―pay attention to how their students learn‖
(p. 10). Teachers must instruct in ways that best facilitate student learning. In today‘s
technological culture, this means facilitating learning through engagement, which is attained as a
byproduct of technological immersion (Jackson et al, 2011). Accordingly, professional
development designers must construct programs that allow teachers to become highly
efficacious. According to Hirsch (2001), former deputy executive director of the National Staff
Development Council, professional development must be ―results driven, standards based, and
job embedded‖ (p. 13). To accomplish this goal, all professional development must address
three questions:
1. What are all students expected to know and do?
2. What must teachers know and do to ensure student success?
3. On what must staff development focus to meet both goals?
The crux of this study is to provide foundation related to self-efficacy antecedents so that
professional development can be employed to breakdown self-efficacy barriers and possibly
raise efficacy levels as well. If professional development is to be designed in such a way as to
provide teachers with the knowledge and tools to ensure student success, these foundational
efficacy inputs must be addressed and accounted for in professional development planning and
execution.
27
Chapter Summary
The rationale for this study is a simple one and is deeply rooted in the researcher‘s
passion and concern for the plight illustrated by the conceptual framework. The United States
trails the world in critical STEM areas. The highly complex industries of science, technology,
engineering, and math are being filled with a populace made up of an increasingly diminished
number of Americans. The literature indicates that Americans are not trained to a degree that
makes them attractive or capable in this market. The deficiency can be traced to the education
received by American students, and this problem is rooted in the lack of engagement of today‘s
learners. Today‘s students have different expectations for learning. Classrooms that feel remote
and barren of those things that hold their attention and, in fact, contribute to critical thinking and
highly complex learning, cannot titillate them. Teachers demonstrate low technological self-
efficacy and, as a result, fail to integrate technology into the classroom despite the wealth of
research demonstrating its benefits. Although the term digital divide is reserved for the
difference between the haves and the have nots, in this case, the digital divide is between the
digital natives and the digital immigrants, or those who were not born into the digital world but
have adopted technology to some degree (Prensky, 2005), a category to which most current
teachers belong. Student performance is stifled by teachers‘ unwillingness to provide proven
student engagement and effectiveness opportunities due to a lack of trust in their own
technological ability. The results of this study may provide direction for professional
development designers as they prepare teachers to integrate, providing valuable information
useful for not only eliminating efficacy barriers but also for efficacy enhancement. It is not
sufficient to merely identify those with high or low self-efficacy levels. Without foundational
information related to antecedents, this information is not useful. It is the identification of those
28
things that specifically contribute to the construction of technological self-efficacy that can be
used to address the problem.
29
CHAPTER III
METHODOLOGY
This chapter includes information related to the methodology utilized in this study. The
methodology narrative begins with information about the research questions, research
hypotheses, and null hypotheses. Additionally, the research design is discussed in detail with
regard to the various components of the study such as the variables for the examination (both
independent and dependent), setting, participants, and sample. A review of the instrumentation
used and the procedures for collecting and analyzing data are included as well. The chapter
concludes with a narrative concerning the pertinent statistical power measures.
Research Question and Hypotheses
Within this study, there is but one research question. The question and its research
hypotheses and null hypotheses are listed below.
Research question R1. Are Bandura‘s (1997) four noted sources of self-efficacy
(mastery experience, vicarious experience, verbal persuasion, and physiological and affective
state) significant predictors of teachers‘ classroom technology integration?
Research hypothesis H1. Mastery experience is a significant individual predictor of
teachers‘ classroom technology integration.
Research hypothesis H2. Vicarious experience is a significant individual predictor of
teachers‘ classroom technology integration.
Research hypothesis H3. Verbal persuasion is a significant individual predictor of
teachers‘ classroom technology integration.
Research hypothesis H4. Physiological and affective state is a significant individual
predictor of teachers‘ classroom technology integration.
30
Research hypothesis H5. The combination of Bandura‘s four noted sources of self-
efficacy is a significant predictor of teachers‘ classroom technology integration.
Null hypothesis H01. Mastery experience is not a significant predictor of teachers‘
classroom technology integration.
Null hypothesis H02. Vicarious experience is not a significant predictor of teachers‘
classroom technology integration.
Null hypothesis H03. Verbal persuasion is not a significant predictor of teachers‘
classroom technology integration.
Null hypothesis H04. Physiological and affective state is not a significant predictor of
teachers‘ classroom technology integration.
Null hypothesis H05. The combination of Bandura‘s four sources of self-efficacy is not a
significant predictor of teachers‘ classroom technology integration.
Research Design
The researcher‘s intent for this correlation and multiple regression study was for it to be
designed as a non-probability cross-sectional survey using volunteers. This type of design
affords the opportunity for timely and efficient measurement of current practice and attitude.
According to Campbell and Stanley (1963), any design that lacks full experimental control
should not necessarily be discounted, and for this reason, the type of investigation suggested
should be encouraged, provided experimental shortcomings are acknowledged. Studies of this
nature should not be avoided but should, rather, be acknowledged as equivocal. The study was
conducted using two survey instruments disseminated via email to a sample population. Both
instruments yielded electronic data that was then collated and contained in a single database for
31
analysis purposes. The data were analyzed using IBM Statistical Product and Service Solutions
(SPSS) Statistics version 20.0 software.
Variables. In this multiple regression study, there are four independent variables and
one dependent variable. The four sources of self-efficacy, as defined by Bandura (1997),
mastery experiences, vicarious experiences, verbal persuasion, and physiological and affective
state, and the degree to which they impact the decision-making of the respondents are noted as
being the four independent variables. The single dependent variable is the Level of Innovation of
each respondent. It is the Level of Innovation that defines the degree to which an instructor
integrates technology into the classroom.
Setting, participants, and sample. For the purposes of this study, the researcher found
it necessary for the sake of expediency and applied practice to collect data and conduct the
research in a nearby regional district. To obtain participants for this study, volunteers were
secured from the population of 781 instructors from various schools serving secondary students
in the district. This occurred once approval had been obtained from both the school district and
the Institutional Review Board of The University of West Florida. Expediency was necessary
for this project due to an anticipated large influx of doctoral dissertation studies in the area, and it
was prudent to begin data collection in a receptive and unsaturated research environment. Data
were gathered using a non-probability volunteer sample through the utilization of email
solicitation from the approved district‘s secondary teaching population in accordance with their
guidelines. This was to provide data from members of all efficacy levels as well as to ensure a
large sample size for generalizeable results. Because participation in this study was voluntary, as
an incentive for participating, respondents were notified of their eligibility to have their names
entered for a random drawing for a $500 Visa gift card if they chose. This incentive was
32
selected due to its sweeping and universal appeal. There are issues neither with bias nor inequity
using this incentive, and only those respondents completing both instruments and the
demographic questions were eligible. To ensure the rights of the respondents, there were three
necessary protective measures undertaken. Institutional Review Board approval from both the
university and the school district were obtained. These measures were in place prior to the start
of the study. Additionally, consent forms accompanied the electronic surveys and were
necessarily completed by each respondent. There were no physical risks associated with this
study, all responses are confidential, and all respondents will remain anonymous. Further, any
respondent so desiring may have access to the research results upon request.
Instrumentation. For the purposes of this study it was necessary to utilize two
instruments. On each instrument, respondents were asked to provide a unique username that
included a minimum of six characters and one numeral. This coding requirement was necessary
to link each of the participant‘s surveys together for the purpose of associating the data to the one
respondent without compromising his or her anonymity.
First, an adaptation to an existing instrument was required in order to rate the influence of
the four sources of self-efficacy for individuals. This adaptation was necessary due to the
shortage of instruments in this field. The researcher‘s original intent was to craft an instrument
for the purpose of measuring a generic source influence upon self-efficacy levels, however, upon
further study, specifically with regard to Bandura‘s (2006) Guide for Constructing Self-efficacy
Scales, there is no ―all-purpose measure of perceived self-efficacy‖ (p. 307). Further, ―the self-
efficacy belief system is not a global trait but a differentiated set of self-beliefs linked to distinct
realms of functioning‖ (Bandura, 2006, p. 307). In short, self-efficacy levels are contextual. It is
reasonable to assume that those things that influence self-efficacy are likely to be contextual as
33
well. Therefore, the instrumentation for this study was appropriately and contextually based,
despite the original intent. The Sources of Self-Efficacy Instrument (SoSEI) was adapted based
on a credible, valid, and reliable instrument from a similarly designed study in a different
research discipline. The instrument used as the foundation for the adaptation is known simply as
the instrument authored by Muretta (2004) and acknowledged in his work, Exploring the Four
Sources of Self-efficacy. However, collaboration with subject matter experts and established and
recognized researchers was utilized for further confidence in order to provide a measure of
weight for each of Bandura‘s (1997) four noted sources of self-efficacy in each teacher‘s
technological decision-making process. The SoSEI is formatted as a ten choice scale, Likert-
type, electronic survey, utilizing radio buttons for selection of ratings associated with a self-
perceived level of belief for each example. There are three examples representing three
antecedents provided for each of two tasks. The examples are associated with each of the four
sources of self-efficacy and are arranged in order of strength of antecedent. There are but three
antecedents utilized for the purpose of making the survey manageable and measureable. The
sums of each antecedent for each source of self-efficacy were used as the independent variable
inputs for each participant in the study. Demographic questions and consent accompany the
instrument as well. Demographic questions are included for the purpose of providing richness to
the study, possibly giving rise to areas of further study. Additionally, some demographic
information was necessarily obtained using this instrument for the purpose of any
correspondence provided such correspondence was required by the respondent (i.e. research
results information, incentive award notification, etc.).
Utilization of the Levels of Teaching Innovation (Moersch, 2009) Digital Age Survey
instrument (LoTi), a well-known and widely recognized measure of technological integration
34
aligned with the International Society for Technology in Education, having been used in
countless dissertations (Moersch, 2009) surveying tens of thousands of respondents (Moersch,
2001), provided the determination of the teachers‘ integration levels. The LoTi consists of 37
Likert-type items, each referring to a technological action, preference, or practice. Users are
directed to select one of eight radio buttons associated with an individual frequency of
occurrence with regard to each item. Response choices for each item include: never, at least
once a year, at least once a semester, at least once a month, a few times a month, at least once a
week, a few times a week, and daily. The numeric values, 0 to 7, correspond respectively.
According to Moersch (2002) this instrument has been ―formally evaluated for validity and
reliability‖ (p. 26). Having been determined through extensive research (Moersch, 1995, 2001)
to have a reliability measure of .74 for internal consistency using Cronbach‘s Alpha statistic, this
proprietary instrument is designed to measure classroom teachers‘ implementation of the various
tenets of digital-age literacy. There are eight levels to which a teacher may be scored.
Level 0 – non-use. At this level, the use of research-based best practices may or may not
be evident. Practices in class are devoid of digital tools or resources. According to Moersch
(2009), there are several factors contributing to this lack of digital application: competing
priorities, lack of access, and a teacher‘s perception that its use is contextually inappropriate.
Level 1 – awareness. Moersch (2009) states that the Level 1 instructional focus is on
information dissemination. Digital tools, for those at this level, are primarily used for one of
three reasons: as support technology for the lecture-style instructional model, for management
tasks such as taking attendance, accessing e-mail, and using electronic grade books, or as a
reward for students.
35
Level 2 – exploration. For instructors at a Level 2, content understanding, mastery
learning, and direct instruction are emphases (Moersch, 2009). Digital resources are used for
extension and enrichment as well as information collection. Student multimedia products are
common.
Level 3 – infusion. According to Moersch (2009), Level 3 instructors focus their
instruction on higher order thinking and engaged learning. Digital tools are used for the purpose
of carrying out teacher-directed tasks such as problem solving, decision-making, and
experimentation that emphasize high levels of cognitive processing.
Level 4a – integration: mechanical. At this level, according to Moersch (2009),
students use digital resources inherently and are motivated by student ownership of questions,
content, process, and products. The instructor facilitates an environment in which students are
engaged in real world issues and authentic problem solving. However, the instructor relies
heavily on outside resources such as colleagues or prepackaged materials.
Level 4b – integration: routine. As with Level 4a, Level 4b is characterized by inherent
use of digital tools and resources by the student. Students are similarly motivated. However, the
instructor at this level is working within his or her comfort level with regard to promoting an
inquiry-based teaching model. Additional characteristics of instructors at this level include
emphases on personal goal setting and self-monitoring by students as well as highly cognitive
processing involved with issues resolution and examination of content (Moersch, 2009).
Level 5 – expansion. According to Moersch (2009), at a Level 5 student collaborations
beyond the classroom are utilized for authentic problem solving and issues resolution. The
complexity and sophistication of the digital resources used are commensurate with the teacher‘s
36
experiential learning model of instruction as well as students‘ levels of complex thinking and
understanding of content.
Level 6 – refinement. At Level 6, collaborations beyond the classroom are the norm
(Moersch, 2009). The classroom at this level is learner-based and is supported by unlimited
access to current digital applications. There is no division between instruction and digital
resources. According to Moersch (2009), the use and access of digital resources provides a
seamless medium for all the tasks associated with problem solving, collaboration, student
reflection, and product development.
The level to which each teacher was rated based on his or her self-reported responses
served as the dependent variable for the study. Both of the instruments were disseminated
electronically and each populated the main database used for data analysis.
Procedures. Once approval was obtained from the Institutional Review Board (IRB)
from both the university and the school district, it became necessary to conduct a pilot study as
described in the next section for the purpose of establishing specific instrument reliability for the
SoSEI. Once reliability was determined to be acceptable, online surveys with digital informed
consent were disseminated throughout the targeted schools via e-mail, according to the protocols
established by the school district. The proprietary method for the dissemination of the LoTi
instrument was utilized. Users were asked to respond to a web link embedded within an e-mail
that directed them to set up an individual, free, and secure LoTi account, protected by password,
associated with the online data collection form associated with the researcher‘s study. Users
were able to log in and out of the account at their leisure without having to begin the survey
again. A Google Docs Survey Form was utilized to disseminate the SoSEI since the LoTi
37
instrument‘s proprietary structure did not allow for adaptation or addition. After collection was
complete, data was imported into SPSS for statistical analysis.
Data analysis. The first issue to address was the reliability and validity of the SoSEI
instrument. It was the researcher‘s original intention to use the pre-established reliability of
Muretta‘s (2004) instrument, from which the SoSEI was adapted, as the given reliability for the
SoSEI, but further consideration was given. Because the instrument was, in fact, an adaptation
of Muretta‘s Likert-type instrument, it was determined that in order to further establish its
reliability, a local pilot study was needed. According to Colton and Covert (2009), pilot studies
are necessary because readability, clarity, and reader perception are of paramount importance,
and nothing in the realm of understanding should be taken for granted.
For the pilot study, the SoSEI was electronically disseminated to a local faculty of
approximately 90 individuals with the expectation that a minimum of 30 would respond. In fact,
58 responses were gathered over the course of this pilot study. Of the 58 responders, 12 were
male and 46 were female. The mean age of those responding was 47.26 years and the mean
number of years of experience in the teaching field was 18.15 years. Fifty-four of the 58
response cases were determined to be valid as a result of four failing to include a complete data
set for the procedure. Results of this survey were entered into SPSS software and tested for
reliability using the Cronbach alpha statistic. Warner (2008) indicates that the Cronbach alpha
has become ―the most popular form of reliability assessment for multiple-item scales‖ (p. 854).
It is a measurement of internal consistency (Colton & Covert, 2009). In effect, this statistic
represents a coefficient indicating the reliability of the measurement scale chosen by the
researcher (Warner, 2008), or, in other words, how well items correlate with each other.
According to Colton and Covert (2009), a Cronbach alpha coefficient of .70 is the minimum
38
indicating an acceptable internal reliability, and a coefficient of 1.0 indicates a perfect
correlation. The pilot study responses generated a Cronbach alpha of .939 which was certainly
within the acceptable range outlined by these coefficient factors and, as a result, reliable.
With regard to instrument validity, association with Muretta‘s (2004) instrument,
pertinent literature review, and research expert acknowledgement were sufficient. According to
Muretta (2004), the original instrument was designed with every possible effort being made to
keep the research uniform with commonly held methods of self-efficacy scale construction and
that, according to Bandura‘s (2006) guide. In Muretta‘s instrument two tasks were required to
ensure instrument validity. Correspondingly, the adapted version uses two as well. In all, the
adapted instrument requires 24 separate items, four sources of self-efficacy, and three levels of
antecedent strength built into the items in order to keep the design simple and measureable. The
SoSEI was not scrambled; rather, the three items for each self-efficacy source were kept
appropriately and uniformly grouped.
Due to the fact that this was a multiple regression study and provided that a viable sample
was obtained, the researcher‘s intent was to analyze the gathered data using IBM SPSS Statistics
software in an attempt to determine which of Bandura‘s four sources, if any, have any predictive
bearing upon technology integration levels in the classrooms of the respondents. Although the
intent of using multiple regression is to measure predictability, many researchers use this
technique as evidence of probable causal effects (Warner, 2008). To address the research
question and hypotheses, each of the independent variables (sources of self-efficacy) found to be
correlated with the dependent variable was analyzed individually with regard to its predicted
contribution upon the teachers‘ levels of innovation (dependent variable). Additionally, the
39
combined contribution of the three correlated sources, one was later found not to be correlated,
upon Level of Innovation was analyzed.
Statistical power. According to Lipsey (1990, Table 1.1), the probability of attaining
statistical significance in an educational study, or average statistical power of this research
domain, is .13 for detecting a small effect, .47 for detecting a medium effect, and .73 for
detecting a large effect. This is to say that if a small effect were present in the reviewed
educational studies and others, the statistical tests would indicate significance 13% percent of the
time. Correspondingly, significant medium effects would be detected 47% of the time and
significant large effects would be detected 73% of the time. According to Lipsey (1990), a
reasonable power expectation for most research contexts is .90 or .95. Although the numbers for
educational research seem to indicate that the typical study is underpowered to predict small,
medium, and high effects, they do provide initial useful information. For the purposes of this
study, medium and high effects of the sources of self-efficacy are of consequence and it is
around this condition that statistical power and sampling were constructed.
It is the researcher‘s determination that a power criterion of .95, in accordance with
Lipsey‘s (1990) statement regarding reasonable research, far exceeds the common recommended
minimum standard of .80 (Cohen, 1988; Warner, 2008) and as a result, is acceptable for this
study. With two general experimental groups (those with high self-efficacy and those with low
self-efficacy) taking part in the research and with the stated minimum power expectation of .47
for the medium effect size in the educational context, a requirement of 210 viable samples is
necessary to ensure a .95 power level. This is based on an effect size of .50, the closest effect
size to .47 noted in Lipsey‘s (1990) Table 6.5, which notates the approximate sample size, in this
40
case 105 participants per experimental group, needed to attain various criterion levels of power
for a range of effect sizes at an alpha of .05.
Chapter Summary
The methodology described in this chapter is straightforward and based on accepted
research practice. The study is a non-probability cross-sectional survey design conducted in, and
using volunteers from, a regional school district. The instruments utilized for data collection,
both the SoSEI and the LoTi, are accepted, reliable, valid, and efficient. Correlation analysis and
multiple regression analysis conducted by the researcher using IBM SPSS Statistics software
determined which of the sources of self-efficacy contributed to teachers‘ classroom technology
integration and to what degree.
41
CHAPTER IV
RESULTS
The purpose of this study was to explore factors contributing to the technological self-
efficacy levels of teachers and their eventual impact on classroom technology integration. As a
result, the study was designed to address one straightforward research question: Are Bandura‘s
(1997) four noted sources of self-efficacy (mastery experience, vicarious experience, verbal
persuasion, and physiological and affective state) significant predictors of teachers‘ classroom
technology integration? The researcher collected and analyzed data from two separate, self-
reported surveys: the Sources of Self-efficacy Instrument (SoSEI), which provided for a self-
assessment of efficacy inputs with regard to technology, and the Levels of Teaching Innovation
(LoTi), which provides a numeric indicator of technological integration. The remainder of this
chapter includes the results of this non-probability, cross-sectional study.
Descriptive Statistics
This study was conducted in a single school district in the southeastern region of the
United States. From a total population of 781 instructors employed at the 19 institutions serving
a secondary population of middle and high school students, 215 participated in the study. This
number exceeded the required number of participants (210) as noted in Chapter 3. Each
respondent completed both surveys and connected them properly through the coding process
stipulated within the instruments and previously described in Chapter 3 of this dissertation.
Of the 215 participants, the majority was female (Table 1). With regard to the
respondents‘ educational levels, the responses ranged from those whose highest educational
attainment was a Bachelor‘s degree to those whose highest level of attainment was a Doctoral
42
degree. More respondents indicated that their highest educational attainment was a Master‘s
degree than all other categories combined (Table 1).
Respondents were asked to indicate their ages individually and by age grouping. The
mean age of those responding was 46.1 years old and ranged from 23 to 68 (Table 1).
Respondents were also asked to indicate in two ways how many years of service they had
completed in the teaching field. They were asked to note specifically as well as by choosing one
of four separate categorical groups designated by a range of years of experience (Table 1). The
vast majority of responders had been in the teaching profession for 10 or more years (Table 1.)
The mean term of service in the teaching profession was 16.4 years. No data were collected with
regard to ethnicity or affluence.
Of the 215 respondents, three, as a result of their LoTi responses were found to be at
Level 0 (non-use). Thirty-eight respondents were found to be at Level 1: awareness; 92 were
found to be at Level 2: exploration; 41 were found to be at Level 3: infusion; 19 were found to be
at Level 4a: integration (mechanical); 17 were found to be at Level 4b: integration (routine);
three were found to be at Level 5: expansion; and two were found to be at Level 6: refinement
(Table 1).
43
Table 1
Gender, Education Level, Age, Longevity of Participants, and Level of Innovation (N=215)
Demographic categories f Percent
Gender
Female 158 73.5%
Male 49 22.8%
No response 8 3.7%
Education level
Bachelors 83 38.6%
Masters 112 52.0%
Specialist 12 5.6%
Doctoral 5 2.3%
No response 3 1.4%
Age by group
21 - 30 25 11.6%
31 - 40 34 15.8%
41 - 50 73 34.0%
> 50 82 38.1%
No response 1 0.5%
Years teaching
< 5 22 10.2%
5 - 9 38 17.7%
10 - 20 78 36.3%
> 20 77 35.8%
Level of innovation
0: Non-use 3 1.4%
1: Awareness 38 17.7%
Table 1 (continues)
44
Table 1 (continued)
Gender, Education Level, Age, Longevity of Participants, and Level of Innovation (N=215)
Demographic categories f Percent
Level of innovation (continued)
2: Exploration 92 42.8%
3: Infusion 41 19.1%
4a: Integration, Mechanical 19 8.8%
4b: Integration, Routine 17 7.9%
5: Expansion 3 1.4%
6: Refinement 2 0.9%
Note. f = frequency
Data Analysis
To answer the sole research question in this study, it was first necessary to screen for a
violation of assumptions with regard to the independent variables. According to Osborne and
Waters (2002), multiple regression can only accurately estimate the relationship between
dependent and independent variables if a linear relationship exists. The assumption that each of
the four sources of self-efficacy is a predictor of a teacher‘s technological integration in the
classroom was investigated by determining whether or not a statistical correlation existed
between the independent variables and the dependent variable. If variables are correlated,
knowing the value of one score allows researchers to predict the other score.
One might assume that the stronger the correlation, the more precise the prediction, but
according to Warner (2008) this is a faulty assumption. ―Correlations provide imperfect
information about the true strength of predictive relationships between variables‖ (Warner, 2008,
p. 280). Correlations do, however, provide the basic information for other types of analysis such
45
as multiple regression analysis (Warner, 2008), and for this reason it was prudent to begin with
this procedure first. According to Warner it is common that scores on variables fall short of the
strictest requirements for a particular level of data, and in actual practice it is often difficult to
make a determination of the data type as a result. Because there is some debate regarding
whether or not the LoTi instrument generates ordinal or interval level data (H. Brown, personal
communication, October 3, 2012), and a self-reported rating scale such as the SoSEI ―probably
[does] not have true equal interval measurement properties‖ (Warner, 2008, p. 9), two separate
correlation procedures were performed for triangulation and, as a result, increased confidence.
Using the Pearson Product-Moment Correlation procedure (Pearson r) as well as the Spearman‘s
rho (Spearman r) Correlation procedure within the IBM SPSS Statistics software package, it was
found that three of the four specific sources of self-efficacy were significantly correlated to the
Level of Innovation determined by the LoTi at an alpha level of .05 (Table 2). According to
Hunt, Tyrrell and Nicholson (2002), correlation coefficients of less than 0.5 are described as
weak using the Pearson r procedure. None of the significant correlations approach this number.
Physical and affective state was not found to be significantly correlated with the Level of
Innovation (Table 2).
Once correlation was determined for mastery experience, vicarious experience, and
verbal persuasion, multiple regression statistical analysis was conducted to determine the degree
to which each could predict the Level of Innovation score.
46
Table 2
Correlation of Independent Variables and Dependent Variable
Correlation statistic Independent variable Sig. (2-tailed) r n
Pearson r
Mastery Experience .000 .277* 210
Vicarious Experience .001 .225* 208
Verbal Persuasion .001 .226* 207
Physical/Affective State .078 .121 213
Spearman r
Mastery Experience .001 .227* 210
Vicarious Experience .001 .220* 208
Verbal Persuasion .001 .233* 207
Physical/Affective State .051 .134 213
Note. Study sample, N=215.
*Correlation is significant at the 0.01 level (2-tailed).
Research hypothesis H1. The purpose for H1 was to investigate whether or not mastery
experience is a significant predictor of teachers‘ Level of Innovation. Using the Pearson
Product-Moment Correlation procedure (Pearson r), mastery experience was found to be
significantly and positively correlated with Level of Innovation, r = .277, p < .05. Of the three
sources to be found correlated using the Pearson r, mastery experience is noted as being the best
predictor of a teacher‘s Level of Innovation. A significant and positive correlation was found
using the Spearman‘s rho procedure as well, r = .227, p < .05, although not the strongest among
the four sources (Table 2). Due to the correlation determined using both methods, mastery
experience was investigated as an independent variable in the multiple regression procedure,
Level of Innovation being the dependent variable. The coefficient for mastery experience
47
(2.462) manifested through the regression procedure is noted as being significantly different than
zero, p = .015 which is less than the alpha level of 0.05 (Table 3).
Table 3
Regression Output
Independent Variable B Std. Error Beta t Sig.
(Constant) .292 .600 .486 .628
ME .050 .020 .294 2.462 .015
VE -.014 .020 -.134 -.679 .498
VP .013 .019 .124 .681 .497
Note: ME = Mastery Experience, VE = Vicarious Experience, VP = Verbal Persuasion,
Dependent Variable: Level of Innovation
Research hypothesis H2. The purpose for H2 was to investigate whether or not vicarious
experience is a significant predictor of teachers‘ Level of Innovation. Again using the Pearson
Product-Moment Correlation procedure, vicarious experience was found to be significantly and
positively correlated with Level of Innovation, r = .225, p < .05. Significant and positive
correlation was found using the Spearman‘s rho procedure as well, r = .220, p < .05 (Table 2).
For this reason, the independent variable of vicarious experience was included in the regression
analysis. The coefficient for vicarious experience (-.679) manifested through the regression
procedure was not noted as being significantly different than zero, p = .498 which is not less than
the alpha level of 0.05 (Table 3).
Research hypothesis H3. The purpose for H3 was to investigate whether or not verbal
persuasion is a significant predictor of teachers‘ Level of Innovation. Using the Pearson
Product-Moment Correlation procedure, verbal persuasion was found to be significantly and
positively correlated with Level of Innovation, r = .226, p < .05. Significant and positive
48
correlation was found using the Spearman‘s rho procedure as well, r = .233, p < .05 (Table 2).
Using this Spearman r statistical procedure, verbal persuasion was found to be the best predictor
of teachers‘ Level of Innovation among the four sources of self-efficacy, albeit by a very narrow
margin (Table 2). Because a correlation was found between the dependent variable of teachers‘
Level of Innovation and the independent variable of verbal persuasion, the independent variable
was included in the regression analysis. The coefficient for verbal persuasion (.681) was not
noted as being significantly different than zero, p = .497 which is not less than the alpha level of
0.05 (Table 3).
Research hypothesis H4. The purpose for H4 was to investigate whether or not physical
and affective states are significant predictors of teachers‘ Level of Innovation. Using the Pearson
Product-Moment Correlation procedure, the physical and affective states category was found to
have no significant correlation with Level of Innovation, r = .121, p > .05 (Table 2). Using the
Spearman‘s rho procedure, the researcher could find no statistical significance, r = .131, p > .05.
As a result, physical and affective state was not used as an independent variable in the multiple
regression analysis. If this uncorrelated category were to be included as part of the analysis, the
possibility of either a Type I or Type II error exists when considering whether or not to reject the
null hypothesis.
Research hypothesis H5. The purpose for H5 was to determine whether the three
correlated sources of self-efficacy had any combined predictive capability concerning teachers‘
Level of Innovation. Regression results indicated R2 = .081 (adjusted R
2 = .067), F(3,193) =
5.704, p = .001 for predicting teachers‘ Level of Innovation (Table 4). The independent
variables, or predictors, mastery experience, vicarious experience, and verbal persuasion
combine to account for 8.1% of the variance (6.7% adjusted) in teachers‘ Level of Innovation.
49
Table 4
Regression Model Summary
Model R R square Adjusted R square Std, error of the estimate
1 .285a .081 .067 1.267
Note: a. Predictors: (Constant), Mastery Experience (ME), Vicarious Experience (VE), Verbal
Persuasion (VP)
Chapter Summary
In this chapter, descriptive data with regard to study respondents was provided.
Participants‘ responses with regard to gender, education level, age, and years of experience were
collected and reported. To investigate the research question and research hypotheses, analysis
using Pearson r, Spearman r, and multiple regression statistical procedures were performed and
the results reported. These statistical procedures indicated individual correlations between three
of the four sources of self-efficacy noted by Bandura (1997) and the dependent variable, Level of
Innovation as derived using the LoTi instrument. Mastery experience, vicarious experience, and
verbal persuasion were found to have significant correlations with the Level of Innovation.
Physical and affective state was found to have no significant correlation. As a result of these
findings, multiple regression analysis was conducted. Although three of the four noted sources
of self-efficacy were found to be correlated with the dependent variable of teachers‘ Level of
Innovation, only mastery experience, or having previously successfully completed a task, was
found to be a significant predictor.
50
CHAPTER V
DISCUSSION
The issue of the degree of technological integration in the classroom in light of teachers‘
technological self-efficacy levels has been investigated extensively in the past (Anderson,
Groulx, & Maninger, 2011; Beas & Salanova, 2006; Hill, Smith, & Mann, 1987; Hodges,
Stackpole-Hodges, & Cox, 2008; Holt & Brockett, 2012; Koh & Frick, 2009; Lee & Tsai, 2010;
Milman & Molebash, 2008). A teacher having high technological self-efficacy yields a greater
likelihood that they will integrate to a higher degree than those having a lower degree of self-
efficacy (Beas & Salanova, 2006). The researcher‘s intent was to use Bandura‘s Social Learning
Theory (1977) and specifically the definitions of self-efficacy (Bandura, 1977) and their sources
(Bandura, 1997) as constructs upon which to base the study. It was the researcher‘s objective to
investigate the noted sources of self-efficacy as predictors of teachers‘ integration levels.
Although predictors cannot necessarily be accounted for as contributory (Warner, 2008),
they do provide information concerning prospective areas of focus. Correspondingly, these
predictors also provide information regarding unimportant or insignificant contributions as well.
The implications of the results are not only found in which null hypotheses were rejected, but
also by those which could not be rejected.
Summary of the Study
The sample for this study included 215 participants from a population of 781 secondary
instructors from 19 different middle and high schools in a single school district in the
southeastern region of the United States. The researcher utilized a non-probability, cross-
sectional research design using electronic surveys disseminated through email and voluntary,
self-reported responses.
51
Are Bandura‘s (1997) four noted sources of self-efficacy (mastery experience, vicarious
experience, verbal persuasion, and physiological and affective state) significant predictors of
teachers‘ classroom technology integration? This is the sole research question addressed in this
study. In order to determine whether or not the sources of self-efficacy are an area of focus with
regard to technology integration in the classroom, an individual correlation analysis was
performed using the sources of self-efficacy data obtained from the SoSEI instrument and the
Level of Innovation manifested through the use of the LoTi instrument.
The results of the correlation analysis indicated a significant, albeit small, correlation
between three of the four sources of self-efficacy and the Level of Innovation. Only physical
and affective state was determined not significantly correlated. For this reason, the researcher
was not able to include that source in the multiple regression analysis and consequently failed to
reject the null hypothesis H04: Physiological and affective state is not a significant predictor of
teachers‘ classroom technology integration. Bandura (1997), as noted in Chapter 2 of this
dissertation, contends that each of the four sources, mastery experience, vicarious experience,
verbal persuasion, and physical and affective state, plays a role in the development of
contextualized self-efficacy. In this study, in the context of technological self-efficacy among
teachers, Bandura‘s contention is unsupported.
The remaining three sources of self-efficacy, mastery experiences, vicarious experiences,
and verbal persuasion, were found to be statistically and positively correlated with the Level of
Innovation and were consequently utilized in a multiple regression analysis. The analysis
indicated that only mastery experience was an individual significant predictor of teachers‘ Level
of Innovation, and 8.1% of the variance in teachers‘ innovation levels could be attributed to the
combined effects of mastery experiences, vicarious experiences, and verbal persuasion. These
52
results allow the researcher to reject the null hypotheses H01: Mastery experience is not a
significant predictor of teachers‘ classroom technology integration and H05: The combination of
Bandura‘s four sources of self-efficacy is not a significant predictor of teachers‘ classroom
technology integration. However, based on these findings the researcher cannot reject null
hypothesis H02: Vicarious experience is not a significant predictor of teachers‘ classroom
technology integration or null hypothesis H03: Verbal persuasion is not a significant predictor of
teachers‘ classroom technology integration.
Implications of the Study
Whether or not technological integration is a decisive determinant of student success or
increased achievement in learning is beyond the scope of this study. This study was conducted
to simply investigate the four sources of self-efficacy and their predictive capability with regard
to technological integration in the classroom. The anticipated ancillary benefit of this research is
the possibility of leveraging the information gained against educational professional developers‘
planning strategy. This is to say that those charged with educators‘ professional development
might be afforded telling and useful information that provides foundation for instruction and
practices that lead to a more highly efficacious technological educator pool. Despite the
methodology‘s inherent limitations with regard to the lack of proof of causality, the findings do
give evidential direction, as stated by Warner (2008), to an area upon which to focus when
ultimately trying to determine causality or manipulate predictive inputs.
Of the four sources of self-efficacy, only three were found to have any significant
correlation with teachers‘ Level of Innovation. Of these three, only one was found to have any
significant predictive capability: mastery experience. This result seems to corroborate, or even
extend, Albion and Ertmer‘s (2002) finding that the most significant predictor of self-efficacy for
53
computer use among future teachers was frequency of use. Because there is a positive
correlation between mastery experience and teachers‘ technological integration level and since
mastery experience does indeed have significant predictive capability, the active implications of
the study are associated with this single source.
Passive implications of these findings include the compulsory omission of any attention
to verbal persuasion, vicarious experiences, and physical and affective state when concerned
with raising the technological self-efficacy levels of teachers as they were not found to have any
significant bearing or predictive capacity. Bandura (1997) indicates that self-efficacy and its
sources are contextual. In the context of teacher self-efficacy with regard to technological
integration, only mastery experience is significant and warranting attention.
The implications for this study, in the context of teachers‘ self-efficacy and technological
integration, extend to a myriad of integrated and ancillary parties. Professional development
designers, school and district-level officials, students, and most notably, teachers may realize
impact from practical application of the findings contained herein.
Implications for professional development designers. Professional development
specialists are tasked with ensuring that teachers are equipped with the skills and strategies
necessary for student success (Hirsch, 2001). Murphy and Calway (2008) state that the research
focus for professional development is to enable teachers to advance beyond mere sufficiency.
Guskey (2009) notes a scarcity of this type of research-based foundation, citing a lack of the
necessary sound, valid, and contextual evidence that provides for effective professional
development. The findings of this study assist in filling this gap by providing direction for these
specialists as they prepare teachers to go beyond mere technological utility to technological
integration. The correlation results and multiple regression analyses provide valuable
54
information for teacher efficacy enhancement as well as research-based rationale for certain
activities or strategies for professional development designers to avoid due to their insignificance
or unproven worth with regard to teachers‘ technological pedagogy or environment.
Enactive mastery experiences, or those previous experiences with either positive or
negative result outcomes, are noted as being the single greatest influencing factor upon self-
efficacy (Bandura, 1997). Albion and Ertmer (2002) indicate that frequency of use is the
strongest predictor of technological self-efficacy. Similarly, the statistical analyses in this study
find mastery experiences to be the only one of the four sources of self-efficacy defined by
Bandura (1997) to be correlated to, and a significant predictor of, teachers‘ technological
integration. The literature and the findings suggest that professional development designers
should create experiences for teachers in training situations that contribute to this source.
Teacher confidence and efficacy levels increase as the teachers successfully deploy and utilize
technological tools in training situations. Additional study may be warranted to determine
whether designers must ensure that these experiences have positive outcomes in light of local
policy expectations.
This concept of experiential mastery training is positioned in direct opposition to the
show and tell model of professional development where specialists demonstrate the use of
technological resources while teachers watch. While cognitive modeling is an important and
recognized technique within Bandura‘s (1977) Social Learning Theory, the findings from this
study suggest that it is a moot practice when considering professional development for the use or
implementation of technological tools or resources. Even though vicarious experience and
verbal persuasion are both positively correlated with technological integration, and even though
55
both share characteristics with cognitive modeling, regression analysis findings indicate that
neither vicarious experience nor verbal persuasion is a significant predictor.
For professional development designers to effectively impact teachers and their
technological development and integration, consideration may be given to the findings of this
study as well as their seeming verification of Bandura‘s (1977) statement regarding the singular
influence of mastery experiences. Opportunities for technological mastery experiences must be
provided for teachers in order to contextually and completely fulfill the threefold consideration
requirement for effective professional development as outlined by the National Staff
Development Council (Hirsch, 2001):
1. What are all students expected to know and do?
2. What must teachers know and do to ensure student success?
3. On what must staff development focus to meet both goals?
As statement two suggests, teachers must be technologically capable and highly
efficacious to ensure student technological proficiency, a must for the students of today (Merrill
& Daugherty, 2010).
Implications for schools and school districts. Multiple regression analysis suggests
that 8.1% of the variance in technological integration scores can be attributed to the combined
effects of mastery experiences, vicarious experiences, and verbal persuasion. If technological
integration is to be a focus area for the decision makers in a school or district, social constructs,
or those strategies that contribute to teacher learning, must be considered. The self-efficacy level
of teachers is a determining factor in an environment where the primary decision maker with
regard to classroom archetype is the teacher. Unless schools or districts are willing to impose
policy in this area, these entities must provide necessary support for a favorable end for the
56
decision makers (Walker, 2007). In effect, institutions of education must support those practices
that build self-efficacy and eliminate those practices that are ineffective or contrary to the
mission.
The findings of this study indicate that enactive mastery experiences positively and
significantly correlate with technological integration levels and as such, provide direction for
school and district policy. These institutions must enable professional development specialists to
create mastery experiences for teachers through budgetary appropriation as well as through the
elimination of archaic or divergent policy—two areas commonly acknowledged as barriers to
technological integration (Wang & Reeves, 2004). Teachers are the most important factor in
student learning (Pilcher & Largue, 2009). It is incumbent upon schools and districts to provide
support and policies enabling them to more effectively accomplish their collective mission.
Implications for students. A student‘s teacher is the most important variable affecting
his or her learning (Pilcher & Largue, 2009). Because student engagement and technology play
such an important foundational role in the learning of today‘s students (Brill & Park, 2008; Chen
& Looi, 2011; Danielson, 2007; Kukulska-Hulme, 2009; Prensky, 2005; Reid & Solomonides,
2007; Stuber, 2007; Willis, 2011), and with teachers being the most important variable for
learning, there are important implications for students when considering teachers‘ technological
improvement through increased self-efficacy. As teachers are afforded opportunities to increase
their technological self-efficacy through intentionally increased mastery experiences, students
have improved potential for increased engagement and ownership of the classroom resulting in
increased learning. This will, in turn, also perpetuate an increased marketability among the
nation‘s students.
57
Students of highly efficacious teachers who have a high degree of technological
confidence and integration are empowered, motivated, and energized (Brill & Park, 2008). They
are afforded the opportunity to experience sustained autonomy and engagement (Brill & Park,
2008), both noted positive factors on achievement and learning (Svanum & Bigatti, 2009; Van
Ryzin, 2011). Students in technology rich classrooms have opportunity to be constantly engaged
with current, relevant information and as a result, practice critical thinking and learning skills in
a ubiquitous manner (Berson & Berson, 2006). In short, raised teacher self-efficacy levels will
positively impact students‘ capacity for learning.
As a result of this expanded knowledge base and increased ability to execute critical
thinking skills students of highly efficacious teachers will likely be more marketable in the ever
changing (Friedman, 2006) and increasingly complex STEM fields upon completion of, and as a
result of, their technologically rich educational experiences (Appleton & Lawrenz, 2011; Merrill
& Daugherty, 2010). They will have an improved likelihood of possessing the high order
thinking skills required by the complex nature of the fields (Jamison, 2008; Smolin & Lawless,
2011) and as a result, be equipped for the competitive market (Friedman, 2006; Jamison, 2008).
Implications for teachers. Finally, and most importantly, there are implications for
teachers found in the results of this study. The first implication to discuss is the likely increase
in highly efficacious teachers. Once professional development designers leverage the findings of
this study and Bandura‘s (1977) own findings regarding the singular importance of mastery
experience, they will understand how to best support teachers, creating opportunities for positive
and successful mastery experiences. Teacher confidence will most assuredly grow with
increased frequency of use (Albion and Ertmer, 2002). In accordance with Bandura‘s (1997)
58
statement regarding the domain-specific nature of self-efficacy, as technological confidence
grows, technological self-efficacy will grow.
Ancillary implications born of this increased self-efficacy among teachers as suggested
by the existing body of literature may include higher degrees of integration (Brinkerhoff, 2006;
Harrison et al., 1997; Hill, Smith, & Mann, 1987), more engaging instruction (Appleton &
Lawrenz, 2011; Brunvand & Byrd, 2011; Casey & Jones, 2011; Errey & Wood, 2011; Jackson et
al., 2011; Terrion & Aceti, 2012; Willis, 2011) and increased student ownership (Moersch,
2009). This will, in turn, enable teachers to work with these digital native students in a way that
they can relate to as suggested by Prensky (2005) and allow for an increase in achievement
(Chen & Looi, 2011; Danielson, 2007; King-Sears & Evmenova, 2007; Kukulska-Hulme, 2009;
Reid & Solomonides, 2007; Stuber, 2007; Willis, 2011).
As mastery experiences are provided and teacher self-efficacy is raised, the level of
integration in the classroom will likely be correspondingly raised. Because teachers are the
primary decision makers with regard to the classroom archetype (Albion, 2001; Okojie,
Olinzock, & Okojie-Boulder, 2006) and because teachers base classroom setup and technological
decisions largely on their own level of comfort (Barnyak & McNelly, 2009), avoiding those
techniques that they are less comfortable with (Bandura, 1977), higher comfort levels and higher
self-efficacy levels among teachers will result in more technologically advanced classrooms.
The more they integrate and experience, the higher their self-efficacy level will become.
As teacher efficacy levels are raised, their classroom archetypes may change. Moersch‘s
(2009) levels of innovation indicate increasing ownership of the classroom environment and an
evolving teacher role toward that of facilitator as technology integration in the classroom
increases. This idea of teacher as facilitator marks a paradigm shift of thought for many teachers
59
(Brunvand & Byrd, 2011; Danielson, 2007; Wang & Reeves, 2004) and as a result, a potentially
major implication. With the teachers‘ raised levels of efficacy and increased technological
inclusion, students in their classes are able to more fully sustain engagement and take their
learning to new and more robust levels thus creating opportunities for teachers to extend the
boundaries of their typical classroom (Brill & Park, 2008).
Limitations
There are limitations to this study. Some are inherent and some are incidental. The
primary limitation lies in the fact that the data produced within the instruments was self-reported.
That is to say that each respondent was asked to accurately and honestly rate themselves, in
every instance, according to his or her own belief system. Even though the intent was to
minimize this limitation through the guarantee of anonymity, slightly skewed data is a possibility
due to participants‘ bias, degree of self-knowledge, and perception of the constructs being
measured (Warren, 2010).
Another limitation of this study is the fact that it was conducted in a single school district.
With varying policies, budgets, and cultures characterizing the national landscape, geographic
bias or control may be a factor in the responses received.
An additional limitation exists in the fact that this study was conducted electronically.
By its very nature, the study results may actually be skewed by the vehicle through which it is
conducted. Even though an incentive with universal appeal was offered to those participating,
those teachers with some sort of resistance to technology may have balked at participating due to
the fact that the study was conducted via technological means. Additionally, there was an
unrelated and untimely email restriction affecting dissemination within the district that went
undiscovered for an indeterminate amount of time within the survey window. It is impossible to
60
fully realize the limitations to this study caused by this incidental event even though an ample
sample size was still attained.
Two final limiting factors to address are based on the fact that the study data was
analyzed through multiple regression analysis. First, it is limited by the chosen instrumentation
and the strictest definitions of variable types of measurements. Second, the use of a non-
experimental design is a limitation.
Many researchers, according to Warner (2008), claim that statistical measurement types,
by their strictest definitions, limit the statistical operations available, and there are legitimate
reasons to expand the applications to those data resultants that fall somewhere between the
definitions. The LoTi instrument utilized in this study produces just such data (H. Brown,
personal communication, October 3, 2012). The researcher employed triangulation using two
separate correlation procedures to offset this possible limitation.
Additionally, an investigation into causal factors is not exemplary using this method due
to the fact that it is a non-experimental design (Warner, 2008). Despite predictor variables very
often pointing toward causality, the outcomes found in this study provide no absolute proof that
any of the sources of self-efficacy are causal factors in a teacher‘s level of technology in the
classroom. They are merely assessed as to their contributions upon the variance in, and their
correlations with, the dependent variable, in this case the Level of Innovation of the classroom
teacher. In other words, in a non-experimental design the independent variables can be used to
significantly predict, rather than cause, outcomes when other predictor variables are controlled.
Recommendations for Further Study
Further study following the findings contained herein is certainly warranted. To provide
depth and an added richness to the quantitative findings, qualitative inquiry, likely conducted as
61
personal interview, may be considered. In effect, this second phase of the study would complete
an exploratory sequential mixed methods design as described by Creswell and Plano-Clark
(2011). Driving conceptual questions for individuals of both high and low technological self-
efficacy levels may include:
• To what degree do you believe technological integration into the classroom to be
important?
• Why are you comfortable with the technologies that you use frequently?
• What is your biggest fear with regard to new technology?
• What excites you about technology?
• What are the characteristics of effective instruction? What does it look like in the
classroom?
• When is change good? When is change bad?
• What techniques or strategies are most effective in making you comfortable with a
new technological tool?
Because mastery experiences, as defined by Bandura (1997), are found to be a significant
correlate and predictor of the degree to which instructors integrate technology as evidenced by
their Level of Innovation determined by Moersch‘s (2009) LoTi instrument, and because the
Levels of Innovation from the studied district indicate that the overwhelming majority (80.9%)
are at Levels zero through three, it stands to reason that further study should be conducted along
these veins specifically.
One avenue to be addressed certainly lies within the domain of success or failure.
Although one might be able to predict the degree to which a teacher technologically integrates
based on his or her mastery experiences, is there any change or difference in the degree of the
62
affect dependent upon whether or not the task experience in question is completed successfully?
Is the level of integration predicted more or less accurately with a successful end than with a
failure? And what specific practices within the district are contributing to the low Levels of
Innovation? Is there a lack of experiential opportunity? This concept leads to the next suggested
area of study.
Another possibility for follow-up study is in the realm of professional development as it
relates to mastery experiences for teachers. Common barriers acknowledged with regard to
technological integration often include time or budgetary concerns (Norris & Soloway, 2011).
What practices can professional development designers create to provide enactive mastery
experiences that are most time and cost efficient?
A final area of suggested study concerns differences among respondents with regard to
race and gender. Usher and Pajares (2006) suggest that efficacy predictors such as mastery
experience, vicarious experience, and verbal persuasion may be more relevant in one gender than
the other or in one ethnic group or another. In the technological context, this may be relevant for
academic professional development designers and as such, an area of possible exploration.
Chapter Summary
Bandura (1977) acknowledges that the contextual strength of a person‘s convictions or
beliefs in large part determines their actions. This study was conducted with the intent of
providing information and insight with regard to technological integration of classroom teachers
from the perspective of this self-efficacy and specifically, the inputs upon it. The results indicate
that mastery experience is the only significant predictor of teachers‘ classroom technology
integration among Bandura‘s (1997) four noted sources of self-efficacy. A small portion of the
variance in the Level of Innovation, or level of technological integration, can be attributed to the
63
combined effects of mastery experience, vicarious experience, and verbal persuasion. Included
in this chapter are a summary of the study, limitations of the study, implications of the findings
for professional development designers, schools and school districts, students, and teachers. The
chapter concludes with avenues of further study born of the researcher‘s results and other noted
research determined to be ancillary or tangential to the scope of this study.
64
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APPENDIXES
77
APPENDIX A
ELECTRONIC INFORMED CONSENT
78
Electronic Informed Consent
The purpose of this research is to gain insight into factors contributing to the technological self-
efficacy levels of teachers. I am asking secondary-level teachers of all technological ability
levels to complete this electronic survey. More specifically, you will be asked to rate your
confidence level in given technological tasks. The potential benefit of this study is improved and
more complete research for foundational professional development practices with regard to
technological integration and, as a result, more highly efficacious teachers. There are no potential
risks in participating in this study, physical or otherwise. The survey will take about 15 minutes
to complete. Your responses will be automatically compiled in a spreadsheet and cannot be
linked to you. All data will be stored in a password protected electronic format. The results of the
study will be used for scholarly purposes only. By selecting the "Yes" button in the ―Informed
Consent Acknowledgement‖ item below you acknowledge that you have read this information
and agree to participate in this research. You are free to withdraw your participation at any time
without penalty. If you have any questions, feel free to contact me at [email protected].
Informed Consent Acknowledgement *
O Yes. I have read the informed consent information and agree to participate in the
research.
O No. I do not agree to participate in the research.
79
APPENDIX B
DEMOGRAPHIC SURVEY
80
Demographic Survey
What is your gender?
O Male
O Female
What is your age (in years)? _____
How long have you been a teacher (in years)? _____
Do you wish to receive a copy of the results of this study?
O Yes
O No
Do you wish to be entered in the random drawing for a $500 Visa gift card?
O Yes
O No
If you answered "yes" to either of the previous two questions (study results or random drawing),
please type your e-mail address into the space provided below.
__________________________________________
81
APPENDIX C
SOURCES OF SELF-EFFICACY INSTRUMENT
82
Sources of Self-efficacy Instrument (SoSEI)
MASTERY EXPERIENCE - Technological Device
Use the scale below each item to rate how sure you are that you can use a technological
device in the given situations. Please select the answer that corresponds to your confidence
level. You are not identifiable by these responses.
1. Use a technological device when I’ve successfully used the device before with no
difficulty. Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
2. Use a technological device when I’ve successfully used the device before with some
difficulty.
Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
3. Use a technological device when I’ve been unsuccessful in all previous attempts to use the
device.
Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
83
VICARIOUS EXPERIENCE - Technological Device
Use the scale below each item to rate how sure you are that you can use a technological device in
the given situations. Please select the answer that corresponds to your confidence level. You are
not identifiable by these responses.
4. Use a technological device when I’ve watched someone use the device with no difficulty,
but have never attempted to use the device myself or been told that I was capable of using
the device.
Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
5. Use a technological device when I’ve watched someone use the device with some
difficulty, but have never attempted to use the device myself or been told that I was capable
of using the device.
Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
6. Use a technological device when I’ve watched someone attempt to use the device
unsuccessfully and have never attempted to use the device myself or been told that I was
capable of using the device.
Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
84
VERBAL PERSUASION - Technological Device
Use the scale below each item to rate how sure you are that you can use a technological device in
the given situations. Please select the answer that corresponds to your confidence level. You are
not identifiable by these responses.
7. Use a technological device when I’ve been told that I am capable and would have no
difficulty, but have never attempted to use the device myself or watched anyone attempt to
use the device. Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
8. Use a technological device when I’ve been told that I am capable even though it would be
difficult, but have never attempted to use the device myself or watched anyone attempt to
use the device. Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
9. Use a technological device when I’ve been told that I am not capable of using the device
and have never attempted to use the device myself or watched anyone attempt to use the
device. Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
85
PHYSICAL/AFFECTIVE STATE - Technological Device
Use the scale below each item to rate how sure you are that you can use a technological device in
the given situations. Please select the answer that corresponds to your confidence level. You are
not identifiable by these responses.
10. Use a technological device when I’m feeling energized and cheerful.
Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
11. Use a technological device when I’m feeling physically and emotionally normal. Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
12. Use a technological device when I’m feeling fatigued and stressed. Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
86
MASTERY EXPERIENCE - Software
Use the scale below each item to rate how sure you are that you can use software in the given
situations. Please select the answer that corresponds to your confidence level. You are not
identifiable by these responses.
13. Use software when I’ve successfully used the software before with no difficulty.
Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
14. Use software when I’ve successfully used the software before with some difficulty. Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
15. Use software when I’ve been unsuccessful in all previous attempts to use the software. Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
87
VICARIOUS EXPERIENCE - Software
Use the scale below each item to rate how sure you are that you can use software in the given
situations. Please select the answer that corresponds to your confidence level. You are not
identifiable by these responses.
16. Use software when I’ve watched someone use the software with no difficulty, but have
never attempted to use the software myself or been told that I was capable of using the
software. Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
17. Use software when I’ve watched someone use the software with some difficulty, but
have never attempted to use the software myself or been told that I was capable of using
the software.
Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
18. Use software when I’ve watched someone attempt to use the software unsuccessfully
and have never attempted to use the software myself or been told that I was capable of
using the software.
Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
88
VERBAL PERSUASION - Software
Use the scale below each item to rate how sure you are that you can use software in the given
situations. Please select the answer that corresponds to your confidence level. You are not
identifiable by these responses.
19. Use software when I’ve been told that I am capable and would have no difficulty, but
have never attempted to use the software myself or watched anyone attempt to use the
software.
Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
20. Use software when I’ve been told that I am capable even though it would be difficult,
but have never attempted to use the software myself or watched anyone attempt to use the
software.
Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
21. Use software when I’ve been told that I am not capable of using the software and have
never attempted to use the software myself or watched anyone attempt to use the software. Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
89
PHYSICAL/AFFECTIVE STATE - Software
Use the scale below each item to rate how sure you are that you can use software in the given
situations. Please select the answer that corresponds to your confidence level. You are not
identifiable by these responses.
22. Use software when I’m feeling energized and cheerful.
Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
23. Use software when I’m feeling physically and emotionally normal.
Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
24. Use software when I’m feeling fatigued and stressed. Rate your level of confidence.
0 1 2 3 4 5 6 7 8 9 10
Cannot do
at all
Certain can do
90
APPENDIX D
LOTI DIGITAL AGE SURVEY FOR TEACHERS
91
92
93
94
95
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