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Factors influencing technology at a secondary school
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Content
Running head: FACTORS INFLUENCING TECHNOLOGY INTEGRATION 1
FACTORS INFLUENCING TECHNOLOGY INTEGRATION AT A SECONDARY SCHOOL
by
Valerie S. S. Bland
A Dissertation Presented to the
FACULTY OF THE USC ROSSIER SCHOOL OF EDUCATION
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF EDUCATION
December 2019
Copyright 2019 Valerie S. S. Bland
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 2
ACKNOWLEDGEMENTS
Thank you to my amazing family. This was truly a group effort! Benjy and Nikki you are my
greatest achievements and I am so very proud of the both of you.
To my parents, thank you for always believing in me. You have never wavered in your support
of me.
Mahalo to my friends who offered me kind words whenever I needed to be picked up.
To my dear friend Dr. Deyon Nagato, you kept encouraging me throughout this long process
even when I didn’t feel like being encouraged.
Finally, I would like to acknowledge my dissertation committee, Dr. Lawrence Picus (chair), Dr.
Brandon Martinez, and Dr. Monique Datta. Mahalo nui loa for all of your time, thoughtful
feedback and patience. I could not have completed this journey without your support and
guidance.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 3
TABLE OF CONTENTS
Acknowledgements 2
List of Tables 5
List of Figures 8
Chapter One: Overview of the Problem 10
Background of the Problem 11
Statement of the Problem 13
Inquiry Purpose 14
Methodology 15
Limitations and Delimitations 15
Definition of Terms 16
Organization of this Study 17
Chapter Two: Review of the Literature 18
Twenty-First Century Skills 18
Technology Integration 19
Task Value 24
Self-Efficacy 25
Conclusion 28
Chapter Three: Methodology 30
Population and Sample 32
Instrumentation 36
Data Collection 38
Analysis 39
Validity and Reliability 42
Chapter Four: Results 44
Phase I – Data Collection and Data Analysis 45
Pre-Training Participant Demographics 46
Phase II – Post-Training Data Collection and Analysis 65
Post-Training Participant Demographics. 66
Findings for the First Research Question 85
Findings for the Second Research Question 86
Phase III - Qualitative Data Collection and Analysis 87
Benefits of Using Technology 87
Characteristics of Effective Professional Development 90
Motivation for Participating in Professional Development 90
Characteristics of Teachers that Integrate Technology 91
Role of Technology in Education 92
Challenges to Effective Technology Use 94
Overcoming Technology Obstacles 94
Summary 94
Chapter Five: Discussion 96
Summary of Findings 97
Limitations 100
Implications for a Further Study 101
Conclusion 102
References 103
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 4
Appendix A Interview Questions 113
Appendix B MSLQ Revision 116
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 5
LIST OF TABLES
Table 1: Observation and Treatment Dates 32
Table 2: Examples of the Original Questions, and Their Modified Versions 37
Table 3: Cronbach’s Alpha 45
Table 4: Secondary Survey Participants 46
Table 5: Participant Gender 46
Table 6: Teacher Ethnicity 47
Table 7: Teacher Experience 47
Table 8: Breakdown of Participants by School 48
Table 9: Pre-Test Task Value Mean by Gender 52
Table 10: Pre-Test Task Value Levene’s Test by Gender 53
Table 11: Pre-Test Task Value by Ethnicity 53
Table 12: Test of Homogeneity of Variances 54
Table 13: ANOVA 54
Table 14: Pre-Test Task Value by Faculty Experience 55
Table 15: Test of Homogeneity of Variances 55
Table 16: ANOVA 55
Table 17: Grade Level Mean Scores 56
Table 18: Pre-Test Self-Efficacy Mean by Gender 61
Table 19: Pre-Test Self-Efficacy Levene’s Test by Gender 62
Table 20: Pre-Test Self-Efficacy by Ethnicity 62
Table 21: Self-Efficacy Homogeneity of Variances Ethnicity 63
Table 22: ANOVA Ethnicity 63
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 6
Table 23: Pre-Test Self-Efficacy by Faculty Experience 64
Table 24: Self-Efficacy Homogeneity of Variances Faculty Experience 64
Table 25: ANOVA Faculty Experience 64
Table 26: Pre-Test Self-Efficacy by Grade Level 65
Table 27: Pre-Test Self-Efficacy Levene’s Test by Grade Level 65
Table 28: Post-Test Participants 66
Table 29: Post-Test Participant Gender 66
Table 30: Post-Test Teacher Ethnicity 67
Table 31: Post-Test Faculty Experience 67
Table 32: Post-Test Breakdown by School 68
Table 33: Gender Post-Test Mean Scores 72
Table 34: Gender Post-Test Independent-Samples t-test 73
Table 35: Post-Test Task Value by Ethnicity 73
Table 36: Post-Test Task Value Homogeneity of Variances 74
Table 37: Post-Test Task Value ANOVA 74
Table 38: Post-Test Task Value by Teacher Experience 74
Table 39: Post-Test Task Value Homogeneity of Variances 75
Table 40: Post-Test Task Value ANOVA 75
Table 41: Grade Level Post-Test Mean Scores 75
Table 42: Grade Level Post-Test Independent-Samples t-test 76
Table 43: Post-Test Self-Efficacy by Gender 81
Table 44: Post-Test Self-Efficacy Independent-Samples t-test by Gender 82
Table 45: Post-Test Self-Efficacy by Ethnicity 82
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 7
Table 46: Post-Test Self-Efficacy Homogeneity of Variances by Ethnicity 83
Table 47: Post-Test Self-Efficacy ANOVA by Ethnicity 83
Table 48: Post-Test Self-Efficacy by Teacher Experience 84
Table 49: Post-Test Homogeneity of Variances by Teacher Experience 84
Table 50: Post-Test Self-Efficacy ANOVA by Teacher Experience 84
Table 51: Post-Test Self-Efficacy by Grade Level 85
Table 52: Post-Test Self-Efficacy Independent-Samples t-test by Grade Level 85
Table 53: Overall Mean Scores 86
Table 54: Item Mean Scores 87
Table 55: Frequency of Technology Integration 88
Table 56: Level of Technology Integration using SAMR Model 90
Table 57: Frequency of Professional Development Participation 91
Table 58: Characteristics of Teachers that Integrate Technology 92
Table 59: Role of Technology in Education 93
Table 60: Memorable Technology Experience 93
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 8
LIST OF FIGURES
Figure 1: Pre-test Task value total score. 49
Figure 2: Pre-test Task value Q4. 49
Figure 3: Pre-test Task value Q10 50
Figure 4: Pre-test Task value Q16. 50
Figure 5: Pre-test Task value Q23. 51
Figure 6: Pre-test Task value Q26. 51
Figure 7: Pre-test Task value Q27. 52
Figure 8: Pre-test self-efficacy total score. 57
Figure 9: Pre-test self-efficacy Q5. 57
Figure 10: Pre-test self-efficacy Q6. 58
Figure 11: Pre-test self-efficacy Q12. 58
Figure 12: Pre-test self-efficacy Q15. 59
Figure 13: Pre-test self-efficacy Q20. 59
Figure 14: Pre-test self-efficacy Q21. 60
Figure 15: Pre-test self-efficacy Q29. 60
Figure 16: Pre-test Self-efficacy Q31. 61
Figure 17: Post-test task value total score. 69
Figure 18: Post-test task value Q4. 69
Figure 19: Post-test task value Q10 70
Figure 20: Post-test task value Q16. 70
Figure 21: Post-test task value Q23. 71
Figure 22: Post-test task value Q26. 71
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 9
Figure 23: Post-test task value Q27. 72
Figure 24: Post-test self-efficacy total. 77
Figure 25: Post-test self-efficacy Q5. 77
Figure 26: Post-test self-efficacy Q6. 78
Figure 27: Post-test self-efficacy Q12. 78
Figure 28: Post-test self-efficacy Q15. 79
Figure 29: Post-test self-efficacy Q20. 79
Figure 30: Post-test self-efficacy Q21. 80
Figure 31: Post-test self-efficacy Q29. 80
Figure 32: Post-test self-efficacy Q31. 81
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 10
CHAPTER ONE: OVERVIEW OF THE PROBLEM
Maile Schools Maui Middle School (MSMMS) and Maile Schools Maui High School
(MSMHS) are private schools whose leaders invested in technology for their students through
technology integration projects during the 2014–15 school year. Administrators opted for a 1:1
program to increase technology integration in both the MSMMS and MSMHS classrooms. From
the onset of the program, they realized providing additional hardware for students without any
additional support for teachers would not increase technology integration levels in the school.
School administrators wanted to gather baseline data regarding technology integration practices
prior to implementation to craft an effective, coherent, and coordinated professional development
plan to be delivered to teachers prior to and concurrent with the device rollout.
Prior to submitting the requests for the technology integration projects at the middle
school and high school, the administrators assessed technology integration at both schools. As a
part of the application for technology integration projects for the three schools, the faculty
completed platform evaluations during the Spring 2013 semester. As a result of faculty feedback
from the evaluations, Maile Schools Maui moved the students and faculty from the Windows
platform to a Macintosh platform. Maile Schools Maui had been on Windows machines since
Fall 2003 in preparation for the 1:1 high school rollout in Spring 2004. This study examined task
value and self-efficacy of the faculty members at both MSMMS and MSMHS.
Generally speaking, task value has to do with the importance an individual attributes to
an activity. Hulleman, Durik, Schweigert, and Harackiewicz (2008) stated task value can be
indicative of how much effort an individual will invest in a task. When individuals find a task
useful or important they tend to engage in that task, and will more likely be successful with that
task. Determining a task is enjoyable is not as strong a predictor of performance (Johnson &
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 11
Sinatra, 2013). Faculty members who demonstrate high task value should be more engaged in
the series of workshops. Additional support could be provided to those faculty members
demonstrating low task value.
Self-efficacy of faculty is important because there is a connection between how well an
individual believes they will do in a given task, and how much they are willing to engage in that
task (Bandura, 2012) . Researchers connected level of technology integration with computer
self-efficacy (Bauer & Kenton, 2005; Brinkerhoff, 2006; Ertmer, Ottenbreit-Leftwich, Sadik,
Sendurur, & Sendurur, 2012;). They found teachers with high levels of computer self-efficacy
are more likely to include technology in their classroom. One aspect of social cognitive theory
contends that behavior is influenced by an individual’s perception of their ability to perform a
certain behavior (Compeau & Higgins, 1995). If a person believes they will be successful in an
activity, they will be more likely to participate in the activity (Bandura, 1986, 2012).
Background of the Problem
Educating students today presents different challenges than in the past, as millennial
students, who were born into an era of much media stimulation, need a different educational
program than is currently available in many schools (Beyers, 2009). Modern schooling was
developed in the 18th and 19th centuries. Beyers (2009) contended students no longer need to
memorize text. The children of today expect to interact with their educational environments.
They tend to choose activities in which they can participate and not be passive audience
members (Beyers, 2009).
Wagner (2010) also argued schools today are out of step with children’s educational
needs. However, Wagner focused on the job market students will enter upon graduation.
Wagner contended K-12 institutions are preparing students for jobs which will no longer exist
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 12
when they graduate. Students no longer need to be taught the traditional three Rs of education:
reading, writing, and arithmetic. Wagner (2010) reasoned schools need to teach seven survival
skills: critical thinking and problem solving, collaboration across networks and leading by
influence, agility and adaptability, initiative and entrepreneurialism, effective oral and written
communication, accessing and analyzing information, and curiosity and imagination. Many of
Wagner’s seven survival skills closely align with twenty-first century skills (Partnership for 21st
Century Skills [P21], 2007).
Twenty-first century skills (P21, 2007) are the 4 Cs: communication, collaboration,
creativity, and critical thinking. Proponents of teaching 21st century skills contend schools have
students memorize formulas for a math problem, but not how to think about how and when to
apply this formula in a real-life context (Kay, 2009; Umphrey, 2010; Wagner, 2010). In
addition, it is still essential to have students learn basic skills. However, these basic skills are no
longer enough for students to function in the work world. Rather than memorizing a plethora of
isolated information, twenty-first century students should be able to locate the information they
require, analyze, and synthesize this information, and then apply it to their needs.
Technology is often regarded as the tool which can engage millennial learners, and
facilitate the acquisition of twenty-first century skills. Students utilize multiple modes of
technology in their personal lives. They also select items which are inherently interactive
(Beyers, 2009). Terrion and Aceti (2012) suggested educators should take advantage of the
natural pull of technology and utilize it in the classroom.
Beyond enhancing student engagement, technology should be incorporated into the
curriculum because students need varied experiences with technology. The world is increasingly
connected by technology (Friedman, 2007). Technology has become a part of everyday life.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 13
The federal government so valued the incorporation of technology into schools that it was
included as a part of the No Child Left Behind Act Of 2001 (Pub. L. No. 107-110, S 115, Stat.
1425, 2002). However, in the rush to comply with NCLB, teachers and students were provided
many new technology tools without knowing how to effectively and meaningfully incorporate
these tools into their curriculum (Lowther, Inan, Ross, & Strahl, 2012). Ross, Smith, Alberg,
and Lowther (2004) noted, in their observations of over 10,000 K-12 classrooms, that, in the vast
majority of cases, the technology was either rarely used or used for drill and practice types of
activities.
Statement of the Problem
Technology initiatives are seen as a way to infuse activities which would foster twenty-
first century learning in schools (Hung, Lee, & Lim, 2012; Rosen, Hobson, & Khan, 2003;
Rutkowski, Rutkowski, & Sparks, 2011). On an instinctive level, adding technology to the
classroom makes sense. Technology utilized effectively can be a powerful tool for educators.
However, they need support to become effective technology integrators. It is not enough to
provide hardware and software for instructors, as rarely, if ever, does providing hardware in
isolation cause students to be engaged in technology-rich lessons which include opportunities to
cultivate their twenty-first century skills. It is not uncommon for administrators to believe
purchasing the technology is enough to effect meaningful change (Potter & Rockinson-Szapkiw,
2012). Technology rollout plans which have yielded gains in student achievement tend to share a
component: a comprehensive professional development plan for the teachers implementing the
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 14
new technology (Lowther, Ross, & Morrison, 2003; Mouza, 2008; Penuel, 2006; Potter &
Rockinson-Szapkiw, 2012).
Professional development should be a part of a schoolwide technology implementation
(Chesbro & Boxler, 2010; Lawless & Pellegrino, 2007; Plair, 2008). Teachers need to be
assisted in their quest to weave technology throughout the curriculum. Without adequate
training, educators tend to treat technology as a stand-alone component, which is often left out of
the curriculum or left as the responsibility of the computer teacher (Ross et al., 2004). Some
researchers note much professional development is ineffective (Chesbro & Boxler, 2010;
Lawless & and Pellegrino, 2007; Plair, 2008). Particularly ineffective is professional
development focused on technology and taking place in only one training session. Darling-
Hammond and Richardson (2009) explained one-shot, drive-by, training sessions are ineffective.
For professional development to have a lasting effect on teacher practice, the training should be
sustained over a period of time and be a part of an improvement plan which connects curriculum,
standards, and assessment.
Inquiry Purpose
The purpose of this research project was to identify factors preventing teachers from
integrating technology within their classrooms. This study assessed the teachers’ current goal
orientation, task value, and computer self-efficacy as well as how these relate to technology
integration. The findings of this study are additional data points in the discussion regarding
technology integration at the middle school and high school. The information can be utilized to
inform the schools’ upcoming technology integration projects.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 15
Research Questions
1. What is the influence of technology-focused professional development on teachers’ task
value for using a computer as an instructional tool?
2. What is the influence of technology-focused professional development on teachers’ self-
efficacy for using a computer as an instructional tool?
Methodology
This study consisted of a self-report survey. The same survey was used as the pre-test
and post-test tool. The teacher survey was used to gather information about goal orientation,
task value, and self-efficacy. MSMMS and MSMHS teachers participated in a series of four
workshops to assist them in their transition from the PC to the Mac platform. The pre-test was
administered prior to the first workshop. The post-test was given after the third workshop.
Limitations and Delimitations
A limitation was the assumption that questionnaire responses were honest and accurately
reflected participants goal orientation, task value and self-efficacy. Participation was limited to
teachers who volunteered to complete both the pre-test and the post-test. Due to the relatively
small number of MSMMS and MSMHS faculty, the number of teachers participating in this
study was small. The researcher is an employee of the school who regularly provides
instructional support to the MSMMS faculty. The participants in the study may have been
influenced by their relationships with the researcher.
This study was limited by the time available to gather data. Middle school and high
school teachers were included because each school is implementing a technology project, and
moving platforms in the 2014–2015 school year. The sample was not chosen randomly. The
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 16
two groups participating in this study were existing groups. It is highly unlikely that results are
generalizable to different grade levels or to other schools within the district or state.
Definition of Terms
Technology integration: where technology is incorporated within the regular curriculum.
Rather than having a separate technology course where presentation application is taught,
students would learn how to utilize the application when preparing a presentation for the course
in which a presentation is required (Bauer & Kenton, 2005; Brinkerhoff, 2006).
21st century skills: These skills are communication, collaboration, critical thinking, and
creativity (P21, 2007).
Goal orientation: a social cognitive theory studying why students engage in their work
(Hicks Anderman & Anderman, 2008). It is usually broken down into either performance
(extrinsic) or mastery (intrinsic) orientations (Duncan & McKeachie, 2005).
Task value: Motivated reasons for engaging in a task (Johnson & Sinatra, 2013)
Self-efficacy: An individual’s belief in his own capabilities (Bandura, 2012)
Computer self-efficacy (CSE): This is an individual’s sense of his own ability to use a
computer (Compeau & Higgins, 1995).
Technology beliefs: This is an individual’s beliefs as to the value and utility of
technology (Brinkerhoff, 2006).
One-to-one (1:1) program: Generally, a program where every student has their own
laptop or tablet to use (Holcomb, 2009; Lowther et al., 2003).
SAMR model: describes a teacher’s level of technology integration. The four levels are
substitution, augmentation, modification, and redefinition (Puentedura, 2012). The first two
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 17
levels are considered enhancement in this model. The next two levels are considered
transformation.
MSMHS: Maile Schools Maui High School
MSMMS: Maile Schools Maui Middle School
SBKH: Standards Based Kula Hawaiʻi “The Standards Based Kula Hawaiʻi (SBKH)
project is guided by the principle that all students who graduate from Maile Schools must meet
or exceed a set of content standards that are embedded in Hawaiian culture and which reflect
21st century skills.” (Kamehameha Schools, 2012)
Kapiʻina: A merit based salary scheduled being piloted at Maile Schools. "Ka Piʻina"
refers to “the climb, ascent, rise” to the top or summit as we elevate our education work force
and build capacity while focusing on recruitment, retention and rewards (Kamehameha Schools,
2009).
WEO: Working Exit Outcomes Framework for students. This framework emphasizes a
global perspective (interdependence), growth (cultural and intellectual), relationships, seeking
knowledge (Kamehameha Schools, 2010).
Teacher professional development: This differs from education which teachers receive
prior to working in a school. Teacher professional development refers to the training the teacher
receives once he has a teaching assignment (Guskey, 1986).
Organization of this Study
Chapter One is an introduction to the research. Chapter Two describes the review of
literature relevant to this research. Chapter Three describes the methodology of this study.
Chapter Four discusses the findings of this research. Chapter Five shares the conclusions and the
recommendations arising from this study.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 18
CHAPTER TWO: REVIEW OF THE LITERATURE
Research shows schools in the United States need to improve (Clemmitt, 2011
Rotherman & Willingham, 2009; Wahlberg, 2011). People in the business world state many of
today’s youths are unprepared for life after school. These students graduate lacking the skills
necessary to be successful in the competitive work world (Wagner, 2010). “The threats to our
education system seem pretty clear – and the biggest challenge is not funding. The challenge
rather is how our education system will produce citizens who can succeed” (Kay & Greenhill,
2011, p. 41). As the world economy has changed, schools need to keep up to meet these needs
(Kay & Greenhill, 2011; Wagner, 2010). The well-paying manufacturing jobs have, for the most
part, disappeared. Schools must now prepare students for careers which require a new set of
skills (Jacobs, 2010; Wagner, 2010).
Twenty-First Century Skills
The role of the school has changed, and students are no longer gathering information as
they advance through the grades rather they need to be taught how to utilize the information that
is available to them (Gunn & Hollingsworth, 2013; Wagner, 2010). Information is readily
accessible, so students must now learn how to sift through this information and how to apply it to
varying situations. Wagner (2010) put forth seven survival skills, while P21 (2007) has four Cs.
The International Society for Technology in Education (2007) also has a set of six standards for
students aligned with Wagner’s survival skills and P21’s four Cs.
Critical thinking and problem solving is the first of Wagner’s (2010) survival skills. It is
also one of the four Cs (P21, 2007). In the new economy, teachers cannot continue to prepare
students for factory jobs. Students will need to think critically about novel situations and be
effective problem solvers. In the past, it was possible to make a comfortable living in a
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 19
manufacturing job. Critical thinking and problem-solving skills were needed primarily by
management (Wagner, 2010, 2012). Employers now ask that even those holding entry-level
positions think on their feet and problem solve without the help of management (Wagner, 2010).
Another important skill employers seek in applicants is effective communication and
collaboration. To be nimbler and to reduce costs, many companies reduced management levels.
As previously mentioned, this reduction required more workers to take on traditional leadership
roles (Wagner, 2010). In addition, working in a team is increasingly important. It is expected
that employees work in a collaborative environment. Effectively conveying ideas within a team
requires individuals communicate clearly (Wagner, 2010, 2012). These twenty-first century
skills are essential for student success in the today’s economy. The next challenge is how to
teach these skills in the classroom. Technology is often seen as a vehicle to assist teachers with
cultivating these twenty-first century skills in their students.
Technology Integration
Twenty-first century skills are essential for student success both while in school and
beyond. An obstacle to incorporating these skills into the curriculum is determining a strategy
for successful implementation. Rotherman and Willingham (2009) pointed out a need for
improved assessments, curriculum, training and teaching to create the 21st century learning
environment. Twenty-first century skills and technology are so closely intertwined because of
the potential of technology to support these ambitious goals (Chesbro & Boxler, 2010). A
common way in which technology has been brought into schools is through laptop initiatives.
One-to-one laptop programs have become very popular in the United States (Penuel, 2006; Suhr,
2008). Holcomb (2009) wrote that, in 2007, nearly 25% of U.S. high schools would have run
some version of a laptop program. The general elements of a 1:1 program are that a portable
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 20
computer is provided for each student, students can take their laptops home, and students have
access to a wireless network to access the internet and printing services on campus. Despite the
popularity of one-to-one initiatives, there is not much rigorous research on the effectiveness of
these programs (Maninger & Holden, 2009; Penuel, 2006). Often, the studies relied heavily on
participants’ feedback (Grimes & Warschauer, 2008).
The research on laptop programs has been mixed (Holcomb, 2009; Penuel, 2006). In
general, the less successful implementations were when laptops were provided to students and
teachers with little or no training (Holcomb, 2009). The recipients of the new computers were
expected to instantly become effective technology integrators. There were more positive results
when teachers and students were educated regarding expected laptop use prior to the actual
rollout, in addition to instructional and technical support throughout the life of the program
(Grimes & Warschauer, 2008; Holcomb, 2009; Manchester, Muir & Moulton, 2004; Morrison,
Ross, & Lowther, 2009; Waters, 2009).
In Mouza’s 2008 study, the laptop program did bring about changes in instruction. This
study looked at students in the third and fourth grades over school year. Each grade had one
control class and one experimental class. The experimental classes were led by teachers with
prior experience integrating technology within their curriculum. The teachers in the
experimental group stated they could think beyond 45-minute lessons. They began planning
with concepts and units in mind, rather than individual lessons. Morrison et al. (2009) observed
increased cooperative learning opportunities provided by teachers. This study looked at students
in grades 5, 6, and 7 in five different schools in the second year of laptop implementation. There
were 12 experimental classes and 9 control classes. Teachers also took on a facilitator role,
rather than acting as the content expert. The teachers found they could have more inquiry based
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 21
units with the aid of the laptops because the students could easily access whatever factual
information was required of the project. In this study (Morrison et al., 2009), the laptop teachers
were provided with instructional support to aid them in this transformative process.
Dawson, Cavanaugh and Ritzhaupt (2008) found similar results. The authors observed
teachers in over 400 classrooms over a period of a school year. They saw distinct differences in
how the teachers operated within their classrooms. Teachers had adopted more student-centered
instructional methods, as well as increased amounts of times doing meaningful technology
activities. Teachers were no longer using the computer as a replacement for a paper and pencil.
They were using the computers for problem-based lessons which required students to use critical
thinking skills.
An additional positive outcome of one-to-one programs was improvement in student
writing skills (Holcomb, 2009; Lowther et al., 2003; Suhr, 2008). A teacher in Mouza’s (2008)
study hypothesized the instant feedback for misspellings and grammatical errors students receive
when using word processing programs may be responsible for the growth in students’ writing.
Holcomb’s (2009) review of the research found students could focus on the writing process
rather than on the process of writing when using laptops. The net result was students who
frequently used their laptops to write became more polished writers.
Grimes and Warschauer (2008) discussed students’ improvement in more detail.
Students were more likely to write multiple drafts of assignments. It was easier to produce
another draft with a laptop and a printer as compared to hand-printing another draft. The word-
processed documents were legible as compared to the pre-laptop papers, so teachers could read
them more quickly, and provide timely feedback to students. The school district also purchased
a subscription to an online service which compared students’ writing assignments with those in a
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 22
data bank. This allowed students to upload their papers ahead of time and receive feedback and
make appropriate corrections prior to turning in the assignments to their teachers for grading.
In addition to writing, after 1:1 adoption students also showed growth in the area of
informational literacy (Dawson & Cavanaugh, 2008; Grimes & Warschauer, 2008; Holcomb,
2009). In subject areas such as social studies and English, research was required with the writing
assignments. In successful implementations, students receive the training necessary to analyze
the wealth of information to which they have.
Zucker and Hug (2008) observed a Denver science and technology high school. They
found students used their laptops to gather as well as analyze data. In their freshman year,
students used the laptops to run simulations in their physics classes. By the time they were in the
twelfth grade, the students had already grasped the concepts displayed in the simulations. They
used the laptops to participate in inquiry-based projects with other students acting as facilitators.
Student achievement was not specifically analyzed in this study. However, the authors pointed
out that while an average of 3% of students nationwide take the AP physics exam, at this school
30% of students complete the exam.
Students even made gains in mathematics, although it seemed the most difficult subject
area for teachers to utilize technology in meaningful ways, as evidenced by the amount of time
laptops were used during instruction (Holcomb, 2009; Jenkins, Clinton, Purushotma, Robison, &
Weigel, 2006). Math teachers often used the laptops as a tool for additional practice with basic
facts. Trimmel and Bachmann (2004) did find students who used the computers for math
showed improved ability at spatial relations.
There are other less costly ways to incorporate technology into a classroom besides
providing a computer for every student. Clickers are a common piece of technology which is
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 23
often utilized in the classroom to increase student engagement (Boyle & Nicol, 2011; Carle,
Jaffe, & Miller; 2009; Filer, 2010; Preszler, Dawe, Shuster, & Shuster, 2007; Terrion & Aceti,
2012). Boyle and Nicol (2011) used clickers to facilitate student interactions and discussions. In
a large classroom, it is normally difficult to incorporate significant amounts of student
discussions. In this study, the professor utilized responders to deploy questions to the groups.
Groups of students discussed the question and sent in their responses via the clicker. With the
aid of this technology, the teachers quickly gathered group or individual responses. Teachers
could quickly assess understanding, and then either reteach a challenging concept, or offer
assistance to the individuals or groups who are having difficulty. In Manuguerra and Petocz’s
(2011) study, the students expressed a more positive outlook of technology and the subject
matter when clickers were used in the course. Carle et al. (2009) also examined a course in
which teachers used clickers to elicit student responses. Students stated they could no longer
hide when they attended class. In this example, the technology motivated the students to come to
class prepared. Another positive aspect of using wireless responders is that it encouraged
students to interact in class (Filer, 2010). Students could respond without being singled out.
They did not feel the pressure of sharing only correct answers with the teachers. The use of
responders aided the teacher in creating a safe environment for students.
Mo (2011) found technology had a positive effect if it was integrated into undergraduate
curriculum. In this case, Blackboard Vista was the technology tool. The professor used
Blackboard in both semesters of the same auditing course. In both semesters, Blackboard was
used for the email function, and to house the course materials. In one semester, the instructor
used paper and pencil quizzes to assess student learning. In another semester, the instructor
utilized the assessment tool within Blackboard for administering student quizzes. The students
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 24
could re-take the quizzes as many times as they wanted. The quizzes were not required, and they
did not count towards the students’ final grades. The students in the semester with the online
quiz tool received better overall course grades than the students in the semester with the paper
and pencil quizzes. Mo suggested the students used the quizzes to review the material as needed.
The researcher hypothesized students could access the technology to most benefit their learning.
Task Value
Task value is the value that an individual attributes to a task or activity. Task values are
usually divided into three different categories: utility, attainment and intrinsic (Johnson &
Sinatra, 2013). Utility task values refer to valuing a task because it is useful. Attainment task
value refers to finding value in the tasks which confirm beliefs about oneself. The last type of
task value is intrinsic value which is the valuing of a task simply because it is interesting
(Johnson & Sinatra, 2013).
Wigfield and Eccles (1992) discussed the importance of task value in individuals’
decision making. If an individual has low task value for an activity, they are not very likely to
participate in it. The authors suggested task value could also be linked to performance. Task
value and performance are not as closely linked in the younger grades. As students get older,
performance tends to stabilize. Wigfield and Eccles (1992) suggested, as students get older and
find they are consistently not performing well in a subject area, it may be easier to reason that
something is not useful rather than that they lack a critical ability.
Wigfield et al. (1997) did a longitudinal study on task values in children. The study was
conducted over a three-year period with 615 primarily White, elementary school-aged children.
The study looked at competence beliefs and task values in math, reading music and sports.
Children were asked about their performance in these subject areas. Teachers and mothers were
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 25
also evaluated students’ performance levels. The researchers found children’s’ task value tended
to stabilize as they got older. There were gender differences in competence beliefs in certain
subject areas, and these competence beliefs eventually affect task value. Boys had significantly
more positive competence beliefs in the areas of math and sports. Girls had more positive
competence beliefs in reading and music. Over time, children’s task value decreased in all four
subject areas. Wigfield et al. (1997) theorized children’s task value may be more heavily
influenced by significant adults at younger ages. The older the students get, the more closely task
value may be linked to performance. As students experience less success, they may fail to see
the value in a subject. Task value is closely related to engagement. Utility and attainment task
value tend to increase performance (Johnson & Sinatra, 2013), and task value can be influenced
by instructors (Johnson & Sinatra, 2013).
Self-Efficacy
Teachers need to integrate technology into classrooms in more meaningful ways. This
requires teachers who are both proficient in technology and their content area. Most teachers
feel confident in their teaching in their content area but need assistance to include technology. In
addition, there are multiple levels of technology integration. Most models of technology
integration range from four to five levels (Holland, 2001; Puentedura, 2012; Welsh, Harmes, &
Winkelman, 2011). At the most basic level, technology is used as a replacement tool. An
example of student work product at this level is a paper typed out on a computer using a word
processing program. The computer is used in place of a paper and pencil. At the ideal, or most
advanced level, technology changes the way a teachers and students interact, and the types of
products that students create. An example of student work product at this level touches multiple
subject areas. Students may need to research a topic, and then write a stance on the topic. They
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 26
would select a platform (blog, wiki, or Twitter) to share their opinions with a larger community.
Students would receive feedback from the teacher, peers, and members in the community
affected by this issue. Students would be expected to defend and/or revise their stance based on
their research and feedback from others. The ideal level of technology integration can be
intimidating to teachers just beginning on their journey to incorporate technology into their
instruction, particularly those struggling with basic computer use.
Social cognitive theory suggests people are more likely to participate in an activity in
which they perceive they are skilled at (Bandura, 2012; Compeau & Higgins, 1995). There are
many factors which influence people’s behavior. However, for the purposes of this study, the
focus was on Bandura’s (2012) concept of self-efficacy, and how it affects choices about which
behaviors a person decides to undertake. Computer self-efficacy (CSE) refers to an individual’s
perception of his ability to utilize a computer to complete a task (Compeau & Higgins, 1995).
Celik and Yesilyurt (2013) pointed out teachers with low CSE are less likely to include
technology in their lesson plans and less likely to persevere and overcome challenges in the
lesson should difficulty arise. Shu, Tu, and Wang (2011) stated many segments of society are
more dependent upon technology. They refer to this as technology dependence, and this
dependence is especially true in education. The call for increased technology proficiency on the
part of both students and teachers is well documented (Friedman, 2007; Morris, 2010; Plair,
2008; Rutkowski, Rutkowski, & Sparks, 2011). Shu et al. (2011) examine the interplay between
increased technology dependence and CSE. Their research found high technology dependence in
combination with low CSE produced anxiety in users. Teachers with low CSE would have to
learn new skills to teach in a 21st century classroom and have to overcome anxiety associated
with the technology.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 27
Professional development for teachers in the area of technology integration can be help to
overcome some of the uneasiness that teachers feel toward using computers in the classroom
(Brinkerhoff, 2006; Holland, 2001; Plair, 2008; Rutkowski et al., 2011). Simply holding
workshops in technology integration does not guarantee lasting change in teacher practice. One-
shot workshops are largely ineffective. Teachers need to be supported beyond the training if the
goal is lasting instructional change (Gonzales, Pickett, Hupert, & Martin, 2002; Higgins &
Spitulnik, 2008).
Professional Development
As Guskey (1986) found, professional development for educators is how policy makers
expect to bring about change in schools (Guskey, 1986). The author noted, “In other words, staff
development programs are a systematic attempt to bring about change-change in the classroom
practices of teachers, change in their beliefs attitudes, and change in the learning outcomes of
students.” (Guskey, 1986, p. 5).
Professional development plays a key role in change in education. According to Guskey
(1986), the end goal of traditional professional development activities would be to change a
teacher’s way of thinking. Once their way of thinking is changed, they would then change their
professional practice, and then the change in student outcomes would follow. Guskey’s model
suggests that, rather than attempt to change attitudes and thinking first, staff development should
attempt to affect teacher professional practice first, then the learner outcomes would be affected,
which would then ultimately change a teacher’s way of thinking.
In a discussion of change in educational settings, Fullan (2006) put forth seven core
premises on which successful change is founded: a focus on motivation, capacity building, with a
focus on results, learning in context, changing context, a bias for reflective action, tri-level
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 28
engagement, and persistence and flexibility in staying the course. Fullan also stated the behavior
often needs to change before the beliefs of the individual changes. Change in an individual’s
beliefs will happen as a result of reflecting on the changes as well as of learning and practicing in
the context of practice. Successful professional development should focus on changing teacher
action and behavior, rather than on changing teacher beliefs.
Conclusion
In general, there are several common strands in successful technology implementation
plans. There is often buy-in from all stakeholders, and there is a plan for professional
development. Teachers have time to get comfortable with the laptops and receive instruction on
how to utilize the new technology prior to the student rollout (Lile, 2008; Manchester et al.
2004; Waters, 2009), and there is technology support, as well as instructional support throughout
the life of the laptop program. There are also clear goals in place prior to the laptops being given
to the students. Finally, a computer for every student does not mean the teacher no longer needs
to teach. The laptop does not replace sound instruction (Warschauer, Grant, Del Real, &
Rousseau, 2004). The success of the programs depends more on the teaching methods used than
on the technology tool itself.
Effective technology integration can be a powerful tool for educators. It can help create
learning environments which facilitate the development of 21st century skills heralded by
business professionals and educators (Wagner, 2010, 2012). Research on technology’s effect on
student achievement is relatively sparse (Lawless & Pellegrino, 2007). However, when
technology initiatives adhere to best practices for implementation, they can change teacher
practice and student learning. It is not enough to provide students and teachers hardware and
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 29
applications. There needs to be a plan to ensure students engage in activities which benefit their
learning. Teachers need support to help them to transform their classrooms.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 30
CHAPTER THREE: METHODOLOGY
A mixed-methods design was chosen for this study. The quantitative data were collected
through surveys. The same survey was administered to faculty members both prior to and at the
conclusion of a series of professional development activities. Qualitative questionnaires were
then administered to a sample of faculty members, and administrators. The qualitative data were
used to help explain some of the quantitative findings.
There were multiple goals of this study. The first goal was to examine if professional
development, which was focused on technology integration, had any effect on teachers’ task
value or self-efficacy for using laptops in the classroom. A second goal was to determine if there
was a relationship between years of teaching experience or subject area taught and teachers’
reported levels of task value and self-efficacy. The final goal of this study was to utilize teacher
interviews to explain any differences in teachers’ task value or self-efficacy for using laptops in
the classroom. Two research questions guided this study:
1. What is the influence of technology-focused professional development on teachers’ task
value for using a computer as an instructional tool?
2. What is the influence of technology-focused professional development on teachers’ self-
efficacy for using a computer as an instructional tool?
This study used methods utilized by other technology integration implementation studies
(Brinkerhoff, 2006; Ertmer et al., 2012; Watson, 2006). The data pertained to current teacher
levels of task value and self-efficacy as well as expressed teacher needs in implementing both the
1:1 laptop program at MSMMS and the technology integration project at MSMHS. Specifically,
the data inform the professional development plans for both MSMMS and MSMHS. Along with
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 31
the technology projects which began in school year 2014–2015, the faculty and student body also
transitioned from the Windows platform to the Macintosh platform.
This study used a mixed-methods approach. The sequential explanatory strategy was
utilized (Creswell, 2009). The sequential explanatory strategy collects the quantitative data prior
to qualitative data. Quantitative data collection used the Pre-Test-Post-Test with Nonequivalent
Groups Design (Creswell, 2009). The Nonequivalent Groups Design was used because the two
groups selected for this research were inherently different. These groups are of different sizes
and members teach different grade level students. The two groups moved to the Macintosh
platform in the 2014-15 school year, and both had 1:1 laptop programs in place in 2014-15. The
elementary school faculty was not used for this study because they did not move to the
Macintosh platform until the 2015-16 school year. In addition, the elementary school had yet to
be approved for a 1:1 device program. Due to these factors, the technology professional
development employed at the elementary school was very different than the technology
professional development at both the middle and high school.
Teachers were surveyed prior to a series of professional development workshops. At the
conclusion of the workshops, the teachers received the same survey. The first Mac integration
workshop featured the basics of Garage Band and how to use this tool to help students to author
original music. The next session built upon the skills learned in the first Garage Band session.
It focused upon podcasting as an educational tool. The third session featured integrating iPhoto
and Keynote in the content areas, and the fourth session demonstrated digital storytelling in the
classroom using iMovie. Teachers received instruction on how to use the various Macintosh
specific applications. However, the common theme which ran through all four sessions was how
to integrate these applications within the curriculum. Teachers were provided specific examples
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 32
of how to use these applications in their classrooms. It was emphasized that the technology
should help to enhance student understanding of the material being taught and should not be the
focus of the instruction.
Table 1
Observation and Treatment Dates
Group 2/2014 2/2014 3/2014 4/2014 4/2014 4/2014 5/2014
MS
Faculty
O1 X1 X2 X3 X4 O2 O3
HS
Faculty
O1 X1 X2 X3 X4 O2 O3
O1: initial quantitative survey
O2: second quantitative survey
O3: qualitative questionnaire
X1: Introductory Garage Band session
X2: Garage Band II
X3: iPhoto and Keynote
X4: Digital storytelling with iMovie
Each of the research questions were addressed through a survey given to the faculty at
MSMMS and MSMHS. The survey was also used to gather background information from all of
the teachers in the study. The survey gathered demographic information, as well as information
regarding task value for integrating technology in the classroom, and self-efficacy for integrating
technology in the classroom of the participants.
Population and Sample
Maile Schools Maui middle and high schools were chosen as the research site because
they were in the process of two technology integration projects. The middle school was
proposing a 1:1 laptop program for students. The high school was proposing a professional
development project for faculty. In addition, both schools were moving from the Windows
based platform to the Macintosh platform.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 33
Maile Schools Maui has an elementary school (K-5), a middle school (6-8), and a high
school (9-12) sitting on a 180-acre campus on the slopes of Haleakalā on the island of Maui.
Maile Schools Maui opened in 1996 and started with the elementary school. The middle school
was opened in 2002, and the high school in 2004. The faculty at both MSMMS and MSMHS
were used for this study.
Maile Schools Maui is a private Episcopal school with a college-preparatory mission.
Maile Schools has a Native Hawaiian preference admissions policy, so nearly all of the students
are part Hawaiian. At the time of the study, there were 324 students enrolled at Maile Middle
School in grades six through eight. There were 496 students enrolled at Maile High School in
grades nine through twelve. The student was evenly divided in terms of gender. Students came
from a wide range of socioeconomic backgrounds. One-fourth of the students were classified as
orphan/indigent. There were spaces reserved for students considered to be orphan (a parent has
died) or indigent (parent/guardian income falls at or near the poverty line). The students still
needed to meet the minimum criteria set by admissions to enter the school.
The middle school was approved for a for 1:1 laptop program for rollout in the 2014-15
school year. The administrators at the middle school supported a 1:1 program for several
reasons. The first is administrators believed increased technology was a way to get teachers to
infuse technology-rich lessons into the curriculum. The second was teachers agreed to
participate in professional development to prepare themselves for the laptops. Finally, in
alignment with best practices, administrators believed that, through increasing technology
integration and a focus on quality instruction, student learning would improve as well.
The high school’s 1:1 laptop program has been in place since 2004. This was the first 1:1
laptop program at Maile Schools. When the laptops were rolled out, administrators thought
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 34
providing the students and teachers with the hardware was enough for meaningful technology
use. This was common thinking in education during early implementations of laptops (Maninger
& Holden, 2009). More than 10 years since the insertion of laptops into the curriculum show this
is not the case. The MSMHS faculty was never provided the professional development that now
accompanies most new technology integration plans. Funding was approved for MSMHS for the
2014-15 school year for new devices for faculty and students as well as professional
development for the faculty. Professional development was to be provided for the teachers
specifically in the area of technology integration. The assumption by administrators at both
schools was that, if technology-focused professional development was provided to the teachers,
then the teachers would incorporate the technology into their curriculum in deep and meaningful
ways. There had been several more 1:1 programs rolled out at other Maile Schools campuses
throughout Hawaii since 2004. In all of the other program rollouts, there was more support
provided for technology integration. Additional staff members were provided and professional
development funds for staff, faculty and administration accompanied the devices with ensuing
rollouts.
Despite the support on campus for technology integration, relatively few faculty members
utilized the services of the instructional technology specialist or curriculum coordinator regularly
prior to the technology projects. Very few teachers accessed the technology for activities other
than internet searches, drill activities or word processing. With the recent approval to increase
the technology to which students have access, and the professional development support for
teachers, the administrators want teachers to use technology in more meaningful ways.
The faculty at both schools participated in this study. At the time of the investigation,
there were 32 faculty members at the middle school, and 50 faculty members at the high school.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 35
There was one principal, and one vice-principal at MSMMS. There were two principals, and one
vice-principal at MSMHS. All faculty members were invited to participate in the quantitative
component of the study. Selected faculty members that completed the quantitative questionnaire
were asked to complete the qualitative questionnaire.
Maile Schools Maui has a highly educated, experienced faculty, and a relatively
technology-rich environment. The Maile Schools faculty was comprised of 49% Native
Hawaiian/Other Pacific Islanders, 17% White Americans, 29% Asian Americans, 10% other
Pacific Islanders, 2% American Indian or Latino. Approximately 90% of the faculty was
credentialed and teaching in their area of specialty, and 85% of the faculty holds master’s
degrees. Eighty percent of the faculty had at least 10 years of teaching experience.
As stated earlier, the high school’s 1:1 laptop program is over a decade old. While the
middle school campus did not have a 1:1 ratio, students did have access to computers. There was
one full-time lab where the teachers could schedule time to access the computers. There was
another lab where teachers could schedule access for a couple of periods each day. There were
two full-time computer teachers for the students, a full-time curriculum coordinator, and a full-
time librarian. The middle school and elementary school faculty shared a full-time instructional
technology specialist. The high school had its own full-time instructional technology specialist.
Each division had its own full-time curriculum coordinator. There were after school professional
development opportunities and B credit courses focusing on technology. The faculty could also
have either the instructional technology specialist or the curriculum coordinator work with an
individual or team to integrate technology into the curriculum.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 36
Instrumentation
The teacher survey was used to gather some demographic information (length of time
teaching, subject area, grade level teaching), information regarding task value, and self-efficacy.
The researcher modified the Motivated Strategies for Learning Survey (MSLQ; Pintrich, Smith,
Garcia, & Mckeachie, 1993) to be domain specific.
Section one of the teacher survey (see Appendix B) included personal and professional
information. Personal information was teacher name, and ethnic background. Professional
information was teaching experience and subject area. Section Two of the Teacher Survey was a
modified version of a scale developed by Pintrich et al. (1993) was utilized. Work on the MSLQ
instrument began in 1986 (Duncan & McKeachie, 2005). It was tested and revised in 1986, 1987
and 1988. The validity and reliability of this instrument is well established (Duncan &
McKeachie, 2005). The MSLQ in its entirety has 81 Likert-type items (see Appendix B). There
are two major sections of the MSLQ. The first section measures motivation, and the second
section measures learning strategies. The motivation section consists of 31 items. The sub
scales in the motivation section are intrinsic goal orientation, extrinsic goal orientation, task
value, control beliefs about learning, and test anxiety. The learning strategies section consists of
50 items. The test can be administered in its complete form, or any of the motivation or learning
strategies scales, or combination of the scales can be administered separately. For this study,
only two of the motivation scales of the MSLQ were used. One of the value components (task
value), and one of the expectancy components (self-efficacy for learning and performance) was
administered to the participants. All questions on the MSLQ asked users to select a level from a
seven point scale ranging from one to seven. A choice of one indicates that the response is “not
at all true of me,” while a seven indicates the statement is “very true of me.”
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 37
There were modifications made to the wording in the MSLQ to make the questions
applicable to in-service teachers. Some modifications from the task value subscale (10, 23), and
the self-efficacy for learning and performance (10, 23, are listed in the table below). The original
questions for the self-efficacy and task value subscales, as well as the modified versions of the
questions, can be found in Appendix A. The complete teacher survey along with instructions
modified for this study can be found in Appendix B.
Table 2
Examples of the Original Questions, and Their Modified Versions
Original Modified
10. It is important for me to learn the
course materials in this class.
10. It is important for me to learn the
materials in this Mac integration
training.
15. I’m confident I can understand the
most complex material presented by
the instructor in this course.
15. I’m confident I can understand the
most complex material presented by
the instructor in this series of iLife
trainings.
21. I expect to do well in this class. 21. I expect to do well in this iLife
training.
23. I think the course material in this class
is useful for me to learn.
23. I think the material in this iLife
training is useful for me to learn.
All teachers who completed the modified MSLQ after the final professional opportunity
were asked to participate in the qualitative questionnaire. Twenty-two of the 32 teachers who
completed second quantitative survey also agreed to answer the qualitative questionnaire. There
were 12 teachers whose mean total score for task value was between 26 and 38, and whose mean
total score for self-efficacy was between 33 and 51 on the second survey (mid-range). There
were five teachers whose total score for task value was lower than 26, and whose total score for
self-efficacy was lower than 33 on the second survey (low-range). These scores for task value
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 38
and self-efficacy were at least one standard deviation lower than the mean total score. There
were five teachers whose total score for task value was higher than 38, and whose total score for
self-efficacy was higher than 51 on the second survey (high range). A total score higher than 38
for task value, and higher than 51 for self-efficacy or above is at least one standard deviation
higher than the mean total score.
Data Collection
Institutional review board and Maile Schools approval to conduct research was
completed in February 2014. Quantitative data were collected through surveys. Surveys were
selected as the primary source for collecting data for a couple of reasons. At the time of the
study, the upcoming 1:1 program and platform transition were just one of several changes
occurring at Maile Schools Maui. Throughout the 2013-14 school year, there were multiple
initiatives happening on both campuses. The MSMMS principal was approached in December
2013 for permission to conduct research in the spring 2014 semester. The MSMHS principals
were approached in January 2014 for permission to conduct research in the spring 2014
semester. Administrators were mindful of the time required of the teachers to perform their
regular job functions. To be allowed to perform the research at the school, administrators asked
that the time asked of the faculty be kept to a minimum.
Qualtrics was used to collect survey information. Qualtrics is an online survey
instrument in which researchers can use to gather data. The strength of the tool is that
participants enter their responses via personal computer. There is no transcribing from paper
into the computer. This reduces the amount errors caused by researchers’ incorrect
interpretations of participant responses, or input errors. In February 2014, teachers were asked to
complete the survey during one of their preparatory periods prior to the first Macintosh
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 39
integration workshop. They had one week to complete the survey. At the time there were 82
faculty members at Maile Schools. Of the 82 faculty members, 51 faculty members completed
the modified MSLQ prior to training. This was a 62% response rate. Of the 51 surveys returned,
30 came from middle school teachers, and 21 were from high school teachers. The modified
MSLQ, which teachers received at the end of the survey, was returned by 32 of the 51
distributed. This was a 63% return rate. The post-test questionnaire was only distributed to the
teachers who completed the modified MSLQ prior to the training. The qualitative questionnaires
were distributed to teachers in May 2014. They were sent to the teachers who had completed
both the pre-and post-training surveys and had agreed to complete the qualitative questionnaire.
Constructs measured by the survey were CSE and task value regarding technology. Teachers
answered a series of open-ended questions on the qualitative survey. The qualitative surveys
were distributed via email to the teachers who consented to participate. Of the 32 teachers who
completed both pre- and post-training quantitative surveys, 22 consented to participate in the
qualitative survey. The response rate was 69% for the qualitative survey. Thirteen of the
completed surveys were from middle school teachers for a 65% response rate. Nine of the
surveys were completed by high school teachers for a 75% response rate. The survey was
created using Microsoft Word. Teachers completed the surveys utilizing MS Word. All of the
completed surveys were returned via email still in a MS Word file.
Analysis
All quantitative data were analyzed using the statistical program SPSS, version 23, and
25. The independent variables used in the quantitative analysis were Time of Trial (pre or post
PD), Teacher Ethnicity, Grade Level Taught, and Years of Experience as a Teacher. The
dependent variables analyzed were the scores on the modified version of the MSLQ using only
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 40
the selected subscales for motivation. Initial data examined were descriptive statistics, including
means, medians, and modes for each dependent variable and frequencies for the independent
variables. The assumptions of normal distribution and equality of variance of the dependent
variables were assessed using the Shapiro-Wilkes and Levene’s tests, respectively. If any
violations of these assumptions had been found, appropriate data transformation techniques
would have been applied as indicated by the nature of the violation noted. Scale reliability for
the Likert scales used in this administration of the modified MSLQ were assessed using
Cronbach’s alpha statistic with values equal to or greater than 0.70 taken as representing
acceptable scale reliability. Initial equivalence of the groups on the dependent variables was
assessed using a One-Way Analysis of Variance Procedure. If the groups were determined to be
equivalent prior to the professional development treatment, further statistical analysis would
have proceeded without further data modification. If the groups were found to be statistically
significantly different on either of the dependent variables, the scores of the dependent variables
were standardized and further analysis was conducted on the standardized values. The
experiment-wide level of significance was set a priori at α = .05.
To answer the first research question, survey data were entered into SPSS statistics
program for analysis. The paired-samples t-test was used to determine whether the mean
differences between the before professional development, and after professional development
scores teachers’ task value were significant. Further analysis attempted to determine if there was
a relationship between gender, grade level taught, years of teaching experience, ethnicity and
teachers’ task value for using laptops in the classroom. Data collected from the survey was
inputted into SPSS statistics program for analysis. A t-test of independent samples was used to
compare the task value scores for males and females, and for the middle school and the high
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 41
school faculty. A one-way between-groups ANOVA was utilized to examine the influence of
age and ethnicity on task value scores. Qualitative questionnaires completed by select teachers
helped to explain any quantitative differences in teachers’ task value for using laptops in the
classroom. Creswell’s six-step approach was used to analyze this qualitative data (Creswell,
2009). Dedoose, an online application for analyzing qualitative and mixed-methods research,
was also used to assist in coding and analysis.
To answer the second research question, survey data were entered into SPSS statistics
program for analysis. The paired-samples t-test was used to determine whether the mean
differences between the before professional development, and after professional development
scores on teachers’ self-efficacy were significant. Further analysis of the second research
question attempted to determine if there was a relationship between gender, grade level taught,
years of teaching experience, ethnicity and teachers’ self-efficacy for using laptops in the
classroom. Data collected from the survey was inputted into SPSS statistics program for
analysis. A t-test of independent samples was used to compare the self-efficacy scores for males
and females, and for the middle school and the high school faculty. A one-way between-groups
ANOVA was utilized to examine the influence of age, and ethnicity on self-efficacy scores.
Qualitative questionnaires completed by select teachers and administrators helped to explain any
quantitative differences in teachers’ self-efficacy for using laptops in the classroom.
Creswell’s (2009) six-step approach was used to analyze this qualitative data. The first
step requires organizing all data. In this case, all responses were submitted in MS Word files and
were uploaded to the Dedoose website. The responses on the questionnaire were categorized by
research question. Reading through all data is the second step of Creswell’s process. In this
step, the researcher looks for general trends from responses. What are respondents
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 42
communicating? How credible and in-depth are the responses? For this project reading the data
entailed trying to determine if respondents were simply trying to complete the questionnaire or
made a genuine attempt to thoroughly answer the questions. The third step in Creswell’s
approach requires coding the qualitative data. This requires taking the responses and then
segmenting the responses into categories. A response to a particular question would be broken
up and analyzed one sentence at a time. Sentences would then be placed into specific categories
during the coding step. Creswell’s fourth step is taking these categories from the coding process
to determine themes for analysis. In general, five to seven themes are likely to emerge from this
stage. The fifth step is highlighting the interconnectedness of the research themes. A narrative,
visuals or figures may be used by the researcher to illustrate the relationship between the
research themes. Creswell’s final step of qualitative data analysis requires the interpretation of
the data. What were the lessons learned as a result of reviewing the data? It could mean looking
at data from literature and determining how the current research findings are similar or different.
What actions should be taken as a result of the data that was collected? This is where the
researcher may develop a plan of action based upon his interpretation of the data.
Validity and Reliability
There are six motivation scales in the MSLQ (Duncan & McKeachie, 2005). In this
study, two of the six motivation scales were used. Only two of the scales were utilized as only
task value and self-efficacy were examined in this study. According to the manual for
administering the MSLQ (Pintrich, Smith, Garcia, & McKeachie, 1991), the scale which
measures task value consists of six items (4, 10, 16, 23, 26, 27), the mean score on this scale is
5.54 with a standard deviation of 1.25. The task value scale has an alpha of 0.90. The scale
which measures self-efficacy for learning and performance consists of eight items (5, 6, 12, 15,
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 43
20, 21, 29, 31), the mean score on this scale is 5.47 with a standard deviation of 1.14. The self-
efficacy scale for learning and performance has an alpha of .93 (Pintrich et al., 1991).
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 44
CHAPTER FOUR: RESULTS
This study focused on the influence of professional development on teachers’ comfort
with integrating technology. The purpose of this study was to explain how professional
development affects teachers’ task value and self-efficacy for utilizing laptops in the classroom.
The study design consisted of a two-phase explanatory sequential mixed-methods research
approach (Creswell, 2009). In the first phase, middle school and high school teachers completed
a modified form of the MSLQ (Pintrich et al., 1991). The MSLQ is a Likert-type scale in which
responses range from 1, which indicates the statement is “very untrue of me” to 7, which
indicates the statement is “very true of me.” The survey addressed the teachers’ beliefs
regarding learning how to integrate Mac OS devices into the classroom as well as their beliefs
regarding their ability to learn the skills required to integrate Macintosh OS devices. This
chapter contains an analysis of the data using descriptive and inferential statistics to describe and
explain teachers’ perspectives toward the integration of technology into the school curriculum.
Phase I entailed quantitative data collection (pre and post professional development) and data
analysis. Phase II consisted of qualitative data collection, data analysis, and interpretation of
data.
The measuring instruments for this study consisted of two parts. The Likert-type scale
items from the modified MSLQ (Pintrich et al., 1991) and the written responses from the open-
ended survey provided to select faculty members. The teacher survey that measured task value
and self-efficacy was administered to both the middle school and the high school faculty, both
before and after a series of four PC to Mac transition in-service training sessions. The survey
consisted of five demographic items, and 14 Likert-type scale items that addressed teacher self-
efficacy and task value for using the Macintosh computer as an instructional tool. The Likert-
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 45
type scale labels for both the self-efficacy and task value was on a scale of 1 to 7, with 1
meaning very untrue of me, 2 meaning not true of me, 3 meaning somewhat not true of me, 4
meaning neutral, 5 meaning somewhat true of me, 6 meaning true of me, and 7 meaning very
true of me. All 51 of the pre-test participant survey responses, and all 32 of the post-test
participant survey responses were used to calculate Cronbach’s alpha for the survey items, as
seen in Table 3. The items were grouped by value component. Self-efficacy items were grouped
together, and task value items were grouped together. The Cronbach’s Alpha for the original
MSLQ Self-Efficacy component was .930. The Cronbach’s Alpha for the original MSLQ for
Task Value component was .900. Cronbach’s alpha demonstrated a strong reliability of survey
items regarding teachers’ self-efficacy, and task value for using the computer as an instructional
tool. The Cronbach’s Alpha values on the modified version of the MSLQ were slightly higher
for both the Self-Efficacy component (pre-test and post-test) and the Task Value component
(pre-test and post-test) as compared to the original MSLQ values.
Table 3
Cronbach’s Alpha
Value Component a
Self-Efficacy Pre-Test
Self-Efficacy Post-Test
.955
.961
Task Value Pre-Test .913
Task Value Post-Test .902
Phase I – Data Collection and Data Analysis
To explain teachers’ perceptions toward the integration of technology within the
classroom, this study addressed the following research questions:
1. What is the influence of technology-focused professional development on teachers’ task
value for using a computer as an instructional tool?
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 46
2. What is the influence of technology-focused professional development on teachers’ self-
efficacy for using a computer as an instructional tool?
The responses were collected to address the research questions yielded the following data.
Microsoft Excel and SPSS 23, and 25 were used to assist in the statistical analysis.
Pre-Training Participant Demographics
Participants for the study were MSMMS and MSMHS faculty members. Two separate
email messages with the survey links were sent to the middle school and the high school faculty.
The teachers had one week to complete the pre-training survey. Fifty-one faculty members
completed the initial survey. In the 2013-14 school year, there were 32 middle school faculty
members, and 50 high school faculty members. There were 82 secondary teachers. This yielded
a 62% return rate. Table 4 represents the overall response rate of the secondary school faculty.
Table 4
Secondary Survey Participants
N %
Completed Survey 51 62
Total Faculty 82 100
Of the 51 teachers who took the survey, 33 were female, and 18 were male. Table 5
represents a breakdown of participants by gender.
Table 5
Participant Gender
N %
Male 18 35
Female 33 65
Total 51 100
The survey also collected information about teacher ethnicity and years of experience.
The categories on the survey were Hawaiian/Part-Hawaiian, White, Asian/Part-Asian, Pacific
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 47
Islander/Part Pacific Islander, Mixed, or Other. Participants could only select one category for
self-identification. Table 6 illustrates self-reported teacher ethnicity.
Table 6
Teacher Ethnicity
N Percent (%)
Hawaiian/Part-Hawaiian 23 45
White/Part-White 7 14
Asian/Part-Asian 16 31
Pacific Islander/Part-Pacific
Islander
1 2
Mixed 4 8
Other 0 0
Total 51 100
Faculty experience was captured in bands of years. The categories participants could
select from were 0-4, 5-9, 10-14, 15-19, 20-24 and 25+ years. Table 7 represents self-reported
teacher experience in years.
Table 7
Teacher Experience
Years N Percent (%)
0-4 4 8
5-9 4 8
10-14 16 31
15-19 11 21
20-24 8 16
25+ 8 16
Total 51 100
The middle school faculty accounted for 30 of the responses. The middle school faculty
yielded a 94% response rate. The high school faculty accounted for 21 of the respondents. The
high school faculty yielded a 42% response rate. Table 8 represents the response rate by
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 48
division. The researcher was working at the middle school at the time of data collection.
Faculty could have felt more obligated to complete the survey. In addition, the administration at
the middle school did require teachers to complete more paperwork on a consistent basis. The
culture of the middle school could be responsible for the higher completion rate.
Table 8
Breakdown of Participants by School
Responses Possible
Response Rate
(%) Percent of Total
Middle School 30 32 94 59
High School 21 50 42 41
Total 51 82 62 100
Pre-test task value. Figure 1 shows the total score participants received on the task value
items of the MSLQ. In the original MSLQ ,these were items 4, 10, 16, 23, 26, and 27. Each
item ranged from 1 to 7. A score of 1 was assigned to a response of “not at all true of me” while
7 was assigned to a response of “very true of me.” With six questions, the minimum score was
7, and the maximum was 42. The mean score for task value on the pretest was 33.27 with the
standard deviation of 7.456. The mean score for each task value item was 5.545 with a standard
deviation of 1.243. Figures 2 through 7 show the score frequency and distribution for each of the
individual test items measuring task value. Participants whose score was in the low range had an
individual item score was neutral to negative. Participants whose score was in the average range
had an individual item score which landed between “somewhat true of me” to “true of me.”
Participants in the high range had an individual item score which was between “true of me” to
“very true of me.”
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 49
Figure 1. Pre-test Task value total score.
Figure 2. Pre-test Task value Q4.
Q4: I think I will be able to incorporate the skills of what I’m learning about podcasts into my
teaching practice.
The mean score for task value item 4 was 4.94 with a standard deviation of 1.489.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 50
Figure 3. Pre-test Task value Q10
Q10: It is important for me to learn the course materials in this Mac integration training.
The mean score for task value item 10 was 5.86 with a standard deviation of 1.371
Figure 4. Pre-test Task value Q16.
Q16: I am very interested in learning how to use iLife apps (Garage Band, iMovie, iTunes,
iPhoto) in my classroom.
The mean score for task value item 16 was 5.53 with a standard deviation of 1.678.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 51
Figure 5. Pre-test Task value Q23.
Q23: I think the material in this iLife training is useful for me to learn.
The mean score for task value item 23 was 5.76 with a standard deviation of 1.258.
Figure 6. Pre-test Task value Q26.
Q26: I like the subject matter of this iLife training.
The mean score for task value item 26 was 5.63 with a standard deviation of 1.455.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 52
Figure 7. Pre-test Task value Q27.
Q27: Understanding how to utilize iLife in my classroom is very important to me.
The mean score for task value item 27 was 5.73 with a standard deviation of 1.328.
Task value pre-test scores by demographic. The mean task value score for male
participants on the pre-test was 33.89. The mean score for female participants was 32.94.
Table 9
Pre-Test Task Value Mean by Gender
gender N Mean Std. Deviation Std. Error Mean
Pre_Task_Value Male 18 33.89 8.050 1.898
Female 33 32.94 7.220 1.257
An independent-samples t-test was administered to compare the task value scores on the
pre-test. There was no statistical difference in scores for male participants (M = 33.89, SD =
8.050) and female participants (M = 32.94, SD = 7.220; t (49) = .431, p = .668, two-tailed).
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 53
Table 10
Pre-Test Task Value Levene’s Test by Gender
Levene's Test for Equality of
Variances t-test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Pre_Task_Value Equal variances
assumed
.380 .540 .431 49 .668
Equal variances
not assumed
.417 31.922 .679
A one-way between-groups ANOVA was administered to explore the influence of
ethnicity on task value scores, as measured by the modified MSLQ. There was no statistical
difference between the five groups at the p <.05 level. F (4, 46) = .945, p = .447). The
difference in scores did not violate Levene’s test for homogeneity of variances. The group sizes
were also relatively small except for Hawaiian/Part-Hawaiian, and Asian/Part-Asian.
Table 11
Pre-Test Task Value by Ethnicity
Pre_Task_Value
N Mean
Std.
Deviation
Std.
Error
95% Confidence
Interval for Mean
Minimum
Lower
Bound
Upper
Bound
Hawaiian/Part-Hawaiian 23 32.04 7.923 1.652 28.62 35.47 8
White/Part-White 7 31.29 7.387 2.792 24.45 38.12 22
Asian/Part-Asian 16 34.50 7.510 1.877 30.50 38.50 14
Pacific Islander/Part-Pacific
Islander
1 42.00 42
Mixed 4 36.75 2.217 1.109 33.22 40.28 34
Other 0
Total 51 33.27 7.457 1.044 31.18 35.37 8
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 54
Table 12
Test of Homogeneity of Variances
Levene
Statistic df1 df2 Sig.
Pre_Task_Value Based on Mean .974 3 46 .413
Based on Median .881 3 46 .458
Based on Median and with
adjusted df
.881 3 41.071 .459
Based on trimmed mean .929 3 46 .434
Table 13
ANOVA
Pre_Task_Value
Sum of Squares df Mean Square F m
Between Groups 211.022 4 52.755 .945 .447
Within Groups 2569.135 46 55.851
Total 2780.157 50
A one-way between-groups ANOVA was administered to explore the effect of faculty
experience in years on task value scores, as measured by the modified MSLQ. There was no
statistical difference between the six groups at the p <.05 level. F (5, 45) = .943, p = .463. The
difference in scores did not violate Levene’s test for homogeneity of variances.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 55
Table 14
Pre-Test Task Value by Faculty Experience
Pre_Task_Value
n Mean
Std.
Deviation Std. Error
95% Confidence Interval
for Mean Minimum Maximum
Lower Bound Upper Bound
0-4 4 35.00 7.348 3.674 23.31 46.69 26 41
5-9 4 35.00 5.033 2.517 26.99 43.01 30 42
10-14 16 35.69 5.594 1.399 32.71 38.67 25 42
15-19 11 29.82 8.704 2.624 23.97 35.67 8 38
20-24 8 32.00 9.842 3.480 23.77 40.23 14 42
25+ 8 32.75 7.305 2.583 26.64 38.86 22 41
Total 51 33.27 7.457 1.044 31.18 35.37 8 42
Table 15
Test of Homogeneity of Variances
Levene
Statistic df1 df2 Sig.
Pre_Task_Value Based on Mean .702 5 45 .625
Based on Median .637 5 45 .673
Based on Median and with
adjusted df
.637 5 32.512 .673
Based on trimmed mean .672 5 45 .647
Table 16
ANOVA
Pre_Task_Value
Sum of Squares df Mean Square F Sig.
Between Groups 263.583 5 52.717 .943 .463
Within Groups 2516.574 45 55.924
Total 2780.157 50
The mean task value score for middle school participants on the pre-test was 32.40. The
mean score for high school participants was 34.52.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 56
Table 2
Grade Level Mean Scores
grade_level n Mean
Std.
Deviation
Std. Error
Mean
Pre_Task_Value Middle
School
30 32.40 8.629 1.575
High School 21 34.52 5.316 1.160
An independent-samples t-test was administered to compare the task value scores on the
pre-test. There was no statistical difference in scores for middle school teachers (M = 32.40, SD
= 8.629) and high school teachers (M = 34.52, SD = 5.316; t (49) = -1.001, p = .322, two-
tailed).
Pre-test self-efficacy. Figure 8 shows the total score that participants received on the
self-efficacy items of the MSLQ. In the original MSLQ these were items 5, 6, 12, 15, 20, 21, 29,
and 31. Each item ranged from 1 to 7. A score of 1 was assigned to a response of “not at all true
of me” while 7 was assigned to a response of “very true of me.” With seven questions, the
minimum score was 8 and the maximum was 56. The mean score for task value on the pretest
was 42.78 with the standard deviation of 11.33. The mean score for each task value item was
5.348 with a standard deviation of 1.416. Figures 9-17 show the frequency and distribution for
each of the items that measured self-efficacy.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 57
Figure 8. Pre-test self-efficacy total score.
Figure 9. Pre-test self-efficacy Q5.
Q5: I believe I will learn a lot in this series of iLife (iPhoto, iTunes, Garage Band, iMovie)
training.
The mean score for self-efficacy item 5 was 5.16 with a standard deviation of 1.447.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 58
Figure 10. Pre-test self-efficacy Q6.
Q6: I’m certain I can understand the most difficult material presented in the readings for this
iLife training.
The mean score for self-efficacy item 6 was 5.16 with a standard deviation of 1.58.
Figure 11. Pre-test self-efficacy Q12.
Q12: I’m confident I can understand the basic concepts taught in this iLife training.
The mean score for self-efficacy item 12 was 5.67 with a standard deviation of 1.424.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 59
Figure 12. Pre-test self-efficacy Q15.
Q15: I’m confident I can understand the most complex material presented by the instructor in
this series of iLife trainings.
The mean score for self-efficacy item 15 was 5.12 with a standard deviation of 1.728.
Figure 13. Pre-test self-efficacy Q20.
Q20: I’m confident I can do an excellent job on the tasks in these iLife sessions.
The mean score for self-efficacy item 20 was 5.29 with a standard deviation of 1.701.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 60
Figure 14. Pre-test self-efficacy Q21.
Q21: I expect to do well in this iLife training.
The mean score for self-efficacy item 21 was 5.41 with a standard deviation of 1.615.
Figure 15. Pre-test self-efficacy Q29.
Q29: I’m certain I can master the skills being taught in this iLife training.
The mean score for self-efficacy item 29 was 5.27 with a standard deviation of 1.65.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 61
Figure 16. Pre-test Self-efficacy Q31.
Q31: Considering the difficulty of this training, the trainers and my skills, I think I will do well
in these iLife sessions.
The mean score for self-efficacy item 31 was 5.45 with a standard deviation of 1.501
Self-efficacy pre-test scores by demographic. The mean task value score for male
participants on the pre-test was 45.11. The mean score for female participants was 41.52.
Table 18
Pre-Test Self-Efficacy Mean by Gender
gender n Mean Std. Deviation Std. Error Mean
Pre_Self_Efficacy Male 18 45.11 11.682 2.753
Female 33 41.52 11.108 1.934
An independent-samples t-test was administered to compare the task value scores on the
pre-test. There was no statistical difference in scores for male participants (M = 45.11, SD =
11.682) and female participants (M = 41.52 SD = 11.108; t (49) = 1.085, p = .283, two-tailed).
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 62
Table 19
Pre-Test Self-Efficacy Levene’s Test by Gender
Levene's Test for
Equality of Variances t-test for Equality of Means
F Sig. t df Sig. (2-tailed)
Pre_Self_Efficacy Equal variances
assumed
.098 .756 1.085 49 .283
Equal variances
not assumed
1.069 33.566 .293
A one-way between-groups ANOVA was administered to explore the effect of ethnicity
on self-efficacy scores, as measured by the modified MSLQ. There was no statistical difference
between the five groups at the p <.05 level. F (4, 46) = .886, p = .480. The difference in scores
did not violate Levene’s test for homogeneity of variances. These group sizes were small when
examining pre-test self-efficacy scores by ethnicity.
Table 20
Pre-Test Self-Efficacy by Ethnicity
Pre_Self_Efficacy
n Mean
Std.
Deviation
Std.
Error
95% Confidence Interval for
Mean
Min Lower Bound Upper Bound
Hawaiian/Part-
Hawaiian
23 41.96 11.463 2.390 37.00 46.91 18
White/Part-White 7 39.86 9.890 3.738 30.71 49.00 31
Asian/Part-Asian 16 42.63 12.811 3.203 35.80 49.45 17
Pacific Islander/Part-
Pacific Islander
1 56.00 . . . . 56
Mixed 4 50.00 2.582 1.291 45.89 54.11 47
Other 0
Total 51 42.78 11.330 1.587 39.60 45.97 17
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 63
Table 21
Self-Efficacy Homogeneity of Variances Ethnicity
Levene
Statistic df1 df2 Sig.
Pre_Self_Efficacy Based on Mean 1.443 3 46 .242
Based on Median 1.034 3 46 .386
Based on Median and with
adjusted df
1.034 3 40.701 .388
Based on trimmed mean 1.338 3 46 .274
Table 22
ANOVA Ethnicity
Pre_Self_Efficacy
Sum of Squares df Mean Square F Sig.
Between Groups 459.064 4 114.766 .886 .480
Within Groups 5959.564 46 129.556
Total 6418.627 50
A one-way between-groups ANOVA was administered to explore the effect of faculty
experience in years on self-efficacy scores, as measured by the modified MSLQ. There was no
statistical difference between the six groups at the p <.05 level. F (5, 45) = 1.454, p = .224.
The difference in scores did not violate Levene’s test for homogeneity of variances.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 64
Table 23
Pre-Test Self-Efficacy by Faculty Experience
Pre_Self_Efficacy
n Mean
Std.
Deviation
Std.
Error
95% Confidence Interval
for Mean
Min Max
Lower
Bound
Upper
Bound
0-4 4 51.50 4.435 2.217 44.44 58.56 45 55
5-9 4 47.50 6.557 3.279 37.07 57.93 40 56
10-14 16 44.19 12.183 3.046 37.70 50.68 17 57
15-19 11 42.64 9.780 2.949 36.07 49.21 20 53
20-24 8 40.88 13.389 4.734 29.68 52.07 18 56
25+ 8 35.38 11.338 4.009 25.90 44.85 20 54
Total 51 42.78 11.330 1.587 39.60 45.97 17 57
Table 24
Self-Efficacy Homogeneity of Variances Faculty Experience
Levene
Statistic df1 df2 Sig.
Pre_Self_Efficacy Based on Mean 1.210 5 45 .320
Based on Median .798 5 45 .557
Based on Median and with
adjusted df
.798 5 36.183 .559
Based on trimmed mean 1.114 5 45 .366
Table 25
ANOVA Faculty Experience
Pre_Self_Efficacy
Sum of Squares df Mean Square F Sig.
Between Groups 892.894 5 178.579 1.454 .224
Within Groups 5525.733 45 122.794
Total 6418.627 50
The mean task value score for male participants on the pre-test was 45.11. The mean
score for female participants was 41.52.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 65
Table 26
Pre-Test Self-Efficacy by Grade Level
grade_level n Mean Std. Deviation Std. Error Mean
Pre_Self_Efficacy Middle School 30 40.17 12.078 2.205
High School 21 46.52 9.190 2.005
An independent-samples t-test was administered to compare the task value scores on the
pre-test in self-efficacy. There was a statically significant difference in scores between middle
school faculty (M = 40.17 SD = 12.078) and high school faculty (M = 46.52 SD = 9.190; t (49)
= -2.033, p = .048, two-tailed). The high school faculty had significantly higher self-efficacy
scores on the pre-test than the middle school faculty.
Table 27
Pre-Test Self-Efficacy Levene’s Test by Grade Level
Levene's Test for
Equality of Variances t-test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Pre_Self_Efficacy Equal variances
assumed
2.663 .109 -2.033 49 .048
Equal variances not
assumed
-2.133 48.601 .038
Phase II – Post-Training Data Collection and Analysis
Once the teachers had completed the four training sessions, those who participated in
the pre-training questionnaire were asked to participate in the post-training survey. The pre and
post-training questionnaires were identical. Two separate email messages with the survey links
were sent out to the 51 faculty members who completed the pre-test survey. The teachers had
one week to complete the post-training survey.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 66
Post-Training Participant Demographics.
Thirty-two faculty members completed the post-test survey. This is a 63% return rate for
the post-test. Table 28 represents the post-test survey response rate. While the percentage of
faculty participating appears lower considerably from 62% of faculty to 39%, the response rate is
relatively consistent. There was a 62% response rate for the pre-test survey, there was a 63%
response rate on the post-test survey, and a 69% response rate on the qualitative survey.
Table 28
Post-Test Participants
N % of Pre-Test % of Faculty
Completed Post-Test 32 63 39
Of the 32 teachers who took the survey 18 were female, and 14 were male. Respondents
of the survey identified as 56% female and 44% male. Table 29 represents a breakdown of
participants by gender.
Table 29
Post-Test Participant Gender
N %
Male 14 44
Female 18 56
Total 32 100
The survey also collected information about teacher ethnicity and years of experience.
The categories on the survey were Hawaiian/Part-Hawaiian, White/Part-White, Asian/Part-
Asian, Pacific Islander/Part Pacific Islander, or Mixed. Participants could only select one
category for self-identification. Table 30 illustrates self-reported teacher ethnicity.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 67
Table 30
Post-Test Teacher Ethnicity
N %
Hawaiian/Part-Hawaiian 13 41
White/Part-White 3 9
Asian/Part-Asian 12 38
Pacific Islander/Part-Pacific
Islander
Mixed
0
4
0
13
Total 32 100
Faculty experience was captured in bands of years. The categories participants could
select from were 0-4, 5-9, 10-14, 15-19, 20-24 and 25+ years. Table 31 represents self-reported
teacher experience.
Table 31
Post-Test Faculty Experience
Years N %
0-4 3 9
5-9 1 3
10-14 9 28
15-19 6 19
20-24 5 16
25+ 8 25
Total 32 100
The middle school faculty accounted for 20 of the responses, which comprised 63% of
the respondents. The middle school faculty yielded a 67% response rate. The high school
faculty accounted for 12 of the responses, which comprised 38% of the respondents. The high
school faculty yielded a 57% response rate. Table 32 represents the response rate by division.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 68
Table 32
Post-Test Breakdown by School
Responses Possible Response % % of Total
Middle School 20 30 67 63
High School 12 21 57 38
Total 32 51 63 100
Figure 17 shows the total score that participants received on the task value items of the
MSLQ. In the original MSLQ these were items 4, 10, 16, 23, 26, and 27. Each item ranged
from 1 to 7. A score of 1 was assigned to a response of “not at all true of me” while 7 was
assigned to a response of “very true of me.” With six questions, the minimum score was 7 and
the maximum was 42. The mean score for task value on the post-test was 32.22 with the
standard deviation of 6.534. The mean score for each task value item on the post-test was 5.37
with a standard deviation of 1.089. Figures 18-23 show the score frequency and distribution for
each of the test items measuring task value on the post-test. There was very little difference
between the pre-test and post-test scores for task value. The pre-test mean score was 33.27 and
the post-test mean score was 32.22. The pre-test mean item was 5.545 while it was 5.37 on the
post-test.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 69
Figure 8. Post-test task value total score.
Figure 9. Post-test task value Q4.
Q4: I think I will be able to incorporate the skills of what I’m learning about podcasts into my
teaching practice.
The mean score for task value item 4 was 4.72 with a standard deviation of 1.508.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 70
Figure 10. Post-test task value Q10
Q10: It is important for me to learn the materials in this Mac integration training.
The mean score for task value item 10 was 5.72 with a standard deviation of 1.198.
Figure 11. Post-test task value Q16.
Q16: I am very interested in learning how to use iLife apps (Garage Band, iMovie, iTunes,
iPhoto) in my classroom.
The mean score for task value item 16 was 5.50 with a standard deviation of 1.27.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 71
Figure 12. Post-test task value Q23.
Q23: I think the material in this iLife training is useful for me to learn.
The mean score for task value item 23 was 5.44 with a standard deviation of 1.413.
Figure 13. Post-test task value Q26.
Q26: I like the subject matter of this iLife training.
The mean score for task value item 26 was 5.47 with a standard deviation of 1.27.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 72
Figure 14. Post-test task value Q27.
Q27: Understanding how to utilize iLife in my classroom is very important to me.
The mean score for task value item 27 was 5.38 with a standard deviation of 1.289.
Task Value Post-Test Scores by Demographic. The mean task value score for male
participants on the pre-test was 31.4286. The mean score for female participants was 32.8333.
Table 33
Gender Post-Test Mean Scores
Gender N Mean Std. Deviation Std. Error Mean
Task_Value Male 14 31.4286 5.66656 1.51445
Female 18 32.8333 7.23757 1.70591
An independent-samples t-test was administered to compare the task value scores on the
post-test. There was no significant difference in scores for males (M = 31.4286, SD = 5.66656)
and females (M = 32.8333, SD = 7.23757; t (30) = -.597, p = .555, two-tailed).
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 73
Table 34
Gender Post-Test Independent-Samples t-test
Levene's Test for
Equality of Variances t-test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Task_Value Equal variances
assumed
.641 .430 -.597 30 .555 -1.40476
Equal variances not
assumed
-.616 29.993 .543 -1.40476
A one-way between-groups ANOVA was administered to explore the effect of ethnicity
on post-test task value scores, as measured by the modified MSLQ. There was a statistically
insignificant difference between the four groups at the p <.05 level. F (3, 28) = .415, p = .744.
The difference in scores did not violate Levene’s test for homogeneity of variances.
Table 35
Post-Test Task Value by Ethnicity
Task_Value
n Mean
Std.
Deviation
Std.
Error
95% Confidence
Interval for Mean
Minimum
Lower
Bound
Upper
Bound
Hawaiian/Part
Hawaiian
13 31.4615 6.15921 1.70826 27.7396 35.1835 20.00
White/Part-White 3 35.0000 8.88819 5.13160 12.9205 57.0795 25.00
Asian/Part-Asian 12 31.5833 7.41569 2.14072 26.8716 36.2950 17.00
Mixed Ethnicity 4 34.5000 4.04145 2.02073 28.0691 40.9309 31.00
Total 32 32.2188 6.53395 1.15505 29.8630 34.5745 17.00
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 74
Table 36
Post-Test Task Value Homogeneity of Variances
Levene
Statistic df1 df2 Sig.
Task_Value Based on Mean .740 3 28 .537
Based on Median .324 3 28 .808
Based on Median and with
adjusted df
.324 3 21.929 .808
Based on trimmed mean .737 3 28 .539
Table 37
Post-Test Task Value ANOVA
Task_Value
Sum of Squares df Mean Square F Sig.
Between Groups 56.321 3 18.774 .415 .744
Within Groups 1267.147 28 45.255
Total 1323.469 31
A one-way between-groups ANOVA was administered to explore the effect of teacher
experience on post-test task value scores, as measured by the modified MSLQ. There was no
significant difference between the four groups at the p <.05 level. F (5, 26) = .400, p = .844.
The difference in scores did not violate Levene’s test for homogeneity of variances.
Table 38
Post-Test Task Value by Teacher Experience
Task_Value
n Mean
Std.
Deviation
Std.
Error
95% Confidence Interval for
Mean
Min Max Lower Bound Upper Bound
0-4 3 32.6667 2.51661 1.45297 26.4151 38.9183 30.00 35.00
5-9 1 30.0000 . . . . 30.00 30.00
10-14 9 30.3333 8.13941 2.71314 24.0768 36.5898 17.00 40.00
15-19 6 33.5000 6.18870 2.52653 27.0054 39.9946 26.00 42.00
20-24 5 35.2000 6.87023 3.07246 26.6695 43.7305 24.00 42.00
25+ 8 31.6250 6.61033 2.33710 26.0986 37.1514 25.00 42.00
Total 32 32.2188 6.53395 1.15505 29.8630 34.5745 17.00 42.00
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 75
Table 39
Post-Test Task Value Homogeneity of Variances
Levene
Statistic df1 df2 Sig.
Task_Value Based on Mean 1.136 4 26 .362
Based on Median .615 4 26 .656
Based on Median and with
adjusted df
.615 4 21.140 .657
Based on trimmed mean 1.052 4 26 .400
Table 40
Post-Test Task Value ANOVA
Task_Value
Sum of Squares df Mean Square F Sig.
Between Groups 94.627 5 18.925 .400 .844
Within Groups 1228.842 26 47.263
Total 1323.469 31
The mean task value score for middle school faculty on the pre-test was 31.30. The mean
score for high school faculty was 33.75.
Table 41
Grade Level Post-Test Mean Scores
Grade Level n Mean Std. Deviation Std. Error Mean
Task_Value 6-8 20 31.3000 7.30609 1.63369
9-12 12 33.7500 4.90130 1.41488
An independent-samples t-test was administered to compare the task value scores on the
post-test. There was no significant difference in scores for middle school faculty (M = 31.30, SD
= 7.30609) and high school faculty (M = 33.75, SD = 4.90130; t (30) = -1.028, p = .312, two-
tailed).
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 76
Table 42
Grade Level Post-Test Independent-Samples t-test
Levene's Test
for Equality of
Variances t-test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Task_Value Equal variances
assumed
3.342 .077 -1.028 30 .312 -2.45000
Equal variances
not assumed
-1.134 29.51
3
.266 -2.45000
Figure 24 shows the total score participants received on the self-efficacy items of the
MSLQ. In the original MSLQ these were items 5, 6, 12, 15, 20, 21, 29, and 31. Each item
ranged from 1 to 7. A score of 1 was assigned to a response of “not at all true of me” while 7
was assigned to a response of “very true of me.” With seven questions, the minimum score was
8 and the maximum was 56. The mean score for task value on the post-test was 42.25 with the
standard deviation of 9.854. The mean score for each task value item was 5.281 with a standard
deviation of 1.232. Figures 25-32 show the frequency and distribution for each of the items that
measured self-efficacy.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 77
Figure 15. Post-test self-efficacy total.
Figure 16. Post-test self-efficacy Q5.
Q5: I believe I will learn a lot in this series of iLife (iPhoto, iTunes, Garage Band, iMovie)
training.
The mean score for self-efficacy item 5 was 5.53 with a standard deviation of 1.244.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 78
Figure 17. Post-test self-efficacy Q6.
Q6: I’m certain I can understand the most difficult material presented in the readings for this
iLife training.
The mean score for self-efficacy item 6 was 4.94 with a standard deviation of 1.585.
Figure 18. Post-test self-efficacy Q12.
Q12: I’m confident I can understand the basic concepts taught in this iLife training.
The mean score for self-efficacy item 12 was 5.47 with a standard deviation of 1.414.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 79
Figure 19. Post-test self-efficacy Q15.
Q15: I’m confident I can understand the most complex material presented by the instructor in
this series of iLife trainings.
The mean score for self-efficacy item 15 was 5.13 with a standard deviation of 1.385.
Figure 20. Post-test self-efficacy Q20.
Q20: I’m confident I can do an excellent job on the tasks in these iLife sessions.
The mean score for self-efficacy item 20 was 5.25 with a standard deviation of 1.459.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 80
Figure 21. Post-test self-efficacy Q21.
Q21: I expect to do well in this iLife training.
The mean score for self-efficacy item 21 was 5.38 with a standard deviation of 1.338.
Figure 22. Post-test self-efficacy Q29.
Q29: I’m certain I can master the skills being taught in this iLife training.
The mean score for self-efficacy item 29 was 5.19 with a standard deviation of 1.401.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 81
Figure 23. Post-test self-efficacy Q31.
Q31: Considering the difficulty of this training, the trainers and my skills, I think I will do well
in these iLife sessions.
The mean score for self-efficacy item 31 was 5.38 with a standard deviation of 1.264.
Self-efficacy post-test scores by demographic. The mean task value score for the male
faculty on the pre-test was 42.4286. The mean score for female faculty was 42.1111.
Table 43
Post-Test Self-Efficacy by Gender
Gender N Mean Std. Deviation Std. Error Mean
Self_Efficacy Male 14 42.4286 9.77213 2.61171
Female 18 42.1111 10.19740 2.40355
An independent-samples t-test was administered to compare the task value scores on the
post-test. There was no significant difference in scores for male faculty members (M = 42.4286,
SD = 9.77213) and female faculty members (M = 42.1111, SD = 10.19740; t (30) = .089, p =
.930, two-tailed).
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 82
Table 44
Post-Test Self-Efficacy Independent-Samples t-test by Gender
Levene's Test for
Equality of Variances t-test for Equality of Means
F Sig. t df Sig. (2-tailed)
Self_Efficacy Equal variances
assumed
.125 .726 .089 30 .930
Equal variances not
assumed
.089 28.637 .929
A one-way between-groups ANOVA was administered to explore the effect of ethnicity
on post-test self-efficacy scores, as measured by the modified MSLQ. There was no significant
difference between the four groups at the p <.05 level. F (3, 28) = .916, p = .446. The
difference in scores did not violate Levene’s test for homogeneity of variances.
Table 45
Post-Test Self-Efficacy by Ethnicity
Self_Efficacy
N Mean
Std.
Deviation
Std.
Error
95% Confidence
Interval for Mean Min
Lower
Bound
Upper
Bound
Hawaiian/Part
Hawaiian
13 42.0769 8.55825 2.37363 36.9052 47.2486 27.00
White/Part-White 3 47.0000 9.53939 5.50757 23.3028 70.6972 37.00
Asian/Part-Asian 12 39.5000 12.19165 3.51943 31.7538 47.2462 18.00
Mixed Ethnicity 4 47.5000 3.87298 1.93649 41.3372 53.6628 42.00
Total 32 42.2500 9.85377 1.74192 38.6973 45.8027 18.00
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 83
Table 46
Post-Test Self-Efficacy Homogeneity of Variances by Ethnicity
Levene Statistic df1 df2 Sig.
Self_Efficacy Based on Mean .994 3 28 .410
Based on Median .694 3 28 .564
Based on Median and with
adjusted df
.694 3 22.112 .566
Based on trimmed mean .927 3 28 .441
Table 47
Post-Test Self-Efficacy ANOVA by Ethnicity
Self_Efficacy
Sum of Squares df Mean Square F Sig.
Between Groups 269.077 3 89.692 .916 .446
Within Groups 2740.923 28 97.890
Total 3010.000 31
A one-way between-groups ANOVA was administered to explore the effect of teacher
experience on post-test self-efficacy scores, as measured by the modified MSLQ. There was no
significant difference between the four groups at the p <.05 level. F (5, 26) = .593, p = .705.
The difference in scores did not violate Levene’s test for homogeneity of variances.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 84
Table 48
Post-Test Self-Efficacy by Teacher Experience
Self_Efficacy
n Mean
Std.
Deviation
Std.
Error
95% Confidence Interval
for Mean Min Max
Lower
Bound Upper Bound
0-4 3 46.3333 5.03322 2.90593 33.8301 58.8366 41.00 51.00
5-9 1 44.0000 . . . . 44.00 44.00
10-14 9 40.5556 10.90999 3.63666 32.1694 48.9417 18.00 54.00
15-19 6 45.5000 8.68907 3.54730 36.3814 54.6186 30.00 56.00
20-24 5 45.0000 10.95445 4.89898 31.3983 58.6017 27.00 56.00
25+ 8 38.2500 10.96423 3.87644 29.0837 47.4163 18.00 56.00
Total 32 42.2500 9.85377 1.74192 38.6973 45.8027 18.00 56.00
Table 49
Post-Test Homogeneity of Variances by Teacher Experience
Levene
Statistic df1 df2 Sig.
Self_Efficacy Based on Mean .330 4 26 .855
Based on Median .216 4 26 .927
Based on Median and with
adjusted df
.216 4 24.009 .927
Based on trimmed mean .302 4 26 .874
Table 50
Post-Test Self-Efficacy ANOVA by Teacher Experience
Self_Efficacy
Sum of Squares df Mean Square F Sig.
Between Groups 308.111 5 61.622 .593 .705
Within Groups 2701.889 26 103.919
Total 3010.000 31
The mean task value score for the middle school faculty on the pre-test was 39.85. The
mean score for high school faculty was 46.25.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 85
Table 51
Post-Test Self-Efficacy by Grade Level
Grade Level N Mean Std. Deviation Std. Error Mean
Self_Efficacy 6-8 20 39.8500 10.65376 2.38225
9-12 12 46.2500 7.04692 2.03427
An independent-samples t-test was administered to compare the task value scores on the
post-test. There was no significant difference in scores for middle school faculty (M = 39.85 SD
= 10.65376) and high school faculty (M = 46.25, SD = 7.04692; t (30) = -1.847, p = .075, two-
tailed).
Table 52
Post-Test Self-Efficacy Independent-Samples t-test by Grade Level
Levene's Test for
Equality of
Variances t-test for Equality of Means
F Sig. t df Sig. (2-tailed)
Self_Efficacy Equal variances
assumed
2.121 .156 -1.847 30 .075
Equal variances not
assumed
-2.043 29.614 .050
Findings for the First Research Question
This section describes the findings for the first research question. A paired-samples t-test
was conducted to evaluate the impact of technology-focused professional development on
teachers’ task value for using a computer as an instructional tool. There was not a significant
difference between the teachers’ scores on the modified MSLQ on the pre-test and the post-test.
The decrease observed in mean score from the pre-test (M=32.9063, SD=8.43327) to the post-
test (M=32.2199, SD=6.53395), t(31) = .403, p>.001 (two-tailed) was not statistically significant.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 86
The mean decrease in scores was .68750 with a 95% confidence interval ranging from -2.79295
to 4.16795. The professional development seemed to have little effect on teachers’ task value for
utilizing computers as instructional tools. The analysis did not identify the influence of
professional development on the participants’ beliefs regarding the effectiveness of using a
computer as an instructional tool. There was a slight decrease in the overall mean scores when
comparing the pre-test and the post-test scores.
Findings for the Second Research Question
This section describes the findings for the second research question. A paired-samples t-
test was conducted to evaluate the impact of technology-focused professional development on
teachers’ self-efficacy for using a computer as an instructional tool. There was not a significant
difference in the teachers’ scores on the modified MSLQ on the pre-test (before training) and the
post-test (after training). There increase in scores observed from the pre-test (M=41.7188,
SD=12.31635) to the post-test (M=42.2500, SD=9.85377), t(31) = -.376, p>.002 (two-tailed) was
not statistically significant. The mean increase was .53125 with a 95% confidence interval
ranging from -3.41263 to 2.35013. The professional development that the teachers participated
in had a small, though insignificant impact on the teachers’ belief in their ability to use
computers as an instructional tool.
Table 53
Overall Mean Scores
Mean SD
Task Value Pre 33.27 7.456
Task Value Post 32.22 6.534
Self-Efficacy Pre 42.78 11.33
Self-Efficacy Post 42.25 9.854
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 87
Table 54
Item Mean Scores
Mean Item SD
Task Value Pre 5.545 1.243
Task Value Post 5.37 1.089
Self-Efficacy Pre 5.348 1.416
Self-Efficacy Post 5.281 1.232
Phase III - Qualitative Data Collection and Analysis
Once the post-treatment quantitative surveys were scored, teachers were divided into
three categories. The three categories were high, medium and low. Teachers whose total scores
on the modified MSLQ were more than one standard deviation below the mean total score in
both of the independent variables (task value and self-efficacy) were placed in the low category.
Teachers whose scores were more than one standard deviation above the mean total score in both
task value and self-efficacy were placed in the high category. All teachers whose scores fell
within one standard deviation of the mean for both task value and self-efficacy were considered
“medium.”
Of the 32 teachers who completed the post-treatment survey, there were 22 who then
completed the open-ended written questionnaire. Questions were asked about a range of topics
related to technology use in the classroom. This section discusses the findings obtained through
the open-ended questionnaire.
Benefits of Using Technology
Teachers in all three groups listed technology’s benefits to students as building their
confidence, adding efficiency to their workflow, providing them frequent and immediate
feedback, improving their learning, and providing them opportunities to practice tasks. Both high
and low range groups added technology can motivate and engage students. Teachers in the high
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 88
range for task value noted the most benefits to using technology in the classroom. The mid-
range teachers did not mention either engagement or motivation as benefits of technology.
Teachers in the low range for task value saw the least benefit to using technology.
Table 50 provides the mean response for each range group for frequency of technology
integration. The survey responses express faculty utilize technology almost every time there is
an opportunity to do so. The teachers in the low range still integrated technology very close to
almost every time. The mid-range group integrated technology slightly more often than almost
every time. The high group average response was close to always integrating technology.
Table 55
Frequency of Technology Integration
Mean Response Responses
High Range 4.8 5
Mid-Range 4.3 12
Low Range 3.8 5
Likert scale 1=never use; 2=almost never; 3=sometimes; 4=almost every time; 5=always
Table 54 provides the mean score level of technology integration using the SAMR model.
The SAMR model was developed by Puentedura (2012). The SAMR scale has is comprised of
four levels and evaluates the depth of technology integration. The first level is substitution (S),
the second level is augmentation (A), the third level is modification (M) and the fourth level is
redefinition (R). Faculty provided an example of a technology activity within their classroom.
The researcher then utilized the SAMR scale (Puentedura, 2012) to assess the level of
technology integration. If it was determined that the activity was at the substitution level, the
activity was assigned the numeric value of 1; if it was determined that the activity was at the
augmentation level, the activity was assigned the numeric value of 2; if it was determined that
the activity was at the modification level, the activity was assigned the numeric value of 3; if it
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 89
was determined that the activity was at the redefinition level, the activity was assigned the
numeric value of 4.
The substitution level is a straight replacement of one tool for another tool. An example
of a substitution level would be to use a computer to do word processing instead of hand-writing.
The augmentation level is a tool replacement with a functional improvement. Continuing along
the line of a handwritten document, it would be using a word processor but then adding the
functionality of embedded images and links to other pertinent content. The modification level in
the SAMR scale moves beyond a simple improvement to the activity or product. At this level,
there is a significant change. An assignment that was previously handwritten would now be a
website/blog post. In this case students work would now be published to a larger audience which
would potentially allow for feedback beyond the classroom walls. The redefinition level is
where the technology integration is so significant and impactful that prior to the introduction of
the technology the assignment was previously unimaginable. In this case, the student work
would be created in Google where there could be a multitude of students editing the work at
once. In addition, the work could then be published to an interactive site which would allow for
live commenting and continued evolution of the piece. The audience is now worldwide but more
than just sharing, there is an interaction between the author(s) and the audience.
The SAMR scale was utilized to categorize the technology lessons that the teachers
shared. The low range teachers’ lessons averaged a 1.8 which is below augmentation level. The
mid-range teachers’ lessons averaged a 2.1 which is slightly above the augmentation level. The
high range teachers’ lessons averaged a 2.5 which is right between the augmentation and the
modification level.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 90
Table 56
Level of Technology Integration using SAMR Model
Mean Responses
High Range 2.5 5
Mid-Range 2.1 12
Low Range 1.8 5
SAMR level evaluation 1=Substitution; 2=Augmentation; 3=Modification; 4=Redefinition
Characteristics of Effective Professional Development
All groups viewed effective professional development as application-specific, hands-on,
and immediately applicable. Teachers in the mid-range added that it is presented in a conference
format, and those in the high range stated it is theoretical, self-directed, and infuses enthusiasm
for using technology. Teachers in the high range provided the most characteristics of effective
professional development. Teachers in the low range provided the least number of
characteristics of professional development. Teachers in the mid-range listed one more
characteristic of effective professional development as compared to the low range group, even
though the group was the largest of all groups. One teacher in the low group had never
experienced effective technology-focused professional development.
Motivation for Participating in Professional Development
Teachers in the high and mid groups like the new ideas provided in professional
development, which allows them to reflect on their teaching practice. The high group also found
professional development helps to provide ideas which enhance the relevance of instruction.
Teachers in the mid group are motivated to participate in professional development when
stipends are provided. Teachers in the low group did not list any motivation for participating in
professional development, but teachers in both the high group and the mid group listed three
reasons for participating in professional development.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 91
This chart shows the quantifying of open-ended teacher responses to the question asking
how often they incorporate technology into their classroom. A response of always was assigned
5, often was assigned 4, sometimes was assigned 3, rarely was assigned 2, and never was
assigned 1. The mean response for all respondents was close to 3, which equated to a sometimes
response.
Table 57
Frequency of Professional Development Participation
Mean Response Responses
High Range 3 4
Mid-Range 3.1 12
Low Range 2.8 5
Response rating 1=never; 2=rarely; 3=sometimes; 4=often; 5=always
Characteristics of Teachers that Integrate Technology
Teachers in the high group found teachers who integrate technology are energetic,
enthusiastic about the profession, flexible, innovative, open-minded, and are risk takers.
Teachers in the mid group stated teachers who integrate technology are apprehensive,
enthusiastic about the profession, intelligent, and is open-minded. Teachers in the low group
perceived teachers who integrate technology as trying to be trendy, and young.
The teachers in the high group listed all positive attributes for teachers integrating
technology. The high group also listed six unique characteristics. Teachers in the mid group
used four different characteristics to describe faculty which integrate technology. One of the
four characteristics was a negative characteristic. Teachers in the low group attributed two
characteristics to faculty which integrate technology. Both of these attributes have negative
connotations.
To quantify the teachers’ responses for the question about teachers that integrate
technology there was a number assigned to the response. If the teacher listed attributes which
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 92
were generally considered positive, then that response was assigned a 2. If the teacher listed
attributes that are generally considered neutral, then that response was a assigned a 1. If the
teachers listed attributes that are generally considered negative, then that response was assigned a
0.
Table 58
Characteristics of Teachers that Integrate Technology
Mean Responses
High Range 1.8 5
Mid-Range 1.5 11
Low Range 1.0 4
Response rating 0=negative; 1=neutral; 2=positive
Role of Technology in Education
The high group recognized technology helps to prepare students for the real world, allows
for enhanced student expression, and is a tool in a teacher’s toolkit. The mid group also saw
technology as helping to prepare students for the real world, and understood technology allows
for enhanced student expression. They knew students prefer using technology and saw
technology as a tool in a teacher’s toolkit. The low group perceived technology as motivating
students, as students prefer using it, and also viewed technology as part of a toolkit.
All three groups agree technology is a tool in the teacher’s toolkit. It is up to the faculty
to utilize the tool effectively. The teachers in the high and mid group listed the same roles of
technology in education, with the exception that the mid group also noted a student preference
for technology.
To quantify the teachers’ responses for the question about the role of technology in
education there was a number assigned to each response. If the teacher listed attributes which
were generally considered positive, then that response was assigned a 2. If the teacher listed
attributes that were generally considered neutral, then that response was a assigned a 1. If the
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 93
teachers listed attributes that were generally considered negative, then that response was
assigned a 0.
Table 59
Role of Technology in Education
Mean Responses
High Range 1.5 4
Mid-Range 1.2 12
Low Range 1.4 4
Response rating 0=negative; 1=neutral; 2=positive
Teachers in the high range all shared positive memorable experiences integrating
technology. Teachers in the mid-range shared primarily positive memorable experiences with
technology, and teachers in the low-range shared neutral memorable experiences. One of the
experiences described was negative.
To quantify the teachers’ responses for the question about a memorable technology
experience there was a number assigned to each response. If the teacher listed attributes which
were generally considered positive, then that response was assigned a 2. If the teacher listed
attributes that were generally considered neutral, then that response was a assigned a 1. If the
teachers listed attributes that were generally considered negative, then that response was
assigned a 0.
Table 60
Memorable Technology Experience
Mean Responses
High Range 2.0 4
Mid-Range 1.8 11
Low Range 1.0 4
Response rating 0=negative; 1=neutral; 2=positive
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 94
Challenges to Effective Technology Use
Teachers in the high range and in the mid-range saw a need for more application-specific
training, lack of needed peripherals and equipment, and lack of software. They saw the learning
curve for implementing new technology, institutional policies and the time needed to plan
lessons and units which integrate technology as limiting. Teachers in the mid-range also
mentioned the lack of ready-made resources and a need for more coaching support. This group
saw technology as not advanced enough. Teachers in the low range saw classroom management
was an inhibitor to integrating technology. They also indicated hardware issues, training being
overwhelming, platform issues, institutional policies, and the time needed to plan lessons as
negatives.
Overcoming Technology Obstacles
Teachers in all groups stated there was a need for increased budget more integration
support, more time to plan for effective technology integration, and more training. All three
groups agreed that more integration support, more time to plan for effective technology
integration, and more training opportunities were essential to overcome barriers to meaningful
technology integration. Participants in the high and mid-range also highlighted the need for an
increase in budget. The high range group listed a need for access to advanced software as well as
for school policy revision while the mid-range group saw a need more articulation time and for
new technology developments, and the low group stated reliability of network and hardware
needs improvement.
Summary
This study examined the influence of professional development on teachers’ task value
and self-efficacy. It utilized a two-phase mixed-methods, sequential explanatory strategy to
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 95
investigate the research questions (Creswell, 2009). The first portion of the study utilized
quantitative methods. There was a pre-test given to faculty, four training sessions, and the post-
test given to faculty who completed the pre-test. A paired-samples t-test was utilized to analyze
the results of the modified MSLQ. To determine if there were differences between groups there
were two different statistical tests that were conducted. The independent samples t-test was used
to evaluate possible differences in gender groups and grade level groups. The one-way between-
groups ANOVA was used to determine if there was a relationship between teacher experience, or
ethnicity and mean task value or mean self-efficacy scores. There was only one relationship
found to be statistically significant. There high school teachers had significantly higher self-
efficacy scores on the pre-test than their middle school counterparts.
The professional development that was provided to secondary faculty had statistically
insignificant impact on both teachers’ beliefs regarding the effectiveness of using laptops as
instructional tools in the classroom, and their confidence in their ability to utilize laptops as
instructional tools in the classroom. Faculty who returned both the pre and post surveys were
asked to participate in the final qualitative survey. The qualitative questionnaire sought to gain
further insight on teachers’ perceptions about technology and its use in an educational setting.
The qualitative responses were analyzed using Creswell’s six-step approach (Creswell, 2009).
Quantitative responses were used group the respondents into three groups (high, middle, low).
Faculty who scored in high range on the modified MSLQ integrated technology more
frequently than their counterparts. They had a higher level SAMR integration, used more
positive language to describe teachers who integrate technology, and shared the most positive
memorable technology experiences. Faculty who scored in the mid-range on the survey
participated in professional development more frequently than the other two groups.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 96
CHAPTER FIVE: DISCUSSION
This action research study sought to determine the influence of technology-focused staff
development on teachers’ beliefs about utilizing instructional technology in the classroom.
There are many aspects of a school’s culture which can have an impact on the effectiveness of
technology integration. There are the first order barriers discussed by Fullan (1991), such as
access to hardware, the quality of the student network, as well as the level of the available
technology support. Second order barriers are typically intrinsic. Examples are beliefs about
teaching, instructional models, or learning. This study used a mixed-methods approach. The
sequential explanatory strategy, which collects quantitative data prior to qualitative data to bring
depth to the quantitative findings was utilized (Creswell, 2009).
There are many resources available at Maile Schools, and leaders dedicated the funds for
students to have technology in their hands. All secondary students have 11-inch MacBook Air
laptops. These are issued to students at the beginning of the school year and students return the
laptops at the end of the school year. Students are expected to bring their devices to school, fully
charged, on a daily basis. This study examined the readiness of teachers prior to the switch from
the PC platform at the high school (already had a 1:1 laptop program which had been deployed
for approximately 10 years), and the launch of a 1:1 program at the middle school level.
Friedman (2007) suggested the world is in the stage of Globalization 3.0. Individuals can
now communicate, collaborate, and compete on global level. In Globalization 2.0, corporations
were communicating, collaborating, and competing on the global stage. With this new paradigm
where individuals are communicating, collaborating, and competing, Friedman contended the
world has flattened, and he playing field has been leveled for all players. Friedman stated, in this
new phase, students require a different kind of preparation for the work world. The jobs which
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 97
will be needed may not currently exist, so the workers of the future will need to adapt and learn
on the fly. Well developed communication and collaboration skills will also be essential in this
global marketplace. Wagner (2012, 2008) agreed students will need different skills for the work
place than has been needed in the past. Wagner argued most of the schools do not have
programs to develop the skills of communication, collaboration, creativity and critical thinking.
In this global job market place, technology is a key component as it helps to facilitate the
collaboration and communication components in this new flat world.
The current study focused on teacher’s attitudes regarding the tech and integration.
In addition, the study looked at how technology-focused professional development affected
attitudes towards technology and technology integration. Two research questions guided this
study:
1. What is the influence of technology-focused professional development on teachers’ task
value for using a computer as an instructional tool?
2. What is the influence of technology-focused professional development on teachers’ self-
efficacy for using a computer as an instructional tool?
Summary of Findings
Teachers’ task value, or beliefs and attitudes about the value of the computer as an
effective classroom tool, will influence how often teachers utilize the computer. The first
research questions addressed task value for using the computer as an instructional tool in the
classroom. The mean score on the pre-test for the items which measured task value was 33.27
with a standard deviation value of 7.456. The average item score was 5.545 with a standard
deviation value of 1.243. The mean score on the post-test for the items which measured task
value was 32.22 with a standard deviation value of 6.534. The average item score was 5.37 with
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 98
a standard deviation value of 1.089. While there was a slight decrease for the task value score
from the pre-test to the post-test, in both instances, the score falls between 5 (somewhat true of
me) and 6 (true of me) which demonstrates mild agreement with the statement. Overall, the
analysis of the task value items suggest the faculty believe that it is important to utilize laptops in
the classroom.
The teachers believe the laptop is a useful tool in the classroom. The activities teachers
use the computers for themselves mirror the type of activities they have the students using the
computers for. According to Bandura (1986) an individual’s belief in their own competency in a
particular activity will affect their motivation to participate in that activity. If an individual feels
that they are not very good at an activity, they are less likely to want to participate in that
activity. If a person feels confident in their ability for an activity then they are more likely to
want to engage in that activity.
The second research questions addressed self-efficacy for using the computer as an
instructional tool in the classroom. The mean score on the pre-test was 42.78 with a standard
deviation of 11.33. The average item score was 5.348 with a standard deviation of 1.416. The
mean score on the post-test was 42.25 with a standard deviation of 9.854. The average item
score was 5.281 with a standard deviation of 1.232. While there was a slight decrease for the
self-efficacy score from the pre-test to the post-test, in both instances the score falls between 5
(somewhat true of me) and 6 (true of me) which demonstrates mild agreement with the
statement. Overall, the analysis of the self-efficacy section suggests the faculty have confidence
in their ability to integrate technology in the classroom.
When looking at the pre-test and the post-test numbers, there was a decrease in the
overall scores in both the areas that were examined. The overall task value and the self-efficacy
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 99
post-test scores were lower than the pre-test scores. The training, as the treatment, had little
effect on the scores, either positively or negatively. This finding is in alignment with the
research examining professional development for educators. Guskey (1986) noted that, for there
to be change in a teacher’s belief, the teacher would have needed to adopted the new procedure
as well as seen a change in their students’ behavior or achievement. In this research project, the
teachers did all of their learning without implementing any new techniques. The focus of this
professional development was on utilizing laptops in a 1:1 environment. While the teachers had
laptops for their own use, the students did not. The teachers would not have seen any changes in
their students prior to taking the post-test, because all of the training concluded prior to
implementation of student laptops in the classrooms.
Barriers to school change tend to fall into one of two general categories: first order
barriers, or second order barriers (Fullan & Stiegelbauer, 1991). First order barriers are normally
easier to address because these are extrinsic in orientation (Fullan & Stiegelbauer, 1991). In the
case of Maile Schools, the first order barriers are allowing for more articulation time for faculty,
improvements to internet bandwidth, changes of policy regarding use of technology tools,
change in type or quantity of devices to students, or the internet policy. The teachers did list
some first order barriers to effective technology implementation. The time required to plan for
technology integration, as well as the lack of equipment and support were first order barriers
cited by faculty in the qualitative survey. Teachers citing time as a barrier to technology
implementation is found in the research addressing technology integration in the classroom
(Ertmer et al., 2012; Kopcha, 2012).
Second order barriers are more challenging to address because these are intrinsic in their
orientation. These require teachers to make changes in their beliefs about teaching and learning.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 100
The one barrier faculty listed that could be interpreted as a second order barrier was their
learning curve. Their specific comments were about their personal learning curve and how long
it would take them to learn about the software that was being integrated. This could also be
related to time needed to integrate which again is a first order barrier. This finding was
consistent with other research of teachers integrating technology in their classrooms. In Ertmer
et al.’s (2012) study, teachers also listed primarily first order (or external) barriers to their
technology integration. Another external barrier that teachers pointed out was that they felt that
their environment was restrictive. Several teachers in the high group stated they could not
accomplish some of their instructional goals because of the limitations in the environment related
to technology tools and their usage.
The professional development model used attempted to change teachers’ beliefs and
attitudes first, which would then lead to a change in teachers’ classroom practices and change in
students’ learning outcomes. This model is used often in professional development (Guskey,
1986, 2002). Perhaps it would be more effective to simply call out the expectation for the
technology integration, and then be sure to provide the support for the change. Guskey (1986,
2002) argued another model is more effective for teacher professional development. He suggests
that one should first change teachers’ classroom practices, which will then lead to change in
students’ learning outcomes, which will then lead to change in teachers’ beliefs and attitudes.
Limitations
One of the primary limitations was that all of the information about the types and
frequency of technology integration was self-reported. The faculty may have tried to answer in a
way which they thought was more desirable. Had there been more time and resources at hand,
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 101
the information about technology integration would have been gathered through classroom
observations.
The open-ended questions were asked via a survey distributed through an email. Had
there been more time available for the both the faculty and the researcher, the study would have
included in-person interviews with faculty. It is highly likely that respondents may have
provided richer responses if there was a person in front of them to ask follow-up questions.
It is important to provide teachers with quality professional development. The quality of
the professional development may not have been effective in enhancing faculty task value or
self-efficacy for integration. Perhaps an outside vendor providing the professional development
may be more effective in this aspect. It would also have been helpful to provide teachers
professional development methods grounded in research (Mouza, 2009). Examples are allowing
opportunities for active learning, instruction and discussion tied closely to classroom practice, as
well as to provide them with professional development that was not delivered in such a teacher-
directed fashion. It would have also been helpful to gather information about the specific needs
of the faculty rather than making assumptions about the type, method, and topic of professional
development that was delivered.
Implications for a Further Study
A similar study at a public school, which may have a different makeup of the faculty,
could yield different results. There would likely be a different faculty breakdown as well as a
more diverse student population. Another study could have the researcher going into the
classrooms to make observations about the frequency and level of technology integration. In this
study, teachers self-reported reporting regarding frequency of technology integration. There
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 102
could have been different outcomes had there been someone collecting information regarding the
frequency and kinds of technology integrations, rather than relying on self-reported data.
A future study would be to gather information from more of the stakeholders. This could
involve interviewing the students, the parents, and the administrators regarding frequency and
level of technology integration. It would be interesting to see how much alignment there was
between the different groups assessments of frequency of technology integration, as well as types
of technology activities.
Conclusion
According to Hohlfeld, Ritzhaupt, Dawson, and Wilson (2017), technology is no longer a
convenience, as it required to navigate the world. Infusing technology in the schools has been a
way in which policymakers have sought to bridge the digital divide (NCLB, 2001). The key to
advancing most educational initiatives is through professional development (Guskey, 1986;
Mouza, 2008). Teachers need quality research-based professional development to implement the
instructional changes asked of them.
Leadership is also an important component of effective technology integration. It is
important that leadership have a clear vision for technology implementation in their schools. If
they cannot define it, and they do not actively participate, it is unlikely that the effective
utilization of technology will expand beyond small pockets of success (Berrett, Murphy, &
Sullivan, 2012).
Technology initiatives coupled with clear leadership, thoughtful support and dedicated
professionals still hold promise to provide critical learning opportunities for our students.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 103
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FACTORS INFLUENCING TECHNOLOGY INTEGRATION 113
APPENDIX A
Interview Questions
Introduction
You have been selected to participate in this questionnaire because you have been identified as
someone who has a great deal to share about teaching, professional development and technology
integration at this school. This research project as a whole focuses on the improvement of
teaching and learning, with particular interest in understanding how we can improve professional
development experiences to increase the level of technology integration in the school. This study
does not aim to evaluate your techniques or experiences. Rather, I am trying to learn more about
how professional development experiences can impact teaching, and hopefully positively affect
student learning.
A. Interviewee Background How long have you been
_______ in your present position?
_______ at this institution?
Background information on interviewee:
What is your highest degree? ___________________________________________
What was your field of study? ____________________________________________
1. What motivates you to continue teaching?
2. How often do you incorporate technology into your classroom?
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 114
3. Could you provide a specific and recent example of how you’ve used technology in the
classroom? Was it effective, why or why not?
4. What are some obstacles to integrating technology within your content area? Do you feel there
are sufficient supports to overcome these obstacles?
5. In your opinion, how would you overcome these obstacles to technology integration?
6. What do you believe the role of technology is in student learning?
7. Could you describe a memorable experience with students and technology?
8. Are there any particular characteristics that you associate with faculty who are interested in
technology integration?
9. What motivates you to participate in voluntary professional development activities?
10. How frequently do you participate in these activities?
11. Please describe an especially successful professional development experience.
12. What do you need to be successful at incorporating technology, the MacBook Air laptops in
particular, into your curriculum?
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 115
13. Please include any information which you feel is pertinent to this topic which was not
captured in the above questions here. Mahalo nui for your participation!
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 116
APPENDIX B
MSLQ Revision
Value Component: Task Value
4. (Original) I think I will be able to use what I learn in this course or other courses.
(Pre) I think I will be able to incorporate the skills of what I’m learning about podcasts
into my teaching practice.
(Post) I think I will be able to incorporate the skills of what I’ve learned about podcasts
into my teaching practice.
10. It is important for me to learn the course materials in this class.
It is important for me to learn the materials in this Mac integration training.
It was important for me to learn the materials in this Mac integration training.
16. I am very interested in the content area of this course.
I am very interested in learning how to use iLife apps (Garage Band, iMovie, iTunes,
iPhoto) in my classroom.
I am very interested in learning how to use iLife apps (Garage Band, iMovie, iTunes,
iPhoto) in my classroom.
23. I think the course material in this class is useful for me to learn.
I think the material in this iLife training is useful for me to learn.
I think the material in this iLife training was useful for me to learn.
26. I like the subject matter of this course.
I like the subject matter of this iLife training.
I liked the subject matter of this iLife training.
27. Understanding the subject matter of this course is very important to me.
Understanding how to utilize iLife in my classroom is very important to me.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 117
Understanding how to utilize iLife in my classroom is very important to me.
Alpha: .90
Expectancy Component: Self-Efficacy for Learning and Performance
5. I believe I will receive an excellent grade in this class.
I believe I will learn a lot in this series of iLife (iPhoto, iTunes, Garage Band, iMovie)
training.
I believe I learned a lot in this series of iLife (iPhoto, iTunes, Garage Band, iMovie)
training.
6. I’m certain I can understand the most difficult material presented in the readings for
this course.
I’m certain I can understand the most difficult material presented in the readings for
this iLife training.
I’m certain I understood the most difficult material presented in the readings for this
iLife training.
12. I’m confident I can understand the basic concepts taught in this course.
I’m confident I can understand the basic concepts taught in this iLife training.
I’m confident I understood the basic concepts taught in this iLife training.
15. I’m confident I can understand the most complex material presented by the instructor in
this course.
I’m confident I can understand the most complex material presented by the instructor in
this series of iLife trainings.
I’m confident I understood the most complex material presented by the instructor in this
series of iLife trainings.
FACTORS INFLUENCING TECHNOLOGY INTEGRATION 118
20. I’m confident I can do an excellent job on the assignments and tests in this course.
I’m confident I can do an excellent job on the tasks in these iLife sessions.
I’m confident I did an excellent job on the tasks in these iLife sessions.
21. I expect to do well in this class.
I expect to do well in this iLife training.
I did well in this iLife training.
29. I’m certain I can master the skills being taught in this class.
I’m certain I can master the skills being taught in this iLife training.
I’m certain I can master the skills that were taught in this iLife training.
31. Considering the difficulty of this course, the teacher, and my skills, I think I will do
well in this class.
Considering the difficulty of this training, the trainers and my skills, I think I will do
well in these iLife sessions.
Considering the difficulty of this training, the trainers and my skills, I think I did well
in these iLife sessions.
Alpha .93
Abstract (if available)
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Bland, Valerie Suzue Shiraki
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Core Title
Factors influencing technology at a secondary school
School
Rossier School of Education
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Publication Date
11/25/2019
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