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One Hawai’i K-12 complex public school teachers’ level of computer self-efficacy and their acceptance of and integration of technology in the classroom

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One Hawai’i K-12 complex public school teachers’ level of computer self-efficacy and their acceptance of and integration of technology in the classroom

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Content Running head: TECHNOLOGY IN THE CLASSROOM 1

ONE HAWAI’I K-12 COMPLEX PUBLIC SCHOOL TEACHERS’ LEVEL OF COMPUTER
SELF-EFFICACY AND THEIR ACCEPTANCE OF AND INTEGRATION OF
TECHNOLOGY IN THE CLASSROOM

by

Devin Takashi Oshiro

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

May 2014

Copyright 2014 Devin Takashi Oshiro
TECHNOLOGY IN THE CLASSROOM 2

Dedication
This dissertation is dedicated to my family for always stressing the importance of
education, believing in me and for keeping me grounded throughout the program.
To my grandparents, I wish all of you were here to celebrate with me as your love and
support have played such a huge role in my life.
Finally, to my wife, Kim, for all of the time, sacrifice and support you have shown me
over the past three years, I cannot imagine sharing this achievement with anyone else.

TECHNOLOGY IN THE CLASSROOM 3

Acknowledgements
Thank you to my committee chair, Dr. Brandon Martinez who literally walked me
through the steps of my dissertation and always reassured me I would make it. For all of the
“uku in the school jokes” and good times eating malasadas, thank you. Go Niners!
I would also like to thank the second chair of my committee, Dr. John Pascarella for
keeping me focused on what to look for through out my study.
To my third chair, Dr. Teri Ushijima, thank you for your patience helping me navigate
my research application and kindly editing my drafts. You have been such a great role model
and I hope that someday I will achieve your level of excellence.
To my friend Carey and the Grana ‘Ohana: thank you for always keeping things in
perspective and fighting the good fight.
Rich, thank you for showing me the road for life after the MEdT Program. Thank you
Cristy for the unconditional support and for being proof that attitude is more important then
facts.
Finally, to my writing partners, Scott and Glenn: you have both helped me through some
dark nights and seemingly insurmountable odds. I can never thank you enough for all of your
support, and I look forward to many more years of your friendship.
TECHNOLOGY IN THE CLASSROOM 4

Table of Contents
Dedication 2
Acknowledgements 3
List of Tables 6
List of Figures 7
Abstract 8
Chapter One: The Problem and its Underlying Framework 10
Background of the Problem 10
Purpose of the study 16
Research Questions 19
Significance of the Study 19
Assumptions 21
Limitations 21
Delimitations 22
Methodology 22
Definition of Terms 23
Chapter Two: Literature Review 25
Social Cognitive Theory 26
Computer Self-Efficacy and the Impact of SCT 31
Anxiety 34
Technology Acceptance 37
Integration 40
Implications for Computer Self-Efficacy 46
21
st
Century Skills 47
Conclusion 48
Chapter Three: Methodology 50
Research Questions 50
Research Design 51
Population and Sample 52
Instrumentation 55
Data Collection 57
Data Analysis 58
Conclusion 59
Chapter Four: Results 60
Participant Characteristics 60
Findings 63
Frequency of Subscales 64
Findings for the First Research Question 71
Findings for the Second Research Question 73
Findings for the Third Research Question 75
Findings for the Fourth Research Question 77
Predictability of Acceptance 79
Conclusion 81

TECHNOLOGY IN THE CLASSROOM 5

Chapter Five: Conclusions 83
Summary of Findings 85
Conclusions for the First Research Question 85
Conclusions for the Second Research Question 87
Conclusions for the Third Research Question 89
Conclusions for the Fourth Research Question 90
Implications for a Further Study 91
Conclusion 93
References 97
Appendix A 113
Appendix B 120
Appendix C 122
Appendix D 124

TECHNOLOGY IN THE CLASSROOM 6

List of Tables
Table 1: Hawai’i K-12 Complex Teacher Demographics 53
Table 2: K-12 Survey Participants 61
Table 3: Breakdown of Participants by School 62
Table 4: Breakdown of Respondents by Content Area 62
Table 5: Reliability of Efficacy Subscales 64
Table 6: Correlation Analysis of Teachers’ CSE, TAM, PU and PEOU 72
Table 7: Correlation Analysis of Teachers’ CSE and Technology Integration 73
Table 8: Correlation Analysis of STEM Teachers’ CSE, TAM, PU and PEOU 74
Table 9: Correlation Analysis of STEM Teachers’ CSE and Technology Integration 75
Table 10: Correlation Analysis of Humanities Teachers’ CSE, TAM, PU and PEOU 76
Table 11: Correlation Analysis of Humanities Teachers’ CSE and Technology Integration 77
Table 12: Correlation Analysis of Elementary Teachers’ CSE, TAM, PU and PEOU 78
Table 13: Correlation Analysis of Elementary Teachers’ CSE and Technology Integration 79
Table 14: ANOVA of I.V. as a Predictor of Technology Acceptance 79
Table 15: Series of ANOVA Testing Various I.V. as Predictors of Technology Acceptance 80

TECHNOLOGY IN THE CLASSROOM 7

List of Figures
Figure 1: Workforce Readiness (Eisen, Jasinowski & Kleinery, 2005) 11
Figure 2: Albert Bandura’s Social Cognitive Theory Model 27
Figure 3: Technology Acceptance Model (TAM) proposed by Davis (1989) 38
Figure 4: Breakdown of Respondents by Gender 61
Figure 5: Overall Frequency of Responses for Computer Self-Efficacy 65
Figure 6: Frequency of Each Question Relating to CSE 66
Figure 7: Overall Frequency of Responses for Technology Acceptance 67
Figure 8: Frequency Responses for Questions Pertaining to PU 68
Figure 9: Frequency of Responses for Questions Pertaining to PEOU 69
Figure 10: Overall Frequency of Responses to Technology Integration 70
Figure 11: Frequency of Responses for Questions Pertaining to Technology Integration 71

TECHNOLOGY IN THE CLASSROOM 8

Abstract
This study sought to determine what the relationship was between K-12 public school
teachers’ level of computer self-efficacy and their acceptance of and integration of technology in
the classroom in one complex in the state of Hawai`i. Computer Self-Efficacy (CSE) as defined
by Karsten, Mitra & Schmidt (2012), is “an individual’s perception of efficacy in performing
specific computer related tasks within the domain of general computing” (p. 54). Computer self-
efficacy is important to the educational field because it has been found to impose a significant
influence on an individual’s ability to complete a task using computer hardware/software (Shu,
Tu & Wang, 2011). Currently, a large number of teachers experience some level of computer
anxiety or anger when faced with the opportunity or requirement to use these tools. These
negative attitudes affect teachers’ beliefs and willingness to integrate technology in their
classrooms (Wilfong, 2004). The reality is that students living in today’s society lead high-tech
lives outside school and decidedly low-tech lives inside school. This new ‘digital divide’ has
made activities inside school appear to have less real world relevance. The challenge for our
educational system is to use modern technology to create engaging and relevant learning
experiences that mimic the technology that has become a ubiquitous way of life.
Participants were emailed survey links with questions dealing with Computer Self-
Efficacy, Technology Acceptance and Technology Integration. Results indicated statistically
significant findings related to the four research questions explored in this study. Overall, K-12
teachers CSE, technology acceptance, and willingness to integrate technology was found to be
statistically significant, yet the correlation was too weak to make it a significant predictor.
Science, Technology, Engineering and Math (STEM) teachers reported a negative correlation
between Computer Self-Efficacy and technology acceptance and no significance on technology
TECHNOLOGY IN THE CLASSROOM 9

integration. Humanities teachers reported no statistical correlation between Computer Self-
Efficacy and technology acceptance, yet a strong and significant correlation was found between
CSE and technology integration. Finally, elementary school teachers reported a strong and
statistically significant correlation between Computer Self-Efficacy, technology acceptance and
technology integration.
Results of the study provide a basis for complex administrators, but also require a need
for further studies that will add to the current body of knowledge concerning Computer Self-
Efficacy and its relationship with technology acceptance and willingness to integrate technology
in the classroom.

TECHNOLOGY IN THE CLASSROOM 10

CHAPTER ONE: THE PROBLEM AND ITS UNDERLYING FRAMEWORK
This study examines the level of K-12 teachers’ Computer Self-Efficacy (CSE) and how
CSE affects the acceptance and integration of technology in classrooms. CSE is derived from
Bandura’s (1986) Social Cognitive Theory (SCT) in the specific area of self-efficacy. Many
researchers have demonstrated a strong positive link between high levels of CSE and employee
participation, production, and persistence with technology in the workplace (Celik & Yesilyurt,
2013; Chien, 2012; Compeau & Higgins, 1995; Karsten, Mitra & Schmidt, 2012) as well as
determining that CSE is a common factor when determining the acceptance of using technology
(Davis, 1989; 1993; Koufaris, 2002; Yanik, 2010) and integrating technology in the classroom
(Abrini, 2006; Bauer & Kenton, 2005; Ertmer, et al. 2012; Summak et al. 2010). This study
investigates the extent to which a teachers’ CSE affects his/her attitude and integration levels of
technology into the classroom.
Background of the Problem
The American high school diploma is a broken promise that does not reflect adequate
preparation for the intellectual demands needed for postsecondary and employment skills.
According to the American Diploma Project (ADP, 2005) 90% of eighth graders expect to
participate in some form of post secondary education while nearly two-thirds of parents consider
college a necessity for their children. However, only 70% of high school graduates enter a two
or four-year college and 53% of those students take remedial math or English at least once in
his/her college career. When asked whether K-12 schools are preparing students for a good job,
84% of respondents in a study by Eisen, Jasinowski & Kleinert (2005) responded “no.” The
graph below shows how responses have changed from 1997-2005. When employers were asked
to elaborate on the top areas public school students are lacking, the most frequently cited were:
TECHNOLOGY IN THE CLASSROOM 11

reading/comprehension at 38%, math and science knowledge at 51% and basic employability
skills (attendance, timeliness, work ethic, etc.) at 55% (Eisen, Jasinowski & Kleinert, 2005).

Figure 1. Workforce Readiness (Eisen, Jasinowski & Kleinery, 2005)
According to Wagner (2008) high school graduation rates in the U.S. (70%) are
significantly behind countries such as Denmark (96%), Japan (93%), and Italy (79%). The
global economy is becoming more interconnected as the world pushes further ahead into the 21st
century. The “achievement gap” that exists between children from middle- to upper-income
families and minority children from low-income families has never been higher (Wagner, 2008).
The U.S. Department of Education (DOE) (2010), under the leadership of the Obama
Administration recognizes the need for students to develop 21
st
century skills in the school
setting. 21
st
century skills are defined as having expertise in critical thinking, complex problem
solving, collaboration and multimedia communication throughout all content areas (U.S.
Department of Education, 2010). According to Porter et al. (2011) the Common Core State
Standards (CCSS) are explicit in their focus on the skills students need to learn, which includes
21
st
century skills by using technology that has been integrated into the school setting. In 2008,
$680 billion dollars, which is 4.5% of the Gross Domestic Product of the U.S., was spent on
public education in the United States (Odden & Picus, 2008). Furthermore, the U.S. DOE’s
TECHNOLOGY IN THE CLASSROOM 12

Enhancing Education Through Technology (EETT) program, which is part of the No Child Left
Behind Act of 2001 (NCLB) allocated $3.4 billion from 2002-2008 to improve student
achievement through the use of educational technology (U.S. Department of Education, 2009).
Yet, many educators, leaders, and policy makers do not have the same understanding and ease of
use with technology as other professional sectors (U.S. Department of Education, 2010). In fact,
the U.S. Department of Education (2010) reported that only 27 out of 50 states had defined
standards for teacher technology competency, and 26 out of 50 states fully integrated technology
(where students and teachers have access to computer hardware and internet connection) into
their schools.
The National Education Technology Plan 2010 (NETP) intends to implement technology-
based learning and assessment systems that will be pivotal in improving student learning and
generating data that will continually improve education. The NETP also encourages teachers to
incorporate technology as a way of collaborating with other educators in order to combine
strategies for best teaching practices that better prepare and enhance students’ competencies and
expertise through out their schooling years (U.S. Department of Education, 2010). The
challenge for districts is to create lessons, projects, and experiences using technology that
engages students and mirrors the skills he/she will need to learn to be proficient in the work
force. The NETP created goals to promote 21
st
century skills such as: critical thinking, complex
problem solving, collaboration, and multimedia communication (U.S. Department of Education,
2010).
The NETP presented five goals that states should address: learning, assessment,
teaching, infrastructure, and productivity. The first goal is about learning, being able to engage
and empower. According to the U.S. Department of Education (2010), “learners will have
TECHNOLOGY IN THE CLASSROOM 13

engaging and empowering learning experiences both in and out of school that prepare them to be
active, creative, knowledgeable, and ethical participants in our globally networked society” (pg.
16). This means that states should use technology to teach 21
st
century skills to students,
specifically in the Science, Technology, Engineering and Mathematics (STEM) areas. Goal
number two will leverage the power of technology to measure specific areas of learning and use
assessment data to drive classroom instruction (U.S. Department of Education, 2010). Goal two
recommends states develop assessments that provide timely feedback in order to improve student
learning, use technology to improve formative and summative assessment materials and ensure
students have a way to track, monitor and access his/her personal academic records. Goal three
supports teachers by providing connections to other educators using technology, access to
technology-based content when and where teachers want it, the use of social media to create
communities of educators and learners, provide online access for teachers to continue their
professional development through an online forum and, finally, to create a teacher workforce that
is skilled in online instruction. Goal four tackles infrastructure and recommends that states
ensure students have access to the internet in school, teachers and students have at least one
technological device that can connect to the internet, and enable stable, financial decisions to
sustain a network for students and teachers to be a part of. The fifth goal urges states to increase
their productivity, to improve learning while making more efficient use of money, time and staff
(U.S. Department of Education, 2010). To meet these goals, states are being asked to improve
policies by making them more meaningful, and more conducive to a modern, and advanced
technological school setting. Although the number of educators and national education leaders
who promote the use of technology in schools as a means to improve educational outcomes has
TECHNOLOGY IN THE CLASSROOM 14

increased, the reality exists that technology has not lived up to its full potential in schools (Hew
& Brush, 2007; Summak, Samancioglu & Baglibel, 2010; Teo, 2011).
Technology has been used to enhance teaching and learning, since at least the early years
of the industrial revolution; and since that time, there have been numerous attempts to use
different types of technology in hopes of advancing the roles of teachers, and ultimately improve
student learning (Lee & Winzenried, 2009). Currently, computer technology has gone from a
mainframe era where large computers filled offices, proceeding to a desktop era where
computers fit on every desk in every office, and presently computer technology has become
ubiquitous, (Dourish & Bell, 2011) meaning that technology is embedded within every aspect of
our lives. During the industrial revolution access to people, information, and knowledge was
limited, especially in education by the technology that was being used, however, in the current
ubiquitous computing (ubicomp) era, access to people, information, and knowledge occurs in a
shorter time period. The point is that ubicomp is the fulcrum between the current state of
technology and the goals of meeting 21
st
century skills. Significant research (Albrini, 2006;
Bauer & Kenton, 2005; Dourish & Bell, 2011; Ertmer et al., 2011) has been done to illustrate
how the use of technology in the educational setting has not kept pace with the ubicomp era,
meaning people are using technology everyday for non-traditional educational activities, but
rarely is the same technology being used in schools. Developing the idea of “invisible”
computing, Mark Weiser wrote of a world where people would be computing without computers
(Greenfield, 2006). Weiser felt that computing would be something not confined to one thing or
one device, but would rather permeate itself in every aspect of our life. One can infer that
ubiquitous means not only in “every place,” but also in “every thing” (Greenfield, 2006). People
would be able to interact and manage systems fluently and naturally without a second thought as
TECHNOLOGY IN THE CLASSROOM 15

to the power and complexity of the systems on which they were working or within which they
were communicating. Transcending from the minds in research laboratories across the country,
computing and technology has forever changed the way we work, play, live, and in this case –
study, learn, and assess.
Rathbun and West (2003) report that 32% of children had access to a home computer in
1993, 65% of children had access to a home computer in 2000, and according to the U.S. DOE
(2009) 77% of children had access to a computer in 2009. Living in an era where computers
double as phones, Global Positioning Systems (GPS), cameras and MP3 players, users of
ubiquitous technology (ubicomp) need to have a proficient amount of knowledge and self-
efficacy to command such tools. Students living in a ubiquitous computing era find technology
embedded through out daily life and is making life more practical and extraordinary, where
mobile access to information and resources are available 24/7 (Dourish & Bell, 2011).
Technology allows students to create and consume multimedia content collaboratively and share
it worldwide, while also being able to use social networking in the collaboration of ideas and
knowledge.
Outside of the classroom students are able to pursue their passions in their own way, at
their own pace through online education. Technology provides opportunities that are limitless
and in order to better prepare students for the ubiquitous age of technology, public schools are
increasing student access to technology by providing more software, hardware, and connecting
more classrooms to the internet (Downey & Smith, 2011). Students are living in an ubicomp era
where outside of the classroom they are creating multimedia content to share with the world, yet
are struggling to be taught 21
st
century skills by teachers who have only a small understanding
about computer-based education. As technology becomes a more ubiquitous part of the 21
st

TECHNOLOGY IN THE CLASSROOM 16

century learning skills students will need to know as they enter the work force, educators should
agree that it is an integral part of students learning (Rivard, 2010). In their study Fiske and
Hammond (1997) reported that technology in education is a key to the quality of educational
experiences students will receive as schools enter the new millennium. As technology becomes
an ubiquitous piece in the life of a student, it only makes sense to ensure that technology in
schools is not only provided, but that it is supported, and more importantly, it is understood. The
Oregon Institute of Design (2007) states, “kids lead high-tech lives outside school and decidedly
low-tech lives inside school. This new ‘digital divide’ is making the activities inside school
appear to have less real world relevance to kids.” (p. 24). The challenge for our educational
system is to use modern technology to create engaging and relevant learning experiences that
mimic the technology that has become a ubiquitous way of life (U.S. Department of Education,
2010).
Purpose of the study
Each piece of technology requires a certain level of computer self-efficacy (CSE), which
as defined by Karsten, Mitra & Schmidt (2012), is “an individual’s perception of efficacy in
performing specific computer related tasks within the domain of general computing” (p. 54). In
other words, CSE represents a teacher’s individual perception of his/her ability to successfully
use computers/technology to accomplish a task. Computer self-efficacy is important to the
educational field because it has been found to impose a significant influence on an individual’s
ability to complete a task using computer hardware/software (Compeau & Higgins, 1995;
Downey & Smith, 2011; Holden & Rada, 2011; Shu, Tu & Wang, 2011; Wang, 2004). CSE is a
critical factor that affects teachers, as those with higher CSE exhibit better outcomes and a higher
level of CSE leads to more awareness that may influence behavioral decisions and personal
TECHNOLOGY IN THE CLASSROOM 17

feelings towards technology (Lim, 2007). However, a large number of teachers experience some
level of computer anxiety or anger when faced with the opportunity or requirement to use these
tools. These negative attitudes affect teachers’ beliefs and willingness to integrate technology in
their classrooms (Wilfong, 2004).
Teachers’ technology acceptance is based on his/her attitude towards technology and
according to Shakpa & Ferrari (2003), teachers project their beliefs and perceptions on to their
students, who then internalize and accept their teachers’ attitude toward technology as their own.
A teacher’s acceptance of technology is shaped by their CSE and attitude towards computer
based education. Wilfong (2006) found that CSE plays a significant role in predicting the level
of computer anger/anxiety a teacher will experience. Teachers who commonly experience
computer anxiety are not heavy computer users (Shakpa & Ferrari, 2003). Computer anxiety is
defined by Wilfong (2004) as “a negative emotional state and/or negative cognition experienced
by a person when he/she is using a computer or imagining future computer use” (p. 1002) and
affects about 25% of the population, which leads to lower use of computers in the classroom
(Wilfong, 2004). The anxiety/anger that these teachers experience make their attitude to accept
technology as part of their curriculum difficult to change and even harder to implement. Davis
(1989) created the Technology Acceptance Model (TAM) to help explain behavioral intentions
to use computers (Karsten, Mitra & Schmidt, 2012). TAM identifies the level of computer
anxiety a user experiences when tasked to work with a computer. Computer anxiety plays a key
role in determining whether or not teachers are willing to integrate technology in their
classrooms (Wilfong, 2004).
All of the upgrades in technology and the increased availability and support for
classroom computer use does not help the emaciated amount of teachers who have fully
TECHNOLOGY IN THE CLASSROOM 18

integrated computers into their teaching (Wang, Ertmer & Newby 2004). As far back as 1979,
researchers have noted that change is an evolving process that takes enormous amounts of time
(Dawson & Rakes, 2003). An emphasis was placed on the premise that change is unique and
highly personable to each individual and their own experience and that developmental growth in
both feelings and skill sets is required (Loucks & Hall, 1979 as cited in Dawson & Rakes, 2003).
According to the U.S. Department of Education (2010), 56% of districts met their state’s
definition of full technology integration in the school. Wang, Ertmer & Newby (2004) state that
teachers’ use of technology in the classroom are influenced by multiple factors such as access,
personal capabilities, and external constraints like time, equipment, and technical support.
According to Wang, Ertmer & Newby (2004) “Even if every first-order barrier were removed,
teachers would not automatically use technology to achieve the kind of meaningful outcomes
advocated” (p.231). Through technology integration in teaching and learning students will be
able to achieve more, the U.S. Department of Education (2010) identified that technology
integration can take many forms like computer based assessments, online grade books and
testing, computer-based lesson planning as well as computer-based portfolios. The bottom line
is that the technology leads to experiences that help students to learn better and faster.
Based on the Literature Review, CSE is an important factor in the ubiquitous age of
technology. CSE draws its conception from Bandura’s Social Cognitive Theory (SCT), more
specifically self-efficacy, and despite an extensive body of literature rooted in SCT and Self-
Efficacy (Bandura, 1977,1986,1997, Zimmerman, 2000), CSE is a rich area for future research
(Karsten, Mitra & Schmidt, 2012; Dourish & Bell, 2011). Using the TAM, the acceptance levels
and attitudes towards technology (which include perceived use and perceived ease of use)
anxiety and fear become discernable and the level of CSE a teacher has becomes apparent.
TECHNOLOGY IN THE CLASSROOM 19

The purpose of this study is to identify the relationship between teachers’ level of CSE and
their acceptance of and integration of technology in the classroom. The study will focus on the
responses submitted by teachers from one complex in grades K-12 identifying the perception of
factors that draw from studies that identify CSE (Murphy, Coover & Owen, 1989), acceptance of
technology (Legris, Ingham & Collerette, 2003) and technology integration (Wang, Ertmer &
Newby, 2004) by answering the following research questions.
Research Questions
1. What is the relationship between teachers’ Computer Self-Efficacy and their
acceptance and integration of technology into the classroom?
2. What is the relationship between STEM teachers’ Computer Self-Efficacy and their
acceptance and integration of technology into the classroom?
3. What is the relationship between Humanities teachers’ Computer Self-Efficacy and
their acceptance and integration of technology into the classroom?
4. What is the relationship between Elementary school teachers’ Computer Self-
Efficacy and their acceptance and integration of technology into the classroom?
Significance of the Study
Apple, Inc. completed a technology profile on a Hawai’i Department of Education, K-12
complex in July of 2012. The profile included an online self-assessment that reported on five
proficiency levels along the Evolution of Thought and Practice (ETAP). The levels are: Entry,
Adoption, Adaptation, Appropriation, and Innovation. The survey also reports on classroom
technology usage and practices in seven dimensions: General Technology Practices, Curriculum,
and Instruction, Assessment, Communication and Collaboration, Media, Productivity, and
Research. Among the key findings teachers in grades K-8 indicated that progress has been made
TECHNOLOGY IN THE CLASSROOM 20

in classroom application but varies from one dimension to the next. The highest scores were on
the productivity dimension, where 16 teachers reported at the Appropriation and Innovation
stages. Also, nine teachers reported in the research dimension that they were in the appropriation
and innovation phases. The lowest scores were in the General Practice and Media dimensions
where no teachers reported that they were in the appropriation and innovation phases. A
dominant finding indicates that technology integration is the most effective in the context of
inquiry-based classroom instruction. Apple’s profile concluded that teachers must assess
delivery methods in order to effectively integrate technology to improve student learning. In
other words, majority of the teachers in the complex do not use technology to its fullest
capabilities.
The results of the current study will help a K-12 complex in the state of Hawai’i identify
educators who express low levels of self-perception in regards to CSE, and have difficulty
accepting and integrating technology in their classrooms. Sub groups were created to extrapolate
information that will provide the administrative team with significant data on K-12 teachers
based on grade level and content area. Data could be used for future technology integration and
better technology professional development. Identifying the trends of teachers’ CSE is important
because according to relevant studies (Igbaria & Iivari, 1995; Shakpa & Ferrari, 2003; Wilfong,
2006), a teacher’s CSE is vital to their personal acceptance, attitude, anger and anxiety when
integrating computer based learning because students end up adopting those key areas as their
own level of CSE. According to the U.S. Department of Education (2010) the state of Hawai’i
reported in the school year 2005-2006 that 95% of its teachers are meeting the national
technology standards, 100% of the districts fully integrated technology in each of its schools, yet
only 80% of students in Hawai’i are meeting the national technology literacy standards. Hawai’i
TECHNOLOGY IN THE CLASSROOM 21

was also the only state to be in the top three for each category (Alabama was ranked number one
in two categories). There must be an explanation for the discrepancy between proficient teachers
and proficient students. If 100% of schools have integrated technology fully, why are teachers
for this particular K-12 complex self-reporting low to mediocre levels of technology in their
teaching practices?
With the new Common Core State Standards implementation under way and the
expectation by the Obama administration that all students will have mastered 21
st
century skills
upon graduation, it is important for this particular K-12 complex in Hawai’i to ensure educators
have the necessary levels of CSE, acceptance, attitude, and implementation skills to turn K-12
classrooms into high performing educational environments. Technology is a tool that plays a
key role in assuring that students are graduating with 21
st
century skills that prepare them for life
after graduation.
Assumptions
For purposes of this study, it will be assumed that teacher responses on survey questions
were voluntary and honest. Additionally, it was assumed that no prompting occurred and
surveys were given in a timely manner.
Limitations
Participation in this study was voluntary and relied on the willing participation and
honesty of K-12 teachers in a particular Hawai’i complex that was surveyed. As the author of
the survey I was not able to personally address the survey and its purpose to all teachers K-12,
therefore, principal explanations may have varied. Due to the inconvenience of travel and
accessibility, I was unable to personally interview teachers for additional data. Therefore, the
resulting data is based on self-reported perceptions and may not be generalizable among other
TECHNOLOGY IN THE CLASSROOM 22

complexes. Another limitation is that only one complex on the island of O’ahu was surveyed in
addition to the limited amount of teachers at each of the four elementary schools, one middle
school and one high school.
A third limitation was the delayed timeline for this study. A research application was
submitted to the Hawai’i Department of Education in August of 2013, but due to policy the
application was not reviewed until January of 2014 and policy held up approval until February
2014. The lengthy delay and crunched timeline may have altered the state of mind teachers felt
when participating in the study. Finally, the self-reported perceptions from teachers were the
main source of data collected and contractual issues would have prevented the ability to observe
teachers accepting and implementing technology in the classroom. This study simply considered
the self-reported perceptions of K-12teachers’ relationship between CSE and the attitudes
towards accepting and integrating technology in the classroom.
Delimitations
This study was limited by the amount of time and manpower available to distribute
surveys. Also, in the survey instrument used to gather data on K-12 teachers’ CSE the questions
(7) dealing with CSE were taken from three subscales in the original instrument. The questions
used were the most similarly worded to what I was looking to address in my study.
Methodology
This study consisted of online surveys answered by K-12 teachers. The teachers within
one Hawai’i, K-12 Complex included in this study were emailed by their respective principals
with a link to the online survey. The study was created to gather information about K-12
teachers’ present levels of CSE, technology acceptance levels and willingness to integrate
technology into the classroom. Subgroups were created based on content and grade level to
TECHNOLOGY IN THE CLASSROOM 23

provide the administrative team with segregated data that would help in future technology
professional development and integration. A quantitative approach was taken and no interviews
were conducted so the data extrapolated were self-reported perceptions from K-12 teachers.
Participation in the survey was completely voluntary and did not affect any teaching
credentials, tenure, etc. If teachers agreed to participate in the study they would have clicked the
online survey link or pasted the link into the address bar of his/her browser and clicked I
AGREE on the first question of the survey. The survey should have taken 5-10 minutes and was
active for two weeks.
Definition of Terms
1. American Diploma Project (ADP): Government group that provides research on the
value of the American high school diploma by describing it in terms of English and
Mathematical skills graduates must master by the time they leave high school if they
want to succeed in post-secondary school or high-performance, high-growth jobs.
2. CSE: Computer Self-Efficacy is derived from Bandura’s (1986) work with Self-
Efficacy. CSE is defined by Karsten, Mitra & Schmidt (2012) as an individual’s
perception of efficacy in performing specific computer related tasks within the
domain of general computing. Chapter 2 provides a more complete description of
CGI.
3. Evolution of Thought and Practice (ETAP): Apple created a 5-part proficiency
instrument that measures teachers’ level of comfortability with technology in their
teaching practices. The levels are: Entry, Adoption, Adaptation, Appropriation, and
Innovation.
TECHNOLOGY IN THE CLASSROOM 24

4. National Education Technology Plan 2010 (NETP) intends to implement technology-
based learning and assessment systems that will be pivotal in improving student
learning and generating data that will continually improve education. NETP also
encourages teachers to incorporate technology as a means to collaborate with other
educators as a means of combining strategies for best teaching practices that better
prepare and enhance students’ competencies and expertise through out their schooling
years (U.S. Department of Education, 2010).
5. Self-Efficacy: Concept from Social Cognitive Theory (Bandura, 1986) in which Self-
Efficacy is related to an individual’s beliefs in their capabilities to achieve a given
level of success performing a certain task. Chapter two provides a more complete
description of Self-Efficacy.
6. Technology Acceptance Model (TAM): Model created by Davis (1989) to help
explain behavioral intentions to use computers. Chapter two provides a more
complete description of the TAM.
7. Technological Pedagogical Content Knowledge (TPACK). TPACK is a specialized
type of knowledge that supports technology integration through content (Harris and
Hofer, 2011). Mishra and Koehler (2006) have characterized TPACK as the multiple
intersections of a teacher’s knowledge of curriculum content, general pedagogies,
technology, and contextual learning that results from teachers’ current understanding
of curriculum, general pedagogy, technology and learning contexts.
The foundational components of TPACK according to Mishra and Koehler (2006)
are: (a) Technology knowledge (TK), (b) Pedagogical knowledge (PK), and Content
knowledge (CK).
TECHNOLOGY IN THE CLASSROOM 25

CHAPTER TWO: LITERATURE REVIEW
There exists agreement that the American high school diploma is a broken promise that
does not reflect adequate preparation for the intellectual demands needed for postsecondary and
employment skills (Eisen, Jasinowski & Kleinert, 2005; Karoly & Panis, 2004; Kay & Greenhill,
2011; Porter, et al., 2011). Extensive research has shown that in order for students to develop
21
st
century skills (critical thinking, complex problem solving, collaboration and multimedia
communication) in the school setting technology must be integrated (Eisen, Jasinowski &
Kleinert, 2005; Harris & Hofer, 2011; Porter et al., 2011, Young, Young & Shaker, 2012).
Current research has also shown that technology is ever-present and has increasingly become a
ubiquitous part of our daily fabric (Dourish & Bell, 2011; Downey & Smith, 2011; Holden &
Rada, 2011; Shu, Tu & Wang, 2011).
This chapter will provide a review of the literature based on a thematic approach
examining Bandura’s (1977) Social Cognitive Theory (SCT) and how SCT laid the foundation
for Computer Self Efficacy (CSE). Next, aspects of Davis’ (1989) Technology Acceptance
Model (TAM) will provide research that describes possible reasons teachers experience anxiety
and technostress when using technology. Finally, a detailed description of technology
integration theories will be presented, followed by a brief discussion about the importance of 21
st

century skills.
Detailing the importance of Computer Self-Efficacy (CSE) starts with a brief
examination of Bandura’s (1977) Social Cognitive (SCT). Self-Regulation is also a key concept
derived from SCT in which Zimmerman (1989, 2000, 2002 and 2008) described how self-
regulation is correlated with self-efficacy. Five core concepts of SCT will be discussed which
contribute to CSE, but for the purpose of this study the focus will be on the concepts of self-
TECHNOLOGY IN THE CLASSROOM 26

regulation and self-efficacy. Next will be a critique of CSE and how SCT contributes to the
importance of CSE. For example, various studies (Karsten, Mitra and Schmidt, 2012; Shapka
and Ferrari, 2003; and Wilfong, 2006) have yielded results that identify a relationship between
CSE and computer anxiety/anger and why a teacher’s CSE directly impacts his/her students’
feelings and attitudes with computer technology. The Technology Acceptance Model (TAM)
adds to the literature because teachers’ attitude towards technology is a basic determinant on how
high his/her CSE will be (Davis, 1989). Literature on technology integration will contribute to
the growing body of research by illustrating how measures like CSE and TAM affect whether or
not technology is integrated into schools. Finally, the implications for CSE will be shared and
recommendations of where the research can go and the impact 21
st
century skills have on
students.
Social Cognitive Theory
Social Cognitive Theory (SCT) is a psychological model of behavior rooted in the work
of Albert Bandura. Bandura (1977) emphasized that SCT focuses on the idea that learning
occurs in a social context and the majority of what individuals learn is gained through
observations. SCT occurs in triadic reciprocity between personal, behavioral, and environmental
factors in a bidirectional, reciprocal fashion (Bandura, 1977). Therefore, personal, behavioral
and environmental factors form a triad and each factor influences one another. The figure below
illustrates the basic assumption of SCT.
TECHNOLOGY IN THE CLASSROOM 27

Figure 2: Albert Bandura’s Social Cognitive Theory Model
Bandura (1986) captured the essence of the SCT model in his statement “behavior is,
therefore, a product of both self-generated and external sources of influence” (p. 454). Bandura
also cautioned that the reciprocity does not mean symmetry of strength but that environmental
influences may be stronger than behavioral or personal ones. One impression about SCT is that
people possess the ability to influence their own behavior and environment in a purposeful, goal-
oriented way (Bandura, 2001). Therefore, individuals alone have the ability to self-reflect and
regulate behaviors that influence outcomes and environments. Another perception within SCT is
that learning may occur without behavior changing immediately (Bandura, 2001). Learning
therefore, involves not only the acquisition of new behaviors but also cognitive skills, concepts,
knowledge, values and rules over time.
There are five central concepts that are integrated into SCT: 1) Observational
Learning/Modeling, 2) Outcome Expectations, 3) Perceived Self-Efficacy, 4) Goal Setting, and
5) Self-Regulation. The five concepts serve as a framework for understanding human
functioning (Bandura, 2001.)
TECHNOLOGY IN THE CLASSROOM 28

Observational Learning/Modeling is at the core of SCT, which is that people learn
through observation. Individuals learn as a result of watching the behavioral consequences of
models in the environment. According to Bandura, (1977) observational learning takes place
when an individual attends to a model’s behavior, retains the observation in a symbolic form,
processes the observation and finally engages in the new skill learned. Modeling is important
because it helps individuals understand when or why previous behaviors are exhibited.
Outcome expectations help guide individuals to reflect on what consequences will occur
if a particular behavior is performed. Bandura, (2001) notes that the importance of outcome
expectations is a result of the decisions people make about what actions are acceptable and what
actions should be suppressed. The frequency of behaviors will increase when the projected
outcome is valued, and the frequency of behaviors will be avoided when the believed outcome is
unfavorable. An individual’s outlook on the projected outcome is derived from perceived self-
efficacy. In a study by Schunk (1984) high school students who mastered arithmetic division
problems showed an increase in his/her self-efficacy of 140%, a 22% increase in learning rate
and a 160% increase in division skills.
Perceived self-efficacy relates to an individual’s belief in their capabilities to achieve a
given level of success performing a certain task (Bandura, 1997). Parajes, (1996) found that
individuals that possess a higher self-efficacy are more confident in their abilities to achieve
success when compared to peers who have lower self-efficacy. Therefore, self-efficacy plays a
significant role in shaping behavioral expectations because individuals who are more confident
in their abilities tend be more successful. Also, individuals who believe they have a high level of
self-efficacy exhibit higher levels of persistence and more effective strategies when confronted
with a challenge (Parajes, 1996). Not only do individuals succeed more, but persist more when
TECHNOLOGY IN THE CLASSROOM 29

faced with issues that challenge personal self-efficacy notions. Past performance has also been
linked to perceived self-efficacy (Bandura, 1997) therefore, individuals who consistently reach
their goals will attain a higher level of self-efficacy versus an individual who does not attain their
goals. In sum, individuals with high levels of self-efficacy increase his/her confidence, persist
with greater frequency when met with challenging confrontations and have a higher success rate
then individuals with low levels of self-efficacy.
Goal setting reflects a desired, anticipated outcome brought out through forethought, an
envisioning of the future and generating plans of action (Bandura, 1986). Goal setting has a
direct correlation to perceived self-efficacy and outcome expectations because the outcomes
individuals expect from engaging in certain behaviors and the confidence received from attaining
their goals increases an individual’s self-efficacy. Individuals with high levels of self-efficacy
set higher, attainable goals more so than individuals with low levels of self-efficacy (Bandura,
1986). Goals are also a pre-requisite for self-regulation because goals provide standards that
individuals are trying to achieve and provide benchmarks to judge success (Bandura, 1986).
Self -regulation is the self-directive processes and individual beliefs that enable learners
to convert their mental abilities into high performance skills (Zimmerman, 1989). Thoresen &
Mahoney (1974) provide the foundation for self-regulation on the basis of a temporal gradient, in
which the primary concern was the impact of disparities the immediate and delayed responses on
behavioral functioning. Zimmerman (1989) separates himself by assuming a motivational
orientation towards learners that is sustained by a continuum of self-perceptions of efficacy when
performing a task. Self-regulated learning is like SCT in that learning takes place in a triadic
reciprocity influenced by environmental and behavioral events (Zimmerman, 1989). Learners’
self-regulation is viewed by Zimmerman (2008) as a proactive process that individuals use to
TECHNOLOGY IN THE CLASSROOM 30

acquire skill sets, deploy strategies and self-monitor one’s effectiveness rather than a reactive
agent experiencing an external force. Self-regulation is dependent on goal-setting because
individuals exhibit self-regulation when managing their actions and thoughts when attaining a
desired outcome (Schunk, 2001). Individuals monitor their own behaviors and outcomes while
evaluating whether their actions are conducive in the progression of their goals.
In general, learners are described as self-regulated to the degree he/she is
metacognitively, motivationally, and behaviorally active a participant is in his/her own learning
(Zimmerman, 1989). In other words, learners that are self-regulated do not depend on others
such as parents, teachers, or peers to direct his/her own efforts to acquire knowledge. According
to Zimmerman (1986), to qualify specifically as self-regulated learner individuals must “involve
the use of specified strategies to achieve academic goals on the basis of self-efficacy
perceptions” (p. 329). Zimmerman’s assumption relies on three elements: learners’ self-
regulated learning strategies, self-efficacy perceptions of performance skill and commitment to
learning goals. Self-regulated strategies are defined by Zimmerman (1989) as actions and
processes intended to acquire more information and/or skills through methods such as organizing
information, self-consequating (student arrangement of rewards or punishment for success and
failures), rehearsing, or using memory aids. Therefore, learners’ use of self-regulated learning
depends on knowledge, of strategies and decision-making processes and performance outcomes.
The self-efficacy element refers to the perception he/she has about one’s ability to organize and
implement actions necessary to attain a certain level of performance (Bandura, 1986). Self-
regulation is assumed to never be an absolute state of functioning, but varies according to the
social and physical context (Thoresen & Mahoney, 1974). In other words, a learner’s degree of
self-regulation is assumed to be determined situationally. For the purpose of this study the focus
TECHNOLOGY IN THE CLASSROOM 31

of the literature deals with the concepts of Self-regulation and Self-efficacy and its impact on
Computer Self-Efficacy (CSE).
Self-efficacy is domain specific and can therefore have an influence on self-regulation
(Zimmerman, 1989). SCT has also been applied to diverse contexts such as athletics,
organizational behavior, career choice, understanding classroom motivation, learning, and
achievement (Pajares, 1996). Research shows that self-efficacy plays an important role in the
self-regulation of motivation using technology (Bandura, et al., 2003). Most recently, SCT has
been used when explaining the significant influence computer self-efficacy has on a learner’s
performance using computers in the classroom (Compeau & Higgins, 1995).
Computer Self-Efficacy and the Impact of SCT
The first theoretical perspective to receive widespread recognition in the Information
Technology (IT) field was Fishbein and Ajzen’s (1975) Theory of Reasoned Action (TRA). The
TRA articulates that individuals would use computers today if he/she realized that there would
be positive benefits/outcomes associated with computers (Compeau & Higgins, 1995). While
TRA is still used in Information Studies (IS) literature, several researchers (Thompson et al,
1991; Webster and Martocchio, 1992) noted that additional explanatory research is needed. One
such variable comes from Bandura’s (1986) writing on SCT, specifically on the inner workings
of self-efficacy. Bandura (1986) believes that self-efficacy influences expected outcomes, and
the resilience of individuals when faced with adversity. In his 1978 study, Bandura notes that in
the realm of self-efficacy people can “give up because they seriously doubt that they can realize
the required level of performance” (p. 2). Perceived self-efficacy also affects the perceptions of
others in the social environment (Bandura, 1986). Self-efficacy judgments vary in three different
measures: level (difficulty of the task), generality (with certain domains and/or situations), and
TECHNOLOGY IN THE CLASSROOM 32

strength (ability to deliver appropriate coping techniques) (Bandura, 1977). CSE is rooted in
Bandura’s (1986) definition of self-efficacy: “People’s judgments of their capabilities to
organize and execute courses of action required to attain designated types of performances” (p.
391). Bandura (1986) goes on to say that “self-efficacy is not concerned with the skill one has,
but the judgments one has with whatever skills one possesses” (p.391).
The definition of self-efficacy (Bandura, 1986) indicates the importance of distinguishing
between individual skills and the ability to assess one’s abilities and execute courses of action
(Compeau & Higgins, 1995). CSE represents an individual’s perceptions of his/her ability to
accomplish tasks by using computers. According to Compeau and Higgins (1995) CSE was
found to exert a significant influence on individuals’ expectations of the outcomes using
computers. Therefore, CSE refers to the judgment an individual has in relation to his/her
capability to use a computer. Karsten, Mitra and Schmidt (2012) define CSE as “an individual’s
perception of efficacy in performing specific computer related tasks within the domain of general
computing” (p. 54). Therefore, CSE refers to the perceived notion of one’s capability to use a
computer and complete computer related tasks (Karsten, Mitra & Schmidt, 2012). CSE is not
concerned with the task an individual has done in the past, but is focused on the ability he/she
has when applying technology skills to broader tasks (Compeau & Higgins, 1995).
Three dimensions of CSE exist, according to Compeau and Higgins (1995), and were
further accredited by Karsten, Mitra and Schmidt (2012) in a meta-analysis. The three
dimensions are magnitude, strength and generalizability. Magnitude is interpreted to reflect an
individual’s level of capability expected. Individuals with a higher magnitude of CSE are able to
accomplish difficult computing tasks than those with a lower level of CSE and judge themselves
to be able to operate with less support than other with lower CSE. Strength of CSE refers to the
TECHNOLOGY IN THE CLASSROOM 33

confidence an individual has regarding the ability to successfully complete a task. Individuals
with high CSE strength perceive themselves as being able to handle more difficult tasks (high
magnitude) and display a higher level of confidence while performing the given task. Finally,
generalizability refers to an individual’s ability to generalize across domains. With the vast
amount of software configurations and hardware to choose from individuals with high CSE
generally expect themselves to be proficient with different software configurations and systems,
while individuals with low CSE would perceive his/her capabilities limited to varied
software/hardware (Compeau & Higgins, 1995; Karsten, Mitra & Schmidt, 2012).
Research has been completed validating measures of CSE in disciplines such as
education (Ekizoglu and Ozcinar, 2010; Shakpa and Ferrari, 2003), healthcare (Henderson, Dean
and Ward, 1995), business (Karsten and Schmidt, 2008), computer training (Downey and Smith,
2011; Hasan, 2006), among many others. Gender and age also have been found to impact
individuals’ CSE (Reed, Doty & May, 2005; Durndell & Haag, 2002). In relation to Internet
usage and skill, Durndell and Haag (2002) found in their study of 74 males and 76 females in a
Romanian University that males tend to report higher levels of CSE, lower levels of anxiety, and
more positive attitudes with Internet usage. Doty, Reed and May (2005) found that an increase
in age correlated to a decline in CSE due to a significant, negative relationship between age and
learning beliefs. In accordance with technology in education, Salanova et al, (2000) found that
CSE plays a role between computer training and burnout levels among teachers. An increase in
the frequency of computer use, and computer training contributed to an increase in CSE among
teachers and reduced the level of burnout (Salanova, 2000). Relationships can also be established
connecting CSE also has relationships with anxiety (Compeau and Higgins, 1995; Conrad and
Munroe, 2008; Ekizoglu and Ozcinar, 2010; Hsu and Lian, 2006; Igbaria and Parasuraman,
TECHNOLOGY IN THE CLASSROOM 34

1989; Koparan, Sahin and Kuter, 2010; Scott and Walczak 2009; Wilfong, 2006), TAM (Adams,
Nelson and Todd, 1992; Davis, 1989; Holden and Rada, 2011; Karsten, Mitra and Schmidt,
2012; Venkatash and Davis, 1996) and integration (Albrini, 2006; Christensen, 1998; Dooling,
2000; Loveless, 1996; Teo, 2011).
Anxiety
In the case of educators, as the continued importance of computers and the Internet grows
in schools, teachers may experience negative emotions in actual or anticipated interactions with
technology (Shu, Tu & Wang, 2012, Rosen & Wiel, 1995). Bandura, (2001) notes that people
need to believe they can produce desired outcomes by their actions or they will have little
incentive to act or persevere when faced with difficulties. Decreased levels of self-efficacy (CSE
included) lead to higher levels of anxiety, which then lead to lower levels of confidence and
performance. Therefore, anxiety needs to be reduced in order for self-efficacy beliefs to increase
(Conrad & Munro, 2008). According to Spilberger (1972) anxiety is an emotional, unpleasant
reaction that is observable, such as sadness, and tension caused by stressful situations. Maurer
(1994) described computer anxiety as the concern and fear individuals experience when thinking
he/she is using a computer. Most recently, Ceyhan (2002) describes computer anxiety as an
individual avoiding computers and areas where computers are available, preferring to use
computers briefly while taking precautions before using a computer.
Previous research has also created various measures of computer anxiety, for example,
the Computer Anxiety Scale was developed to measure computer anxiety and positive attitudes
with computers (Heinsesen, Glass & Knight, 1987). The Computer Aversion Scale
conceptualized computer anxiety as an aversion comprised of three components: efficacy,
reinforcement, and outcome (LaLomia & Sidowski, 1993; Meier, 1988). The Computer
TECHNOLOGY IN THE CLASSROOM 35

Technology Use Scale (CTUS) was developed with three domains in mind: computer efficacy,
attitudes to technology, and technology related anxiety (Conrad & Munroe, 2008). The results of
the various scales has revealed that higher levels of CSE are associated with positive attitudes
toward technology and lower levels of anxiety, as well, as determining the relationship between
CSE and computer anxiety and its affects on how well new technology will be adopted and
utilized (Conrad & Munroe, 2008). According to Bandura (1998), individuals with negative
conditions lack the adequate skills to manage stressful situations and are more likely to
experience higher levels of anxiety and avoid using technology. It is interesting to note that
Chang (2005) implies that no amount of computer experience can help individuals eliminate
his/her high levels of anxiousness and computer anxiety.
In a study done by Wilfong (2004), 242 university students indicated that CSE played a
significant role in computer anxiety and anger. This is a significant study because one-fourth of
the university population was affected by computer anxiety and 50% of individuals displayed
some level of anxious behavior when using a computer for a task. Wilfong’s (2004) study
concluded that users with lower self-efficacy in experiential domains such as spreadsheets,
photo, or graphic editing software, etc. resulted in higher levels of computer-anxiety and
computer-anger. Wilfong’s (2004) findings support SCT and Bandura’s (1997) idea that a
person’s belief in her abilities affects the completion of goals and levels of motivation to
persevere when faced with difficulties. Individuals who experience computer anxiety retain
negative beliefs towards computers, do not complete technological goals, and find it difficult to
take advantage of life in an ubicomp era.
The negative emotions that teachers experience according to Shu, Tu and Wang (2012)
take the form of fear, anxiety, hostility and resistance in behavior, which prevents teachers from
TECHNOLOGY IN THE CLASSROOM 36

extracting the most from their technology. Research has shown that most teachers agree that
computers are valuable tools, but very few extensively use computers in their classrooms (Rosen
& Weil, 1995; Shu, Tu & Wang, 2012). Teachers who are technophobic avoid teaching with
computers, and if they do teach with computers their anxiety and negative viewpoints are passed
on to their students (Shu, Tu & Wang, 2012). The study also noted that secondary teachers were
more likely to use computers in their classrooms than elementary school teachers. This finding
is disturbing because research shows that children are being exposed to technophobia from a
very early age and the initial exposure to a teacher who is uncomfortable with technology may
lead to more students experiencing technophobia (Rosen & Weil, 2005; Shu, Tu & Wang, 2012).
The term technophopia was expanded by Shu, Tu and Wang (2012) and has been changed to
technostress. Technostress is defined as “any negative effect on human attitudes, thoughts,
behavior and psychology that directly or indirectly results from the use of technology” (p. 924).
Shu, Tu and Wang (2011) list three ways that technostress has evolved: First, modern
technology is integrated into our lives and has brought down the walls between work and life.
Technological changes create a decrease in adaptation caused buy individuals’ inability to cope
with new computer technologies in a healthy manner (Shu, Tu & Wang, 2011). The research
done by Shu, Tu and Wang (2011) also validates Dourish and Bell’s (2011) ubiquitous era in
technology theory where technology is so deeply integrated into our lives society has become
dependent on it. Second, due to the explosion of the global market, and mobile commerce
employees are dealing with an ever-growing amount of information. The influx of information,
coupled with the constant change in technology may cause symptoms of technostress that
include: the inability to concentrate on a single issue, increased irritability towards technology,
and a feeling of loss of control (Shu, Tu & Wang, 2011). Sufferers describe technostress as a
TECHNOLOGY IN THE CLASSROOM 37

fear of computers when using one, or fearing the possibility of using a computer (Shu, Tu &
Wang, 2011). Third, as technology continues to increase, the demand to keep up with current
technology becomes harder and harder for employees. The fallout refers to an individuals’
inability to cope with constantly evolving technology and the required change in cognitive and
social requirements (Shu, Tu & Wang, 2011). Individually, the ability of teachers to keep up
with the increased demands is limited and causes an increase in technostress. As technology
burrows deeper into our lives more educators will experience an increase in technostress (Shu,
Tu & Wang, 2012). Several researchers (Compeau and Higgins, 1995; Downey and Smith,
2011; Lim et al., 2007) also found that a lower level of CSE is correlated to higher levels of
anxiety and stress and ultimately lead to a lower level of job performance. The task of helping
teachers who are technophobic and experiencing technostress may be difficult, but changing
primary school educators’ CSE is important because attitudes regarding CSE are transferred to
students.
Technology Acceptance
Computer Self-Efficacy (CSE) also affects teachers of all levels (K-16) and their
willingness to use technology in the classroom setting. Davis, (1989) created a model that
explains an individual’s intention to use computer systems. The Technology Acceptance Model
(TAM) is based on principles from Fishbein and Ajzen’s (1975) attitude paradigm from
psychology, which specifies how to measure behavior components of attitude, distinguish
between beliefs and attitudes, and specify how external stimuli are linked to beliefs, attitudes,
and behavior (Davis, 1993).
TAM uses two statistically estimated values that make TAM understandable and
simplistic (King & He, 2005). TAM proposes two predictors that determine behavioral
TECHNOLOGY IN THE CLASSROOM 38

intentions: perceived usefulness (PU) as defined by Davis (1989) is “the degree to which a
person believes that using a particular system would enhance his or her job performance”
(p.320). PU is not hypothesized to have an impact on Perceived ease of use (PEOU) because PU
concerns deal with the expected overall impact of system use on job performance (Davis, 1993).
In other words, PU deals with the process individuals go through and the outcomes he produces.
Perceived ease of use (PEOU), is defined by Davis (1989) as “the degree to which a person
believes that using a particular system would be free of effort” (p. 320). PEOU is hypothesized
by Davis (1993) to have significant effects on PU. PEOU pertains only to the performance
impacts related to the process of using the system, for example, the perceptions individuals have
when learning a new software program. Figure 2 illustrates the TAM model, which has been
cited and tested by numerous researchers (Barki & Hartwick, 1994; Davis, 1993; King & He,
2005; Lee, 2003; Mathieson, Peacock & Chin, 2001; Venkatash & Davis, 1996). External
stimuli affect both PU and PEOU; however, PEOU has a direct causal effect on PU, while PU
has no direct causal effect on PEOU. According to the TAM, individuals’ attitude toward using
technology stems from two beliefs: PU and PEOU.

Figure 3. Technology Acceptance Model (TAM) proposed by Davis (1989)
TAM provides administrators with a useful model when identifying which teachers are
willing to use technology in their classrooms and which teachers may be suffering from
technophobia. Igbaria and Iivari (1995) further validated TAM in their study of 109 companies
TECHNOLOGY IN THE CLASSROOM 39

in Finland where the number of employees for each company ranged from 89-28,859 employees,
noting that TAM suggests that if individuals believe computer technology will yield positive
outcomes he/she will be more likely to use computers. Igbaria and Iivari (1995) also developed
a computer usage model based on Bandura’s (1986) Social Cognitive Theory (SCT) which
proposed that CSE affects individuals’ anxiety, which influences PEOU, PU and system usage.
The study also showed that PEOU plays a major role in affecting the use of technology through
its direct effect on PU and enjoyment. Lee (2003) conducted a historical accumulation of TAM
citations and studies. In the study it was found that the first two TAM articles (Davis, 1989;
Davis et al., 1989) received 424 journal citations by the year 2000, (Lee, 2003) and by 2003 the
number grew to 698 (Lee, 2003). According to Lee (2003), Davis’ (1989) study was shown to
have a valid and reliable measurement for both PU and PEOU across different settings and
different information systems. Overall, Lee (2003) provided a meta-analysis of 101 articles
published between 1986-2003 and found that TAM has progressed continually during that time
and was elaborated on by researchers.
A limitation of TAM is that the model does not consider how individuals’ expectations of
abilities affect behavior, however, Igbaria and Iivari (1995) note that in SCT individuals may
believe that a specific course of action will yield a certain outcome, but if an individual’s self-
efficacy is doubted it does not affect their behavior choice. This argument is important because
it emphasizes that self-efficacy plays a role in behavior. One of the major problems Lee (2003)
found was that TAM studies were conducted when the tasks were too broad. Karahanna and
Straub (1999) recognized that the research findings could not be generalized under “task-
dependent situations” (p. 767) and for the future TAM should specify tasks more granularly.
Research has found that the simplicity of TAM is also a limitation (King and He, 2005; Lee,
TECHNOLOGY IN THE CLASSROOM 40

2003) because the simplicity makes TAM difficult to put into practice. Mathieson, Peacock and
Chin (2001) note that a limitation of TAM is that the model assumes usage is a conscious choice,
and there are no barriers preventing an individual access to technology. However, research has
shown that access to resources does affect technology usage and situations may prevent
individuals from using technology, which TAM will miss the variances like access restriction
(Mathieson, 1991; Mathieson, Peacock & Chin, 2001; Taylor & Todd, 1995).
Holden and Rada (2011) have studied TAM more recently and concluded that the most
significant predictor of self-efficacy for technology is determined by the frequency technology is
used and the attitude the teacher has towards technology. TAM can also identify a teacher’s
belief towards educational technology and their willingness to change classroom practices and
incorporate technology on a daily basis (Holden & Rada, 2011). Relevant studies (Shakpa and
Ferrari, 2003; Celik and Yesilyurt, 2013; Wilfong, 2006) revealed that attitude towards
technology are regarded as the biggest indicator if a teacher will/will not integrate technology in
the classroom.
Integration
Research indicates that computer technology is an effective means for exploring
educational opportunities, but most teachers neither use technology as part of their delivery of
instruction nor integrate technology into their curriculum (Bauer & Kenton 2005, Summak,
Samanicoglu & Baglibel, 2010). Significant research has also found that a higher level of CSE
creates a positive attitude toward using technology, which also leads to greater system use
(Davis, 1989; Igbaria & Iivari, 1995; Usta & Korkmaz, 2010). For the purpose of this study,
system use refers to the use of technology in the classroom. Milken (1999) found that 62% of
teachers surveyed felt that technology was a powerful tool for teaching and that student
TECHNOLOGY IN THE CLASSROOM 41

motivation increases with the use of technology. Technology is very versatile as it has the ability
to store, manipulate, and retrieve information that engages student learning and focus, yet the
same technology that permeates itself in the ubiquitous world we live in has not been fully
incorporated in the nation’s schools (Bauer and Kenton, 2005). Studies have shown that
educators are under-prepared to integrate technology into his/her instruction in meaningful ways
(Bauer and Kenton, 2005; Albrini, 2006). In fact, according to Bauer and Kenton (2005) only
one-third of teachers reported that they were well prepared to use technology during classroom
instruction. In the end, technology by itself does not support learning only when it is well
integrated into a learning environment does the real potential show (Summak et al., 2010).
According to Fuller (2000) and Loveless (1996) when technology was first being
introduced in classrooms in the late 1980’s curriculum was poorly planned and teachers were not
trained. In addition, teachers did not fully understand the role computers would play and often
felt that his/her job would be in jeopardy while other teachers felt that technology would
interfere with teacher-student relationships (Bauer and Kenton, 2005). Hooper and Rieber
(1999) described five phases of teachers’ use of technology: (a) familiarization, (b) utilization,
(c) integration, (d) reorientation, and (e) evolution. According to Hooper and Reiber (1999)
most teachers do not proceed past the utilization phase because teachers become complacent
with his/her limited use of technology and lack the commitment to using technology as part of
the curriculum while abandoning technology at the first signs of trouble. Only during the
integration phase do teachers breakthrough. During the integration phase, Hooper and Reiber
(1999) assert that teachers make a conscious decision to designate technology as part of the
curriculum. Therefore, Hooper and Reiber (1999) define technology integration as “a reliance
on computer technology for regular lesson delivery” (p. 522).
TECHNOLOGY IN THE CLASSROOM 42

The term technology integration has been used and defined by a number of researchers
(Albrini, 2006; Bauer & Kenton, 2005; Hew & Brush, 2007; Hooper & Reiber, 1999; Miller,
2007; Lovelss, 1996). Some of the research examined the types of teachers’ computer usage in
the classroom, other studies mention how teachers carry out familiar activities more reliably,
while still others describe technology integration in terms of teachers using technology to
develop thinking skills in students (Hew and Brush, 2007). Harris and Hofer (2011) describe
successful technology integration as being rooted primarily in curriculum content and through
content-related learning. Shulman (1986) first explicated the interrelationship of pedagogy and
content in relation to teacher effectiveness. Mishra and Koehler (2006) elaborated Sulman’s
model to address the need for teachers to understand how technology, pedagogy, and content
affect each other. Mishra and Koehler (2006) describe the total package for complex knowledge
as the technological pedagogical content knowledge (TPACK). TPACK is a specialized type of
knowledge that supports technology integration through content (Harris & Hofer, 2011). Mishra
and Koehler (2006) have characterized TPACK as the multiple intersections of a teacher’s
knowledge of curriculum content, general pedagogies, technology, and contextual learning that
results from teachers’ current understanding of curriculum, general pedagogy, technology and
learning contexts.
The foundational components of TPACK according to Mishra and Koehler (2006) are:
(a) Technology knowledge (TK), (b) Pedagogical knowledge (PK), and Content knowledge
(CK). Technology content refers to knowledge about different analog and digital technologies.
Content knowledge is the knowledge of content and subject matter taught. Finally, Pedagogical
knowledge is the methodology and processes of teaching, which includes classroom
management, assessment and lesson planning. Researchers have put notable emphasis on
TECHNOLOGY IN THE CLASSROOM 43

TPACK in regards to pre-service teachers (Jang & Chen, 2010; Kereluik et al., 2010; Ward &
Overall, 2010) because the knowledge supports the decision-making processes and skills
necessary to choose appropriate technology in support of learning. Other examples of
technology integration definitions are from Ogle et al. (2002), where they state, “purposeful use
of instructional technology in development base practices into daily routines” (p. 120). For the
purposes of this study, the definition of technology integration comes from Summak et al.
(2011), where they state that “the use of technology to achieve learning goals and to empower
student learning throughout the instructional program” (p. 1726).
In a review of 48 studies (43 peer-reviewed journals) Hew and Brush (2006) described
six barriers teachers face when looking to integrate technology in the classroom: (a) resources,
(b) institution, (c) subject culture, (d) attitudes and beliefs, (e) knowledge and skills, and (f)
assessment. The lack of resources, include one of the following: (a) technology, (b) access to
available technology, (c) time, and, (d) technical support. Lack of technology includes,
computers, printers, and other types of software. According to Pelgrum (2001) less than
adequate hardware/software creates little opportunity for teachers to integrate technology into the
curriculum. Even when technology is available at a school, he/she may not have access to it.
According to Karchmer (2001), access to technology involves providing the proper amount of
locations where teachers and students can use technology. Teachers need time to preview
websites, locate photos, and make presentations, and the lack of time teachers have to work on
lessons using technology is also another barrier (Hew & Brush, 2006). Lack of technical support
is another barrier to effective technology integration because teachers need assistance with the
use of different technologies and, according to Cuban et al. (2001), more often than not,
TECHNOLOGY IN THE CLASSROOM 44

technical support was overwhelmed with teacher requests and were not able to respond swiftly
and adequately.
Fox and Henri (2005) found that school leadership can be an institutional barrier that
either helps/hinders teachers with technology integration. School leadership may also be strong
in attaining technology for teachers to integrate, but not provide teachers enough time to
integrate the technology into the curriculum. In a study by Lawson and Cumber (1999) if
teachers are not given the support or time to integrate technology into school wide curriculum,
the newest technology will sit on the side for no one to use. The next barrier is subject culture,
which, according to Goodson and Mangan (1995) is a, “general set of institutionalized practices
and expectations which have grown up around a particular school subject, and shapes the
definition of that subject as a distinct area of study” (p. 614). Subject cultures have long-
standing histories reinforced by school practices going back generations; therefore, teachers are
reluctant to adopt technology that does not seem compatible with the norms of a subject culture
(Goodson & Mangan, 1995).
Teachers’ attitudes and beliefs also play a role as another barrier to technology
integration. According to Hew and Brush’s (2006) study, beliefs about teaching, technology,
and integration determine an educator’s opinion. Ultimately, the decision to integrate technology
comes from individual teachers and their beliefs/attitudes toward technology (Ertmer, 2005).
Teachers who used technology as a means of “keeping students busy” did not see the relevance
of technology integration into curriculum (Hew & Brush, 2006). The lack of specific knowledge
and skills has been identified as a major barrier to technology integration (Hew & Brush, 2006).
Research has shown that a lack of specific technology knowledge and skills is the most common
reasons given by teachers for not using technology (Downey & Smith, 2011; Ekizoglu &
TECHNOLOGY IN THE CLASSROOM 45

Ozcinar, 2010; Koparan, Sahin & Kuter, 2010; Shu, Tu & Wang, 2011). Technology
management in the classroom is also a skill teachers need to possess. According to Lim et al.
(2003) in a classroom that has been technologically integrated, teachers need to have the
technology-related classroom management skills to ensure every student has an equal
opportunity to use a computer, or know how to solve a technical problem when students run into
them. Finally, the last barrier towards integrating technology into classrooms is assessment. The
No Child Left Behind Act (2001) has created a set of high stakes testing that students participate
in (Porter et al., 2011). High stakes testing has serious consequences attached to it in the forms
of graduation for students, rewards or sanctions for schools and school districts, and the
pressures of such testing may also serve as a barrier to technology integration (Hew & Brush,
2006). According to Butzin (2004) the pressure to meet higher standards and pass high stakes
testing, coupled with the need to cover a vast scope of material is daunting for any teacher.
Unfortunately, teachers feel that he/she can cover more material by standing in front of the class
rather than using technology because planning and implementing technology lessons takes too
much time away from covering material that will be on the test (Butzin, 2004).
Research studies demonstrate that the use of technology improves student learning, can
help improve students’ scores on standardized tests, improve students’ inventive thinking and
improve students’ self-concept and motivation (Albrini, 2006, Rakes, Fields, & Cox, 2006;
Summak et al., 2010; Teo, 2011). Removing barriers and providing resources, training, and an
understanding for teachers is the key step to ensuring that 100% of schools have technology
integrated into the curriculum (Hew& Brush, 2006).

TECHNOLOGY IN THE CLASSROOM 46

Implications for Computer Self-Efficacy
CSE plays a central role in several information technology theories and frameworks, thus
becoming an important construct for researchers of informational systems (Karsten, Mitra &
Schmidt, 2012). Drawing a parallel to SCT (more specifically self-efficacy) CSE provides
organizations with helpful implications to increase self-efficacy with technology and lessen
anxiety. Igbaria and Iivari (1995) found that organizational support indirectly influences feelings
regarding CSE. Higher organizational support using technology resulted in higher levels of self-
efficacy because more resources would be offered to help users become more proficient (Igbaria
& Iivari, 1995). Igbaria and Iivari (1995) also found that increasing the amount of training and
educational support also increased levels of self-efficacy and PEOU, which is important because
it allows for a wider selection of various software tools that can be offered to users.
Chien (2012) suggests that companies pay attention to the needs of their employees when
learning a new system. For example, creating custom-made system functions for employees of
various backgrounds was found to increase effectiveness and loyalty to new training tools.
Organizations should also probe instructors’ instructional attitudes, as well as instructional
methods and overall e-learning skills beforehand because instructors with sufficient experience
with computers and e-learning can positively increase training effectiveness (Chien, 2012).
Similar to Rosen and Weil’s (1995) study, an instructor’s CSE attitude is apparent to students
and in turn students will mimic the ideas and attitudes of their instructor. If organizations want
their training programs to increase CSE trainers and instructors, those serving as trainers and
instructors also need to have high levels of CSE as well as e-learning effectiveness.
Holden and Rada (2011) found that the use of TAM and organizational support directly
influences feelings regarding CSE. Higher organizational support using technology resulted in
TECHNOLOGY IN THE CLASSROOM 47

higher levels of self-efficacy because more resources would be offered to help users become
more proficient (Holden & Rada, 2011). Holden and Rada (2011) also found that increasing the
amount of training and educational support increased levels of self-efficacy and PEOU, which is
important because teachers who demonstrate positive attitudes and perceptions (as well as high
self-efficacy) toward technology usage may be more likely to use the technology in their
classrooms.
Igbaria, Iivari and Maragahh (1995) attest that computer technology training should be
perceived as useful and enjoyable. Igbaria, Iivari and Maragahh (1995) have also argued that the
complex interaction between humans and computers needs to be taken into consideration and
system designs need to be compatible with user abilities. Dourish and Bell, (2011) would attest
that ubicomp validates the notion that computer technology can embed itself within the daily
fabric of human interactions. The advancements in computer technology have created a bridge
between time and space in which humans have instant access to each other (Dourish & Bell,
2011). Finally, organizations need to design computer technology training around the concept of
increasing knowledge, and Computer Self-Efficacy (CSE) because it will lead to a reduce
barriers and resistance while increasing the perception of ease of use (Igbaria, Iivari &
Maragahh, 1995). Centering computer technology trainings on individuals’ abilities will affect
performance because individuals will possess the necessary skills and motivation to stay on task
in the face of demands and failures (Bandura, 1977).
21
st
Century Skills
Educators are now charged with the responsibility to equip students with the skills to
succeed in the 21st century (Bellanca et al., 2010; Dale, 2005). New skills to succeed in the 21
st

century include global awareness; financial, economic, business, and entrepreneurial literacy;
TECHNOLOGY IN THE CLASSROOM 48

environmental literacy; health literacy; and civic literacy—must be integrated into the core
subjects (Wagner, 2008). The U.S. Department of Education (2010) has defined 21
st
century
skills as having expertise in critical thinking, complex problem solving, collaboration, and
multimedia communication through out all content areas. As advances in technology are made
in the ubiquitous world of technology the importance for students to become equipped with
information, media, and technology skills have become absolutely essential (Wagner, 2008).
Proficiency in an ubicomp world involves accessing and evaluating information efficiently and
effectively using information accurately while managing the flow of information from a variety
of creative sources (Partnership for 21st Century Skills, 2012).
In a study of CEOs from Fortune 500 companies, educational policy experts and school
leaders, Wagner (2008) identified seven critical skills that students world-wide lack in order to
compete in a global economy: (a) critical thinking and problem solving, (b) collaboration across
networks and leading by influence, (c) agility and adaptability, (d) initiative and
entrepreneurship, (e) effective oral and written communication, (f) accessing and analyzing
information, and (g) curiosity and imagination. The challenge for teachers becomes teaching 21
st

century skills when not all teachers are aware what those skills are.
Conclusion
Computer technology is ubiquitous and influences almost every area of our life; from
home to work to our social lives each context requires a certain level of computer literacy.
Computer-related technology has become an indispensible part of public school education where
many districts have integrated technology to improve student engagement and learning outcomes
(Shu, Tu & Wang, 2011). Computer self-efficacy (CSE) is rooted in SCT, and more specifically,
the self-efficacy framework of Bandura (Karsten, Mitra & Schmidt, 2012). SCT emphasizes that
TECHNOLOGY IN THE CLASSROOM 49

learning occurs in a social context and much of what is learned is through observation (Bandura,
1977). For Bandura (1986), self-efficacy was used to define an individual’s judgment of their
abilities to complete a task and as an important measure for fearful and avoidant behavior.
Individuals who perceive themselves as having a high CSE handle more difficult tasks and
display a higher level of confidence, while those who see themselves having low CSE tend to
give up because they seriously doubt that they can reach their goals (Bandura, 1978). CSE also
affects anxiety levels in individuals and more specifically terrify educators with technophobia
(Rosen & Wiel, 1995). A teacher’s CSE is vital to both students (who often mimic their
teacher’s perceived CSE, positive or negative) and organizations. Several scholars, Igbaria and
Iivari (1995), Chien (2012) and Shu, Tu, and Wang (2011) provided suggestions for
organizations in an attempt to make technology training account for user ability, while increasing
knowledge and self-efficacy and creating a perceived ease of use. Currently, CSE remains an
area yet to be fully researched and given that we are living in a ubiquitous computing era that is
becoming more technologically dependent, it is imperative for schools to explore the levels of
teachers’ self-efficacy, especially since teacher attitudes are absorbed by his/her students (Rosen
& Weil, 1995; Shu, Tu, & Wang, 2012). Chapter three will revisit the four research questions,
discuss the methodology, research design, population sample, instrumentation, data collection
and data analysis.

TECHNOLOGY IN THE CLASSROOM 50

CHAPTER THREE: METHODOLOGY
Technology has evolved into a ubiquitous part of the 21
st
century (Dourish & Bell, 2011)
and the technological skills associated with student learning and preparation of students for
success in the work force, should be an integral part of students’ learning (Rivard, 2010). This
study seeks to describe the levels of Computer Self-Efficacy (CSE) of K-12 teachers in a K-12
complex and how CSE affects the acceptance and integration of technology in classrooms. The
enactment of this study was analyzed to investigate whether the principles of CSE (Celik &
Yesilyurt, 2012; Compeau & Higgins, 1995; Karsten, Mitra & Schmidt, 2012; Wilfong, 2004)
have a correlation with Davis’ (1989) Technology Acceptance Model (TAM), and technology
integration (Albrini, 2006; Bauer & Kenton, 2005; Hew & Brush, 2007) into classroom
instruction and practice. This chapter includes the research questions, research design,
population and sample, instrumentation and procedures for data collection and analysis.
Research Questions
The research questions for this study are:
1. What is the relationship between teachers’ Computer Self-Efficacy and their
acceptance and integration of technology into the classroom?
2. What is the relationship between STEM teachers’ Computer Self-Efficacy and their
acceptance and integration of technology into the classroom?
3. What is the relationship between Humanities teachers’ Computer Self-Efficacy
and their acceptance and integration of technology into the classroom?
4. What is the relationship between Elementary school teachers’ Computer Self-
Efficacy and their acceptance and integration of technology into the classroom?

TECHNOLOGY IN THE CLASSROOM 51

Research Design
Data collected was used in previous surveys used to measure Computer Self-Efficacy
(CSE), the Technology Acceptance Model, (TAM) and Technology Integration questions drawn
the Manual for the Use of the Motivated Strategies for Learning Questionnaire (MSLQ). The
methods used in these previous studies were relevant because they have been shown to be valid,
reliable and have been used in a number of additional studies by various researchers (Albrini,
2006; Lee, 2000; Torkzadeh & Van Dyke, 2010). The questions that were used to measure CSE
are from Murphy, Coover & Owen, (1989). In the original instrument there were 32 questions
and three subscales. For this study the scales were broken-down and seven questions were taken
from the entire scale. The questions chosen were the closest related to what the researcher was
looking for and limited so that respondents would not feel overwhelmed with the amount of
questions being asked. The questions that were used to measure TAM is from Davis (1989) and
finally, the questions that were used to measure technology integration come from the MLSQ
from Pintrich et al. (2007). In the original instrument there were 91 questions and three sub
sections. The seven questions chosen make up the entire sub scale labeled self-efficacy for
learning and performance. This was a quantitative research study (Salkind, 2011) and non-
experimental study that consisted of an online survey for Hawai’i K-12 teachers, with the goal to
determine the relationship between the CSE of teachers in a Hawai’i K-12 Complex and the
acceptance of and integration of technology in the classroom based on the self- reported
perceptions of the teachers. .
The research questions were addressed through an online survey that was accessible via a
link emailed to K-12 teachers by their respective principals. The results were used to analyze the
perceived relationship of K-12 teachers’ CSE, acceptance of technology and the levels of
TECHNOLOGY IN THE CLASSROOM 52

technology integration in classrooms. Only the initial results were used for this study, however
the Hawai’i K-12 Complex will be able to use the results of the survey to provide meaningful
and effective technology professional development for its K-12 teachers.
Population and Sample
The Hawai’i K-12 Complex serves students who will need to develop 21
st
skills in the
school setting, described by the U.S. Department of Education (2010) as having expertise in
critical thinking, complex problem solving, collaboration and multimedia communication
through out all content areas. The complex is made up of four elementary schools (grades K-6)
one middle school (grades 7-8), and one High School (grades 9-12). This particular complex
was chosen by the researcher given the position held as a teacher in this complex, a good
working relationship with both teachers and administrative personnel and the representation of
the researchers sub questions (STEM, Humanities and Elementary). According to the U.S.
Department of Education (2010) $3.4 billion has been allocated from 2002-2008 to improve
student achievement through the use of educational technology. Yet many of the existing
educators, as well as current educational leaders and policy makers do not have the same
understanding and ease of use with technology as other professional sectors (U.S. Department of
Education, 2010).
Table 1 below provides a comparison of teacher demographics for the high school,
middle school and elementary schools in this study.

TECHNOLOGY IN THE CLASSROOM 53

Table 1
Hawai’i K-12 Complex Teacher Demographics
Complex
Teachers Total Licensed
Years
Experience
5+ Years
at the
School
Classes Taught
by Teachers
Meeting NCLB
Requirements
Advanced
Degree
2011-2012 294 99.43% 11.60 61.17% 97.17% 37.33%

High School
Teachers Total Licensed
Years
Experience
5+ Years
at the
School
Classes Taught
by Teachers
Meeting NCLB
Requirements
Advanced
Degree
2011-2012 118 96.6% 12.1 71% 89% 45.8%

Middle School
Teachers Total Licensed
Years
Experience
5+ Years
at the
School
Classes Taught
by Teachers
Meeting NCLB
Requirements
Advanced
Degree
2011-2012 45 100% 13.8 71% 96% 44.4%

Elementary School 1
Teachers Total Licensed
Years
Experience
5+ Years
at the
School
Classes Taught
by Teachers
Meeting NCLB
Requirements
Advanced
Degree
2011-2012 37 100% 12.7 62% 100% 48.6%

Elementary School 2
Teachers Total Licensed
Years
Experience
5+ Years
at the
School
Classes Taught
by Teachers
Meeting NCLB
Requirements
Advanced
Degree
2011-2012 23 100 8.2 39 100 30.4
TECHNOLOGY IN THE CLASSROOM 54

Table 1, continued
Elementary School 3
Teachers Total Licensed
Years
Experience
5+ Years
at the
School
Classes Taught
by Teachers
Meeting NCLB
Requirements
Advanced
Degree
2011-2012 24 100% 8.8 50% 100% 20.8%

Elementary School 4
Teachers Total Licensed
Years
Experience
5+ Years
at the
School
Classes Taught
by Teachers
Meeting NCLB
Requirements
Advanced
Degree
2011-2012 47 100% 14 74% 98% 34%

There are a total of 294 teachers in the complex, and 99.43% are licensed. The average
years of experience is 11.60 and 61.17% of the teachers have been at their school for five or
more years. In addition, 97.17% of teachers meet the No Child Left Behind Act (NCLB)
requirements to be a highly qualified teacher and 37.33% of teachers have an advanced degree.
The High School has 118 teachers, 96.6% are licensed, with 12.1 years of average
service. There are 71.0% of the teachers that have five or more years of service, 89.0% meet the
NCLB requirements and 37.33% have an advanced degree. The Middle School has 45 teachers,
100% are licensed, and average 13.8 years of service. There are 71.0% of the teachers that have
five or more years of service, 96% meet the NCLB requirements and 44.4% have an advanced
degree.
The first Elementary school has 37 teachers, 100% are licensed and average 12.7 years of
service. There are 62.0% of the teachers that have five or more years of service, 100% meet
NCLB requirements and 48.6% have an advanced degree. The second Elementary school has 23
teachers, 100% licensed and average 8.2 years of service. There are 39.0% of the teachers that
TECHNOLOGY IN THE CLASSROOM 55

have five or more years of service, 100% meet NCLB requirements and 30.4% have an advanced
degree. The third Elementary school has 24 teachers, 100% licensed, and average 8.8 years of
service. There are 50% of the teachers that have five or more years of service, 100% meet
NCLB requirements and 20.8% have an advanced degree. The fourth Elementary school has 47
teachers, 100% licensed and average14.0 years of service. There are 74% of the teachers that
have five or more years of service, 98% meet NCLB requirements and 34.0% have an advanced
degree.
Instrumentation
The researcher developed the survey protocol and instrumentation used (see Appendix
A). The protocol was designed to elicit responses from teachers to describe levels of Computer
Self-Efficacy (CSE), as it relates to the perception of acceptance attitudes towards technology
and levels of integrating technology in the classroom. An online survey was used to garner the
maximum amount of participation from teachers in the Hawai’i K-12 Complex. Because of
resource constraints, the researcher was neither able to distribute the surveys personally or
conduct observations or interviews with any teachers; therefore the survey instrument was
delivered via a link embedded within an email principals from each school sent to their teachers.
Teachers were asked to click on the link within the email in order to take the survey. The survey
measured the three constructs of: Computer Self-Efficacy (CSE), Technology Acceptance
(TAM) levels and willingness to integrate technology in the classroom. Therefore, the survey
was separated into four critical areas: Advanced Level of Computer Skills, Perceived Use (PU),
Perceived Ease of Use (PEOU) and willingness to integrate technology (Murphy, Coover &
Owens, 1989; Davis, 1989; Pintrich et al., 2007). In order to determine item reliability a
Chronbach’s Alpha was conducted using SPSS (Salkind, 2010).
TECHNOLOGY IN THE CLASSROOM 56

Questions from the three surveys (Murphy, Coover and Owen, 1989; Davis, 1989;
Pintrich et al. 2007) had varying Likert scales. For the purpose of continuity in the current study
all responses were changed to a six-point Likert scale ranging from Strongly Disagree to
Strongly Agree. Chronbach’s alpha was run to see if there was a change from 0.97 (Murphy,
Coover & Owen, 1989) in the Advanced Computer Skills due to the exclusion of certain
questions and modifying the wording of the original items to fit modern context (ex. Using a
computer to organize information changed to using a cloud based program/Web 2.0 to organize
and store information). A Reliability and validity test was conducted to see if the Chronbach’s
alpha for PE changed from 0.97 (Davis, 1989) and if the Chronbach’s alpha for PEOU changed
from 0.86 (Davis, 1989) due to inserting the word “technology” in place of “electronic mail.”
Finally, the Chronbach’s alpha was conducted on the questions drawn from the MSLQ to see if
there was a change from 0.93 (Pintrich et. al., 2007) by using only one of the sub scales from the
original instrument and changing the questions to fit beliefs on technology integration in the
classroom.
The first part of the survey was used to gather perceptions about current teachers’ level of
computer skills and self-efficacy with computers. Measuring CSE first is important because
many researchers have demonstrated a strong positive link between high levels of CSE and
employee participation, production, and persistence with technology in the workplace (Celik &
Yesilyurt, 2013; Chien, 2012; Compeau & Higgins, 1995; Karsten, Mitra & Schmidt, 2012) as
well as determining that CSE is a common factor when determining the acceptance of using
technology (Davis, 1989; 1993; Koufaris, 2002; Yanik, 2010) and integrating technology in the
classroom (Abrini, 2006; Bauer & Kenton, 2005; Ertmer, et al. 2012; Summak et al. 2010).
TECHNOLOGY IN THE CLASSROOM 57

The second and third part of the survey used Davis’ (1989) TAM to identify K-12
teachers’ Perceived Use (PU) and Perceived Ease of Use (PEOU). According to Wilfong, (2004)
teachers’ acceptance of technology is shaped by their CSE and attitude towards computer based
education. The TAM, PU, and PEOU have been used in over 88 studies and have been cited
over 2,000 times (Lee, 2003). The last part of the survey used modified questions from the
MLSQ to elicit responses that relate to teachers implementing technology in the classroom.
Wang, Ertmer & Newby (2004) state that teachers’ use of technology in the classroom are
influenced by multiple factors such as access, personal capabilities, and external constraints like
time, equipment, and technical support, but the bottom line is that technology leads to
experiences that help students to learn better and faster.
Data Collection
Data was collected through an online survey that teachers were asked to take during their
non-instructional time. Principals from the high school, middle school, and each of the four
elementary schools emailed their respective faculty members with a link to the survey and
consent. Participation in the survey was completely voluntary and did not affect any teaching
credentials, tenure, etc. If teachers agreed to participate in the study they clicked the online
survey link or pasted the link into the address bar of their browser and clicked I AGREE on the
first question of the survey. The survey should have taken 5-10 minutes and was active for two
weeks so that the researcher would have enough time to analyze and report the data. The results
of the current study will provide a Hawai’i K-12 Complex with a better understanding between
the relationships of CSE, technology acceptance and integration
The link that was provided to all K-12 teachers in the complex that lead to the online
survey; once each teacher had completed the survey the results were automatically sent and
TECHNOLOGY IN THE CLASSROOM 58

collected by Qualtrics. All of the data collected was kept confidential and data would remain
safeguarded through the policies and procedures from both the University of Southern California
(USC) and the Hawai’i State Department of Education (DOE).
Data Analysis
Information from the survey instrument assisted the researcher in answering the research
questions. For the first research question: What is the relationship between teachers’
Computer Self-Efficacy (CSE) and their acceptance and integration of technology into the
classroom the independent variable (IV) is the CSE of teachers and the dependent variable
(DV) is the acceptance level and willingness to integrate technology into the classroom. For
the current study a correlational analysis was used to determine the relationship between
Computer Self-Efficacy and its impact on technology acceptance and integration.
For the rest of the sub-research questions: What is the relationship between STEM
teachers’ Computer Self-Efficacy and their acceptance and integration of technology into the
classroom? What is the relationship between Humanities teachers’ Computer Self-Efficacy
and their acceptance and integration of technology into the classroom? What is the
relationship between Elementary school teachers’ Computer Self-Efficacy and their
acceptance and integration of technology into the classroom? A correlational analysis was
first run to ensure the CSE, TAM, and Technology Integration was correlated across
Elementary, Humanities and STEM teachers. Since the variables were highly correlated a
simple analysis of variance (ANVOA), or one-way analysis of variance was run as there is
only one grouping dimension (Salkind, 2011). The ANOVA identified the variance that is
due to differences between individuals within groups and the differences between groups
(Salkind, 2011). Also associated with the ANOVA is the F-test, which looks at the overall
TECHNOLOGY IN THE CLASSROOM 59

difference between groups (Salkind, 2011). The higher the F-test value is over one, the more
likely there is a significance relationship between various areas of content, years of
experience, schools and the research questions.
Conclusion
This study focuses on the perceived relationship of K-12 teachers’ level of CSE and the
acceptance of and integration of technology in the classroom. An online survey was used to
determine the degree to which CSE affects acceptance levels and willingness to integrate
technology into classrooms. Through the correlational analysis the researcher was able to
identify the relationship between CSE, Technology Acceptance and Technology Integration for
STEM, Humanities and elementary teachers. All of the information collected provides a Hawai’i
K-12 Complex administrative team with invaluable information that may help to guide future
technology development. Chapter four will provide the results from the data that were collected
in order to answer the research questions for this study.

TECHNOLOGY IN THE CLASSROOM 60

CHAPTER FOUR: RESULTS
The purpose of this study is to identify the relationship between Hawai’i K-12 public
school teachers’ level of Computer Self-Efficacy (CSE) and their acceptance of and willingness
to integrate of technology in the classroom. The first research question sought to determine what
the relationship is between K-12 teachers’ CSE and their acceptance of and integration of
technology into the classroom. Secondly, the relationship between STEM teachers’ CSE and
their acceptance of and integration of technology into the classroom was studied. The third
question examined the relationship between Humanities teachers’ CSE and their acceptance of
and integration of technology into the classroom. The fourth research question analyzed the
relationship between Elementary school teachers’ CSE and their acceptance of and integration of
technology into the classroom.
This chapter represents the results of the data analysis for each of the research questions.
Participation characteristics including, gender, school, and content will be presented in the
following section. The subsequent section will present the outcomes of the data analysis as
related to the research questions. A discussion of the results, application and suggestions for
future studies will ensue in Chapter Five. Outcomes of this study should be interpreted with
caution as the sample size was not an accurate representation of the entire complex, nor was the
gender ratio equally distributed.
Participant Characteristics
Participants for the study were 292 K-12 teachers (2012-2013 statistics) from a Hawai’i
complex in the 2013-2014 school year. An email was sent to six principals (four elementary, one
middle school and one high school) to forward an online survey link to their teachers. Teachers
had 14 days to complete and submit their surveys. One hundred-nineteen surveys were
TECHNOLOGY IN THE CLASSROOM 61

completed, however, six respondents declined to participate, resulting in 113 valid participants.
In the school year 2012-2013 there were 292 teachers in the complex, therefore, 113 participants
yielded a 39% return rate (See table 2).
Table 2
K-12 Survey Participants
N %

Valid 113 94.9
Excluded 6 5.1
Total 119 100.0

Of the 113 teachers taking the survey 90 were female, 22 were male, 0 transgender and 1
declined to answer. Respondents of the survey identified as 80% female, 19% male, 0%
transgender and 1% declined to answer. Figure 4 breaks down the participation rates based on
gender.

Figure 4. Breakdown of Respondents by Gender
There are four elementary schools, one middle school and one high school in this
particular K-12 complex. Elementary School A had 14 responses, which made up 14% of
respondents. Elementary School B had 0 responses, Elementary School C had 21 responses,
TECHNOLOGY IN THE CLASSROOM 62

which made up 19% of respondents. Elementary School D had 12 responses, which made up
11% of respondents. The middle school had 28 responses, which made up 25% of respondents
and the high school had 38 responses, which made up 34% of respondents. Table 3 represents the
breakdown of participants based on schools.
Table 3
Breakdown of Participants by School

Overall, elementary school teachers made up 41% of responses, while Secondary/High
School teachers made up 59% of responses. At the secondary/high school level 30% of
respondents were Science, Technology, Engineering and Math (STEM) Teachers. 40% of
respondents were Humanities (Language Arts and Social Studies), and 30% of respondents
taught other subjects (Foreign language, Special Education, Art, etc.). Table 4 breaks down the
responses based on the demographics of research questions 2, 3, and 4.
Table 4
Breakdown of Respondents by Content Area
Content Area
Answer Responses %
STEM 20 30%
Humanities 26 40%
Other 20 30%
Elementary 47 41%
Total 113 100%
Answer Response %
Elementary School A 14 12%
Elementary School B 0 0%
Elementary School C 21 19%
Elementary School D 12 11%
Middle School 28 25%
High School 38 34%
Total 113 100%
TECHNOLOGY IN THE CLASSROOM 63

Findings
The research questions that guided this study were:
1. What is the relationship between teachers’ Computer Self-Efficacy and their
acceptance of and integration of technology into the classroom?
2. What is the relationship between STEM teachers’ Computer Self-Efficacy and their
acceptance of and integration of technology into the classroom?
3. What is the relationship between Humanities teachers’ Computer Self-Efficacy and
their acceptance of and integration of technology into the classroom?
4. What is the relationship between Elementary school teachers’ Computer Self-
Efficacy and their acceptance of and integration of technology into the classroom?
The following sections will present the results of analysis of each question.
To measure the relationship between teachers’ CSE and their acceptance of and
integration of technology into the classroom, respondents completed an online survey.
Participants responded to thirty questions; nine questions related to CSE from Murphy, Coover
& Owen, (1989) which were taken from the original scale of 32 questions. The original scale of
questions was broken to meet the wording that the researcher was looking for in this study.
Fourteen questions related to a study conducted by Davis (1989), which has become known as
the Technology Acceptance Model (TAM). Seven of the questions pertained to Perceived
Usefulness (PU) and seven pertained to Perceived Ease of Use (PEOU). Finally, seven questions
relating to technology integration were drawn from the Manual for the Use of the Motivated
Learning Strategies Questionnaire (MLSQ) from Pintrich et al. (2007). The questions were
taken from the original 81 questions in the instrument and the entire subscale labeled self-
efficacy for learning and performance was used.
TECHNOLOGY IN THE CLASSROOM 64

A reliability analysis of the survey was used to measure reliability of the survey
questions. This reliability analysis measured four question series: Q_5 series for CSE; Q_6
series for PU; Q_7 for PEU (Q_6 and Q_7 series for overall Technology Acceptance) and Q_8
for Technology Integration. Analysis of the full scale of thirty questions for reliability resulted
in a Cronbach’s alpha of α= .755 suggesting an above average level of statistical reliability.
Analysis of the five subscales resulted in Cronbach’s alpha of α= .557 for CSE, α= .878 for the
overall Technology Acceptance, α= .862 for PU, α= .832 for PEOU (PU and PEOU make up the
total TAM scale) and α= .804 for Technology Integration. All subscales with the exception of
“CSE” were found to be highly reliable, however, factors such as a small or diverse set of
participants when compared to the study by Murphy, Coover & Owen (1989). Another reason
the reliability of CSE is acceptable is based on Salkind’s (2011) reminder that Cronbach’s Alpha
is rounded to the tenths place, therefore, α= .557 is rounded to .6, which is poor, but acceptable.
(see Table 5).
Table 5
Reliability of Efficacy Subscales
Factor N of items Cronbach's Alpha α= Survey questions
Full Scale 30 .755 Series 5-8
CSE 9 .557 Series 5
TAM 14 .878 Series 6-7
PU 7 .862 Series 6
PEOU 7 .832 Series 7
Tech Integration 7 .804 Series 8

Frequency of Subscales
A frequency analysis was run for CSE (N= 113) and resulted in a mean of 34.45 and
standard deviation of 5.902; the result of the responses were negatively skewed, and leptokurtic
output. K-12 teachers in this complex reported an above average level of CSE (see figure 5).
TECHNOLOGY IN THE CLASSROOM 65

Figure 5. Overall Frequency of Responses for Computer Self-Efficacy
Figure 6 details the frequency of answers pertaining to CSE (Q_5 series) it was found
that the frequency of answers was diverse. In Q_2 the responses had a normal distribution; Q_ 4
and Q_6 also had a normal distribution, but were platykurtic in nature suggesting that majority of
the answers were in or about the median; Q_1, Q_3, Q_6, Q_7, Q_9 reported a negatively
skewed distribution; Only Q_ 5 reported a positively skewed frequency of answers, which
indicated that teachers are more comfortable using the internet to communicate instead of writing
letters.
TECHNOLOGY IN THE CLASSROOM 66

Figure 6. Frequency of Each Question Relating to CSE
The TAM (N= 110) results from a frequency analysis resulted in a mean score of 56.40
and standard deviation of 5.875, which is negatively skewed, and leptokurtic, which means that
majority of the data is peaked and small changes happen less frequently because historical values
TECHNOLOGY IN THE CLASSROOM 67

have clustered by the mean. K-12 teachers in this complex reported a high level of Technology
Acceptance (see Figure 7).

Figure 7. Overall Frequency of Responses for Technology Acceptance
Figure 8 details the frequency of answers pertaining to the TAM (Q_6 and Q_7 series).
The Q_6 series are based on PU and the Q_7 series is based on PEOU. It was found that the
frequency of answers for PU (See figure 8) was negatively skewed and all of the questions were
platykurtic, with Q_4 being the only question to resemble a more balanced distribution. Thus,
respondents frequently accept the PU of technology.
TECHNOLOGY IN THE CLASSROOM 68

Figure 8. Frequency Responses for Questions Pertaining to PU
The frequency of answers for PEOU (See figure 9) was either normally or negatively
skewed. Q_1, Q_2 and Q_3 were normally distributed, but platykurtic, resulting in majority of
responses to those questions at or around the median. Q_4 and Q_5 were normally distributed
and slightly skewed negatively, while Q_6 and Q_7, were normally distributed and negatively
skewed. Thus, respondents had mixed perceptions towards the PEOU of technology.
TECHNOLOGY IN THE CLASSROOM 69

Figure 9. Frequency of Responses for Questions Pertaining to PEOU
The frequency analysis run on Technology Integration (N= 111) resulted in a mean score
of 31.07 and standard deviation of 4.104, which is negatively skewed, and leptokurtic. K-12
teachers in this complex reported an above average level of willingness to integrate technology
in classrooms (see Figure 10).
TECHNOLOGY IN THE CLASSROOM 70

Figure 10. Overall Frequency of Responses to Technology Integration
The frequency of answers for Technology Integration (See figure 11) was all negatively
skewed. Q_3 and Q_6 were only slightly negatively skewed, where as Q_1, Q_2, Q_4, Q_5 and
Q_7 were negatively skewed. Thus, majority of respondents agreed on the importance of
technology integration in the classroom.

TECHNOLOGY IN THE CLASSROOM 71

Figure 11. Frequency of Responses for Questions Pertaining to Technology Integration
Findings for the First Research Question
This section will describe the findings for the first research question: What is the
relationship between teachers’ CSE and their acceptance of and integration of technology into
the classroom? A Pearson’s product-moment correlation coefficient was computed to assess the
relationship between Hawai’i K-12 teachers’ CSE and Technology Acceptance levels. Table 6
summarizes the results. There was a high level of significance (at the 0.05 level) between CSE
TECHNOLOGY IN THE CLASSROOM 72

and the TAM that also produced a positive and weak correlation, r= 0.267, N= 107, p= 0.05.
There was also a high level of significance (at the 0.01 level) between CSE and PU that also
resulted in a strong and positive correlation, r= 0.460, N= 108, p= 0.001. The correlation
analysis between CSE and PEOU produced a non-statistical significance and a negative
correlation between the two variables, r= -0.149, N= 108, p= 0.123. PU and PEOU are the two
sub factors that make up the TAM (Davis, 1989). In relation to each other the correlation
analysis indicated that PU has a high level of significance (at the 0.01 level) with the overall
TAM, as well as a strong and positive correlation, r= 0.747, N=110, p= 0.001. In addition,
PEOU also has a high level of significance (at the 0.01 level) with the overall TAM, as well as a
strong and positive correlation, r= 0.582, N= 110, p= 0.001.
Table 6
Correlation Analysis of Teachers’ CSE, TAM, PU and PEOU
Correlations
CSE Acceptance PU PEOU
Computer Self-Efficacy
Pearson Correlation 1 .267
**
.460
**
-0.149
Sig. (2-tailed) 0.005 0.001 0.123
N 110 107 108 108
Acceptance
Pearson Correlation .267
**
1 .747
**
.582
**

Sig. (2-tailed) 0.005 0.001 0.001
N 107 110 110 110
Perceived Usefulness
Pearson Correlation .460
**
.747
**
1 -0.105
Sig. (2-tailed) 0.001 0.001 0.276
N 108 110 111 110
Perceived Ease of Use
Pearson Correlation -0.149 .582
**
-0.105 1
Sig. (2-tailed) 0.123 0.001 0.276
N 108 110 110 111

**. Correlation is significant at the 0.01 level (2-tailed).

TECHNOLOGY IN THE CLASSROOM 73

A Pearson’s product-moment correlation coefficient was computed to assess the
relationship between Hawai’i K-12 teachers’ CSE and Technology Integration levels. Table 7
summarizes the results. The results showed a high level of significance (at the 0.01 level)
between CSE and Technology Integration. The data also yielded a strong and positive
correlation, r= 0.445, N= 108, p= 0.001.
Table 7
Correlation Analysis of Teachers’ CSE and Technology Integration
Correlations
CSE Integration
Computer Self-Efficacy

Pearson Correlation

1

.445
**

Sig. (2-tailed) 0.001
N 110 108

Technology Integration
Pearson Correlation .445
**
1
Sig. (2-tailed) 0
N 108 111
**. Correlation is significant at the 0.01 level (2-tailed).

Findings for the Second Research Question
This section will describe the findings for the second research question: What is the
relationship between STEM teachers’ CSE and their acceptance of and integration of technology
into the classroom? A Pearson’s product-moment correlation coefficient was computed to assess
the relationship between Hawai’i STEM teachers’ CSE and their Technology Acceptance levels.
Table 8 summarizes the results. There was no statistical significance between CSE and the
overall TAM; a weak and negative correlation was found between CSE and the overall TAM, r=
-0.295, N= 20, p= 0.206. A high level of significance (at the 0.05 level) was discovered between
CSE and PU; results also found a strong and positive correlation, r= 0.506, N= 20, p= 0.023. A
high level of significance (at the 0.01 level) was found between CSE and PEOU; the results
TECHNOLOGY IN THE CLASSROOM 74

generated a strong and negative correlation between CSE and PEOU, r= -0.692, N= 20, p=
0.001.
Table 8
Correlation Analysis of STEM Teachers’ CSE, TAM, PU and PEOU
Correlations
CSE Acceptance PU PEOU
Computer Self-
Efficacy
Pearson Correlation 1 -0.295 .506
*
-.692
**

Sig. (2-tailed) 0.206 0.023 0.001
N 20 20 20 20
Acceptance
Pearson Correlation -0.295 1 0.312 .517
*

Sig. (2-tailed) 0.206 0.18 0.02
N 20 20 20 20
Perceived Usefulness
Pearson Correlation .506
*
0.312 1 -.652
**

Sig. (2-tailed) 0.023 0.18 0.002
N 20 20 20 20
Perceived Ease of Use
Pearson Correlation -.692
**
.517
*
-.652
**
1
Sig. (2-tailed) 0.001 0.02 0.002
N 20 20 20 20
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).

A Pearson’s product-moment correlation coefficient was computed to assess the
relationship between Hawai’i STEM teachers’ CSE and Technology Integration levels. Table 9
summarizes the results. The results did produce a positive correlation, but there was not enough
strength in the significance to garner a strong correlation between STEM teachers’ CSE and
Technology Integration, r= 0.389, N= 20, p= 0.09.

TECHNOLOGY IN THE CLASSROOM 75

Table 9
Correlation Analysis of STEM Teachers’ CSE and Technology Integration

Correlations
CSE Integration
Computer Self-Efficacy
Pearson
Correlation
1 0.389
Sig. (2-tailed) 0.09
N 20 20
Technology Integration
Pearson
Correlation
0.389 1
Sig. (2-tailed) 0.09
N 20 20

Findings for the Third Research Question
This section will describe the findings for the third research question: What is the
relationship between Humanities teachers’ CSE and their acceptance of and integration of
technology into the classroom? A Pearson’s product-moment correlation coefficient was
computed to assess the relationship between Hawai’i Humanities teachers’ CSE and Technology
Acceptance levels. Table 10 summarizes the results. For the purpose of this study only
Language Arts and Social Studies responses were classified as Humanities. No statistical
significance was found between Humanities teachers’ CSE and the overall TAM; a weak and
positive correlation was also found between CSE and the overall TAM, r= 0.201, N= 43, p=
0.196. At the 0.01 level the data is highly significant between CSE and PU; there is also a strong
and positive correlation between CSE and PU, r= 0.454, N= 43, p= 0.002. There was no
significant correlation between CSE and PEOU, but a weak and negative correlation was found
between CSE and PEOU, r= -0.216, N= 43, p= 0.164.

TECHNOLOGY IN THE CLASSROOM 76

Table 10
Correlation Analysis of Humanities Teachers’ CSE, TAM, PU and PEOU
Correlations
CSE Acceptance PU PEOU
Computer Self-
Efficacy
Pearson Correlation 1 0.201 .454
**
-0.216
Sig. (2-tailed) 0.196 0.002 0.164
N 44 43 43 43
Acceptance
Pearson Correlation 0.201 1 .691
**
.554
**

Sig. (2-tailed) 0.196 0 0
N 43 45 45 45
Perceived Usefulness
Pearson Correlation .454
**
.691
**
1 -0.219
Sig. (2-tailed) 0.002 0 0.149
N 43 45 45 45
Perceived Ease of Use
Pearson Correlation -0.216 .554
**
-0.219 1
Sig. (2-tailed) 0.164 0 0.149
N 43 45 45 45
**. Correlation is significant at the 0.01 level (2-tailed).

A Pearson’s product-moment correlation coefficient was computed to assess the
relationship between Hawai’i Humanities teachers’ CSE and Technology Integration levels.
Table 11 summarizes the results. The result established a high level of significance (at the 0.05
level) between CSE and Technology Integration that also yielded a strong and positive
correlation, r= 0.308, N= 43, p= 0.044.

TECHNOLOGY IN THE CLASSROOM 77

Table 11
Correlation Analysis of Humanities Teachers’ CSE and Technology Integration

Correlations
CSE Integration
Computer Self-Efficacy
Pearson Correlation 1 .308
*

Sig. (2-tailed) 0.044
N 44 43
Technology Integration
Pearson Correlation .308
*
1
Sig. (2-tailed) 0.044
N 43 45
*. Correlation is significant at the 0.05 level (2-tailed).

Findings for the Fourth Research Question
This section will describe the findings for the fourth research question: What is the
relationship between Elementary school teachers’ CSE and their acceptance of and integration of
technology into the classroom? A Pearson’s product-moment correlation coefficient was
computed to assess the relationship between Hawai’i K-6 Elementary teachers’ CSE,
Technology Acceptance and Integration levels. Table 12 summarizes the results. There was no
statistical significance found between K-6 Elementary teachers’ CSE and the overall TAM;
results indicate a strong and positive correlation, r= 0.366, N= 44, p= 0.15. A high level of
significance (at the 0.05 level) was found between K-6 Elementary teachers’ CSE and PU; a
strong and positive correlation was reported, r= 0.457, N= 45, p= 0.002. There was no
statistically significant correlation between CSE and PEOU, but a very weak and positive
correlation was discovered between CSE and PEOU, r= 0.34, N= 45, p= 0.823.

TECHNOLOGY IN THE CLASSROOM 78

Table 12
Correlation Analysis of Elementary Teachers’ CSE, TAM, PU and PEOU
Correlations
CSE Acceptance PU PEOU
Computer Self-
Efficacy
Pearson Correlation 1 .366
*
.457
**
0.034
Sig. (2-tailed) 0.015 0.002 0.823
N 46 44 45 45
Acceptance
Pearson Correlation .366
*
1 .827
**
.653
**

Sig. (2-tailed) 0.015 0 0
N 44 45 45 45
Perceived Usefulness
Pearson Correlation .457
**
.827
**
1 0.114
Sig. (2-tailed) 0.002 0 0.457
N 45 45 46 45
Perceived Ease of Use
Pearson Correlation 0.034 .653
**
0.114 1
Sig. (2-tailed) 0.823 0 0.457
N 45 45 45 46
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).

A Pearson’s product-moment correlation coefficient was computed to assess the
relationship between Hawai’i K-6 Elementary teachers’ CSE and Technology Integration levels.
Table 13 summarizes the results. There was a very high level of significance (at the 0.01 level)
found between K-6 Elementary teachers’ CSE and Technology Integration. Results also indicate
that there is a strong and positive correlation between K-6 Elementary teachers’ CSE and
Technology Integration, r= 0.539, N= 45, p= 0.001.

TECHNOLOGY IN THE CLASSROOM 79

Table 13
Correlation Analysis of Elementary Teachers’ CSE and Technology Integration
Correlations
CSE Integration
Computer Self-Efficacy
Pearson Correlation 1 .539
**

Sig. (2-tailed) 0.001
N 46 45
Technology Integration
Pearson Correlation .539
**
1
Sig. (2-tailed) 0.001
N 45 46
**. Correlation is significant at the 0.01 level (2-tailed).

Predictability of Acceptance
In order to assess if technology acceptance can be predicted, a multi-linear regression was
run consisting of factors such as level of CSE, gender, level of school (K-6 or 7-12) and content
area. The ANOVA reported when grouping these factors together there is no statistical
significance, r= 0.698 and therefore, the combined independent variables are not significant
predictors of technology acceptance among teachers (See table 14).
Table 14
ANOVA of I.V. as a Predictor of Technology Acceptance

ANOVA
a

Model Sum of Squares df
Mean
Square
F Sig.
1
Regression 46.025 4 11.506 0.552 .698
b

Residual 1208.579 58 20.838
Total 1254.603 62
a. Dependent Variable: Acceptance
b. Predictors: (Constant), Content Area, Level of School, CSE, GenderQ2

Taking out the gender variable, another multi-linear regression was run testing three
variables simultaneously. The first regression tested if CSE alone was a significant predictor of
TECHNOLOGY IN THE CLASSROOM 80

technology acceptance. The resulting ANOVA results show that CSE is not a significant
predictor of technology acceptance, r= 0.431. The second regression tested whether both CSE
and Level of School (K-6 or 7-12) were significant predictors of technology acceptance.
ANOVA results indicate that CSE and level of school are not significant predictors of
technology acceptance, r= 0.734. The third regression tested the variables CSE, Level of School
(K-6 or 7-12), or Content Area (Elementary, STEM or Humanities) are predictors of technology
acceptance. Resulting ANOVA tests show that the combined variables of CSE, level of school,
and content area are not significant predictors of technology acceptance, r=0.536. Table 15
summarizes the findings.
Table 15
Series of ANOVA Testing Various I.V. as Predictors of Technology Acceptance

ANOVA
a

Model Sum of Squares df
Mean
Square
F Sig.
1
Regression 12.773 1 12.773 0.627 .431
b

Residual 1241.83 61 20.358
Total 1254.603 62
2
Regression 12.854 2 6.427 0.311 .734
c

Residual 1241.749 60 20.696
Total 1254.603 62
3
Regression 45.15 3 15.05 0.734 .536
d

Residual 1209.453 59 20.499
Total 1254.603 62
a. Dependent Variable: Acceptance
b. Predictors: (Constant), Computer Self-Efficacy
c. Predictors: (Constant), Computer Self-Efficacy, Level of School
d. Predictors: (Constant), Computer Self-Efficacy, Level of School, Content Area

TECHNOLOGY IN THE CLASSROOM 81

Conclusion
The current study resulted in statistically significant findings related to the four research
questions explored in this study. Though the findings were found to be statistically significant,
they are not strong. Question one examined the relationship between teachers’ CSE and their
acceptance of and integration of technology into the classroom. There was a significant, but
weak finding relating CSE to overall TAM. A highly significant and strong correlation was
found between CSE and PU. There was a statistically negative and non-significant correlation
between CSE and PEOU. Finally, there was a strong and highly significant correlation between
CSE and Technology Integration.
Secondly, the relationship between STEM teachers’ CSE and their acceptance of and
integration of technology into the classroom was studied. There was a negative and non-
significant correlation between CSE and the overall TAM. There was a positive, strong and
significant correlation between CSE and PU. A negative and highly significant correlation was
found between CSE and PEOU. Finally, a non-statistical significance that was positive and
weak was found between STEM teachers’ CSE and Technology Integration.
The third research question examined the relationship between Humanities teachers’ CSE
and their acceptance of and integration of technology into the classroom. There was a weak,
positive and non-correlation between CSE and the overall TAM. There was a strong, positive
and highly significant correlation between CSE and PU. A negative and non-significant
correlation was found between CSE and PEOU. Finally, a high level of significance was found
to be strong and positive between Humanities teachers’ CSE and Technology Integration.
The final research question examined the relationship between elementary teachers’ CSE
and their acceptance of and integration of technology into the classroom. There was positive and
TECHNOLOGY IN THE CLASSROOM 82

significant correlation between CSE and the overall TAM. There was a positive, strong and
highly significant correlation between CSE and PU. A positive, but non-significant correlation
was found between CSE and PEOU. Finally, a high level of significance was found to be strong
and positive between Elementary teachers’ CSE and Technology Integration.
Chapter five will begin with a brief review of the results and a discussion relating to
literature on CSE, the TAM, and Technology Integration. Finally, recommendations for future
study that focuses on CSE, TAM, and Integration will be presented.

TECHNOLOGY IN THE CLASSROOM 83

CHAPTER FIVE: CONCLUSIONS
When asked about expectations, 90% of average eighth grade students expect to receive a
high school diploma and participate in some form of post secondary education and nearly two-
thirds of parents consider college a necessity for their children (ADP, 2005). According to
Wagner (2008) high school graduation rates in the U.S. (70%) are significantly behind countries
such as Denmark (96%) and Japan (93%). The global economy is becoming more
interconnected as the world pushes further ahead into the 21st century. To encourage 21
st

century thinking the United State’s DOE Enhancing Education Through Technology (EETT)
program, which is part of the No Child Left Behind Act of 2001 (NCLB) allocated $3.4 billion
from 2002-2008 to improve student achievement through the use of educational technology (U.S.
Department of Education, 2009). However, many K-12 educators do not have the same
understanding and ease of use with technology as other professional sectors. Currently, 26 out of
50 states fully integrated technology into their schools (U.S. Department of Education, 2010).
Computer technology has gone from a mainframe era where large computers filled
offices, advancing to a desktop era where computers fit on every desk, in every office, and
presently computer technology has become ubiquitous, (Dourish & Bell, 2011) meaning that
technology is embedded within every aspect of our lives. Students living in an ubicomp world
have allowed technology to make life more practical and extraordinary, where mobile access to
information and resources are available 24/7 (Dourish & Bell, 2011). The viewpoint of students
is that the technology used in school is not as engaging as the technology used outside of class
(Albrini, 2006; Bauer & Kenton, 2005; Dourish & Bell, 2011; Ertmer et al., 2011). These
studies highlight the lack of correlation between the pace with which technology in the schools
TECHNOLOGY IN THE CLASSROOM 84

are keeping up with the ubicomp era, meaning people are using technology everyday for non-
traditional educational activities, but rarely is the same technology being used in schools.
A number of studies found a correlation between teachers’ Computer Self-Efficacy
(CSE) and students’ attitudes toward the use of technology in the classroom (Rosen & Weil,
2005; Shu, Tu & Wang, 2012 and Wilfong, 2004). Therefore, one way to increase student
engagement is not only to increase the frequency of technology used in schools, but also to
increase the level of CSE for teachers. As professional development is a main strategy used to
provide technology training it is worth noting the specific areas that teachers may need help in
and an area worth investigating.
The purpose of this study was to add to the body of knowledge of CSE, technology
acceptance, and willingness to integrate technology in the classroom. This study focused on four
elementary schools (grades K-6), one middle school (grades 7-8), and one High School (grades
9-12), in one K-12 complex in the state of Hawai’i, and sought to answer the following research
questions based on teachers’ self-reported perceptions: (1) What is the relationship between
teachers’ CSE and their acceptance of and willingness to integrate technology into the
classroom? (2) What is the relationship between STEM teachers’ CSE and their acceptance of
and willingness to integrate technology into the classroom? (3) What is the relationship between
Humanities teachers’ CSE and their acceptance of and willingness to integrate technology into
the classroom? (4) What is the relationship between Elementary school teachers’ CSE and their
acceptance of and willingness to integrate technology into the classroom? To collect data to
address these questions, 292 K-12 teachers were provided an online survey (Hawai’i Department
of Education, 2013). There were 119 teachers who participated in the survey. Data was
TECHNOLOGY IN THE CLASSROOM 85

collected using questions from previous surveys to measure CSE, the Technology Acceptance
Model (TAM), and Technology Integration.
The questions that were used to measure CSE are from Murphy, Coover & Owen, (1989).
In the original instrument there were 32 questions and three subscales. For this study the scales
were broken and seven questions were taken from the entire scale. The questions chosen were
the closest related to what the researcher was looking for and limited so that respondents would
not feel overwhelmed with the amount of questions. The questions that were used to measure
TAM is from Davis (1989) and finally, the questions that were used to measure technology
integration come from the Manual for the Use of the Motivated Strategies for Learning
Questionnaire (MLSQ) from Pintrich et al. (2007). In the original instrument there were 91
questions and three sub sections. The seven questions chosen is the entire sub scale entitled self-
efficacy for learning and performance.
Summary of Findings
The following sections provide the conclusions that were drawn for each of the four
research questions in this study. Each research question will be discussed and how the findings
relate to CSE, Technology Acceptance and Willingness to Integrate technology in the classroom
will be included in the analysis. Finally, recommendations will be made for both the complex
and for further study.
Conclusions for the First Research Question
The first research question explored the relationship between K-12 teachers’ CSE and
their acceptance of and willingness to integrate technology into the classroom. An email with a
link to the online survey was sent to the six principals (four elementary, one middle school and
one high school) in the complex who then forwarded the link to their respective teachers in early
TECHNOLOGY IN THE CLASSROOM 86

February 2014. The span of the study was two weeks and there were a total of 113 participants
who agreed to partake in the study. Research provides us with a number of hypotheses for the
results. There was a high level of significance between CSE and the TAM that also produced a
positive and weak correlation. According to Bandura’s (1994) Social Cognitive Theory self-
efficacy is influenced whether a person is aware of the tasks or skills needed when faced with a
new situation. Bandura (1994) also suggests that individuals who have higher levels of efficacy
tend to approach challenges more successfully. Therefore, teachers who have higher levels of
CSE will be more successful accessing and participating in an online survey then their peers who
do not have high levels of CSE. In this experiment, 113 of 292 teachers agreed to participate.
CSE may have played a part in influencing those not only willing, but capable of sharing their
opinions on CSE, Technology Acceptance and Integration. Similar to the findings of Holden and
Rada (2011), the most significant predictor of technology acceptance is determined by the
frequency technology is used and the attitude the teacher has towards technology. CSE alone is
not enough to predict a teacher’s technology acceptance, which is shown in the data.
There was also a high level of significance between CSE and Perceived Usefulness (PU)
that also resulted in a strong and positive correlation. The correlation analysis between CSE and
Perceived Ease of Use (PEOU) produced a non-statistical significance and a negative correlation
between the two variables. Davis, (1989) defines two factors that make up his TAM. The first,
PU is defined as, “the degree to which a person believes that using a particular system would
enhance his/her or her job performance” (p. 320). K-12 teachers with higher levels of CSE will
find that technology in the classroom is very useful to their practice. As noted in the study by
King and He, (2005) an individual’s CSE will cause he/she to believe that a specific course of
action will yield a certain outcome, but if an individual’s self-efficacy is doubted it does not
TECHNOLOGY IN THE CLASSROOM 87

affect their behavior choice. Even though a teacher may not have the highest level of CSE,
he/she may still find technology to be useful in the classroom.
Davis (1989) defines PEOU as, “the degree to which a person believes that using a
particular system would be free of effort” (p. 320). K-12 teachers with higher levels of CSE
were found to have lower levels of PEOU. Mathieson, Peacock and Chin (2001) note that a
limitation of TAM is that the model assumes usage is a conscious choice, and there are no
barriers preventing an individual access to technology. However, research has shown that access
to resources does affect technology usage and situations may prevent individuals from using
technology (Mathieson, 1991; Mathieson, Peacock & Chin, 2001; Taylor & Todd, 1995). So
while K-12 teachers may feel that technology is useful, barriers might be in place that makes it
difficult for teachers to access the technology’s full potential.
Finally, the results showed a high level of significance between CSE and Technology
Integration. The data also yielded a strong and positive correlation. CSE plays a significant role
in K-12 teachers’ willingness to integrate technology in the classroom. Significant research has
also found that a higher level of CSE creates a positive attitude toward using technology, which
also leads to greater system use (Davis, 1989; Igbaria & Iivari, 1995; Usta & Korkmaz, 2010).
K-12 teachers’ integration of technology into the classroom benefits the curriculum that students
are learning. As Harris and Hofer (2011) describe successful technology integration as being
rooted primarily in curriculum content and through content-related learning.
Conclusions for the Second Research Question
The second research question investigated the relationship between STEM teachers’ CSE
and their acceptance of and integration of technology into the classroom. There was no
statistical significance between CSE and the overall TAM; a weak and negative correlation was
TECHNOLOGY IN THE CLASSROOM 88

found between CSE and the overall TAM. A high level of significance was also found between
CSE and PEOU; the results generated a strong and negative correlation between CSE and PEOU.
The results did produce a positive correlation, but there was not enough strength in the
significance to garner a strong correlation between STEM teachers’ CSE and Technology
Integration. Research indicates that PEOU affects technology acceptance (Davis, 1989) and may
influence the willingness to integrate technology in the classroom (Bauer & Kenton, 2005).
STEM teachers reported that the higher CSE they felt lead to less Technology
acceptance. CSE and technology acceptance are very personal perceptions and it was found that
years of service do have a negative affect on the ability of teachers to use, manage, and navigate
technology and the changes along with it (Hew & Brush, 2007). For privacy reasons, years of
service were not included in the demographics section of the survey. The negative correlation
between years of service and CSE can have a significant impact the way teachers use technology
to manage classrooms, and also how technology is viewed, used, and the impact on learning.
Fox and Henri (2005) found that school leadership can be an institutional barrier that either
helps/hinders teachers with technology integration. School leadership may also be strong in
attaining technology for teachers to integrate, but not provide teachers enough time to integrate
the technology into the curriculum.
Institutional barriers are one way that affects teachers’ PEOU of technology in the
classroom. In a study by Lawson and Cumber (1999) if teachers are not given the support or
time to integrate technology into school wide curriculum, the newest technology will be seen as
difficult to use and sit on the side for no one to use. Time to plan lessons may also be a factor for
STEM teachers, who also need time to preview websites, locate photos, and make presentations,
and the lack of time teachers have to work on lessons using technology is also another barrier
TECHNOLOGY IN THE CLASSROOM 89

(Hew & Brush, 2006). STEM teachers are part of a larger faculty and staff may not have the
resources to resolve technical issues in a timely manner. Lack of technical support is another
barrier to effective technology integration because teachers need assistance with the use of
different technologies and, according to Cuban et al. (2001), more often than not, technical
support was overwhelmed with teacher requests and were not able to respond swiftly and
adequately.
Institutional Barriers that cause negative attitudes toward technology may lead to teachers
feeling less prepared integrating technology into the classroom during instructional time. In fact,
according to Bauer and Kenton (2005) only one-third of teachers reported that they were well
prepared to use technology during classroom instruction.
A high level of significance was discovered between CSE and PU; results also found a
strong and positive correlation. Shu, Tu and Wang (2011) found that science and math are the
two content areas where technology is used the most. The results of the various scales have also
revealed that higher levels of CSE are associated with positive attitudes toward technology use
and lower levels of anxiety (Conrad & Munroe, 2008). For further research investigators should
look into figuring out what institutional barriers (if any) are creating a negative correlation for
STEM teachers’ Computer Self-Efficacy and technology acceptance.
Conclusions for the Third Research Question
The third research question examined the relationship between Humanities teachers’ CSE
and their acceptance of and integration of technology into the classroom. No statistical
significance was found between Humanities teachers’ CSE and the overall TAM; as well as no
significant correlation between CSE and PEOU. Humanities teachers felt that PEOU, though not
statistically significant has a negative correlation with personal CSE levels. As several studies
TECHNOLOGY IN THE CLASSROOM 90

have shown (Bower & Kenton, 2005; Cuban et. al, 2001; Fox & Henri, 2005; Hew & Brush,
2006; Lawson & Cumber, 1999) barriers negatively affect teachers’ attitudes and may cause
more anger and anxiety towards accepting and implementing technology into classroom settings.
According to Bandura (1998), individuals with negative conditions lack the adequate skills to
manage stressful situations and are more likely to experience higher levels of anxiety and avoid
using technology.
A highly significant correlation was found between CSE and PU; there is also a strong
and positive correlation between CSE and PU. The results also established a high level of
significance between CSE and Technology Integration that also yielded a strong and positive
correlation. With the vast amount of software configurations and hardware to choose from
individuals with high CSE generally expect their PU to be high and individuals are a lot more
willing to integrate new software and hardware into the classrooms (Compeau & Higgins, 1995;
Karsten, Mitra & Schmidt, 2012).
Conclusions for the Fourth Research Question
The fourth research question examined the relationship between Elementary school
teachers’ CSE and their acceptance of and integration of technology into the classroom. There
was no statistical significance found between K-6 Elementary teachers’ CSE and the overall
TAM. There was also no statistically significant correlation between CSE and PEOU. A high
level of significance was found between K-6 Elementary teachers’ CSE and PU; a strong and
positive correlation was reported. There was a very high level of significance found between K-6
Elementary teachers’ CSE and Technology Integration. Results also indicate that there is a
strong and positive correlation between K-6 Elementary teachers’ CSE and Technology
Integration. At the elementary level students are very observant of their teacher’s behavior and
TECHNOLOGY IN THE CLASSROOM 91

often tend to mimic their teacher’s attitudes and perceptions (Chien, 2012). Individuals who
perceive themselves to have a high level of CSE handle more difficult tasks and display a higher
level of confidence, while those who see themselves having low CSE tend to give up because
they seriously doubt that they can reach their goals (Bandura, 1978). A teacher’s CSE is vital for
students, who often mimic their teacher’s perceived CSE, positive or negative (Igbaria and Iivari
1995; Chien 2012; Shu, Tu and Wang 2011).
Implications for a Further Study
Currently, the research on technology is expanding at an exponential rate; what is current
today will not be current tomorrow. Though CSE is rooted in Bandura’s (1977) earliest theories
of Social Cognitive Theory CSE research will continue to grow. The survey instrument used in
this study was a compilation of questions used in three previous studies. Questions pertaining to
CSE should use one of the entire sub scales, if not all 32 questions, to mirror the calculated
Cronbach’s alpha. Due to the lack of time and contractual issues observations and interviews
with teachers were not conducted. A mixed methods approach would have revealed more about
CSE, technology acceptance and willingness to integrate perceptions. A recommendation for
further research is to investigate if CSE is a significant predictor of technology acceptance and
willingness to integrate technology or is there another variable to pursue. The current study
suggested that K-12 teachers’ CSE are significantly correlated with overall technology
acceptance, PU and willingness to integrate technology in the classroom. Results of the study
also indicate that CSE has no significance predicting PEOU, in fact, CSE has a negative
correlation with PEOU. This relationship could be further explored to determine future topics
when planning technology professional development.
TECHNOLOGY IN THE CLASSROOM 92

Additionally, further investigation is needed into what made variations in individual
teachers’ responses. The male-female response rate was overwhelmingly female (19% male vs.
80% female) and roughly 39% of teachers participated in the study. Had the number of
participants increased, as well as the male representation, the results might have been different.
Gender and age also have been found to impact individuals’ CSE (Reed, Doty & May, 2005;
Durndell & Haag, 2002). In relation to Internet usage and skill, Durndell and Haag (2002) found
in their study of 74 males and 76 females in a Romanian University that males tend to report
higher levels of CSE, lower levels of anxiety, and more positive attitudes with Internet usage.
The outcome of a study such as this could inform professional development activities and teacher
support programs that are available to teachers as they become more familiar with the ubiquitous
role of computers.
Second, studies that are focused on CSE, technology acceptance, and integration should
be conducted in both online and hard copy versions. By only choosing one method of
distribution and collection limits all possible candidates. After all, Shu, Tu and Wang (2012)
validate Dourish and Bell’s (2011) ubiquitous era in technology theory where technology is so
deeply integrated into our lives that society has become dependent on it, however, add that the
imbedded dependency on technology has also created negative effects on human attitudes,
thoughts, behavior and psychology that directly or indirectly results from the use of technology.
If a teacher were experiencing anger, anxiety or confusion about an online survey having a hard
copy would ensure the maximum amount of respondents.
Third, another area to consider is adding in years of service to the survey. Due to
Department of Education compliance rules including years of service in the survey was not an
acceptable demographic question. As noted earlier, outcomes of this study should be construed
TECHNOLOGY IN THE CLASSROOM 93

with caution as the sample size was 39% of the entire complex population of teachers, and the
gender ratio was not equally distributed. In addition, only one complex in the state of Hawai’i
was surveyed. The resulting data cannot be generalized and might have been different if there
was a larger complex, or one that was classified as rural, urban or even if it was expanded to
include private schools. There is an immense amount of research that may be contrary to the
findings which implies that a need for further research on CSE, Technology Acceptance and
Integration is recommended.
Fourth, although high levels of significance were found between CSE and overall TAM
and Integration, the strength of each correlation was too weak to conclude CSE as a significant
predictor of technology acceptance and willingness to integrate technology. Furthermore, there
was a clear connection that CSE has no significance in predicting PEOU. Future studies should
focus on details explaining why one factor of the TAM is correlated with CSE and the other is
not, while looking into possible barriers of technology in the educational setting.
The general limitations of this study provide areas of potential further research. A similar
study conducted with a higher participation rate, a more diverse demographic of teachers,
complete use of questions from previous studies, years of service, hard copies of the survey and a
mixed methods approach to data gathering could yield more significant results.
Conclusion
In the 21st-century technological advances and ubicomp living continue to drive the
world economy, lifestyle, and education. Educators must leverage technology and provide
students with real world and engaging experiences. For example, using a variety of project and
problem-based learning opportunities that connect across content areas while focusing on inquiry
in a collaborative learning environment, both within and beyond the classroom while still
TECHNOLOGY IN THE CLASSROOM 94

encouraging students to make appropriate use technology. At a low-level of engagement
students could be taking tests, turning in assignments and communicating with teachers online.
At a middle level of engagement students could be using personal knowledge and skills in
various content areas (math, science and language arts) to complete a long term project such as a
science fair, or planning a family vacation using Microsoft Xcel, Word and Powerpoint to gather
and present research. At a high level of engagement students plan the criteria and grading rubric
in a collaborative environment to research and create a virtual book about a subject being taught
in one or more of the content areas.
Technology is very versatile as it has the ability to store, manipulate and retrieve
information that engages student learning and focus, yet the same technology that permeates
itself in the ubiquitous world we live in has not been fully incorporated in the nation’s schools
(Bauer & Kenton, 2005). Technology by itself does not support learning; only when it is well
integrated into a learning environment does the real potential show (Summak et al., 2010).
Teachers will need to have the high levels of CSE to accept and integrate technology into
classrooms because computer-related technology has become an indispensible part of public
school education (Shu, Tu, & Wang, 2011), not only as a way to increase graduation rates (70%
in 2008), but to ensure high school graduates are college and career ready.
This study initially set out to gather information for district administrators and the
Complex Area Superintendant (CAS) about whether teachers’ (STEM, Humanities, and
elementary) CSE affects the acceptance of and willingness to integrate technology into the
classroom. These inquiries served as the basis for the research questions used in this study.
Results of the study found K-12 teachers’ perceptions were at high levels of significance for each
of the variables, but are not strong enough to make significant predictors.
TECHNOLOGY IN THE CLASSROOM 95

Using research gathered from this study the complex administration team of this
particular Hawai’i K-12 complex is equipped to separate the data by grade level and content area
to provide meaningful technology professional development for its teachers. Frost, (2002)
recommended 15-60 hours of professional development to ensure a comprehensive integration of
technology. Effective technology professional development needs to be ongoing and carried out
over time, rather then presented in one-day workshops. Also, professional development should
be delivered in the context of the teacher's subject area, which for this complex deals with
technology acceptance and willingness to integrate technology. Finally, peer coaches and
mentors can be highly effective in helping teachers implement a new skill or increase levels of
willingness to integrate technology into the classroom.
Organizational support from the complex administrators indirectly influences feelings
regarding CSE. Higher organizational support using technology results in higher levels of self-
efficacy because more resources would be offered to help users become more proficient (Chien,
2012). Igbaria and Iivari (1995) also found that increasing the amount of training and
educational support also increased levels of self-efficacy and Perceived Ease of Use (PEOU),
which is important because it allows for a wider selection of various software tools that can be
offered to users and PEOU is the variable which is not a significant out come of CSE.
Chien (2012) would suggest that the complex administration team pay attention to the
needs of teachers when planning technology professional development. For example, focusing
on how to use cloud based programs from teachers who are currently implementing such
programs into the curriculum. The complex administration team should also probe presenters’
instructional attitudes, as well as instructional methods and overall e-learning skills beforehand
because instructors with sufficient experience with computers and e-learning can positively
TECHNOLOGY IN THE CLASSROOM 96

increase training effectiveness (Chien, 2012). Similar to Rosen and Weil’s (1995) study, an
instructor’s attitude toward technology is apparent to students and in turn students will mimic the
ideas and attitudes of their instructor. If the complex administration team wants their training
programs to increase the level of K-12 teachers’ CSE, those serving as trainers and instructors
also need to have high levels of CSE as well as e-learning effectiveness.
Holden and Rada (2011) found that the use of TAM and organizational support directly
influences feelings regarding CSE. Higher organizational support using technology resulted in
higher levels of self-efficacy because more resources would be offered to help users become
more proficient. Holden and Rada (2011) also found that increasing the amount of training and
educational support increased levels of self-efficacy and PEOU, which is important because
teachers who demonstrate positive attitudes and perceptions (as well as high self-efficacy)
toward technology usage may be more likely to use the technology in their classrooms.
Children growing up in an ubicomp era integrate technology into every aspect of life.
Students can upload or download assignments, submit tests or create video projects without ever
using a hardbound textbook. Technology can evolve classrooms into 21
st
century inquiry centers
where real world experiences flourish everyday. In order for high school graduation rates to rise,
the barriers for integrating technology in the classroom need to fall, and CSE levels of teachers
need to rise. In the end, improving the quality of educational experiences for students start with
the quality of teacher knowledge, even if it comes from the cloud.

TECHNOLOGY IN THE CLASSROOM 97

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TECHNOLOGY IN THE CLASSROOM 113

Appendix A
Survey Questions

Hawai’i K-12 Public School Teachers’ Level of Computer Self-Efficacy and Their Acceptance
of and Integration of Technology in the Classroom

Q1 Consent to Participate:
 I AGREE to participate in the study: Hawai’i K-12 Public School Teachers’ Level of
Computer Self-Efficacy and Their Acceptance of and Integration of Technology in the
Classroom and understand that I can change my mind about participating in this project at
any time before submitting my answers.
 I DO NOT agree to participate in the study: Hawai’i K-12 Public School Teachers’ Level of
Computer Self-Efficacy and Their Acceptance of and Integration of Technology in the
Classroom

Q2 Which of the following best describes you?
 Male
 Female
 Trans gender
 Decline to answer

Q3 Which school do you teach at?
 Fort Shafter Elementary
 Moanalua Elementary
 Red Hill Elementary
 Salt Lake Elementary
 Moanalua Middle School
 Moanalua High School

Q4 What content area do you associate yourself teaching the most?
 English/Language Arts
 Mathematics
 Science
 Special Education
 Foreign Language
 Social Studies
 Other (Art, band, technology, physical education, guidance, etc.)

TECHNOLOGY IN THE CLASSROOM 114

Q5 The following questions ask about your Computer Self-Efficacy (CSE) in the
classroom. Remember there are no right or wrong answers, just answer as accurately as possible.
Use the scale below to answer the questions. If you think the statement is very true of you, click
strongly agree if a statement is not at all true of you, click strongly disagree.
Strongly
Disagree
Disagree Somewhat
Disagree
Somewhat
Agree
Agree Strongly
Agree
I am confident I
can describe the
function of
computer hardware
(keyboard,
monitor, etc.)
     
I am not very
confident about my
ability to use the
Internet.
     
I am confident I
understand
terms/words related
to computer
hardware
     
I am confident I
can troubleshoot
computer
problems.
     
I am more
confident using
letters or the
telephone to
communicate with
people, rather than
the Internet.
     
I am confident I
can learn to use a
variety of cloud
based programs
(dropbox, google
drive, etc.)
     
I am not very
confident using
search engines like
Google or Yahoo.
     
TECHNOLOGY IN THE CLASSROOM 115

I am confident I
can explain why a
program/software
will or will not run
on a given
computer.
     
I am confident that
using the Internet
makes it easier to
keep in contact
with people.
     

Q6 The following questions ask about your attitude towards the usefulness of technology
(Elmos, Promethean boards, laptops, etc.) in the classroom. Remember there are no right or
wrong answers, just answer as accurately as possible. Use the scale below to answer the
questions. If you think the statement is very true of you, click strongly agree if a statement is not
at all true of you, click strongly disagree.
Strongly
Disagree
Disagree Somewhat
Disagree
Somewhat
Agree
Agree Strongly
Agree
I am confident
my job would
be difficult to
perform
without
technology in
my classroom.
     
I am confident
that using
classroom
technology
improves my
job
performance.
     
I am confident
that using
classroom
technology
allows me to
accomplish
more work.
     
TECHNOLOGY IN THE CLASSROOM 116

I am not very
confident that
using
classroom
technology
enhances my
effectiveness
on the job.
     
I am confident
classroom
technology
enables me to
accomplish
tasks more
quickly.
     
I am confident
that using
classroom
technology
improves the
quality of the
work I do.
     
Overall, I am
confident
classroom
technology is
useful in my
job.
     

Q7 The following questions ask about your attitude towards the ease of use with
technology (Elmos, Promethean boards, laptops, etc.) in the classroom. Remember there are no
right or wrong answers, just answer as accurately as possible. Use the scale below to answer the
questions. If you think the statement is very true of you, click strongly agree if a statement is not
at all true of you, click strongly disagree.
Strongly
Disagree
Disagree Somewhat
Disagree
Somewhat
Agree
Agree Strongly
Agree
I often become
confused when
using
classroom
technology.
     
TECHNOLOGY IN THE CLASSROOM 117

Interacting
with
classroom
technology
requires a lot
of mental
effort.
     
I find it
difficult to
recover from
errors
encountered
while using
technology in
my classroom.
     
Technology in
my classroom
is rigid and
inflexible to
work with.
     
I find it
cumbersome
to use
classroom
technology.
     
The
technology in
my classroom
provides
helpful
instruction.
     
Overall, I find
technology in
the classroom
easy to use.
     

TECHNOLOGY IN THE CLASSROOM 118

Q8 The following questions ask about your attitude towards implementing technology in your
classroom. Remember there are no right or wrong answers, just answer as accurately as possible.
Use the scale below to answer the questions. If you think the statement is very true of you, click
strongly agree if a statement is not at all true of you, click strongly disagree.
Strongly
Disagree
Disagree Somewhat
Disagree
Somewhat
Agree
Agree Strongly
Agree
I believe I
provide a
better
learning
experience by
using
technology.
     
I am
confident I
understand
the basic
concepts of
using
technology in
my
classroom.
     
I am
uncertain I
can explain
the most
difficult
material
using
technology.
     
I am
confident
integrating
technology in
my class will
improve
student
learning.
     
TECHNOLOGY IN THE CLASSROOM 119

I expect to
provide
higher quality
instruction
integrating
technology
into my
classroom.
     
I am
uncertain I
can master
the skills to
use
technology in
my
classroom.
     
Overall, I am
confident
when
integrating
technology
into my
classroom.
     

TECHNOLOGY IN THE CLASSROOM 120

Appendix B
Computer Self-Efficacy Scale
(Murphy, Coover, & Owen, 1989)
Very Little Quite a lot
Confidence of Confidence
I feel confident in adding and deleting
information from a data file
1 2 3 4 5
I feel confident in escaping/exiting from
the program/software
1 2 3 4 5
I feel confident in copying an individual
file
1 2 3 4 5
I feel confident in copying a disk 1 2 3 4 5
I feel confident in making selections from
an onscreen menu
1 2 3 4 5
I feel confident in moving the cursor
around the monitor screen
1 2 3 4 5
I feel confident in using a printer to make a
“hardcopy” of my work
1 2 3 4 5
I feel confident in using the computer to
write a letter or essay
1 2 3 4 5
I feel confident in handling a floppy disk
correctly
1 2 3 4 5
I feel confident in entering and saving data
(numbers or words) into a file
1 2 3 4 5
I feel confident in storing software
correctly
1 2 3 4 5
I feel confident in getting rid of files when
they are no longer needed
1 2 3 4 5
I feel confident working on a person
(microcomputer)
1 2 3 4 5
I feel confident in getting the software up
and running
1 2 3 4 5
I feel confident in calling-up a data file to
view on the monitor screen
1 2 3 4 5
I feel confident in organizing and managing
files
1 2 3 4 5
I feel confident in explaning why a progam
(software) will or will not run on a given
computer
1 2 3 4 5
I feel confident in troubleshooting
computer problems
1 2 3 4 5
I feel confident in writing simple programs
for the computer
1 2 3 4 5

TECHNOLOGY IN THE CLASSROOM 121

I feel confident in describing the function
of computer hardware (keyboard, monitor,
disk drives, computer processing unit)
1 2 3 4 5
I feel confident in understanding
terms/words relating to computer hardware
1 2 3 4 5
I feel confident in understanding
terms/words relating to computer software
1 2 3 4 5
I feel confident in understanding the three
stages of data processing: input,
processing, output
1 2 3 4 5
I feel confident in learning to use a variety
of programs (software)
1 2 3 4 5
I feel confident in getting help for problems
in the computer system
1 2 3 4 5
I feel confident in learning advanced skills
within a specific program (software)
1 2 3 4 5
I feel confident using the computer to
organize information
1 2 3 4 5
I feel confident using the computer to
analyze number data
1 2 3 4 5
I feel confident logging onto a mainframe
computer system
1 2 3 4 5
I feel confident logging off the mainframe
computer system
1 2 3 4 5
I feel confident working on a mainframe
computer
1 2 3 4 5

TECHNOLOGY IN THE CLASSROOM 122

Appendix C
Technology Acceptance Model Scale
(Davis, 1989)

Rate your opinion on the questions below using the 5-point Likert scale ranging from:
1=Strongly disagree, 2=Disagree, 3=Neither disagree nor disagree, 4=Agree and 5=Strongly
agree

Strongly Strongly
Disagree Agree
Using (application) improves the quality of
the work I do
1 2 3 4 5
Using (application) give me greater control
over my work
1 2 3 4 5
Application enables me to accomplish tasks
more quickly
1 2 3 4 5
Application enables me to accomplish tasks
more quickly
1 2 3 4 5
Application supports critical aspects of my
job
1 2 3 4 5
Using (application) increases my
productivity
1 2 3 4 5
Using (application) increase my job
performance
1 2 3 4 5
Using (application) allows me to
accomplish more work than would
otherwise be possible
1 2 3 4 5
Using (application) enhances my
effectiveness on the job
1 2 3 4 5
Using (application) makes it easier to do
my job
1 2 3 4 5
Overall, I find the (application) useful in
my job
1 2 3 4 5
Using (application) increases my
productivity
1 2 3 4 5
Using (application) increases my job
performance
1 2 3 4 5
Using (application) enhance my
effectiveness on the job
1 2 3 4 5
Overall I find the (application) useful in my
job
1 2 3 4 5
I find (application) cumbersome to use 1 2 3 4 5

TECHNOLOGY IN THE CLASSROOM 123

Learning to operate (application) is easy for
me
1 2 3 4 5
Interacting with the (application) is often
frustrating
1 2 3 4 5
I find it easy to get the (application) to do
what I want it to do
1 2 3 4 5
The (application) is rigid and inflexible to
interact with
1 2 3 4 5
It is easy for me to remember how to
perform tasks using the (application)
1 2 3 4 5
Interacting with the (application) requires a
lot of mental effort
1 2 3 4 5
My interaction with the (application) is
clear and understandable
1 2 3 4 5
I find it takes a lot of effort to become
skillful at using the (application)
1 2 3 4 5
Overall, I find the (application) easy to use 1 2 3 4 5

TECHNOLOGY IN THE CLASSROOM 124

Appendix D
Expectancy Component in MSLQ
(Pintrich, P., Smith, D., Garcia, T., & Mckeachie, W. (1993).

Not at All Very True
True of Me of Me
I believe I will receive an excellent grad in
this class.
1 2 3 4 5 6 7

I’m certain I can understand the mot
difficult material presented in the readings
for this course.
1 2 3 4 5 6 7

I’m confident I can understand the basic
concepts taught in this course.
1 2 3 4 5 6 7

I’m confident I can understand the most
complex material presented by the
instructor in this course.
1 2 3 4 5 6 7

I’m confident I can do an excellent job on
the assignments and tests in this course.
1 2 3 4 5 6 7

I expect to do well in this class. 1 2 3 4 5 6 7

I’m certain I can master the skills being
taught in this class.
1 2 3 4 5 6 7

Considering the difficulty of this course,
the teacher, and my skills, I think I will do
well in this class.
1 2 3 4 5 6 7

Abstract (if available)

Abstract This study sought to determine what the relationship was between K-12 public school teachers’ level of computer self‐efficacy and their acceptance of and integration of technology in the classroom in one complex in the state of Hawai`i. Computer Self‐Efficacy (CSE) as defined by Karsten, Mitra & Schmidt (2012), is “an individual’s perception of efficacy in performing specific computer related tasks within the domain of general computing” (p. 54). Computer self‐efficacy is important to the educational field because it has been found to impose a significant influence on an individual’s ability to complete a task using computer hardware/software (Shu, Tu & Wang, 2011). Currently, a large number of teachers experience some level of computer anxiety or anger when faced with the opportunity or requirement to use these tools. These negative attitudes affect teachers’ beliefs and willingness to integrate technology in their classrooms (Wilfong, 2004). The reality is that students living in today’s society lead high‐tech lives outside school and decidedly low‐tech lives inside school. This new ‘digital divide’ has made activities inside school appear to have less real world relevance. The challenge for our educational system is to use modern technology to create engaging and relevant learning experiences that mimic the technology that has become a ubiquitous way of life. ❧ Participants were emailed survey links with questions dealing with Computer Self‐Efficacy, Technology Acceptance and Technology Integration. Results indicated statistically significant findings related to the four research questions explored in this study. Overall, K-12 teachers CSE, technology acceptance, and willingness to integrate technology was found to be statistically significant, yet the correlation was too weak to make it a significant predictor. Science, Technology, Engineering and Math (STEM) teachers reported a negative correlation between Computer Self‐Efficacy and technology acceptance and no significance on technology integration. Humanities teachers reported no statistical correlation between Computer Self‐Efficacy and technology acceptance, yet a strong and significant correlation was found between CSE and technology integration. Finally, elementary school teachers reported a strong and statistically significant correlation between Computer Self‐Efficacy, technology acceptance and technology integration. ❧ Results of the study provide a basis for complex administrators, but also require a need for further studies that will add to the current body of knowledge concerning Computer Self‐Efficacy and its relationship with technology acceptance and willingness to integrate technology in the classroom.

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Asset Metadata

Creator Oshiro, Devin Takashi (author)

Core Title One Hawai’i K-12 complex public school teachers’ level of computer self-efficacy and their acceptance of and integration of technology in the classroom

School Rossier School of Education

Degree Doctor of Education

Degree Program Education (Leadership)

Publication Date 04/24/2014

Defense Date 03/17/2014

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

Tag computer self‐efficacy,OAI-PMH Harvest,self‐efficacy,technology acceptance model,ubiquitous computing

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Martinez, Brandon (committee chair), Pascarella, John, III (committee member), Ushijima, Teri (committee member)

Creator Email devinoshiro@gmail.com,doshiro@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-384654

Unique identifier UC11296399

Identifier etd-OshiroDevi-2414.pdf (filename),usctheses-c3-384654 (legacy record id)

Legacy Identifier etd-OshiroDevi-2414.pdf

Dmrecord 384654

Document Type Dissertation

Format application/pdf (imt)

Rights Oshiro, Devin Takashi

Type texts

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

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

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