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Indonesian teachers' adoption of technology in the K-12 classroom: a TAM-based quantitative study

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Indonesian teachers' adoption of technology in the K-12 classroom: a TAM-based quantitative study

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Content Indonesian Teachers’ Adoption of Technology in the K-12 Classroom:
A TAM-Based Quantitative Study
by
Stephanie Riady
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
(Organizational Change and Leadership (On Line))
August 2023
Copyright 2023 Stephanie Riady
Indonesian teachers’ adoption of technology in the K-12 classroom
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EPIGRAPH
“We need technology in every classroom and in every student and teacher’s hand,
because it is the pen and paper of our time, and it is the lens through which we experience much
of our world.”
David Warlick

Indonesian teachers’ adoption of technology in the K-12 classroom
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DEDICATION
This research is dedicated to my Lord Jesus Christ, “in whom are hidden all the treasures
of wisdom and knowledge” (Colossians 2:3).

Indonesian teachers’ adoption of technology in the K-12 classroom
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ACKNOWLEDGEMENTS
First, I would like to give all glory and thanks to my Lord Jesus Christ. His death on the
cross and resurrection provides the reason for my existence and gives purpose to my life. He
alone provides meaning and direction to my service in the field of education. Thank you, Lord
for allowing me to experience deep joy in my journey as a lifelong learner.
I would like to acknowledge and give my warmest thanks to my dissertation chair, Dr.
Courtney Malloy, for patiently guiding me to complete this dissertation. I would also like to
convey my gratitude to Dr. Kimberly Hirabayashi and Dr. Kathy Stowe for your encouragement
and support as part of my dissertation committee.
I would like to give special thanks to the people who make up my support system. Thank
you to my husband, Chiang, who supported me through my Doctorate program from the very
beginning. Thank you to my children, Theodore, Timothy, Olivia, and Tobias, who provided me
with laughter, joy, and much needed interruptions throughout the writing process. Thank you to
Sus Siti and Sus Maya for keeping the kids busy as I spent time writing. Thank you to my four
grandparents who were always so proud to share my achievements with everyone they met.
Thank you to my parents who embodied what it meant to be in Christian education. Finally,
thank you to my parent in-laws whose prayers sustained me this far.
I am eternally grateful to my friends at the Pelita Harapan Foundation. First, thank you to
Christine Wijaya, who accompanied me from the beginning of my doctorate journey. Thank you
to Dr. Christine Sommers, Dr. Curtis Taylor, Dr. Eric Jobiliong and Christopher Nata, who
helped me as I navigate the new field of quantitative research. Finally, thank you to Deny Sinaga
and the Dian school team, who gave me meaningful feedback based on school ground realities.

Indonesian teachers’ adoption of technology in the K-12 classroom
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TABLE OF CONTENTS
EPIGRAPH ..................................................................................................................................... ii
DEDICATION ............................................................................................................................... iii
ACKNOWLEDGEMENTS ........................................................................................................... iv
LIST OF TABLES ........................................................................................................................ vii
LIST OF FIGURES ....................................................................................................................... ix
ABBREVIATIONS ........................................................................................................................ x
ABSTRACT ................................................................................................................................... xi
CHAPTER 1: INTRODUCTION TO THE STUDY ...................................................................... 1
Context and Background of the Problem ........................................................... 3
Organizational Context and Mission .................................................................. 6
Purpose of the Project and Research Questions ............................................... 10
Importance of the Study ................................................................................... 11
Overview of Theoretical Framework and Methodology .................................. 12
Definitions ........................................................................................................ 14
Organization of the Dissertation ...................................................................... 15
CHAPTER 2: LITERATURE REVIEW ...................................................................................... 17
Introduction ...................................................................................................... 17
Technology and Education ............................................................................... 17
Infrastructure and Hardware ............................................................................ 18
Information and Communication Technologies (ICTs) ................................... 20
Technology-Enabled Learning Approaches ..................................................... 22
Teachers and Technology Adoption ................................................................ 26
Self-Efficacy .................................................................................................... 27
Pedagogical Beliefs .......................................................................................... 30
Instructional knowledge ................................................................................... 32
Professional Development ............................................................................... 33
Access to technology and time for preparation ................................................ 34
Social inequity and digital divide ..................................................................... 35
Other COVID-19 Circumstances ..................................................................... 36
Theoretical Framework: Technology Acceptance Model (TAM) ................... 37
TAM in Education ....................................................................................... 39
Extended TAM Models ............................................................................... 41
CHAPTER 3: METHODOLOGY ................................................................................................ 43
Research Questions .......................................................................................... 43
Overview of Design ......................................................................................... 43
Indonesian teachers’ adoption of technology in the K-12 classroom
vi

Research Setting ............................................................................................... 44
The Researcher ................................................................................................. 45
Method ............................................................................................................. 46
Participants .................................................................................................. 47
Instrumentation ............................................................................................ 47
Data Collection Procedures ......................................................................... 50
Data Analysis .............................................................................................. 50
Validity & Reliability .................................................................................. 52
Ethics ........................................................................................................... 53
Summary .......................................................................................................... 55
CHAPTER 4: RESULTS AND FINDINGS................................................................................. 56
Participating Stakeholders ................................................................................ 57
Survey Participants........................................................................................... 57
Analysis ............................................................................................................ 58
Research Question 1 .................................................................................... 58
Research Question 2 .................................................................................... 80
Research Question 3 .................................................................................... 83
Summary .......................................................................................................... 90
CHAPTER 5: RECOMMENDATIONS....................................................................................... 92
Discussion of Findings ..................................................................................... 92
Recommendations for Practice ........................................................................ 97
Limitations and Delimitations ........................................................................ 102
Recommendations for Future Research ......................................................... 103
Conclusion ..................................................................................................... 105
REFERENCES ........................................................................................................................... 107
APPENDICES ............................................................................................................................ 139
Appendix A: Survey Questionnaire ............................................................... 139

Indonesian teachers’ adoption of technology in the K-12 classroom
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LIST OF TABLES
Table 1: Beacon of Light Foundation’s Strategic Technological Investments ............................... 9
Table 2: Constructs Items and Source .......................................................................................... 48
Table 3: Constructs Items and Composite Means......................................................................... 59
Table 4: Ordinal Scale for Self-Efficacy Scores. .......................................................................... 59
Table 5: Self-efficacy levels based on demographic groups. ....................................................... 61
Table 6: Ordinal Scale for Pedagogical Belief Scores. ................................................................. 63
Table 7: Mean scores of constructivist pedagogical beliefs based on demographic groups. ....... 64
Table 8: Mean scores of traditional pedagogical beliefs based on demographic groups. ............. 66
Table 9: Ordinal Scale for Perceived Usefulness Scores. ............................................................. 68
Table 10: Mean scores of Perceived Usefulness based on demographic groups. ......................... 69
Table 11: Ordinal Scale for Perceived Ease of Use (PEU) Scores. .............................................. 70
Table 12: Mean scores of Perceived Ease of Use (PEU) based on demographic groups. ............ 71
Table 13: Ordinal Scale for Attitudes Scores. .............................................................................. 73
Table 14: Mean scores of Attitudes Toward Technology (ATT) based on demographic groups. 74
Table 15: Ordinal Scale for Intention (IU) Scores. ....................................................................... 75
Table 16: Mean scores of intentions to use technology (IU) based on demographic groups. ...... 75
Table 17: Descriptive Statistics for Actual Use of Technology. .................................................. 77
Table 18: Mean scores of Actual Use of Technology (AU) based on demographic groups. ....... 78
Table 19: Correlation Matrix. ....................................................................................................... 80
Table 20: Coefficients for a Multiple Regression Model Between Five Independent Constructs
(SE, PB-T, PB-C, PU, PEU) and Attitudes Toward Technology (ATT). .................... 84
Table 21: Model Summary for Regression Analysis 1. ................................................................ 85
Indonesian teachers’ adoption of technology in the K-12 classroom
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Table 22: Revised Regression Model with Three Independent Constructs (PB-C, PU, PEU) and
Attitude Toward Technology (ATT). ........................................................................... 85
Table 23: Revised Model Summary for Regression Analysis 1. .................................................. 85
Table 24: Coefficients for a Multiple Regression Model Between Five Independent Constructs
(SE, PB-T, PB-C, PU, PEU) and Intention to Use Technology (IU). .......................... 86
Table 25: Model Summary for Regression Analysis 2. ................................................................ 86
Table 26: Coefficients for a Multiple Regression Model Between Three Independent Constructs
(PB-T, PU, PEU) and Intention to Use Technology (IU). ........................................... 87
Table 27: Revised Model Summary for Regression Analysis 2. .................................................. 87
Table 28: Coefficients for a Multiple Regression Model Between Two Independent Constructs
(PU, PEU) and Intention to Use Technology (IU). ...................................................... 87
Table 29: Second Revised Model Summary for Regression Analysis 2 ...................................... 88
Table 30 Coefficients for a Single Regression Model Between attitude toward technology (ATT)
and intention to use technology (IU). ........................................................................... 88
Table 31: Model Summary for Regerssion Analysis 3 ................................................................. 88
Table 32:Coefficient for a Single Regression Model between intention to use technology (IU)
and actual use of technology (AU). .............................................................................. 89
Table 33: Model Summary for Regression Analysis 4. ................................................................ 89
Table 34: Example of ROI justification to the Board ................................................................... 98
Table 35: Summary of Recommendations for BLF Schools. ....................................................... 98

Indonesian teachers’ adoption of technology in the K-12 classroom
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LIST OF FIGURES
Figure 1: Beacon of Light School Technology Strategy ................................................................ 8
Figure 2: Ajzen’s (1985) Theory of Planned Behavior. ............................................................... 30
Figure 3: Theory of Reasoned Action (Fishbein & Ajzen, 1975) ................................................. 38
Figure 4: Original Technology Acceptance Model (TAM) by Fred Davis (1989) ....................... 38
Figure 5: Dissertation Conceptual Framework Illustration .......................................................... 42
Figure 6: Conceptual Framework with Regression Pathways ...................................................... 95
Figure 7: Conceptual Model Depicting the Regression Findings. ................................................ 96

Indonesian teachers’ adoption of technology in the K-12 classroom
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ABBREVIATIONS

ATT Attitude Toward Technology
AU Actual Use of Technology
BLF Beacon of Light Foundation
IU Intention to Use Technology
PB Pedagogical Beliefs
PB-C Constructivist Pedagogical Beliefs
PB-T Traditional Pedagogical Beliefs
PEU Perceived Ease of Use
PU Perceived Usefulness
SE Self-Efficacy
TAM Technology Acceptance Model
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ABSTRACT
Technology is a powerful tool in education that, when used in the right context, can
improve academic outcomes, teaching delivery, content quality, and access to education.
However, in Indonesia, the adoption of technology within schools remains low and unsystematic.
Furthermore, technology adoption remains plagued by issues relating to unequal infrastructure,
reliability of Internet connection, and data plan availability. However, given the magnitude of
investment that the Indonesian government and private schools have poured into technology, and
the urgency created by the COVID-19 pandemic, it is critical to understand how technology
adoption can be increased to reap its benefits.
Studies have found that teachers are critical in determining whether technology gets
adopted and effectively utilized within a school. Without the teachers’ buy in, technology
adoption often fails to translate to positive educational outcomes. As such, it is important to
understand how school leaders can encourage teachers to utilize technology as a tool to improve
the quality of teaching and learning.
Grounded in Fred Davis’ (1989) Technology Acceptance Model, this quantitative study
attempted to understand the adoption of technology in the classroom among teachers who were
employed under the Beacon of Light Foundation (BLF, a pseudonym). The Beacon of Light
Foundation is a private school chain that include 14 K-12 schools located across the Indonesian
archipelago. At the time of this study, BLF oversaw 11,500 students and over 1,000 teachers
across its schools.
This study sought to understand teachers’ perception of technology across seven
constructs: Self-efficacy, pedagogical beliefs, perceived usefulness, perceived ease of use,
attitude toward technology, intention to use technology, and actual use of technology. This study
Indonesian teachers’ adoption of technology in the K-12 classroom
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constructed a survey instrument consisting of 7 demographic questions and 47 technology-
related questions that were disseminated to Kindergarten and Elementary teachers under the
Beacon of Light Foundation. Teachers were selected for this study based on their employment
status, teaching credentials, and grade levels. The study’s data analysis revealed several
noteworthy findings related to perceptions, correlations, and predictive relationships. This study
provided recommendations based on the findings to systematically increase teacher buy-in
toward technology.

Keywords: Educational technology, technology adoption, teachers, K-12 education, self-
efficacy, pedagogical beliefs, perceived usefulness, perceived ease of use, attitudes toward
technology, intention to use technology, actual use of technology, TAM model.
Indonesian teachers’ adoption of technology in the K-12 classroom

1

CHAPTER 1: INTRODUCTION TO THE STUDY
Over the past decade, technology has drawn significant attention as a powerful tool in K-12
education (Antoniou & Ioannou, 2018). When used in the right context, researchers have found
that technology in the classroom improved academic outcomes by enhancing instructional
design, content development and delivery (Buckner & Kim, 2012); promoted social change by
fostering behavioral and attitudinal changes in students (Buckner & Kim, 2012); and supported
educational innovations including flipped classrooms, individualized learning, peer-to-peer
teaching, and collaboration (Pierce & Cleary, 2016; Buisine et al., 2012). Furthermore, a UK-
based report by Promethean Research (2021) found that 79% of teachers believe educational
technologies helped them perform their teaching responsibilities more effectively, and 80%
believed that educational technologies improved learning outcomes.
The global education technology market size has also continued to grow, reflecting
investors’ optimism in technology’s potential to overcome educational hurdles. A report by
Grand View Research (2022) projected that the global educational technology market size,
which was valued at USD 106.46 Billion in 2021, would grow annually at a 16.5% CAGR until
2030. In a similar direction, a report by Global Industry Analysts (2021) estimated that the global
market for educational technologies and smart classrooms will grow to USD $207.3 Billion by
2026 from USD $84 Billion in 2020, reflecting a CAGR growth rate of 16.3%. Although North
America accounts for the largest regional market for educational technology products, the Asia-
Pacific region has seen a push for digital solutions to enhance operational efficiency, with China,
Indonesia, India, and Malaysia showing high penetration for educational technology products
(Global Industry Analysts, 2021).
Indonesian teachers’ adoption of technology in the K-12 classroom
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Despite technology’s potential to transform students academically and socio-emotionally,
these opportunities remain largely unrealized at the school level (Organization for Economic Co-
operation and Development, 2015; Pierce & Cleary, 2016). According to pre-pandemic data, the
adoption of technology in K-12 educational systems continued to be lower as compared to other
non-education sectors (Liu et al., 2017; Williams et al., 2023), remained unsystematic (Pierce &
Cleary, 2016), and was marked with confusion as to which educational technology products were
worth adopting (Escueta et al., 2017).
The ubiquity of technology has not led to a universally positive attitude toward
technology (Edison & Geissler, 2003). Some people openly embrace technology, while others
feel uncomfortable with technological change. Attitudes, however, are important because they
affect teacher perceptions, judgements, and classroom behaviors (Palak & Walls, 2009). This
study primarily focuses on attitudes toward technology because a positive attitude provides an
open pathway toward exploration of technology to be used for improved educational outcomes
and equity. While a positive attitude in and of itself does not guarantee an effective use of
technology in the classroom, a positive attitude creates openness for teachers to widen the range
of tools that they can use in and outside of the classroom.
Technology’s incorporation into K-12 learning sits largely in the hands of teachers.
Teachers are often credited as being single most important factor that determines whether
technology ends up being incorporated in the classroom (Zhao & Frank, 2003). The role of the
teacher is so critical that even in adverse situations where school leaders do not provide much
support for the adoption of technology in the school, a highly capable teacher still gives
technological projects a high chance of success (Zhao et al., 2002). Therefore, to harness the
potential benefits of technology in education within the Indonesian context, this dissertation used
Indonesian teachers’ adoption of technology in the K-12 classroom
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a quantitative survey questionnaire method to study factors that influence Indonesian teachers’
adoption of technology in the classroom.
Context and Background of the Problem
Indonesia faces a very basic problem of educational equity, access, and quality (World
Bank, 2021). The Indonesian Bureau of Statistics reported that an estimated 4.4 million children
aged 7-18 years were still out of school, despite the stipulation of a nine-year compulsory basic
education law passed in 2003 by the Ministry of Education and Culture (UNICEF, 2021). Even
up until the last decade, Indonesia’s educational system faced the uphill battle of addressing low
levels of learning outcomes. In 2018 and the years prior to that, Indonesian students were below
average in reading, mathematics, and science (Organization for Economic Co-operation and
Development, 2019). Specifically in reading, Indonesia’s average PISA reading literacy was 371
in 2018, which is significantly lower than the OECD average of 487 (Organization for Economic
Co-operation and Development, 2019). A study by Beatty et al. (2021) even found that learning
from 2000 to 2014 declined where an average seventh grader in 2014 had the equivalent
numeracy mastery of a fourth grader in 2000.
The issues facing Indonesia’s educational system are inextricably connected with issues
pertaining to Indonesia’s teacher workforce. Indonesia has a total of 3,017,296 teachers within its
schools (Kementerian Pendidikan dan Kebudayaan Indonesia, 2018). Of these teachers,
2,237,765 teachers work in public schools while 902,531 teachers work within the private
education system. In terms of educational grade levels, 10.5% of teachers teach at the
kindergarten level, 48.5% of teachers teach at the Elementary school level, while the remaining
41% teach at the middle and junior high school levels. Teacher quality remains a concern (Arifa
& Prayitno, 2019) because 17.4% of teachers do not hold an undergraduate degree, and 53.9% of
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teachers do not have teacher certification as required by the Ministry of Education and Culture
(Kementerian Pendidikan dan Kebudayaan Indonesia, 2018). Furthermore, the average score for
the Teacher Competency Exam, or Ujian Kompetensi Guru, was only 53.02 in 2019 and declined
to 50.64 in 2021, which is below the minimum competency level of 55.0 (Arifa & Prayitno,
2019; Kementerian Pendidikan & Riset, 2021). Only seven of Indonesia’s 27 provinces have
average passing scores that exceed 55.0 (Arifa & Prayitno, 2019).
Although Indonesia commits 20% of its national budget resources for education, Indonesia
annually underspends on education (World Bank Group, 2020). Furthermore, Indonesia’s GDP
per capita stands at USD $3,869 in 2020, which is significantly behind neighboring countries
such as Malaysia at USD $10,412 and Singapore at USD $59,798 in the same year (World Bank
Group, 2020). The low GDP levels translate to a lower purchasing ability for Indonesian families
to invest in educational services for their children.
Despite technology’s promise to transform learning and improve student outcomes, the
country’s digital divide makes it difficult for technology to be adopted effectively and at a large
scale. Although the internet penetration has almost quadrupled since 2011, the connectivity gap
continues to exist as only 51 percent of Indonesians had access to the internet in 2021 (World
Bank, 2021). These gaps are exacerbated along regional divisions, with the Java-Bali Island
regions having a larger proportion of adults with access to the Internet as compared to other areas
such as Maluku, Nusa Tenggara, and Papua (World Bank, 2021). The same study by the World
Bank also found that digital connectivity is strongly linked with financial affluence, with 71% of
the highest decile level of per capita consumption having access to the internet, as compared to
only 14% of adults at the lowest decile level of per capita consumption. In this context of
Indonesian teachers’ adoption of technology in the K-12 classroom
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inequality, unequal educational technology adoption could aggravate the situation and create new
lines of inequity in educational access across the country.
The economic landscape has also served as a barrier to the development of technology
solutions in Indonesia. Technology solutions from overseas are often too expensive to be adopted
by Indonesian schools. At the same time, locally developed educational technologies – an
industry that was virtually non-existent in Indonesia prior to 2008 (World Bank, 2018) – have
struggled to thrive due to the country’s low purchasing power. Twelve years later in 2020, the
majority of locally developed educational start-up solutions were still struggling to find a
workable business model that generated profit (World Bank Group, 2020).
The proliferation of new educational technology products has also led to a degree of
confusion in schools, where educators do not have consensus on which products and services are
worth investing in, or the tools to evaluate the usefulness of an educational technology product
(Escueta et al., 2017). Regardless, the push for technological adoption in Indonesian schools
continues to gain momentum both in the public and private sectors. The Indonesian government
has continued to develop free access platforms such as Rumah Belajar, a free access online
learning and content platform for school-aged children, and Merdeka Belajar, an open platform
that allows teachers to provide training and share teaching materials to other teachers and
students. The Education Ministry also launched Guru Berbagi, which allows teachers to share
ICT teaching and learning practices with other teachers in a shared platform.
The COVID-19 pandemic has also accelerated the push for schools to adopt educational
technologies (Middleton, 2020). During the pandemic, governments across the world
implemented policies prohibiting the physical opening of schools. Ninety-four percent of the
world’s student population were forced into emergency remote education, which refers to
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unplanned technology-mediated remote learning (Crompton et al., 2021; Schleicher, 2020;
World Bank Group, 2020) that did not undergo proper planning, design, and development
(Oliveira et al., 2021). This was also the case in Indonesia, where the government’s restrictions
on community activities (pemberlakuan pembatasan kegiatan masyarakat di Indonesia, or
PPKM) required the physical closure of schools.
Due to government and COVID-19 pressures, schools have felt an urgency to figure out
how to effectively use technology in their schools. More specifically, schools have felt the need
to gain a better understanding of the factors affecting their teachers’ intention to use technology
in their classrooms.
Organizational Context and Mission
The Beacon of Light Foundation (BLF, not its actual name) is an educational foundation
in Indonesia that governs 14 schools located in various first and second-tier cities across the
country. The vision of this foundation is to provide excellent, holistic, and transformative
Christian education at the K-12 level. In 2022, the network of schools collectively enrolled over
11,500 students with over 1,000 teachers and academic support staff. Of the total student
population, 10% were enrolled at the kindergarten level, 50% were enrolled at the elementary
level, and 40% were enrolled at the middle and high school levels.
The BLF schools are private schools in Indonesia offering an affordable education with a
tuition range of approximately USD $1,500 to $5,000 per year. The BLF schools are fully
tuition-funded and report to a governing board comprising of Board Advisors and Board
Members, represented by an Operational Committee for day-to-day operations. School-level
initiatives must be presented and approved by these authoritative structures.
Indonesian teachers’ adoption of technology in the K-12 classroom
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Over the past four years, the 14 schools under BLF have adopted an increasing number of
technological solutions to improve student experience and learning outcomes. Between 2019-
2021, the schools executed a new initiative called the Technological Infrastructure Project where,
under the leadership of the Foundation’s Chief Technology Officer, schools remapped and
improved each campus’ technological topology. This project allowed for the improvement of,
among others, Internet and WiFi connectivity, stability, bandwith adequacy, and high Internet
availability (i.e., with minimal down-time).
Even though the upgrade of BLF schools’ technology infrastructure required a large
amount of financial investment, this transformation was largely seen as an important cornerstone
that would enable future technological developments on the software, program, and application
levels. By the start of 2020, the BLF schools had already adopted Microsoft Teams as their office
collaboration system, which was timely since Microsoft Teams subsequently also became the
platform used during the emergency remote learning due to the COVID-19 pandemic. A few
months later, the BLF schools also began to adopt Moodle as their new LMS and EdConnect as
their upgraded Student Information System.
To improve the classroom infrastructure, the BLF schools have begun to roll out their
latest Smart Learning Initiative. This initiative involves transformation across four facets:
content, process, engagement, and physical environment. Each facet involves transformation in
teaching and learning beyond the availability of technology itself. The details of the
transformation under BLF schools’ Smart Learning Initiative are depicted in Figure 1.
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Figure 1
Beacon of Light School Technology Strategy

These initiatives are translated into strategic investments across multiple individual
schools. The newest smart classrooms in the BLF schools are equipped with technologies to
support teaching and learning in the 21
st
century. The classroom equipment includes a 360-
degree tracking camera, interactive smartboards, tablets, smart TVs, and speakerphones. In
addition, the BLF schools have redesigned the smart classrooms to include versatile furniture
that can accommodate multiple configurations to support collaborative learning processes. These
Indonesian teachers’ adoption of technology in the K-12 classroom
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investments are intended to provide greater support for teachers in the classroom, with the hopes
of improving student outcomes.

Table 1
Beacon of Light Foundation’s Strategic Technological Investments
List of Platforms, Devices, Tools
Facets Classroom Type
SC SE SP SPE A B C
Microsoft Office 365 SC SE SP
✓ ✓ ✓
Learning Management System (Moodle,
Microsoft Teams, OneNote)
SC SE SP
✓ ✓ ✓
Student Information System (EdConnect) SC SP
✓ ✓ ✓
PTZ Tracking Camera (360-Degree Camera) SC SE SPE
✓ ✓ ✓
Speakerphone SC SE SPE
✓ ✓ ✓
Clip-on and Handheld Microphone SE SPE
✓ ✓
Interactive Projector/Smartboard SC SE
✓
Access Point SC SP SPE
✓
Document Viewer SC
✓
Tablet (iPad, Android Tablets) SC SE SP SPE
✓
Smart TV SC SE SPE
✓
Versatile Furniture SP SPE
✓ ✓

The BLF schools fully understood that technology investments alone cannot improve
student outcomes. They also recognized that technology initiatives cannot be done in a vacuum,
but must be executed in tandem with other pedagogical, process, and physical transformations.
To organize their thoughts in a structured manner, the BLF schools have adopted the Khan
Octagonal Framework (2005) as a framework to conceptualize their vision of technology-
enabled learning in classrooms. The Octagonal Framework highlights eight distinct dimensions
that intersect with technology. These dimensions include the pedagogical, technological,
interface design, evaluation, management, resource support, ethical, and institutional dimensions.
Each dimension supports the others toward one unified strategy.
Since teachers are at the heart of technology-enabled learning, several of the BLF schools
have also implemented teacher mentorship programs, where teachers who were experienced in
using technology were given the task of mentoring teachers who had less technology experience.
Indonesian teachers’ adoption of technology in the K-12 classroom
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These experienced teachers were elevated to function as role models in the schools, tangibly
showing other teachers how technology can be effectively adopted.
Purpose of the Project and Research Questions
The purpose of this quantitative survey-based study is to understand factors that influence
Indonesian teachers’ adoption of technology in the classroom. The study included elementary
school teachers who have taught in the profession for five or more years, who are employed
under a not-for-profit educational foundation overseeing 14 schools located across first and
second-tier cities throughout Indonesia. Using an extended Technology Adoption Model (TAM)
framework, this dissertation sought to answer the following research questions:
1. What are teachers’ attitudes and beliefs about technology use in the classroom?
2. What are the relationships between the various teacher attitudes and beliefs?
3. To what extent do teachers’ attitudes and beliefs predict intention to use and actual use of
technology?
This study focuses on attitudes toward technology in general, without specifying a
particular brand or type of technology. A general definition of technology was adopted because
the goal of the study is to capture the overall teacher openness toward technologies from the
perspective of attitudes and intentions. Furthermore, this approach is useful considering the
reality that new educational technologies emerge every year, forming a plethora of options that
educators can choose from.
This study uses a general definition of educational technologies. Firmin and Genesi
(2013) defined information and communication technologies (ICTs) as “any product that can
store, retrieve, manipulate, or transmit information electronically in a digital form” (p. 1606) .
These range from infrastructure technologies such as the Internet, hardware technologies such as
Indonesian teachers’ adoption of technology in the K-12 classroom
11

computers, and software and application technologies. This study also includes various types of
technology use, including administrative and instructional uses for in-classroom teaching and
learning.
Importance of the Study
The potential benefits of technology and its ability to optimize educational processes and
student outcomes are clear. Technology can improve academic outcomes (Buckner & Kim,
2012), foster behavioral and attitudinal changes in students (Antoniou & Ioannou, 2018), and
improve learning experiences through the possibility of flipped classrooms, individualized
learning, and peer-to-peer teaching (Pierce & Cleary, 2016; Buisine et al., 2012). If teacher
adoption of technologies increases, there is potential to address some of the longstanding issues
of educational quality and equity in Indonesia.
Furthermore, the risks of failing to adopt educational technologies cannot be understated.
From the macro perspective, there is a risk of lost investment to the Indonesian economy as the
Indonesian government has already committed to investing in technologies at the national level.
Additionally, the growth of Indonesia’s educational technology sector depends on its ability to
gain relevance and acceptance within the K-12 landscape in Indonesia. If educational
technologies fail to gain traction among K-12 teachers in Indonesia, the results will be costly to
the Indonesian economy.
One example is the widely available government-funded platform known as Rumah
Belajar, which was developed by Indonesia’ Ministry of Education and Culture. In 2020, only
57% of students were aware that this platform existed, and teachers hesitate to use it due to
perceptions of poor quality, particularly because of their prior experiences with technology-based
platforms (UNICEF, 2021). Unless the rate of teacher adoption increases, there is an
Indonesian teachers’ adoption of technology in the K-12 classroom
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unquantifiable loss of investment and opportunity for projects like Rumah Belajar, which is
funded using taxpayer money.
Teachers are a key factor in the success or failure of technological adoption in K-12
schools. It is widely accepted that technology itself does not advance learning (Williams et al.,
2023). Technology only becomes useful when teachers use it for pedagogical purposes
(Organization for Economic Co-operation and Development, 2015). Most studies credit teachers
as the dominant factor associated with whether technology ends up being incorporated in the
classroom (Zhao & Frank, 2003). For this reason, it is critical to understand the factors that affect
teacher attitudes and their intention to use technology, as these factors are antecedent to teachers’
eventual use of technology.
Overview of Theoretical Framework and Methodology
A theoretical framework is the structure that summarizes concepts and theories and how
they relate to one another (Eisenhart, 1991; Kivunja, 2018). According to Grant and Osanloo
(2014), the framework also “provides the structure to define how the researcher will
philosophically, epistemologically, methodologically, and analytically approach the dissertation
as a whole” (p. 13) which they likened to the blueprint of a house.
This dissertation used an extended Technology Acceptance Model (TAM) as a guiding
framework to understand teacher adoption of technology in the classroom. TAM, which was
developed by Fred Davis (1989), theorizes that a person’s attitude, intention to use, and actual
use of technology are affected by their perceived ease of use and perceived usefulness of
technology. Over the years, researchers have extended TAM to incorporate various variables that
also influence attitudes, intention to use, and actual technology use. This dissertation will include
two additional variables: self-efficacy, which is based on Albert Bandura’s (1977)
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conceptualization of self-efficacy within his social cognitive theory, and pedagogical beliefs,
which refer to a teacher’s beliefs about teaching, learning, and assessment which underlies their
behavior, educational style, educational processes, and methods in the classroom (Mihaela &
Alina-Oana, 2014).
The TAM model is an appropriate framework for examining factors influencing teacher
adoption of technology in the classroom. The TAM examines the relationship between
independent variables, which in this case are factors affecting technology adoption, and how they
relate to dependent variables, which in this case are the teachers’ intention and actual use of
technology.
This dissertation will employ quantitative research methods to gather and analyze data
(Creswell & Cresswell, 2018). A survey was administered to 444 full-time elementary school
teachers within the BLF schools, as these teachers would be directly implementing technology in
the classroom. The survey contained items that explored teachers’ self-efficacy, pedagogical
beliefs, perceived usefulness, perceived ease of use, attitudes, intentions, and actual use of
technology.
Based on Davis’ (1989) TAM model, the researcher began by conducting an analysis of
the survey results using mean and ordinal data. The researcher then performed a correlational
analysis between the variables to understand the relationship between the variables. Finally, the
researcher conducted a statistical path analysis using statistical regression to arrive at a causal
model that attempted to explain the relationships between the study’s variables. The researcher
hoped to find a best-fit regression model to search for variables that predicted technology
acceptance among teachers. At the end of the dissertation, the researcher identified broad themes
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that emerged through the investigation and discussed these findings in relation to tangible
recommendations to the schools.
Definitions
Below are the key concepts that are critical to understanding this dissertation’s research
questions on factors affecting teacher adoption of technology.
● Actual use of technology (AU) is a variable from the original TAM model that measures
the frequency, time length, and use case of technology in the classroom.
● Attitude toward technology (ATT) is a variable from the original TAM model that refers
to an evaluative response or predisposition that is either favorable/positive or
unfavorable/negative (Fishman et al., 2021; Fishbein & Ajzen, 1975).
● Intention to use technology (IU) is a variable from the original TAM model that refers to
the degree to which users are determined to use a system or product for future use
(Mardiana et al., 2015). Behavioral intention is the antecedent of actual use.
● Educational Technology refers to the broad value chain which includes a) infrastructure
supporting and delivering software services, b) hardware such as computers and network
equipment, and c) instructional content technologies including instructional, test
preparation and assessment, and enterprise software for school management (Pierce &
Cleary, 2016; Chatterji & Jones, 2012)
● Emergency remote learning is distinct from pre-pandemic online learning because it is
done “in a hurry with bare minimum resources and scant time” (Hodges et al., 2020, p.
7). The sudden changes mean that online learning, both in process and technology, did
not undergo proper planning, design, and development (Oliveira et al., 2021) and
providers are expected to adapt on-the-go (Trust & Whalen, 2021).
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● Pedagogical beliefs (PB), a variable in this study’s extended TAM model, refer to a
teacher’s beliefs about teaching, learning, and assessment which underlies their behavior,
educational style, educational processes, and methods in the classroom (Mihaela &
Alina-Oana, 2014).
● Perceived Ease of Use (PEU), a variable from the original TAM model, is defined as the
degree to which an individual believes that his or her use of a particular technology
would be easy and free of effort.
● Perceived Usefulness (PU), a variable from the original TAM model, is defined as the
degree to which an individual believes that his or her use of a particular technology
would enhance productivity or job performance.
● Self-efficacy (SE), a variable in this study’s extended TAM model, refers to one’s
personal belief in one’s ability to exercise control over one’s actions to achieve a certain
level of mastery (Bandura, 2005).
● Technology adoption is defined as the acceptance, integration, and embracement of novel
technology (Granić, 2022). In the context of this dissertation, adoption is specifically
narrowed down to teacher adoption of technology for the purposes of enhancing teaching
and learning (Ibrahim & Ahmed, 2022).
Organization of the Dissertation
This dissertation follows the format of a five-chapter dissertation. Chapter 1 provides a
broad overview of the problem of practice, the background and context of the problem, the
project’s purpose and research questions, the study’s importance, an explanation of the
theoretical framework and methodology, and key definitions. Chapter 2 provides a review of the
existing and relevant literature on educational technology, centralizing the role of the teacher in
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technology integration, and an overview of the theoretical/contextual framework of the study.
Chapter 3 explains the research design and quantitative methodology. Chapter 4 presents the data
results of this research study, within the context of existing literature. Chapter 5 discusses the
study’s overall findings, recommendations, limitations, delimitations, and conclusion from this
dissertation.
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CHAPTER 2: LITERATURE REVIEW
Introduction
This chapter begins with a broad overview of the literature that explores the intersection
of technology in education, including how technology has allowed new technology-based
instructional approaches to emerge. It is followed by literature on the teacher’s centrality in the
process of technological adoption. The third section of this chapter covers challenges faced by
teachers in the adoption of technology. The final section of this chapter discusses the theoretical
framework of the Technology Acceptance Model (TAM) based on Fred Davis’ (1989) original
model. This section also provides the rationale and justification for this study’s Extended TAM
Model which stretches the original framework by including two additional variables that were
not present in the original TAM model.
Technology and Education
Educational technology is a broad term that has continued to evolve over time. In its
broadest definition, educational technology referred to “the use of tools, technologies, processes,
procedures, resources, and strategies to improve learning experiences in a variety of settings,
such as formal learning, informal learning, non-formal learning, lifelong learning, learning on
demand, workplace learning, and just-in-time learning” (Huang et al., 2019). Under this
definition, educational technologies also included technologies that were developed in the early
1900s such as the radio, telephone, and television.
However, as educational technology continued to emerge as a formalized profession in
the last 50 years, educational technology became narrowly defined to refer to technologies that
are internet-based (Huang et al., 2019). More specifically, the term refers to the broad value
chain which includes a) infrastructure supporting and delivering software services, b) hardware
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such as computers and network equipment, and c) instructional content technologies including
instructional, test preparation and assessment, and enterprise software for school management
(Pierce & Cleary, 2016; Chatterji & Jones, 2012)
There are multiple ways to segment existing educational technology products depending
on the analytical perspective taken. Aside from the technological segmentation share above,
(HolonIQ, 2020) published a top 50 list of Southeast Asia EdTech divided by eight sub-
segments: Language Learning, Skills & Jobs, Steam & Coding, Higher Education, Education
Financing, Verification, Management & Learning Environments, and Learning Support,
Tutoring & Test Preparation.
To break down this term further, Pierce and Cleary (2016) and Chatterji and Jones (2012)
described the broad value chain which includes infrastructure supporting and delivering software
services, hardware such as computers and network equipment, and instructional content
technologies including instructional, test preparation and assessment, and enterprise software for
school management.
Infrastructure and Hardware
The availability of basic technologies such as computer hardware, software, Internet
connectivity has continued to grow in both developed and developing countries around the world
(Bulman & Fairlie, 2016). In developed countries like the United States, United Kingdom, and
those that are part of the European Union, virtually all classrooms in public schools are already
fitted out with computers with Internet access. In addition, a large proportion of low-income
families in developed countries have been supported with devices that allow students to use
technology within their homes (U.S. Census Bureau, 2012; European Commission, Directorate-
General for the Information Society and Media, 2013; Bulman & Fairlie, 2016).
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Overall, technology use in Indonesia has lagged (World Bank, 2021). Data from 2019
indicated that in Indonesia 34% of students had access to a computer for schoolwork, 25.8% had
access to at least one computer at home, and 48% had access to the Internet (Graafland, 2018).
These numbers have continued to improve over time, with the percentage of Indonesian students
who had access to a computer at home growing from 21.1% in 2009 to 25.8% in just three years
(Graafland, 2018). The number of Indonesian students who had access to the Internet grew from
8.3% in 2009 to 23.1% in 2012, marking a 14.7% increase. The growth of access was bolstered
by Indonesia’s growing GDP per capita and with the support of government-funded initiatives
such as free Internet quotas in collaboration with state-owned telecommunications companies
and school computer lab CAPEX funding.
However, studies have found that investments in ICT resources alone are not linked to
improved achievements in reading, mathematics, or science. OECD’s 2015 report found that
countries that had a lower student to computer ratio, and countries that relied less on the Internet
at school for schoolwork, had better reading performance. A study by Bulman and Fairlie (2016)
that examined the literature on the effects of computer use in school, found that the net total
effect of ICT investments in schools neither produced a large positive or negative impact in
measurable academic outcomes. Their paper analyzed 14 individual studies conducted in
different countries that analyzed the effects of computers, laptops, Internet, or software
investments in schools to academic performance. They found that 10 studies indicated
insignificant results with three skewed in the negative direction, and one study clearly reported
negative results. Bulman and Fairlie’s study also analyzed 13 individual studies on computer use
at home which showed slightly better results but were still inconclusive overall.
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To explain the reasons behind these findings, Bulman and Fairlie (2016) purport that
while students use computers to complete assignments and access material, computers can also
be distracting as they are often used for non-educational purposes such as games, social
networking and communication with friends, and access to music and videos (Kaiser Family
Foundation, 2010). As a result, while children spend 16 minutes a day on average on the
computer for schoolwork, they also spend 17 minutes per day for playing games and 21 minutes
accessing videos and other entertainment (Kaiser Family Foundation, 2010). For this reason, in
and of itself, technology does not guarantee learning (Firmin & Genesi, 2013), and an emphasis
needs to be placed on how teachers integrate technology into the classroom (Driscoll, 2002).
OECD’s 2015 report echoed the same findings. While the report states that students who
use the computer moderately at schools have somewhat better learning outcomes in mathematics,
reading and science than those who rarely used computers, it also found that students who used
computers frequently “do a lot worse in most learning outcomes, even after accounting for social
background and student demographics” (p. 3). OECD’s 2015 report suggested that within the
classroom, technology can distract from the deep learning that happens through teacher-student
engagement. This distraction can happen in the classroom as well as outside the classroom,
where extreme Internet use, defined as being engaged for six or more hours a day, resulted in
withdrawal from socializing, decreased mental well-being, and reduction of overall academic
performance (Organization for Economic Co-operation and Development, 2015).
Information and Communication Technologies (ICTs)
Firmin & Genesi (2013) defined information and communication technologies (ICTs) as
“any product that can store, retrieve, manipulate, or transmit information electronically in a
digital form” (p. 1606). While ICT products may require infrastructure and hardware as
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described in the previous section, ICT covers a broader range of products that include Microsoft-
based products such as Word, Excel and PowerPoint, Google-based products such as those under
the G-suite for education, and Adobe products.
Scholars find it difficult to define ICT, which is an umbrella term covering diverse forms
of conceptual and methodological approaches. Livingstone (2012) point out that this term
included technologies that deliver one-to-many, such as a teacher teaching to a group of students,
many-to-many, or peer-to-peer technology where content is decentralized and user-generated.
There are also technology-enabled one-to-one learning, which supports individualized learning
between a tutor and a student, and technologies that replace the tutor using machine learning or
independent study in the form of self-paced learning. ICT can also refer to formal technologies
that are integrated into the school, such as the implementation of the smart or interactive
whiteboard, all the way to informal educational games (Livingstone, 2012).
While ICT is not an end in and of itself, the contextual use of specific forms of ICT to
meet pedagogical needs can lead to outcomes that exceed that of traditional classroom-based
teaching and learning (Czerkawski & Lyman III, 2016; Bakia et al., 2012). These outcomes are
frequently measured in terms of mathematical and literacy achievements. When ICT is used in
the service of pedagogy, ICT can create a dynamic and proactive teaching-learning environment
that can stimulate a student’s understanding about a subject (Arnseth & Hatlevik, 2010); allow
for immediate feedback and quality assurance of assessments (Anekwe & Izuchi, 2012); monitor
and assess learning (Jewitt et al., 2011); and provide statistical data for subsequent improvements
(Trimurtini et al., 2021).
ICT also plays a role in improving the range and quality of learning resources for school
leaders, teachers, and students. In a qualitative study of 12 English primary and secondary
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schools, Jewitt et al. (2011) found that tools such as learning platforms were able to gather and
organize learning resources, allow for sharing and collaboration, and offer students the
opportunity to utilize resources both in school and at home. In a study of seven exemplary
schools across the United States, McKnight et al. (2016) found that technology-managed content
helped teachers in keeping their teaching content current, and also allowed for greater richness to
the teacher’s content material.
Overall, there are studies that demonstrate specific cases where technology use
successfully led to increased student outcomes. One study in Semarang, Indonesia found that a
technology-enabled problem-based learning across two high schools resulted in increased
mathematical literacy scores of junior high school students (Wardono et al., 2018),
corresponding to the national goal of improving Indonesia’s PISA scores. In addition, the
problem-based approach improved the independent character of the students in this study.
Recently, scholars have attempted to redefine the definition of success in ICT
implementation beyond the traditional measures of mathematics and literacy achievements. More
emphasis has been placed into technology’s role in shaping the process of learning. Technology
holds the potential for the creation of a stimulating culture that liberates students from the rigid
hierarchies of the traditional curricular approach (Bekerman et al., 2009; Jenkins, 2006), and an
approach where students can play an active role in shaping the course of their personalized
learning (Grant, 2009; Jewitt et al., 2011).
Technology-Enabled Learning Approaches
The concept and definition of student engagement has been studied for over 70 years
(Groccia, 2018). While earlier definitions linked student engagement to participatory efforts
particularly in learning/academic activities, Groccia’s multidimensional definition expands the
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concept “beyond learning behaviors to a broad range of campus activities within and beyond the
classroom.” In other words, students are engaged across disciplinary boundaries where course
content is applied to real problems, and involve the cognitive, affective, and behavioral aspects
of the person (Groccia & Hunter, 2012).
There are several learning approaches that maximize student engagement. The next
section covers several of these approaches and discusses how technology becomes an enabler for
each. The learning approaches include the problem-based learning approach, gamification
strategies, flipped classroom, peer-to-peer teaching, personalized and adaptive learning.
Problem-Based Learning Approaches. Problem-based learning refers to a student-
centered instructional method where learning is directed toward a student’s ability and skills to
apply their knowledge and higher order thinking skills to solve real world problems (Jonassen &
Hung, 2012). Several studies have shown positive results on problem-based learning as an
instructional design. For example, Camilleri and Camilleri (2017) found that in a study of 241
educators, a problem-solving approach resulted in improved educational outcomes for teachers in
learning digital learning resources.
Technology can support problem-based learning approaches in several ways. First, since
problem-based learning is learner centered, technology provides students with a diverse set of
technological tools to demonstrate understanding and solve problems (Taradi et al., 2005).
Furthermore, technology can support the collaborative process required by problem-based
learning approaches, since problem-based learning requires greater flexibility for students to
explore concepts and skills through a collaborative learning process (Tambouris et al., 2012).
Finally, technology allows learners to make technology-enabled connections to the outside world
(Tambouris et al., 2012).
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Gamification Strategies. Gamification refers to an instructional design that aims to
increase learner motivation and engagement through the incorporation of game-based design
elements (Dichev & Dicheva, 2017). In a study that connected student types with gamification
strategies, Bovermann and Bastiaens (2020) found that understanding the students’ profiles is an
important component in the instructional design of online programs. Learning is personalized
according to student profiles, which include their preferences and interests, which affect their
learning needs and motivation.
Flipped Classroom Strategies. To promote active learning, this model inverts what is
traditionally done in class, such as listening to a lecture, with what students do out of class, such
as working on assigned problems (Nouri, 2016). Several context-specific studies found that
students perceived the flipped classroom method effectively improved learning by creating more
meaningful teacher- and peer-scaffolding in and out of the classroom (Nouri, 2016; Betihavas et
al., 2015; Gilboy et al., 2015) resulting in improved academic outcomes (Betihavas et al., 2015).
The flipped classroom methodology involves hybrid learning, which includes a combination of
physical and non-physical learning environments (Schultz & DeMers, 2020).
Peer-to-Peer learning. Peer-to-peer learning or peer-assisted learning occurs when
students help each other learn (Guraya & Abdalla, 2020) and where students learn from one
another inside and outside the classroom (Hanson et al., 2016). While this mode of learning
existed in education prior to the proliferation of technology, technology allowed a growth of
peer-to-peer learning through the configuration of learning platforms (Tang et al., 2022). The
research on peer-to-peer learning is positive with a clear value proposition in improved student
achievement, development of social and autonomy skills, and overall psychological well-being
(Tang et al., 2022).
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Personalized and Adaptive Learning. Personalized learning is defined as the practice of
customized or adaptive instruction based on individual needs and goals (Shemshack & Spector,
2020). Technology-based learning enables shifting centers of focus, where learning oscillates
from the traditional educator at center stage to one where the student is center stage (Schultz &
DeMers, 2020). While personalized learning has happened in the past in the form of tutors and
individual instructors, smart devices and intelligent devices have recently taken over
personalization based on big data technology (Peng et al., 2019).
However, as these studies show, the benefits of technology in pedagogy are context
specific. Leszczynski et al. (2018) found that online learning has proved to be an effective
learning tool for the social sciences and humanities, but less compatible with subjects that
require practical experiences in instruction, such as engineering and the medical sciences.
Schultz & DeMers (2020) also note that online learning has been successfully used in geography
education for many decades, although different studies demonstrated different and opposing
views on its effectiveness.
The outcomes of technology-based learning also vary according to what kind of
technology is used in the classroom. Trust & Whalen (2021) saw that many teachers gravitated
toward technologies focused on teacher-centered presentation, dissemination, organization, and
assessment of information. This is further corroborated by a report titled Common Sense Census:
Inside the 21st Century Classroom (Vega & Robb, 2019), where many educators perceived
technology as valuable to develop students’ 21st century skills, but only 25% of participants used
these tools. In these cases, the technology is simply a reflection of the teacher’s pedagogical
approach, which plays a larger role in determining student outcomes.

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Teachers and Technology Adoption
Across literature, scholars generally agree that teachers play a significant role in the
success and/or failure of technological adoption in schools. While studies show that technology
in and of itself does not advance learning, technology is useful when the teacher is able to use it
for pedagogical purposes (Organization for Economic Co-operation and Development, 2015).
Most studies credit teachers as being the dominant factor associated with whether technology
ends up being incorporated in the classroom (Zhao & Frank, 2003). For this reason, many studies
in schools’ technological adoption focus on the teachers and their attitudes and behavior toward
technology.
The role of the teacher is so critical that even in adverse situations where school leaders
do not provide much support for the adoption of technology in the school, a highly capable
teacher still gives technological projects a high chance of success (Zhao et al., 2002). Ertmer
(1999) also confirms this phenomenon. “Teachers’ agency in changing classroom practice… is
not meant to discount the systemic and cultural nature of the change process. Rather, teachers are
viewed as being key to the change process, coordinating “fit” from within their individual
teaching contexts” (p. 48).
This teacher-centric analytical approach aligned with Rogers’ (1983) diffusion of
innovation theory. Rogers purported that an analysis of technology adoption must consider the
beliefs and attitudes of the adopter, in this case the teacher, as a critical factor. According to
Rogers (1983), innovations go through a diffusion process where the idea is spread, both
purposefully and spontaneously, in a social change process that alters the structure and function
of a social system. The diffusion of innovation is inextricably linked with an individual’s
perception, since the process involves communication, which is a process where participants
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share information toward a mutual understanding. Furthermore, Rogers explains that the idea of
an innovation itself involves the perception of an individual as being new or novel.
There is an extensive body of research that explores various factors influencing teachers’
technology use. Ertmer (1999) distinguished between first and second-order barriers to
technology integration in understanding technology integration. She defined first-order barriers
as extrinsic to teachers (such as access to computers, digital tools, time) and second-order
barriers as intrinsic to teachers (related to beliefs, practices, and willingness to change). This
section begins by covering second-order factors such as self-efficacy, pedagogical beliefs, and
instructional knowledge, followed by first-order factors such as access to technology, time for
preparation, parent-teacher-student dynamic, and the social inequity.
Self-Efficacy
Bandura (2005) defined self-efficacy as a person’s personal belief in his or her ability to
exercise control over their actions to achieve a level of mastery. Self-efficacy is not only about
the possession of skills, but more so with the judgment of what to do with possessed skills
(Bandura, 1994). The concept focuses on an individual’s confidence in what he or she can
achieve within a given context and influences the ability to function at an optimal level (Bradley
et al., 2017). Belief about one’s own self-efficacy affects choices over a course of action, amount
and duration of effort, and emotional response to success (Bandura, 1977).
Measurement of self-efficacy can be done through three dimensions: Magnitude,
strength, and generality (Bandura, 1977). The magnitude of self-efficacy measures the level of
difficulty a person perceives a particular task to be. The strength of self-efficacy measures a
person’s level of confidence to succeed in a particular task. The generality of self-efficacy
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measures the extent self-efficacy beliefs relate to other behavioral domains or across time, since
the magnitude and strength of self-efficacy is typically related to a specific task or domain.
Self-efficacy in technology is strongly tied with technology use. In a review of 35 studies
that examined the relationship between teacher self-efficacy and technological integration, Bakar
et al. (2018) found that almost all studies found a positive relationship between the two factors.
According to Bandura (2005), a teacher’s self-efficacy is influenced by four important
sources of information: mastery experiences, vicarious experiences, social influence, and
physiological states (Bandura, 1977). Many studies have affirmed the connection between these
four components and a teacher’s intent to use technology:
Mastery Experiences. Mastery experiences are developed from a person’s personal
interpretation of his or her past performance to predict future ability to perform a task. Studies
have found that prior technology training (Houghton Mifflin Harcourt, 2018) and technology-
related pedagogical content knowledge training (Mishra & Koehler, 2006) affect future
technological adoption. Kay (2006) also found, in a review of 68 articles studying the
introduction of technology to preservice teachers, that increased strategies resulted in a more
pervasive use of technology among teachers. Furthermore, other studies discovered that small
successes through ongoing professional development increased intent to use technology (Sadaf et
al., 2016; Ertmer et al., 2012; Gomez et al., 2022).
Some studies have found that a teacher’s teaching experience contributed to the
successful adoption of technology in the classroom (Buabeng-Andoh, 2012; Lau & Sim, 2008;
Wong & Li, 2008; Giordano, 2007) because experienced teachers were more comfortable with
their ability to adapt instruction based on student needs (Gorder, 2008). Another interview-based
study of experienced teachers’ technology-based teaching practices found that they were able to
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effectively use technology as part of their hands-on learning pedagogy (Fahrman et al., 2020).
However, other studies found only a weak correlation between teaching experience and use of
technology (Jiale, 2021).
Vicarious Experiences. Vicarious experiences are gained by observing other people
perform a certain task. Bandura (2005) purported that when individuals observe other people’s
successes and failures, they create predictive judgments of their own prospects of success in
performing a similar task. In preservice education, studies have shown that teacher educators can
model positive technology use for preservice teachers (Baert, 2014; Tondeur et al., 2016),
although this was not always the case (Özüdoğru & Çakır, 2020). Another study by Winter et al
(2021) also showed that teachers who successfully used technology relied on the knowledge and
skills of their colleagues.
Verbal Persuasions. Verbal persuasions occur when an individual receives external
feedback on performance. This may affect an individual’s sense of self-efficacy, depending on
the credibility, trustworthiness, and expertise of the persuader. Mtebe (2020) suggested a
“Trainer of Trainees” approach, where trained staff could provide encouragement through verbal
persuasion to their trainees.
Emotional/Physiological States. Emotional and physiological states refer to emotional
arousals that may affect an individual’s sense of self-efficacy in coping with the situation.
Positive emotions can boost an individual’s confidence in his or her skills, while negative
emotions such as anxiety or depression can weaken confidence. In a qualitative study, Howard
(2013) found that teachers who were traditionally labeled as “resistant” against technology
experienced “significant risk, dread and anxiety with the use of technology” – feelings that were
irrational from lack of experience and knowledge.
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Pedagogical Beliefs
Pedagogical beliefs refer to a teacher’s beliefs about teaching, learning, and assessment
which underlies their behavior, educational style, educational processes, and methods in the
classroom (Mihaela & Alina-Oana, 2014). According to Ajzen’s (1985) conceptual framework,
an individual’s beliefs influence attitudes, which affect his or her intentions and actual actions.
Fishbein and Ajzen’s (1975) Theory of Reasoned Action and Theory of Planned Behavior
(Ajzen, The theory of planned behavior, 1991) purport that behaviors are determined by
constructs such as their attitudes – namely positive or negative feelings – about the behavior,
subjective norms – namely other important people’s acceptance of the behavior, and perceived
behavioral control.
Figure 2
Ajzen’s (1985) Theory of Planned Behavior.

Studies have found a strong connection between teachers’ pedagogical beliefs and their
classroom behavior. These studies include qualitative studies (Gil-Flores et al., 2017; Petko,
2012), quantitative studies (Liu et al., 2017; Woolley & Benjamin, 2004), and mixed method
studies (Ertmer et al., 2012).
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There are two constructs of pedagogical beliefs which are labeled as traditional and
constructivist (Chan & Elliott, 2004; Petko, 2012; Woolley & Benjamin, 2004). The traditional
approach focuses on the teacher-centered classroom with knowledge transmission as the main
goal, and the constructivist approach focuses on a student-centered approach. These pedagogical
beliefs permeate through various dimensions of teaching and learning, including curriculum,
assessment, the classroom learning environment, teaching strategies, classroom management
style, and the teacher-parent relationship (Woolley & Benjamin, 2004).
In line with this finding, another group of researchers, Liu et al. (2017) conducted a
survey of 202 teachers using the TAM model and found that the teachers’ pedagogical beliefs
played a determining factor in their willingness to adopt ICT in teaching and learning.
Specifically, the researchers found that teachers who were constructivist-oriented in their outlook
also held a more positive attitude toward ICT.
Pedagogical beliefs not only shape teachers’ attitudes toward technology, but also in the
selection of educational technologies to be used. For example, Martin & Vallance (2008) found
that teachers who believe in a teacher-centered educational approach tended to utilize technology
emphasizing skills acquisition. In contrast, teachers who were more student-oriented embraced
problem-solving technological tools (Lim & Chan, 2007). However, Ertmer et al. (2012) found
that while there was a strong alignment between pedagogical beliefs and technology use,
teachers often chose to apply technology differently from one another. As an example, some
teachers used technology to facilitate their beliefs about bringing greater collaboration in the
classroom, while others used technology to provide students with more choices for assignments
and projects.
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Both constructivist and teaching approaches were not found to be contrasting spectrums
when more of one approach meant less of the other. Woolley and Benjamin (2004) developed a
Teacher Beliefs Survey focusing on the traditionalist and constructivist teaching approaches and
discovered that both perspectives might impact teacher beliefs to different teaching dimensions,
such as views on curriculum, the learner, assessment, and others. They suggested that both
constructivist and traditionalist beliefs should be measured independently from one another.
Instructional Knowledge
Teachers often struggle to find the time and inspiration to design quality content and
activities for their students, following the best practices of instructional design processes and
principles (Khlaif et al., 2021). Teachers regularly grapple, on the job, to figure out whether an
instructional approach would work (Middleton, 2020). In the specific context of the COVID
pandemic, what eventually emerged was a lack of standardization across online instructional
quality. Middleton (2020) coined the term “classroom instructional divergence” to describe the
variance between classrooms, with many teachers experimenting with technology and
pedagogical approaches that were new. Teachers were also often asked to diversify their teaching
content into uncharted territories such as general home health education, mental health, and
pandemic prevention, which goes beyond a teacher’s main expertise (Cheng et al., 2020).
In the area of assessment, many teachers adapted themselves to ICT-enhanced testing
formats such as performance-based formats, constructed responses, sentence-completion
responses, and cloze-procedures (Adedoyin & Soykan, 2020). Results were also often loosely
held because teachers recognized the possibility of “test pollution” where stress, anxiety, health,
or unfamiliarity with online methods interfered with student results (Middleton, 2020).
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To complicate matters further, teachers were often unable to overcome the barriers due to
workload challenges. Workload challenges differ in nature for primary teachers and secondary
teachers. Primary teachers frequently mentioned the additional hours needed to create innovative
teaching aids, while secondary teachers shared additional hours were needed to monitor students,
especially since class sizes are often larger at the upper grade levels (Jain et al., 2021). The
workload challenges make it difficult for teachers to carve out time to explore technologies for
teaching and learning.
Professional Development
Technology-related professional development is one of the key factors to increase teacher
adoption of technology in the classroom (Buabeng-Andoh, 2012). Studies have shown that
professional development programs improve the scale in which teachers adopted technology in
their teaching and learning practices. They were able to do so through the transformation of their
technology literacy, skills with the actual technology use (Liu et al., 2015), pedagogical beliefs,
and beliefs about the value of technology in the classroom – components that were found
inextricably linked in multiple studies.
A study by Lee et al. (2017) found that middle school teachers who participated in two
years of technology professional development improved in their technological literacy and ICT
capabilities. In addition, the study also found that teachers’ pedagogical beliefs often changed
during the second year, leading to a shift in teaching methods. According to the study, these
changes resulted in improved student learning outcomes.
On a similar note, a study by Bowman et al. (2020) highlighted the importance of using
professional development to improve teachers’ beliefs on the value of technology in the
classroom. Technology-related beliefs are affected by exposure to professional development (Liu
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et al., 2015; Bowman et al., 2020), and professional development was shown to be effective in
changing teacher beliefs (Er & Kim, 2017). Naturally, favorable beliefs were ultimately linked
with teachers’ decision to use technology in the classroom (Cheng et al., 2020).
The importance of using technology to transform pedagogy cannot be overstated.
According to Tran (2016), teachers need to develop Technological Pedagogical Content
Knowledge (TPCK) on top of content knowledge and pedagogical knowledge. The support goes
beyond a “training model” to provide an ongoing development alongside the development of
technology.
Access to Technology and Time for Preparation
In line with Ertmer’s (1999) framework of first and second order barriers, one of the most
significant first-order barriers found by Trust & Whalen (2021) was in finding, evaluating, and
using new digital tools and applications. In a study of 277 teachers, respondents shared about
being overwhelmed by sorting through many available tools, turning to free online resources
such as those on YouTube as guiding resources.
Teachers’ ability to explore technological tools is further impacted by technological
concerns. At the very basic level, issues around Internet stability, Internet availability, and access
to adequate data packages are common roadblocks faced by teachers (Rasmitadila et al., 2020).
However, there are also a host of other issues associated with applications and software. These
issues often include downloading errors, installation issues, login problems, and audio/video
issues (Dhawan, 2020). Outside issues related to applications and software, educators and
students also face lifestyle issues with screen fatigue that end up affecting the quality of teaching
and learning (Trust & Whalen, 2021).
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These first-order barriers are inextricably linked with second-order barriers related to
teachers’ digital skills. The National Education Technology Plan (McFarland et al., 2017)
identifies three ongoing digital gaps faced by schoolteachers (as cited by Jain et al., 2021). The
first is dubbed the access gap, which relates to schools’ ability to support teachers in the
infrastructure for online learning, as well as to train teachers in ICT use. The second is the digital
literacy and skills gap, which directly refers to a teacher’s personal abilities, such as in shaping
lesson plans using digital tools, adapting learning materials to an online format, and the
pedagogical skills to teach on online platforms. The last gap is the usage gap, which refers to a
gap between teachers and students in their opportunities to use technology on a regular basis.
Social Inequity and Digital Divide
While social inequity and the digital divide has existed in Indonesia for quite some time,
COVID-19 exposed these disparities. The modality of online learning during COVID-19 meant
that students needed technological tools as a prerequisite for school participation. This
exacerbated existing digital inequalities, because students from low-income families were more
likely to face a lack of access to adequate technological resources or learning spaces in the home
(Manca & Delfino, 2020). In many situations, this could mean that students do not have access to
education at all (Dhawan, 2020).
Additionally, online learning also required infrastructure factors including internet
connection and technical support to be available to students as their learning environment shifted
from the school to the home (Affouneh et al., 2020). Khlaif, Salha, and Kouraichi (2021)
highlighted that the internet is frequently inaccessible for many students, and for others may be
unstable and unreliable. Since technology infrastructure is not evenly distributed across social
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and socioeconomic groups, it contributes to a widening gap in access to quality education
(Dhawan, 2020).
The digital divide is also evident in the availability of quality digital content. It is
particularly evident among teachers, contributing to teachers from high poverty schools teaching
less amount of new materials due to resource constraints (DeWitt, 2020; Herold & Yettick, 2020;
as cited in Middleton, 2020). Some fields also had more digital resources compared with others;
For example, a study on technology-based Chinese character learning by Xu et al. (2021)
discovered that there were more resources for languages based on the Roman script as compared
to the character-based languages like Chinese.
Other COVID-19 Circumstances
The experiences of Indonesian teachers in teaching online during COVID-19 demonstrate
that teachers were generally unprepared for a forced shift into technology-based learning. On the
one hand, the pandemic led to the positive development of myriad curriculum resources for
teachers to use (Schleicher, 2020). On the other hand, Rasmitadila et al. (2020) found that
teachers in Indonesia struggled with instructional strategies, ultimately resulting in a negative
impact on the quality of learning. Teachers also struggled with issues related to school
infrastructure, particularly concerning the accessibility of technology and internet that prohibit
online learning (Bakalar, 2018). Beyond this, studies have demonstrated that teachers were not
prepared to teach online, and student engagement dropped to the point where assignments were
not being submitted (Middleton, 2020). These and other COVID-19 induced changes were
critical because a nationwide pivot to online learning continued to exacerbate lackluster student
achievement.
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Despite these barriers, studies have also pointed to cases where success was seen. In a
study of 600 English as an Additional Language (EAL) pupils between grades 4-8, Thomas et al.
(2021) discovered that the satisfaction of basic psychological needs enabled the students to avoid
negative outcomes such as a lowering of aspirations. In particular, the pupils had a strong sense
of internalized control over course objectives and content, despite having low peer support or
interaction. In a separate study, Gares et al. (2020) found that in the University of Alberta,
student engagement continued to be high. They attributed this to a strong student-instructor
relationship which resulted in excellent class attendance, participation in online activities, and
completion of assignments. Both studies show that internal factors such as motivation need to be
strengthened, whether by personal/individual dispositions or external factors.
Currently, there are no studies specific to Indonesia on how COVID-19 shifted teacher
perceptions regarding technology. However, one report by MDR Education published in 2021
found that educators had a more positive attitude toward technology in the classroom in 2020 as
compared to 2018, with over 54% of teachers reporting that technology was useful and 29% of
teachers reporting that it would be a prerequisite to their ability to teach.
Theoretical Framework: Technology Acceptance Model (TAM)
The Technology Acceptance Model (TAM) was first proposed by researcher Fred Davis
as an instrument to predict human behavior toward the potential acceptance or rejection of
information technology (Granić & Marangunić, 2019; Davis, 1989; Chuttur, 2009). The TAM is
based on the Theory of Reasoned Action (TRA) by Fishbein and Ajzen (1975), which purports
that an individual’s behaviors are preceded by behavioral intentions, and that behavioral
intentions are a function of that individual’s attitudes and subjective norms. Additionally,
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attitudes are shaped by beliefs and perceptions about behaviors, and one’s own evaluations on
those beliefs.
Figure 3
Theory of Reasoned Action (Fishbein & Ajzen, 1975)

Based on the Theory of Reasoned Action, the TAM proposes that technology acceptance
is defined as an individual’s thoughts and actions regarding the use of technology (Siyam, 2019)
which is comprised of two constructs, which are perceived usefulness (PU) and perceived ease of
us (PEU).
Figure 4
Original Technology Acceptance Model (TAM) by Fred Davis (1989)

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Davis defined perceived usefulness as “the degree to which a person believes that using a
particular system would enhance his or her job performance” (Davis, 1989, p. 320). Perceived
ease of use was defined as “the degree to which a person believes that using a particular system
would be free of effort” (Davis, 1989, p. 320), which he further specified as being free from
difficulty or great effort. Perceived ease of use is closely linked with psychologist Bandura’s
concept of self-efficacy, which examined an individual’s belief in the ability to perform a task or
behavior (Bandura, 1977). According to Davis, both variables affect outcome variables on
technology use. The outcome variables include an individual’s attitude toward the technology
(AT), which affects his or her behavioral intention to use (IU) and actual use (AU) of the
technology.
Since its conceptualization, the TAM has continued to evolve and be adopted in extended
models. According to Lee et al. (2003), the model had gone through at least four periods: a
model introduction period, where Davis’ original TAM model was replicated in technology
studies in different settings or longitudinal situations; a model validation period, where
validation studies were conducted on TAM’s original instruments; a model extension period,
where new variables were proposed alongside TAM’s original constructs; and a model
elaboration period, where researchers tried to create the next generation TAM model.
TAM in Education
In the educational context, TAM emerged as a leading paradigm to investigate
technological acceptance (Granić & Marangunić, 2019). Studies have used TAM to understand
acceptance of e-learning (Khafit et al., 2021; Ibrahim et al., 2017; Abdullah & Ward, 2016;
Abramson et al., 2015); online video use in the classroom (Nagy, 2018); Learning Management
Systems (Munir, 2010; Alharbi & Drew, 2014); mobile learning (Al-Emran et al., 2018; Park et
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al., 2012); digital educational games (Dele-Ajayi et al., 2017); active learning classrooms
(Poellhuber et al., 2018); and personalized learning environments (Barrio-García et al., 2015).
Furthermore, meta-analytical studies have approached technology adoption from different
angles, including from the teachers’ perspective (Scherer et al., 2019), student perspective (Al-
Emran et al., 2018), instructional perspective (Cabero Almenara et al., 2021), and subject-
specific areas such as Library and Information Science (Weerasinghe & Hindagolla, 2017). Still,
other studies have examined the correlation between variables within perceived usefulness,
perceived ease of use, attitude toward technology, intention to use technology, and actual use of
technology (Imtiaz & Maarop, 2014).
In a systematic literature review of TAM to examine teacher adoption of technology in
teaching and learning, Granić and Marangunić (2019) pointed out that while TAM was a credible
model for assessment, the framework also presented gaps that needed to be addressed. Different
researchers addressed this by adding variables on top of Davis’ original TAM model: Subjective
norm, which refers to one’s perception on what is important or ought to be performed; computer
self-efficacy, which is a teacher’s belief in his or her ability to perform a computer-based task
(based on Compeau & Higgins, 1995); and facilitating conditions, namely the presence of
organizational or technical resources to support technology use (based on the UTAUT model by
Venkatech et al., 2003).
Other studies also found that TAM did not adequately address the factor of professional
knowledge about teaching and learning with technology, and thus combined TAM with the
Technological Pedagogical Content Knowledge (TPACK) framework proposed by Mishra and
Koehler (2006). The TPACK framework, which is based on Shulman’s (1987) taxonomy,
consisted of three domains of knowledge that included technological, pedagogical, and content
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knowledge. These domains were theorized to have an impact on one or all the TAM variables,
including perceived ease of use, perceived usefulness, attitude toward technology, intention to
use technology, and actual use of technology. One study by Mayer and Girwidz (2019), for
example, found that the TPACK functioned as a superordinate moderator variable that affected
almost all of TAM’s features, thereby effectively predicting user acceptance of the multimedia
application being studied.
Extended TAM Models
Today, researchers have developed many different extended TAM versions. In a meta-
analytic study of previous TAM literature and survey, Lee et al. (2003) found that TAM had
been used to study over 30 types of information systems applications in fields which included
communication, general, office, hospital, educational, and other specialized business systems.
Researchers have proposed adaptations of the TAM model depending on the research need. In a
study that summarized variables used by TAM studies, Lee et al. (2003) listed 25 variables that
were added. Some studies added self-efficacy as a variable, based on Bandura’s Social Cognitive
Theory (1977).
Venkatech and Davis (1996), for example, added variables that they believe were
antecedent to perceived ease of use, which included measures of self-efficacy and system
usability. Other studies added the variables of compatibility, complexity, observability,
trialability, and image, based on Rogers’ (1983) theory of diffusion. Still, in a different study on
the adoption of Windows-based workstations, Lucas and Spitler (1999) extended the TAM by
adding variables which included system quality, norms, and prior performance. Another study on
e-payment systems by Tella (2014) added four additional factors based on the Information
System Success Model (ISSM) by DeLone & McLean (1992; 2003).
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Figure 5
Dissertation Conceptual Framework Illustration

This study contends that attitudes and behavioral intentions are functions of other
psychological variables (Fishman et al., 2021). This dissertation uses an extended version of
TAM that adds two factors, namely self-efficacy and pedagogical beliefs, which are variables
that may affect attitudes, intention to use, and actual use of technology. This study breaks down
pedagogical beliefs into two types, which are constructivist pedagogical beliefs and traditionalist
pedagogical beliefs. This study also investigates the relationship between self-efficacy and the
two pedagogical beliefs with perceived usefulness and perceived ease of use.
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CHAPTER 3: METHODOLOGY
Research methodology refers to a study’s systematic approach to describe, explain and
predict a phenomenon in a way that ensures the validity and reliability of the research (Matthews
& Ross, 2010; Igwenagu, 2016). This chapter provides an overview and rationale of the research
design selected to understand factors that influence Indonesian teachers’ adoption of technology
in the K-12 classroom. The discussion includes sections describing the researcher, research
setting, data sources and methodology, instrumentation, and data analysis.
Research Questions
To understand factors affecting teachers and their adoption of technology in the
classroom, this dissertation study is guided by three research questions:
1. What are teachers’ attitudes and beliefs about technology use in the classroom?
2. What are the relationships between the various teacher attitudes and beliefs?
3. To what extent do teachers’ attitudes and beliefs predict intention to use and actual use of
technology?
Overview of Design
This dissertation study used an extended version of Davis’ (1989) Technology
Acceptance Model as a conceptual framework. An extended model was selected because while
researchers have found TAM to be a credible model for assessment, the framework also
presented gaps that were often addressed through an extension of the original model (Granić &
Marangunić, 2019).
Thus, to address these research questions, this study used a quantitative survey to
examine seven different constructs. Five constructs were taken from Davis’ (1989) original TAM
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model: perceived ease of use (PEU), perceived usefulness (PU), and attitude toward technology
(ATT), behavioral intention to use technology (IU), and actual use of technology (AU).
Two additional constructs were added to create an extended TAM model. These
constructs included self-efficacy (SE) and pedagogical beliefs (PB). Self-efficacy was added
because it was found to be the most used external factor in extended TAM studies (Abdullah &
Ward, 2016). The pedagogical beliefs construct was added because it aligned with Fishbein &
Ajzen’s (1975) theory purporting that beliefs were closely related to attitudes.
Research Setting
This research study specifically focused on schools under the Beacon of Light
Foundation, which govern 14 schools across first and second-tier cities in Indonesia. As of
January 2022, the school system employed a total of 14 school administrators, 831 full-time
teachers and 250 academic support staff who provide educational services for 11,064 students.
Almost all administrators, teachers and staff are of Indonesian nationality.
At the time of study, the school was emerging out of the COVID-19 pandemic school
closures where the schools remained fully or partially closed for 2 years and 4 months. The study
was conducted toward the end of the first fully on-site semester.
The following is a list of digital transformation initiatives that were being implemented
across the 14 schools:
● Implementation of Moodle as a learning management system: In addition to the
technological implementation of the LMS, the school system also strategized the
creation of a small team of teachers in each school who had buy-in into technology
and acted as agents of change within the schools.
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● Implementation of EdConnect as their student information system: Teachers were
required to use EdConnect as the main repository for student data.
● Blended Learning as a teaching modality: With COVID-19 as a catalyst, the schools
integrated technology into learning within the classroom, as well as outside the
classroom in a flipped classroom learning model.
● Physical classroom transformation: The school system prototyped their new
generation of smart classrooms in several of their schools, accompanied by teacher
training on utilizing new classroom technologies.
The Researcher
The researcher currently holds an administrative leadership position in the educational
system that was studied. The researcher holds a Bachelor of Arts in Philosophy and a Master of
Education in Philosophy and Education and is currently pursuing her Doctor of Education degree
at the Rossier School of Education in the University of Southern California. The researcher sits
on the governing body of the schools that were subject to this study, but the researcher was, and
is, not involved in the day-to-day of the school operations. Since the researcher holds a position
that may lead to a conflict of interest, this study implemented protocols that minimize any
conflict of interest that may have arisen. These protocols included the survey being conducted
anonymously whereby participants’ names and details were not linked to their responses.
Participation in the survey was also voluntary.
The researcher’s status as a mother of four children ages nine and below led to a growing
interest concerning technology and education. The experiences of two full years of online
learning during the COVID-19 pandemic allowed the researcher to understand first-hand the joys
and struggles faced by teachers, parents and students related to the use of technology in the
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classroom. These experiences produce a drive in the researcher to help teachers find an
equilibrium where technology can be seamlessly integrated into teaching and learning, instead of
being a barrier for teachers to overcome. The researcher also intends to use the results of this
study to inform policies that will provide greater support for teachers and technology adoption,
particularly for the organization being studied.
Data Sources
At the time of study, the Beacon of Light Foundation employed 831 teachers which
included teachers at the Kindergarten, Elementary and Secondary levels. The study focused on
teachers at the Kindergarten and Elementary school levels because it wanted to focus on
technology use for teachers who taught a wide range of subjects, as opposed to teachers who
specialized in specific subjects such as chemistry, biology, physics, history, or economics, as
might be found in secondary school. The assumption was that specialist subjects may have had
different technology requirements, with some, such as computer science and information
technology classes, requiring more technology use than others, such as physical education and
music classes. By focusing on kindergarten and elementary-level teachers, the study found that
there were 444 teachers who fit the participation criteria of the study. The survey was
administered using an electronic format that was accessible to teachers over a period of two
weeks.
Method
Survey research can use different kinds of data collection methods (Ponto, 2015). This
study used a questionnaire format for data collection. A survey questionnaire is a method where
information is collected from a sample of individuals through responses to questions (Check &
Schutt R., 2012). Survey research can be quantitative, qualitative, or mixed methods, depending
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on the scope of research. This study chose to do a quantitative-based survey because quantitative
methods allow for data collection on a large scale and allows for data to be analyzed in a
straightforward way (Zohrabi, 2013).
Participants
This survey questionnaire was distributed to all teachers who were employed in one of
the 14 schools under the Beacon of Light Foundation. The Beacon of Light school system
employed a total of 14 school administrators and 831 full-time teachers who provided
educational services for 11,064 students. This study narrowed down the survey participants to
full-time teachers at the Kindergarten and Elementary school levels, which numbered 444
teachers in total. While participation in this survey was voluntary, efforts were made to
maximize the number of respondents through the timing of the survey and ensuring the survey’s
ease-of-use through technology.
Several sampling criteria were used to narrow down the group of participants for the
survey. To be eligible to participate in the survey questionnaire, the inclusion criteria were that
participants needed to: 1) be full-time teachers, 2) have a teaching degree at the undergraduate
level, 3) be teachers within the Elementary grade level, and 4) either be homeroom teachers or
subject specialist teachers. Participants could take part in the survey regardless of age, gender, or
years of teaching experience.
Instrumentation
The survey questionnaire was created by combining Davis’ original TAM questions,
questions from several studies that applied the TAM model with a similar research purpose, and
questionnaires that focused specifically on the areas of self-efficacy and pedagogical beliefs. The
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survey contained 47 questions in total, which were broken down into seven constructs as shown
in Table 2. The survey questionnaire can be found in Appendix A.
Table 2
Constructs Items and Source
Construct Items Source
Self-Efficacy 10 Holden & Rada, 2011
Pedagogical Beliefs 14 Wooley & Benjamin, 2004
Perceived Usefulness 7 Davis (1989); Weng et al. (2018)
Perceived Ease of Use 6 Davis (1989)
Attitude Toward Usage 5 Weng et al. (2018)
Intention To use 3 Weng et al. (2018)
Actual Use 2 (none)

This study adopted Davis’ original TAM questions for the sections on perceived
usefulness (PU) and perceived ease of use (PEU), taking six questions for each section
respectively. Because most questions focused on technology’s utility for the teacher, one
question was added related to PU as adopted from the study Weng et al. (2018) relating to a
teacher’s perceived usefulness of technology to catch individual student needs. The study by
Weng et al. (2018) was appropriate to draw from because of the similarity in research focus.
Their study focused on applying TAM on schoolteachers’ intention to use multimedia, which
contained four of the variables studied (perceived usefulness, perceived ease of use, attitude
toward using technology, and intention to use technology).
The items on self-efficacy were based on a survey questionnaire developed by Holden &
Rada (2011). Holden and Rada conducted a study on teachers’ technological self-efficacy and its
relationship with technology acceptance, which the researcher found similar to the scope of this
study. The researchers adapted the findings of Compeau & Higgins (1995) and Venkatech (2000)
on computer self-efficacy and adapted these items for the specific participant audience of
teachers.
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The items on pedagogical beliefs were adapted from the Teacher Beliefs Survey (TBS)
developed by Woolley & Benjamin (2004). The survey by Woolley & Benjamin (2004)
contained 21 statement items that measured three constructs, namely Traditional Management
(TM), Constructivist Teaching (CT), and Traditional Teaching (TT). This study adapted the
survey by eliminating four questions related to the Traditional Management construct, which
focus on classroom behavioral management, since this study’s focus was on pedagogy related to
teaching and learning. Furthermore, three additional statement items that related to the teacher-
parent relationship were also eliminated. The remaining 14 items were retained to understand the
extent to which a teacher employed a traditional versus a constructivist teaching approach, as
evident in areas of curriculum, classroom learning environment, assessment, and teaching
strategies. Of the 14 items, seven items measured the Traditional Teaching while the remaining
seven items measured Constructivist Teachings. Several statement wordings were modified for
contextual purposes, such as the reference on bulletin boards that was broadened to include other
collaborative spaces.
The items on attitude toward technology (ATT) were adopted from a study conducted by
Weng et al. (2018) because of the similarities seen between their research and the scope of this
study. Of the five survey items used by Weng et al. (2018), four were adapted into this study’s
questionnaire, while one question related to pedagogy was added.
Similarly, this study adopted three items on intention to use technology (IU) by selecting
three of five survey items in Weng et al. (2018). In addition, the researcher added two questions
on actual use of technology (AU) to provide a numerical figure on how often BLF teachers
believe they use technology in the classroom, and how the teachers perceived technology as
being implemented in the classroom.
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Data Collection Procedures
The survey was administered electronically to participants who fit the eligibility criteria.
The school system’s Head Office sent out an email message inviting prospective participants to
complete the survey. This email contained a statement on the purpose of the research study, a
statement on the institution’s willingness to participate, an explanation on the anonymity of the
survey, and an invitation to school administrators and staff to participate in the survey.
When the survey was accessed, prospective participants were directed to an informed
consent page. This page provided information about the survey, including the purpose and scope
of the study; explained the voluntary nature of participation, including the participant’s ability to
withdraw at any point in time; and shared how the outcome of the survey would be published.
Participants were required to consent to the study before proceeding with the questionnaire, and
they were aware that participation was of a voluntary nature.
The survey was available over a period of two weeks. A follow-up email was sent every
3-4 days to remind participants to participate in the survey. After the two-week window was
completed, the survey was no longer available for access, even if the participant had already
started the survey beforehand.
Data Analysis
The data generated from the survey questionnaire included basic demographic data about
the participants (e.g., gender, age, teaching experience, academic qualifications, and leadership
position) and information on the participants’ self-reported perceptions on technology across the
study’s constructs, using a Likert scale of 1-4.
The first research question sought to understand teachers’ attitudes and beliefs about
technology use in the classroom. Descriptive statistics were generated for each survey item
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across seven distinct constructs – Self-Efficacy (SE), Pedagogical Beliefs (PB), Perceived
Usefulness (PU), Perceived Ease of Use (PEU), Attitude toward Technology (ATT), Intention to
use Technology (IU), and Actual Use of Technology (AU). The data for Pedagogical Beliefs
(PB) were divided between constructivist beliefs (PB-C) and traditionalist beliefs (PB-T). The
descriptive statistics included the mean scores for each construct, mean scores for each
demographic category, and the ordinal data for each survey question. The data was also linked to
participants’ demographic information to understand if there were any meaningful differences
across demographic groups.
The second research question looked for correlations between the various attitudes and
beliefs. A correlations matrix was generated to understand if any of the variables correlated with
one another. Variables that had a coefficient (r value) of 0.3 or above were found to be
correlated, whereas variables that had a coefficient (r value) below 0.3 were found to have no
correlation.
The third research question focused on the predictive relationship between the constructs.
The study used a regression model between the study’s constructs. The following regression
models were created:
1. A multiple regression model between five independent constructs (SE, PB-T, PB-C, PU,
PEU) and attitude toward technology (ATT)
2. A multiple regression model between five independent constructs (SE, PB-T, PB-C, PU,
PEU) and intention to use technology (IU)
3. A single regression model between attitude toward technology (ATT) and intention to use
technology (IU)
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4. A single regression model between intention to use technology (IU) and actual use of
technology (AU).
A revised regression model was created for the first two multiple regression models,
where non-significant variables were dropped. The data were analyzed for unstandardized
coefficients, standardized coefficients, and the significance (p value), where results with a p
value below 0.05 were considered significant and p value below 0.01 to be highly significant.
The study also analyzed the models using the r value, r square value, and adjusted r square value
to determine the model’s explanatory power.
Validity & Reliability
Validity and reliability are used to demonstrate the rigor of research findings and justify
the trustworthiness of results (Roberts & Priest, 2006). Validity is defined as the degree to which
a study accurately measures a concept (Heale & Twycross, 2015). The four types of validity
include construct, content, criterion, and face validity. Construct and content validity hold the
most relevance to this study.
Construct validity relates to the extent that an instrument or test measures the construct it
intends to measure, while content validity refers to the degree to which the items in a test fully
represent the entire theoretical construct that the test is intended to assess (Roberts & Priest,
2006). To ensure validity, the researcher created a questionnaire that was adapted from
previously developed TAM models. The researcher attempted to find the best-fit questionnaire
for each construct, such that the seven constructs were a combination of survey items from
various instruments. The adapted questionnaire was reviewed by the Beacon of Light
Foundation’s Head Office, who reviewed the survey’s wording to ensure that the questions were
straightforward and clear for participants to understand (Groves et al., 2009; DeMaio &
Indonesian teachers’ adoption of technology in the K-12 classroom
53

Landreth, 2004). Through this process, the researcher also gained feedback on the design of the
survey, particularly the format and graphic layout, which also affected the degree of
measurement error (Sanchez, 1992).
Reliability refers to the stability of an instrument to produce results that are consistent
when measured under the same under the same circumstances with the same instrument (Sürücü
& Maslakci, 2020). Reliability should be present when the instrument is used by different people
(inter-rater reliability) and across different times (test-retest reliability) (Roberts & Priest, 2006).
To increase reliability, this questionnaire was administered at a very specific timeframe within
the school’s academic year to avoid the results being skewed due to major events that may have
affected teachers and their responses.
According to Lavidas et al. (2022), web surveys often yield low response rates, thereby
affecting the reliability and validity of the survey method. The researchers identified several
factors that were found to affect response rates specifically for teachers. These factors were
considered when the researcher planned the design and dissemination of the survey. First, this
study maximized response rates by working closely with the Heads of Schools and Human
Resources Department to communicate how the survey results would inform the institution
regarding future technological support for teachers. Teachers were also given multiple reminders
to complete the survey during the two-week period via email. Finally, the survey was
disseminated on a Friday, which according to some literature yielded better results given the
down-time available for teachers (Lavidas et al., 2022).
Ethics
Drawing on the principles of ethical research involving human participants (Vanclay et
al., 2013), standards outlined by the USC Institutional Review Board (IRB), the Belmont Report,
Indonesian teachers’ adoption of technology in the K-12 classroom
54

and the Indonesian National Research and Innovation Institute (Badan Riset dan Inovasi
Nasional), this study abided by the principles of respect for persons, beneficence, and
distributive justice.
Respect for persons included respecting the participant’s autonomy and protection from
exploitation and harm. This study ensured that participants who completed the survey did so
voluntarily without pressure or coercion from the institution or researcher. The voluntary nature
of this study was clearly stated at the beginning of the survey, along with information that the
willing participant could withdraw from the survey at any given point. Given the researcher’s
position in the Foundation or governing body of the schools, the researcher distributed the survey
through the school administrators to avoid any unjustified pressure on teachers to participate in
this study.
Participants were given an informed consent form prior to taking part in the survey. This
form included statements regarding the research purpose, procedure, risks, and anticipated
benefits, as well as who the targeted participants were. The study ensured that participants
comprehended the information and ticked an informed consent box before proceeding to
complete the survey.
The study followed the ethical principle of beneficence and justice by ensuring that the
study outcome promoted participant welfare. The study maximized teachers’ welfare because it
aimed to uncover factors that are important to teachers in technology adoption. These findings
pointed toward recommendations that were given to schools, to help school leaders better
support teachers inside the classroom. These findings were also made accessible to all teachers
under the Beacon of Light Foundation, so that they could personally reflect and benefit from the
study’s findings.
Indonesian teachers’ adoption of technology in the K-12 classroom
55

Summary
The goal of this chapter has been to outline the research method that was used in the
study to answer the research questions. First, this chapter covered the design overview of the
study, including the quantitative-based survey questionnaire methodology. The study was based
on an extended TAM model with six different constructs. Next, the chapter covered the study’s
methodology, study participants, instrumentation, data collection, and data analysis. Third, the
study discussed issues of validity and reliability, including the researcher’s plan to increase the
degree of the study’s rigor and trustworthiness. Finally, the chapter discussed ways that the study
maintained ethical principles to ensure the outcome promoted the welfare of participants.

Indonesian teachers’ adoption of technology in the K-12 classroom
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CHAPTER 4: RESULTS AND FINDINGS
This chapter reports on the study’s findings. The purpose of this research was to
understand teacher attitudes and beliefs regarding technology, and how these factors relate to the
actual use of technology. This dissertation used Fred Davis’ (1989) Technology Acceptance
Model (TAM) to understand the relationship between four independent variables – self-efficacy
(SE), pedagogical beliefs (PB), perceived ease of use (PEU), and perceived usefulness (PU) –
and their relationship with three dependent variables, namely teachers’ attitudes toward
technology (ATT), intention to use technology (IU), and actual use of technology (AU) in the
classroom. The pedagogical belief (PB) construct was also broken down into two separate sub-
variables, which were traditional pedagogical beliefs (PB-T) and constructivist pedagogical
beliefs (PB-C).
The three research questions that guided this study were:
1. What are teachers’ attitudes, beliefs, intentions, and actual use of technology in the
classroom?
2. What are the relationships between the various teacher attitudes and beliefs?
3. To what extent do teachers’ attitudes and beliefs predict intention to use and actual use of
technology?
To answer these research questions, the researcher disseminated a questionnaire which
consisted of 7 demographic questions and 47 questions across seven variables. The electronic
survey took approximately 10-15 minutes to complete. The survey was conducted in English
because the target population consisted of teachers who regularly taught using English as a
medium of instruction in the classroom. The survey was disseminated to a population of 444
teachers. The survey obtained 389 responses with a response rate of 87.6%. Based on the
Indonesian teachers’ adoption of technology in the K-12 classroom
57

inclusion criteria, there were 365 valid responses that were used for the data of this study, thus
bringing the participation rate to 82.2%.
Participating Stakeholders
The stakeholders that became the focus of this study were teachers under the Beacon of
Light Foundation. The study intentionally chose to focus on teachers at the lower grade levels to
generate results that were more specific to their needs. The population size of eligible
participants in this study consisted of 444 kindergarten and elementary school teachers. This
study required participants to be full-time teachers to ensure that all respondents were the
decision-makers in the classroom. These requirements automatically excluded part-time teachers,
the majority of whom were typically employed as shadow teachers, teaching assistants, or
specialized as subject matter experts in elective subjects. Furthermore, this study only chose to
include teachers who held a formal academic degree in education, either at the undergraduate,
master’s or doctoral levels.
Survey Participants
The electronic survey was sent by email to 444 full-time teachers under the Beacon of
Light Schools. The survey was disseminated through an email that was sent out by the Human
Resources Office at the Central Headquarters. The survey population consisted of 42.0%
Kindergarten teachers and 58.0% Elementary-level teachers, 58% of whom had 0-5 years of
experience, 15.2% with 6-10 years of experience, 15.1% with 11-15 years of experience, and
11.7% with 11-15 years of experience.
At the end of the survey’s two-week period, 389 individuals had responded to the survey,
representing an 87.6% response rate. Out of 389 respondents, 25 respondents did not fit the
criteria of being full-time teachers employed under the Beacon of Light Foundation and were
Indonesian teachers’ adoption of technology in the K-12 classroom
58

thus removed from the survey. As a result, the study used data from the remaining 365
responses, representing 82.2% of the population of 444 teachers under Beacon of Light
Foundation.
The 365 respondents consisted of 302 (82.7%) females and 63 (17.3%) males. In terms of
age, 152 participants (41.6%) were between ages 18-24, 123 (33.7%) were between ages 25-34,
66 (18.1%) were between ages 35-44, and 24 (6.6%) were 45 years or above. Regarding teaching
experience, 321 teachers (64.9%) had 0-5 years of experience, 42 teachers (11.5%) had 6-10
years of experience, 42 teachers (11.5%) had 11-15 years of experience, and 44 teachers (12.1%)
had over 15 years of experience.
Among the participants, 321 (88.0%) held undergraduate teaching degrees, 43
participants (11.8%) were master's degree holders, and 1 participant (0.3%) was a Doctoral
Degree holder. In terms of leadership positions, 304 (83.3%) did not hold a leadership position,
21 (5.8%) were subject coordinators, 30 (8.2%) were year-level leaders, and 10 (2.7%) were
School Principals.
Analysis
This section reports on the survey results and findings as they relate to this dissertation’s
four research questions.
Research Question 1
What are teachers’ attitudes, beliefs, intentions, and actual use of technology in the
classroom?
This study measured seven constructs related to teacher attitudes and beliefs about
technology in the classroom. Five of the seven constructs had composite means that were above
3 on a 4-point Likert scale, indicating that the respondents had a central tendency to agree or
Indonesian teachers’ adoption of technology in the K-12 classroom
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strongly agree with the survey statements. These constructs included Perceived Usefulness (m =
3.46), Perceived Ease of Use (m = 3.21), Attitude Toward Technology (m = 3.46), Intention to
Use Technology (m = 3.31), and Actual Use of Technology (m = 3.25). Meanwhile, the
constructs with the lowest composite mean scores were Self-Efficacy (m=2.77) and Pedagogical
Beliefs (m = 2.91).
Table 3
Constructs Items and Composite Means.
Construct Composite Mean
Self-Efficacy (SE) 2.77
Pedagogical Beliefs (PB) 2.91
Perceived Usefulness (PU) 3.46
Perceived Ease of Use (PEU) 3.21
Attitude toward Technology (AT) 3.46
Intention to Use Technology (IU) 3.31
Actual Use of Technology (AU) 3.25

The next section will discuss the detailed findings for each of the seven constructs. The
data will include mean scores across demographic groups and ordinal scores for each survey item
within the construct.
Survey Findings for Self-Efficacy
Table 4 provides a breakdown of the survey results for the ten items under self-efficacy
using an ordinal scale.
Table 4
Ordinal Scale for Self-Efficacy Scores.
Item Strongly
Disagree
Disagree Agree Strongly
agree
Total
(n)
SE1 I could complete any desired task using
any technological application if there
was no one around to tell me what to do
as I go.
21.64% 26.85% 36.99% 14.52% 365
SE2 I could complete any desired task using
any technological application if I had
never used a technology like it before.
36.99% 37.81% 20.82% 4.38% 365
SE3 I could complete any desired task using
any technological application if I had
only the manuals for reference.
25.48% 41.10% 27.95% 5.48% 365
SE4 I could complete any desired task using
any technological application if I had
2.74% 15.34% 60.55% 21.37% 365
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60

seen someone else using it before trying
it myself.
SE5 I could complete any desired task using
any technological application if I could
call someone for help if I got stuck.
1.64% 6.58% 52.33% 39.45% 365
SE6 I could complete any desired task using
any technological application if
someone else had helped me get started.
4.66% 15.89% 55.89% 23.56% 365
SE7 I could complete any desired task using
any technological application if I had a
lot of time to complete the task for
which the technology was provided.
3.84% 21.10% 50.14% 24.93% 365
SE8 I Could complete any desired task using
any technological application if I only
had the built-in help facility for
assistance.
3.01% 27.12% 57.53% 12.33% 365
SE9 I could complete any desired task using
any technological application if
someone showed me how to do it first.
2.47% 19.73% 52.05% 25.75% 365
SE10 I could complete any desired task using
any technological application if I had
used similar technologies before this one
to do the same task.
1.64% 12.88% 60.00% 25.48% 365

The three items that had the highest percentage of participants who responded “agree” or
“strongly agree” were “I could complete any desired task using any technological application if I
could call someone for help if I got stuck” (SE5), “I could complete any desired task using any
technological application if I had used similar technologies before this one to do the same task”
(SE10), and “I could complete any desired task using any technological application if I had seen
someone else using it before trying it myself” (SE4). The percentage of participants who
responded “agree” or “strongly agree” for these statements were 92%, 85%, and 82%,
respectively.
In contrast, teachers disagreed most with “I could complete any desired task using any
technological application if I had never used a technology like it before” (SE2), “I could
complete any desired task using any technological application if I had only the manuals for
reference” (SE3), and “I could complete any desired task using any technological application if
there was no one around to tell me what to do as I go” (SE1). For these statements, the teachers
who responded “agree” or “strongly agree” were 25%, 33%, and 52%, respectively.
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61

The responses to the survey aligned with Albert Bandura’s social learning theory, which
emphasizes the communal role of observation, modeling, and imitation in the process of
learning. The fifth survey item indicated that teachers value having someone they can ask for
help (SE5), and seeing other teachers succeed in using the same technology (SE4). In contrast,
teachers reported lower level of self-efficacy if they had to rely on a manual (SE3), no prior
learning reference (SE2), or if no one was around to tell the teacher what to do (SE1).
Table 5
Self-efficacy levels based on demographic groups.
Demographic Composite Mean
Gender Male 2.80
Female 2.76
Age 18-24 2.84
25-34 2.75
35-44 2.60
45+ 2.83
Teaching Experience 0-5 2.80
6-10 2.75
11-15 2.68
>15 2.67
Academic Qualifications Undergraduate Education 2.79
Undergraduate Non-Education 2.73
Master's & Doctoral 2.65
Leadership Position Principal 2.71
Subject Coordinator 2.77
Year-level Lead 2.86
None/Others 2.76

The chart above shows self-efficacy levels based on demographic groups. In terms of
gender, findings are similar between males and females (m = 2.80 and m = 2.76, respectively). In
terms of age, teachers between ages 18-24 reported higher levels of self-efficacy (m=2.84),
followed by teachers who were 45 years and above (m = 2.83), teachers ages 25-34 (m = 2.75),
and teachers ages 35-44 (m = 2.60). Self-efficacy scores were highest for teachers with 0-5 years
of teaching experience (m = 2.80) and reduced as experience increased, with teachers with 6-10
years of experience having a mean of 2.75, 11-15 years with a mean of 2.68, and teachers above
15 years with a mean of 2.67.
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Teachers who held an undergraduate education degree had the highest mean self-efficacy
levels (m = 2.79), followed by teachers that held non-education undergraduate degrees (m =
2.73), and post-undergraduate degrees (m = 2.65). Teachers who held a leadership position as
year-level lead had the highest self-efficacy level (m = 2.86), followed by subject coordinators
(m = 2.77). Teachers who held no leadership position had a mean score of 2.76, and principals
had the lowest mean self-efficacy score (m = 2.71).
Summary
In summary, teachers reported a high level of self-efficacy regarding the use of
technology in the classroom. However, self-efficacy levels varied depending on the presence or
absence of internal and external factors. Teachers had higher levels of technological self-efficacy
when they had prior experience in using technology, when they could observe technology role-
modeling, and when they could ask for assistance when needed. Furthermore, the survey
responses revealed that teachers had higher technological self-efficacy levels when they could
call someone for help, if teachers had seen other teachers successful performing the same task,
and if they have used the technology before. In contrast, teachers had lower self-efficacy if they
had never used the technology before, had only the manuals for reference, and if there was no
one to tell them what to do.
These findings align with Albert Bandura’s (1977) social learning theory, which
emphasized the communal role of observation, modeling, and imitation in the process of
learning. According to Bandura, behavior is not only learned through the processes of
conditioning, but also through the process of observational learning. Bandura’s theory is also
well supported by several TAM studies in K-12 educational settings. Several studies have found
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63

that role modeling has a significant impact on teacher self-efficacy and long-term adoption of
technology (Özüdoğru & Çakır, 2020; Oigara & Wallace, 2012).
Survey Findings for Pedagogical Beliefs
Table 6 provides the breakdown of responses for the thirteen items under pedagogical
beliefs. The purpose of this construct is to measure teachers’ beliefs pertaining to the
constructivist and traditional approaches to teaching and learning, to understand any relationship
between both pedagogical beliefs and teachers’ technology use in the classroom. A constructivist
teaching approach has the underpinning of cognitive-based psychology learning theories that
focus on the learner and their learning journey, whereas a traditionalist teaching approach has
behaviorist underpinnings with a focus on the teacher as the focal point of teaching and learning
(Woolley & Benjamin, 2004).
Table 6
Ordinal Scale for Pedagogical Belief Scores.
Items Strongly
disagree
Disagree Agree Strongly
Agree
Total (n)
PB1 I believe that expanding on
students' ideas is an effective way
to build my curriculum.
1.37% 12.05% 56.99% 29.59% 365
PB2 I prefer to cluster students' desks
or use tables so they can work
together.
1.10% 9.32% 55.62% 33.97% 365
PB3 I invite students to create many of
my bulletin boards or other
collaborative spaces.
4.66% 16.71% 56.99% 21.64% 365
PB4 I like to make curriculum choices
for students because they can't
know what they need to learn.
9.04% 39.18% 40.00% 11.78% 365
PB5 I base student grades primarily on
homework, quizzes, and tests.
4.38% 24.38% 56.44% 14.79% 365
PB6 To be sure that I teach students all
necessary content and skills, I
follow a textbook or workbook.
6.58% 35.34% 47.95% 10.14% 365
PB7 I teach subjects separately,
although I am aware of the
overlap of content and skills.
1.37% 10.14% 58.63% 29.86% 365
PB8 I involve students in evaluating
their own work and setting their
own goals.
1.64% 11.51% 64.11% 22.74% 365
PB9 I make it a priority in my
classroom to give students time to
work together when I am not
directing them.
6.85% 26.30% 51.51% 15.34% 365
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64

PB10 My interest in what students can
do independently is mainly for
mandatory assessment purposes.
3.01% 18.90% 62.74% 15.34% 365
PB11 I generally use a teacher's guide
that is given to me, to lead class
discussions of a story or text.
0.82% 18.90% 59.45% 20.82% 365
PB12 I prefer to assess students
informally through observations
and conferences.
3.01% 27.95% 54.79% 14.25% 365
PB13 I find that textbooks and other
published materials are the best
sources for creating my
curriculum.
9.04% 29.86% 44.11% 16.99% 365

The fourteen survey items were divided into seven items that indicate a constructivist
teaching approach (PB1, PB2, PB3, PB8, PB9, PB12, PB14), and seven items that indicate a
traditional teaching approach (PB4, PB5, PB6, PB7, PB10, PB11, PB13). The researcher decided
to eliminate one question (PB14) with the statement “I often create thematic units based on the
students’ interest and ideas” because it is irrelevant, since thematic units are decided centrally by
the curriculum team instead of by the teachers. The composite mean score for constructivist
pedagogical beliefs (PB-C) was 3.34, and the composite mean score for traditionalist
pedagogical beliefs (PB-T) was 3.30.
Overall, a larger proportion of the survey participants agreed with the constructivist
statements as compared to those who agreed with traditionalist statements. An average of 291
participants (80.0%) either agreed or strongly agreed with the six constructivist statements in the
survey, as compared to an average of 255 participants (70.0%) who agreed or strongly agreed
with the traditionalist statements.
Findings for Constructivist Pedagogical Statements
Table 7
Mean scores of constructivist pedagogical beliefs based on demographic groups.
Demographics Composite Mean
Gender Male 3.01
Female 2.99
Age 18-24 2.99
25-34 2.96
Indonesian teachers’ adoption of technology in the K-12 classroom
65

35-44 3.00
45+ 3.17
Teaching
Experience
0-5 2.98
6-10 2.97
11-15 2.96
>15 3.15
Academic
Qualifications
Undergraduate Education 2.98
Undergraduate Non-Education 2.92
Master's & Doctoral 3.10
Leadership Position Principal 2.93
Subject Coordinator 2.96
Year-level lead 2.98
Others 3.00
None 3.00

Males and females had composite mean scores of 3.01 and 2.99, respectively. Older
participants were more likely to agree with the constructivist statements. Participants over the
age of 45 had a composite mean score of 3.17. This was followed by participants in the 35-44
age group with a composite mean score of 3.00. Participants between ages 18-24 had a mean
score of 2.99. Participants between ages 25-34 had the lowest mean score of 2.95.
Participants with over 15 years of experience had the highest mean score of 3.15.
Participants who had 11-15 years of teaching experience had the lowest mean score of 2.96.
Participants with 0-5 years of teaching experience had a mean score of 2.98, while those with 6-
10 years of experience had a mean score of 2.97.
Participants who held a Master’s or Doctorate degree had the highest mean score of 3.10.
Undergraduate education degree holders had a mean score of 2.98, while undergraduate non-
degree holders had a mean score of 2.92. Teachers without a leadership position were more
likely to agree with constructivist statements, with a mean score of 3.00. Year-level leaders and
subject coordinators held a mean score of 2.98 and 2.96 respectively, and principals had a mean
score of 2.93.

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66

Findings for Traditional Pedagogical Statements
Table 8
Mean scores of traditional pedagogical beliefs based on demographic groups.
Demographics Composite Mean
Gender Male 2.88
Female 2.81
Age 18-24 2.88
25-34 2.79
35-44 2.75
45+ 2.79
Teaching
Experience
0-5 2.86
6-10 2.71
11-15 2.75
>15 2.79
Academic
Qualifications
Undergraduate Education 2.83
Undergraduate Non-Education 2.74
Master’s & Doctorate 2.83
Leadership Position Principal 2.67
Subject Coordinator 2.80
Year-level lead 2.77
Others 2.83
None 2.83

Males were more likely to agree with the survey’s traditional pedagogical statements,
with a composite mean score of 2.88, as compared with females with a composite mean of 2.81.
Younger participants were most likely to agree with the survey’s traditional pedagogical
statements, with a mean score of 2.88. Participants ages 25-34 and 45 or above had a mean score
of 2.79. Meanwhile, participants ages 35-44 had the lowest mean score of 2.75.
Based on teaching experience, participants with 0-5 years of experience had the highest
agreement with traditional pedagogical statements, with a mean score of 2.86. This was followed
by participants who had over 15 years of teaching experience, with a mean score of 2.79.
Participants who held 11-15 years of experience held a score of 2.75, while those with 6-10 years
of experience held a score of 2.71.
From the standpoint of academic qualifications, undergraduate education degree holders
and master’s and doctorate degree holders had the same mean score of 2.83. Meanwhile,
undergraduate non-education degree holders had a lower mean score of 2.74.
Indonesian teachers’ adoption of technology in the K-12 classroom
67

Teachers who held no leadership position had a higher mean score of 2.83. Subject
coordinators and year level leaders had a mean score of 2.80 and 2.77, respectively. Principals
had a lower mean score of 2.67.
Summary
This section of the survey attempted to capture the extent to which survey participants
agreed with traditionalist and constructivist pedagogical statements. The pedagogical belief
statements were not directly related to beliefs on technology but collected for the purpose of
understanding the correlation between pedagogical beliefs and technology adoption in research
question 2 and 3.
In summary, this study found that teachers were more likely to agree with constructivist-
oriented pedagogical statements as compared to traditionalist-oriented pedagogical statements.
The results showed that participants’ pedagogical beliefs were oriented toward constructivist
rather than traditionalist beliefs. Teachers reported that they preferred to foster student
collaboration through the clustering of student tables and expanding on student ideas to broaden
the curriculum. In addition, teachers involved students to evaluate their own work and helped
students set their own learning goals. These constructivist arrangements occurred within a larger
traditionalist framework embraced by the school, such as the requirement to teach subjects
separately according to government requirements.
There is no established profile of pedagogical beliefs for Indonesian teachers as a
comparison to the study’s findings. However, several research that examined pre-service and in-
service teachers in nearby Asian countries and found that teachers were more inclined to
subscribe to constructivist-oriented pedagogical beliefs (PB-C) over traditionalist-oriented
pedagogical beliefs (PB-T) (Deng et al., 2014). This is often attributed to recent trends in
Indonesian teachers’ adoption of technology in the K-12 classroom
68

education to promote constructivist approaches to learning at the pre-service and in-service
levels (Deng et al., 2014).
Survey Findings for Perceived Usefulness
The seven survey items under perceived usefulness were intended to capture teachers’
perception on technology’s usefulness as it impacts various teacher needs – the speed of task
accomplishment (PU1), teaching delivery (PU2), productivity (PU3), teaching effectiveness
(PU4), ease of teaching (PU5), student needs (PU7), and overall usefulness (PU6).
Table 9 provides a breakdown of the survey results for the seven items under the
Perceived Usefulness construct. On average, 94.5% of participants either agreed or strongly
agreed with the survey statements, indicating a strong belief that educational technologies do
have a strong use case in the context of teaching and learning.
Table 9
Ordinal Scale for Perceived Usefulness Scores.
Construct Items Strongly
disagree
Disagree Agree Strongly
Agree
Total (n)
PU1 Using technology in the classroom
allows for tasks to be accomplished
more quickly.
0.82% 4.38% 46.03% 48.77% 365
PU2 Using technology in the classroom
improves my teaching delivery.
0.55% 1.37% 42.47% 55.62% 365
PU3 Using technology in the classroom
increases my productivity.
0.55% 3.56% 42.47% 53.42% 365
PU4 Using technology in the classroom
enhances teacher effectiveness.
0.55% 3.01% 42.74% 53.70% 365
PU5 Using technology in the classroom
makes it easier to do my job.
0.55% 3.29% 42.19% 53.97% 365
PU6 I find educational technologies
useful in my class.
0.82% 2.47% 45.48% 51.23% 365
PU7 Using educational technologies
makes it easier to catch individual
student needs.
1.64% 9.32% 53.70% 35.34% 365

There were seven items under perceived usefulness (PU). Of the seven statements,
teachers agreed most with the statement that technology improved teaching delivery (PU2) with
98.09% either agreeing or strongly agreeing with the statement. However, teachers found that
Indonesian teachers’ adoption of technology in the K-12 classroom
69

technologies were less likely to help them catch individual student needs (PU7), with only
89.04% either agreeing or strongly agreeing with the statement.
Table 10
Mean scores of Perceived Usefulness based on demographic groups.
Demographics Composite Mean
Gender Male 3.53
Female 3.45
Age 18-24 3.47
25-34 3.50
35-44 3.35
45+ 3.52
Teaching Experience 0-5 3.48
6-10 3.41
11-15 3.30
>15 3.53
Academic Qualifications Undergraduate Education 3.49
Undergraduate Non-Education 3.36
Master’s & Doctorate 3.36
Leadership Position Principal 3.26
Subject Coordinator 3.37
Year-level lead 3.47
Others 3.47
None 3.47
Male survey respondents were slightly more likely to indicate a perceived usefulness for
technology in the classroom (m = 3.53) as compared to females (m = 3.45). Participants in the
35-44 age sub-group were least likely to view technology as useful (m = 3.35), in contrast with
participants in the 18-24 subgroup (m = 3.47), 25-34 subgroup (m = 3.50), and over 45 sub-group
(m = 3.52). This was also mirrored in the data breakdown based on teaching experience, where
participants with 11-15 years of experience being least likely to view technology as useful with a
score of 3.30, in contrast with those having 0-5 years of experience (m = 3.48), 6-10 years of
experience (m = 3.41), and over 15 years of experience (m = 3.53).
Participants who only held undergraduate education degrees were most likely to see the
perceived usefulness of technology (m = 3.49) as compared to non-undergraduate degree holders
and post-undergraduate degree holders with a mean score of 3.36 for both demographic
subgroups.
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Teachers who held no leadership positions, and those serving as year-level leaders, had
the highest agreement with the perceived usefulness of technology, with a mean score of 3.47.
This was followed by subject coordinators, with a mean of 3.37. Meanwhile, principals were
least likely to see technology as useful relative to the other subgroups (m = 3.26).
Summary
Scherer et al. (2015) proposed a multidimensional approach in understanding teachers’
perceptions on technology’s usefulness. In his definition, a multidimensional approach places
usefulness in the context of a teacher’s specific attainment goals for their job performance. This
study attempted to understand usefulness in the context of different practical applications from
the teacher’s perspective. The survey results found that teachers found technology to be most
useful when it was used to improve teaching delivery in the classroom. Teachers also found that
technology helped them to increase teacher productivity in the classroom. However, teachers
were least likely to find that technology made it easier for them to catch individual student needs.
Survey Findings for Perceived Ease of Use
Table 11 provides a breakdown of the survey results for the six items under the Perceived
Ease of Use construct. On average, 88.0% of participants either agreed or strongly agreed with
the survey statements.
Table 11
Ordinal Scale for Perceived Ease of Use (PEU) Scores.
Construct Items Strongly
disagree
Disagree Agree Strongly
Agree
Total (n)
PEU1
I find it easy to operate
educational technologies in the
classroom.
1.64% 9.32% 53.70% 35.34% 365
PEU2
I find it easy to get technology
to do what I want it to do.
0.82% 9.86% 58.36% 30.96% 365
PEU3
My interaction with technology
is clear and understandable.
0.55% 7.40% 59.45% 32.60% 365
PEU4
I find educational technologies
flexible to interact with.
0.55% 6.58% 61.92% 30.96% 365
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PEU5
It is easy for me to become
skillful at using educational
technologies.
1.37% 11.51% 58.08% 29.04% 365
PEU6
I would find educational
technologies easy to use.
0.82% 7.95% 59.18% 32.05% 365

There were six survey items under perceived ease of use (PEU). Among the six survey
items, participants had the highest agreement with the statement “I find educational technologies
flexible to interact with” (PEU4) with 92.88% of respondents either agreeing or strongly
agreeing with the statement. Participants agreed least with the statement “It is easy for me to
become skillful at using educational technologies” (PEU5), with 87.12% of participants either
agreeing or strongly agreeing with the statement.
Table 12
Mean scores of Perceived Ease of Use (PEU) based on demographic groups.
Demographics Composite Mean
Gender Male 3.31
Female 3.19
Age 18-24 3.26
25-34 3.27
35-44 3.03
45+ 3.17
Teaching Experience 0-5 3.26
6-10 3.19
11-15 3.05
>15 3.14
Academic Qualifications Undergraduate Education 3.25
Undergraduate Non-Education 3.09
Master’s & Doctorate 3.11
Leadership Position Principal 3.10
Subject Coordinator 3.25
Year-level lead 3.23
Others 3.21

Male survey participants were more likely to see technology as easy to use as compared
to female survey participants. Male participants had a mean score of 3.31 while female
participants had a mean score of 3.19.
Participants ages 25-34 were most likely to perceive technology as easy to use, while
participants ages 35-44 were least likely to see technology as easy to use. In order of highest
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mean scores, participants ages 25-34 had a score of 3.27. This was followed by participants ages
18-24 with a mean score of 3.26, participants ages 45 or above with a mean score of 3.17, and
participants between ages 35-44 with a mean score of 3.03.
Participants with the least years of teaching experience were most likely to perceive
technology as easy to use, as compared to the other subgroups. Participants with 0-5 years of
teaching experience had a composite mean of 3.26 followed by participants with 6-10 years with
a composite mean score of 3.19. Participants with over 15 years of experience had a mean score
of 3.14, while participants with 11-15 years of experience had the lowest perceived usefulness of
technology with a mean score of 3.05.
Teachers who held an undergraduate degree in education were most likely to perceive
technology as easy to use, with a composite mean of 3.25. Undergraduate non-education degree
holders had a composite mean of 3.09, while those with master’s or doctorate degrees had a
mean score of 3.11.
Subject coordinators had the highest perceived usefulness mean score of 3.25, followed
by year level leaders with a mean score of 3.23. Teachers with no leadership position had a mean
score of 3.21, while principals had the lowest mean score of 3.10.
Summary
Overall, this study found that teachers perceived technology in general to be easy to use.
The study did not narrow down teachers’ perceptions of technology’s ease of use toward a
specific technology, but instead used a general definition of technology. Overall, the participants
of this study found technology easy to use as demonstrated through their high level of agreement
with all the survey statements under perceived ease of use (PEU). Participants reportedly found
that educational technologies were flexible to interact with, and that their interaction with
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technology was clear and understandable. Participants also found that they could operate
technology with ease. Participants also found it easy to become skillful at using educational
technologies.
Survey Findings for Attitude Toward Technology
Table 13 provides an ordinal breakdown of the survey results for the five items under the
attitude toward technology (ATT) construct. The first item measures the extent to which teachers
believe that it is inherently good to use technology in the classroom, regardless of their personal
feelings and opinion toward it. The second item measures the extent to which it is favorable or
pleasing to the user. The third and fifth items measure whether teachers believe technology helps
with student learning outcomes and interests, as well as the pedagogy of the teacher. The fourth
item measures their general predisposition to using technology in the classroom.
Table 13
Ordinal Scale for Attitudes Scores.
Construct Items Strongly
Disagree
Disagree Agree Strongly
Agree
Total (n)
ATT1 Using technology to support
classroom learning is good.
1.10% 1.37% 41.37% 56.16% 365
ATT2 Using technology to support
classroom learning is favorable.
0.55% 2.74% 47.95% 48.77% 365
ATT3 Using technology in the classroom
enhances student learning outcomes
and interests.
0.82% 3.01% 47.40% 48.77% 365
ATT4 I would love to use educational
technology in my class.
0.55% 4.11% 43.29% 52.05% 365
ATT5 Using technology in class helps me in
my pedagogical approach.
0.55% 4.38% 47.40% 47.67% 365

Overall, the teachers who participated in the survey showed a positive attitude toward
using technology in the classroom, with all five items having over 90% of participants either
agreeing or strongly agreeing with the statements. Of the five statements, participants agreed
most with the statement that “using technology to support classroom learning is good” (ATT1),
with 97.53% of participants agreeing or strongly agreeing with the statement. Of the five
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statements, participants agreed least with the statement “I would love to use educational
technology in my class” (ATT4), however this item also found that 95.07% of participants
agreed or strongly agreed with the statement.
Table 14
Mean scores of Attitudes Toward Technology (ATT) based on demographic groups.
Demographics Composite Mean
Gender Male 3.50
Female 3.45
Age 18-24 3.47
25-34 3.51
35-44 3.35
45+ 3.45
Teaching Experience 0-5 3.50
6-10 3.38
11-15 3.33
>15 3.48
Academic Qualifications Undergraduate Education 3.50
Undergraduate Non-Education 3.31
Master’s & Doctoral 3.38
Leadership Position Principal 3.28
Subject Coordinator 3.38
Year-level lead 3.46
Others 3.47

The survey results showed that the composite mean for male survey participants was
3.50, as compared to the composite mean for female survey participants which was 3.45. The
data showed that participants between ages 25-34 had a higher mean score of 3.51. Participants
ages 18-24 had a mean score of 3.47, while those ages 45 and above had a mean score of 3.45.
Participants between ages 35-44 had the lowest mean score at 3.35.
The data showed that participants with five or less years of experience held a more
positive attitude, with a mean score of 3.50. Participants with over 15 years of experience had the
second highest mean average of 3.48. Participants with 6-10 years of experience had a mean
average of 3.38, while those with 11-15 years of experience had the lowest mean average of
3.33. Teachers with no leadership position showed the most favorable attitude toward technology
with a composite mean of 3.47. This was followed closely by teachers who were also year-level
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leaders, with a mean score of 3.46. Subject coordinators had a mean score of 3.38, while
principals held the lowest mean score of 3.28.
Summary
In summary, this study found that participants showed a positive attitude toward
technology in the classroom, where over 90% of participants either agreed or strongly agreed
with the statements.
Survey Findings for Intention to Use
Table 15
Ordinal Scale for Intention (IU) Scores.
Construct Items Strongly
Disagree
Disagree Agree Strongly
Agree
Total (n)
IU1 I intend to use technology during this
semester.
1.10% 6.30% 48.22% 44.38% 365
IU2 I intend to increase the occurrence of
technology use in my classroom.
1.10% 6.30% 53.70% 38.90% 365
IU3 I intend to use technology to diversify
approaches to learning.
1.10% 7.12% 54.52% 37.26% 365

Overall, the study found that participants self-reported intentionality to use technology in
the classroom. Over 92.6% of participants agreed or strongly agreed with the statement “I intend
to use technology during this semester” (IU1) and “I intend to increase the occurrence of
technology use in my classroom” (IU2), while 91.8% agreed with the statement “I intend to use
technology to diversify approaches to learning” (IU3).
Table 16
Mean scores of intentions to use technology (IU) based on demographic groups.
Demographics Composite Mean
Gender Male 3.34
Female 3.31
Age 18-24 3.36
25-34 3.29
35-44 3.21
45+ 3.40
Teaching Experience 0-5 3.35
6-10 3.20
11-15 3.18
>15 3.38
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Academic Qualifications Undergraduate Education 3.34
Undergraduate Non-Education 3.15
Master’s & Doctorate 3.33
Leadership Position Principal 3.27
Subject Coordinator 3.13
Year-level lead 3.27
None 3.33

The study found that male survey participants were incrementally more likely to intend to
use technology (m = 3.34) as compared with females (m = 3.31). Participants in the 45+ age
subgroup had the highest intention to use technology (m = 3.40), followed by the 18-24 age
subgroup (m = 3.36), 25-34 age subgroup (m = 3.29), and 35-44 age subgroup (m = 3.21).
New teachers with 0-5 years of experience and those with over 15 years of experience
had slightly higher mean scores, with composite means of 3.35 and 3.38, respectively. Those
with 6-10 years of experience had a composite mean of 3.20, and those with 11-15 years of
experience had the lowest composite mean of 3.18.
Teachers who held an undergraduate degree in a non-education field had a lower
intention to use technology (m = 3.15) as compared to undergraduate degree-holders in education
(m = 3.34) and Master’s or Doctorate degree-holders (3.33). Teachers that held no leadership
position had the highest intention to use technology (m = 3.33). Principals and year-level leaders
had a composite mean score of 3.27, while subject coordinators had the lowest mean score with a
value of 3.13.
Summary
In summary, this study found that teachers reported a high intention to use technology in
the classroom across all three survey items. Teachers reported that they intended to use
technology during the semester, and that they intended to increase the occurrence of technology
use in the classroom. Furthermore, teachers reported intentions to use technology to diversity
their approaches to learning.
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Findings for Actual Use of Technology
The survey captured participants’ technology use across different purposes ranging from
preparation (AU1, AU2), teaching delivery (AU3, AU4), administration (AU5, AU7), homework
(AU6), and assessments (AU8). Table 17 shows the descriptive data of the eight survey
questions.
Table 17
Descriptive Statistics for Actual Use of Technology.
Construct
Items (I use technology
for...)
Strongly
Disagree
Disagree Agree
Strongly
Agree
Total (n)
AU1
Creating learning
materials
0.55% 4.38% 43.84% 51.23% 365
AU2
Accessing online
resources for teaching
preparation
0.82% 5.21% 40.00% 53.97% 365
AU3
Delivering learning
materials
0.55% 4.66% 46.85% 47.95% 365
AU4
Enhancing teaching
strategies
0.27% 5.21% 54.52% 40.00% 365
AU5
Managing coursework
materials
1.92% 8.49% 52.60% 36.99% 365
AU6 Assigning homework 4.66% 18.63% 52.05% 24.66% 365
AU7
Managing government
submission documents
3.56% 10.41% 50.14% 35.89% 365
AU8 Delivering examinations 6.58% 20.00% 51.23% 22.19% 365

Teachers reported that they used technology for preparation purposes (AU1 and AU2)
and teaching delivery (AU3 and AU4). 95.07% of participants agreed or strongly agreed that
they used technology to create learning materials (AU1), and 93.97% agreed or strongly agreed
that they used technology to access online resources for teaching preparation (AU2). As a
percentage, 94.8% of participants agreed or strongly agreed that they used technology to deliver
learning materials, and 94.52% agreed or strongly agreed that they used technology to enhance
teaching strategies.
The survey found that technology was used less for administrative purposes such as
managing coursework materials (AU5) and managing government submission documents (AU7).
As a percentage, 89.59% of participants agreed or strongly agreed that they currently used
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technology to manage coursework materials, and 86.03% agreed or strongly agreed that they
currently used technology to manage government submission documents.
Fewer participants agreed that they used technology for the purposes of assigning
homework (AU6) and delivering examinations (AU8). Only 76.71% of participants agreed or
strongly agreed that they were currently using technology to support them in assigning
homework, and only 73.42% of participants agreed or strongly agreed that they were currently
using technology to deliver examinations.
Table 18
Mean scores of Actual Use of Technology (AU) based on demographic groups.
Demographics Composite Mean
Gender Male 3.25
Female 3.25
Age 18-24 3.28
25-34 3.24
35-44 3.13
45+ 3.43
Teaching Experience 0-5 3.28
6-10 3.11
11-15 3.12
>15 3.34
Academic Qualifications Undergraduate Education 3.28
Undergraduate Non-Education 3.11
Master’s & Doctorate 3.22
Leadership Position Principal 3.20
Subject Coordinator 3.11
Year-level Lead 3.24
None 3.26

The study found that there were no gender differences in the actual use of technology in
the classroom, with both males and females having a composite mean of 3.25 in their survey
responses. Participants ages 45 and above reported the highest level of actual technology use in
the classroom, with a mean score of 3.43. This was followed by participants ages 18-24 and 25-
34, with composite mean scores of 3.28 and 3.24, respectively. Participants ages 35-44 had the
lowest mean score of 3.13 for actual use of technology.
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Participants with over 15 years of teaching experience reported the highest level of
technology use in the classroom, with a mean score of 3.34. This was followed by participants
with 0-5 years of teaching experience, with a mean score of 3.28. Participants with 6-10 and 11-
15 years of teaching experience reported a comparatively lower level of actual technology use in
the classroom, with composite means of 3.11 and 3.12, respectively.
Undergraduate degree-holders in education were more likely than their peers to report
actual adoption of technology in the classroom, with a mean score of 3.28. This was followed by
Master’s and Doctorate degree holders, with a mean score of 3.22. Participants who held
undergraduate degrees in a field outside of education reported the lowest level of actual
technology use in the classroom, with a mean score of 3.11.
Teachers without leadership positions reported the highest level of actual technology use
in the classroom, with a mean score of 3.26. This was followed by year-level leaders and
principals, with mean scores of 3.24 and 3.20. Subject coordinators were least likely to report
actual use of technology in the classroom, with a mean score of 3.11.
Summary
Overall, this study found that teachers reported a high level of actual use of technology.
Furthermore, this study sought to understand the purposes for which teachers in the Beacon of
Light Schools were using technology. Of the many possible uses of technology, teachers
reported that they used technology most for the purposes of teaching preparation – such as
creating learning materials and accessing online resources for preparation – and classroom
teaching delivery – such as delivering learning materials and enhancing teaching strategies.
Teachers also used technology for administrative purposes such as managing coursework
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materials and government submission documents. Teachers were less likely to use technology
for the purposes of assigning homework and delivering examinations.
Research Question 2
What are the relationships between the various teacher attitudes and beliefs?
Correlations were calculated to examine the relationships between the various teacher
attitudes and beliefs. As seen in Table 19, there were several correlations that were found to be
significant with a value above 0.3.
Table 19
Correlation Matrix.
SE PB-T PB-C PU PEU ATT IU AU
SE –
PB-T .074 –
PB-C 0.011 .160 –
PU 0.005 .077 .328 –
PEU .113 .100 .259 .498 –
ATT 0.022 .058 .356 .621 .485 –
IU 0.002 .136 .300 .524 .489 .540 –
AU 0.027 .097 .261 .436 .395 .466 .491 –

Finding 1: Self-Efficacy had no correlational relationship with any of the other constructs.
This study found that self-efficacy (SE) did not correlate with any factors, including
perceived usefulness (PU), perceived ease of use (PEU), traditional pedagogical beliefs (PB-T),
constructivist pedagogical beliefs (PB-C), attitude toward technology (ATT), intention to use
technology (IU), or actual use of technology (AU).
Participants’ reported level of self-efficacy (SE) did not correlate with any of the other
seven variables because all r values were well below 0.3 on the correlation matrix. The literature
examining self-efficacy with the other variables in this study is mixed, where some studies
confirmed the hypothesized relations, and other studies. This study confirmed that in the Beacon
of Light school setting, self-efficacy is not found to be correlated with any of the independent
and dependent variables.
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Several studies found some connection between self-efficacy (SE) and technology
adoption (AU) (Gudek, 2019; Joo et al., 2018; Corry & Stella, 2018) while others found no
connection between these factors (Hanafi, 2021). Several studies also noted that self-efficacy
(SE) had an indirect effect toward perceived ease of use (PEU), a factor that is often correlated
with actual use of technology (AU) (Hanafi, 2021).
Finding 2: Teachers who held constructivist-oriented pedagogical beliefs were more likely to
find technology useful (PU), have a favorable attitude toward technology (ATT), and intend to
use technology (IU).
This study found a weak correlation between traditional pedagogical beliefs (PB-T) and
other variables given that the effect size is below 0.3. This study found a moderate correlation
between constructivist pedagogical statements (PB-C) and perceived usefulness, attitude toward
technology, and intention to use technology, since the effect size was 0.328, 0.356, and 0.300,
respectively. Constructivist beliefs were associated with beliefs that technologies are useful,
which is a key variable strongly correlated with a favorable attitude and intention to use
technology.
Many studies have attempted to investigate the relationship between pedagogical beliefs
and technology use, but almost all were done outside the setting of Indonesia. For example, a
study conducted in Turkey on pre-service math teachers found that constructivist beliefs had a
significant influence on overall technology use, while traditional-oriented beliefs led to higher
perceived ease of use (Gurer & Akkaya, 2022). Another study, based in China, found that
constructivist-oriented pedagogical beliefs naturally lead to greater technology use because
technology becomes perceived as a tool to support the student’s personal construction of
knowledge (Deng et al., 2014).
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Finding 3: Perceived Usefulness, Perceived Ease of Use correlated with Attitude, Intention
and Actual Use of Technology
Perceived usefulness of technology (PU) correlated with a higher perceived ease of use
(PEU; r = 0.498), positive attitude toward technology (ATT; r = 0.621), the intention to use
technology (IU; r=0.524), and actual use of technology (AU; r = 0.436). Between the four
correlations, perceived usefulness (PU) was most strongly correlated with attitude toward
technology (ATT) with an r value of 0.621.
The findings in this study aligned with prior research around perceived usefulness (PU)
and actual use of technology (AU). The data from this research study showed that perceived
usefulness (PU) was correlated with attitude toward technology (ATT; r = 0.621), intention to
use technology (IU; r = 0.524), and actual use of technology (AU; r = 0.436). Teachers who had
reported higher mean scores of perceived usefulness (PU) were also more likely to have a
favorable attitude toward technology (ATT), intention to use technology (IU), and actual use of
technology (AU).
In addition to the correlation between perceived usefulness (PU) and perceived ease of
use (PEU), perceived ease of use of technology (PEU) was found to be positively correlated with
attitudes (ATT; r = 0.485), intentions to use (IU; r = 0.489), and actual use of technology (AU; r
= 0.395) in the classroom. Perceived ease of use (PEU) was most strongly correlated with
perceived usefulness (PU) with a coefficient of 0.498.
At the same time, the research around the relationship between technology’s perceived
ease of use (PEU) and actual use of technology (AU) is mixed. While most studies found
perceived ease of use (PEU) to be a determinant of the intention to use technology (IU) (Alharbi
& Drew, 2014; Callum et al., 2014; Ursavaş & Reisoglu, 2017), a small number of studies found
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perceived ease of use (PEU) to have no effect or even a negative significant effect to the
intention to use technology (Attis, 2014; Ngabiyanto et al., 2021).
While the prior studies have mixed results on the relationship between perceived ease of
use (PEU) and actual use of technology (AU), this study found perceived ease of use (PEU) to be
correlated with attitude toward technology (ATT; r = 0.485), intention to use technology (IU; r =
0.489), and actual use of technology (AU; r = 0.395), therefore proving to be a significant
determinant of technology adoption among teachers.
Attitude toward technology (ATT), intention to use technology (IU), and actual use of
technology (AU) are three factors that were positively inter-correlated with one another. Attitude
toward technology (ATT) and intention to use technology (IU) had an r value of 0.540, and
intention to use technology (IU) and actual use of technology (AU) had an r value of 0.491.
Research Question 3
To what extent do teachers’ attitudes and beliefs predict intention to use and actual use
of technology?
This section investigated the predictive relationship between attitudes and beliefs in five
constructs – self-efficacy (SE), pedagogical beliefs (PB-T and PB-C), perceived usefulness (PU),
perceived ease of use (PEU) -- and attitudes toward technology (ATT), intention to use
technology (IU), and actual use of technology (AU). This analysis was conducted in four
separate regression analyses:
1. A multiple regression model between five independent constructs (SE, PB-T, PB-C, PU,
PEU) and attitude toward technology (ATT)
2. A multiple regression model between five independent constructs (SE, PB-T, PB-C, PU,
PEU) and intention to use technology (IU)
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3. A single regression model between attitude toward technology (ATT) and intention to use
technology (IU)
4. A single regression model between intention to use technology (IU) and actual use of
technology (AU).
Multiple Regression Model Between Five Independent Constructs and Attitude toward
Technology (ATT)
Table 20-23 presents the data from a multiple regression of five independent constructs
(SE, PB-T, PB-C, PU, PEU) and one dependent construct (ATT). Each table provides data for
the unstandardized coefficients, standardized coefficients, test statistic (t), and significance
values for all five variables. Three of the five independent variables were found to be significant
predictors of ATT (p < 0.01). The three items were constructivist pedagogical belief (p value =
0.000), perceived usefulness (p value = 0.000), and perceived ease of use (p value = 0.001). SE
and PB-T were not found to be statistically significant predictors of attitudes toward technology.
The adjusted r square, which represents explained variance that can be contributed to all the
predictors in this progression was 0.45 which means that 45% of the variance in attitude toward
technology can be explained by the five independent variables in this model.
Table 20
Coefficients for a Multiple Regression Model Between Five Independent Constructs (SE, PB-T, PB-C, PU, PEU) and Attitudes
Toward Technology (ATT).
Variable
Unstandardized
Coefficients
Standardized Coefficients
B Std. Error Beta t Sig.
(Constant) 0.293 0.146 2.011 0.045
SE 0.016 0.04 0.012 0.394 0.693
PB-T -0.101 0.052 -0.082 -1.946 0.052
PB-C 0.206 0.056 0.163 3.656 0.000
PU 0.672 0.044 0.658 15.11 0.000
PEU 0.145 0.043 0.147 3.396 0.001
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Table 21
Model Summary for Regression Analysis 1.
Model R
R
Square
Adjusted R
Square
Std. Error
of the
Estimate
Change Statistics
R Square
Change
F Change df1 df2
Sig. F
Change
1 .673
a
0.453 0.452 0.44042 0.453 301.78 5 1819 0
a. Predictors: (Constant), PEOU, SE, PBTRAD, PBCONST, PU
A revised model which excluded SE and PB-T was generated. As seen in Tables 22-23,
with SE and PB-T removed, PB-C, PU, and PEU were still found to be significant predictors of
ATT with 45% of the variance explained.
Table 22
Revised Regression Model with Three Independent Constructs (PB-C, PU, PEU) and Attitude Toward Technology (ATT).
Variable
Unstandardized
Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
(Constant) 0.915 0.112 8.204 0.000
PB-C 0.056 0.026 0.063 2.157 0.031
PU 0.516 0.032 0.522 16.007 0.000
PEU 0.188 0.028 0.212 6.763 0.000
a. Dependent Variable: ATT

Table 23
Revised Model Summary for Regression Analysis 1.
Model R R Square
Adjusted R
Square
Std. Error
of the
Estimate
Change Statistics
R Square
Change
F Change df1 df2
Sig. F
Change
1 .676
a
0.457 0.454 0.43121 0.457 203.274 3 726 0
a. Predictors: (Constant), PB-C, PU, PEU
b. Dependent Variable: ATT

Multiple Regression Model Between Five Independent Constructs and Intention to Use
Technology (IU)
Table 24-25 lay out the data for a multiple regression model consisting of five
independent constructs (SE, PB-T, PB-C, PU, PEU) and one dependent construct (IU). The
tables provide the data for the unstandardized coefficients, standardized coefficients, test statistic
(t), and significance values for all five variables. Two of the five independent variables were
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found to be significant predictors for intention to use technology (IU; p < 0.01). The two items
were perceived usefulness (PU; p value = 0.000) and perceived ease of use (PEU; p value =
0.000). In addition, traditional pedagogical beliefs (PB-T) were found to be statistically
significant with 95% confidence with a p value below 0.05. Self-efficacy (SE) and Constructivist
Pedagogical Beliefs (PB-C) were not found to be statistically significant predictors of intention
to use technology.
Table 24
Coefficients for a Multiple Regression Model Between Five Independent Constructs (SE, PB-T, PB-C, PU, PEU) and Intention to
Use Technology (IU).
Variable
Unstandardized
Coefficients
Standardized
Coefficients

B Std. Error Beta t Sig.
(Constant) 0.464 0.211 2.199 0.029
SE -0.096 0.058 -0.068 -1.671 0.096
PB-T 0.164 0.075 0.12 2.184 0.03
PB-C -0.021 0.081 -0.015 -0.259 0.796
PU 0.521 0.064 0.458 8.092 0
PEOU 0.285 0.062 0.259 4.598 0

Table 25
Model Summary for Regression Analysis 2.
Model R
R
Square
Adjusted R
Square
Std. Error
of the
Estimate
Change Statistics
R Square
Change
F Change df1 df2
Sig. F
Change
1 .600
a
0.36 0.357 0.51525 0.36 122.66 5 1089 0
a. Predictors: (Constant), PEOU, PBTRAD, SE, PBCONST, PU

Table 25 shows the results of the predictive variables of this regression model. The r
square, which represents explained variance that can be contributed to all the predictors in this
progression, describes the model’s explanatory power. This model found that the r value of 0.60,
the r square value of 0.36, and adjusted r square value of 0.36 with a standard error of estimate
of 0.52. The adjusted r square value of 0.36 means that 36% of the data can be explained by this
model.
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A revised regression model which excluded SE and PB-C was generated. As seen in
Table 26-27, PU and PEU were still found to be significant predictors of IU with 34.4% of the
variance explained. However, PB-T was not found to be significant.
Table 26
Coefficients for a Multiple Regression Model Between Three Independent Constructs (PB-T, PU, PEU) and Intention to Use
Technology (IU).
Variable
Unstandardized
Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
(Constant) 0.843 0.112 7.512 0
PB-T 0.037 0.022 0.042 1.683 0.093
PU 0.403 0.031 0.372 13.152 0
PEU 0.3 0.028 0.298 10.519 0
a. Dependent Variable: IU

Table 27
Revised Model Summary for Regression Analysis 2.
Model R R Square
Adjusted R
Square
Std. Error
of the
Estimate
Change Statistics
R Square
Change
F Change df1 df2
Sig. F
Change
1 .588
a
0.345 0.344 0.52077 0.345 191.811 3 1091 0
a. Predictors: (Constant), PEOU, PBTRAD, PU

A second revised model which excluded PB-T was generated. As seen in Table 28-29,
with PB-T removed, PU and PEU were still found to be significant predictors of IU with 44.1%
of the variance explained.
Table 28
Coefficients for a Multiple Regression Model Between Two Independent Constructs (PU, PEU) and Intention to Use Technology
(IU).
Variable
Unstandardized
Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
(Constant) 0.952 0.067 14.199 0
PU 0.506 0.021 0.503 24.629 0
PEU 0.232 0.019 0.247 12.119 0
a. Dependent Variable: IU

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Table 29
Second Revised Model Summary for Regression Analysis 2.
Model R
R
Square
Adjusted
R Square
Std. Error
of the
Estimate
Change Statistics
R Square
Change
F
Change
df1 df2
Sig. F
Change
1 .664
a
0.441 0.441 0.44488 0.441
719.80
2
2
182
2
0
a. Predictors: (Constant), PEU, PU
b. Dependent Variable: ATT

Single Regression Model Between Attitude Toward Technology (ATT) and Intention to Use
Technology (IU)
Table 30 presents the data from a multiple regression of one independent construct (ATT)
and one dependent construct (IU). The table provides data for the unstandardized coefficients,
standardized coefficients, test statistic (t), and significance values for both variables.
Table 30
Coefficients for a Single Regression Model Between attitude toward technology (ATT) and intention to use technology (IU).
Variable
Unstandardized
Coefficients
Standardized
Coefficients

B
Std.
Error
Beta t Sig.
(Constant) 0.679 0.149 4.548 0
ATT 0.761 0.043 0.684 17.843 0

A single regression model analysis between attitude toward technology (ATT) and
intention to use technology (IU) found that attitude toward technology (ATT) was a highly
significant predictor of the intention to use technology (IU) (p value = 0.000).

Table 31
Model Summary for Regression Analysis 3.
Model R
R
Square
Adjusted R
Square
Std. Error
of the
Estimate
Change Statistics
R Square
Change
F Change df1 df2
Sig. F
Change
1 .540
a
0.29 0.291 0.54108 0.292 450.6 1 1093 0
a. Predictors: (Constant), ATT

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Table 31 shows the results of the predictive variables in the simultaneous backward
single regression analysis. This regression model has an r value of 0.54, r square value of 0.29,
and adjusted r square value of 0.29 with a standard error of estimate of 0.54. The adjusted r
square value of 0.29 means that 29% of the data can be explained by this model.
Single Regression Model Between Intention to Use (IU) and Actual Use of Technology (AU)
Table 32
Coefficient for a Single Regression Model between intention to use technology (IU) and actual use of technology (AU).
Variable
Unstandardized
Coefficients
Standardized
Coefficients

B Std. Error Beta t Sig.
(Constant) 1.289 0.113 11.371 0
IU 0.592 0.034 0.678 17.56 0

A single regression model between intention to use technology (IU) and actual use of
technology (AU) found that intention to use technology (IU) was a highly significant predictor of
actual use of technology (AU) with a p value of 0.000.
Table 33
Model Summary for Regression Analysis 4.

Model R
R
Square
Adjusted R
Square
Std. Error
of the
Estimate
Change Statistics
R Square
Change
F Change df1 df2
Sig. F
Change
1 .491
a
0.241 0.24 0.53815 0.241 347.04 1 1093 0
a. Predictors: (Constant), IU
Table 32 shows the results of the predictive variables in the simultaneous backward single
regression analysis. This regression model had an r value of 0.491, r square value of 0.24, and
adjusted r square value of 0.24 with a standard error of estimate of 0.54. The adjusted r square
value of 0.24 meant that 24% of the data can be explained by this model.

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Summary
In summary, this chapter provided an analysis of the study’s three research questions
based on 365 survey responses that were collected. The first research question sought to
understand teachers’ attitudes, beliefs, intentions, and actual use in the classroom. Descriptive
data for all constructs were analyzed – including that for self-efficacy (SE), traditional
pedagogical beliefs (PB-T), constructivist pedagogical beliefs (PB-C), perceived usefulness
(PU), perceived ease of use (PEU), attitude toward technology (ATT), intention to use
technology (IU), and actual use of technology (AU). The data included the mean scores for each
construct, mean scores for each demographic category within each construct, and the ordinal
scale for each survey item within that construct.
The second research question sought to understand the relationship between various
teacher attitudes and beliefs. A correlations matrix was created where all r values above 0.3 were
considered significant. As a result, this study found that perceived usefulness and perceived ease
of use were correlated with the attitude, intention, and actual use of technology in the classroom.
Furthermore, having a constructivist perspective to teaching pedagogy was associated with a
higher likelihood to find technology useful and easy to use.
Finally, the third research question sought to understand the extent to which teachers’
attitudes and beliefs predicted the intention to use and actual use of technology. This study chose
to do four regression models, following the conceptual framework’s model for path analysis. The
first multiple regression model studied the relationship between five variables – self-efficacy
(SE), traditional pedagogical beliefs (PB-T), constructivist pedagogical beliefs (PB-C), perceived
usefulness (PU), and perceived ease of use (PEU) – on attitudes toward technology (ATT).
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The regression models were rerun after eliminating non-significant variables, until a best-
fit model was obtained. The second multiple regression model studied the relationship between
five variables – self-efficacy (SE), traditional pedagogical beliefs (PB-T), constructivist
pedagogical beliefs (PB-C), perceived usefulness (PU), and perceived ease of use (PEU) – on
intention to use technology (IU). Similarly, regression models were rerun after eliminating non-
significant variables, until a best-fit model was obtained. The third single regression model
studied the relationship between attitudes toward technology (ATT) and intention to use
technology (IU). The fourth single regression model studied the relationship between intention to
use technology (IU) and actual use of technology (AU).
Overall, the data showed that perceived usefulness (PU) and perceived ease of use (PEU)
were highly significant to both attitude toward technology (ATT) and intention to use technology
(IU). Constructivist pedagogical beliefs (PB-C) had a moderately significant relationship with
attitude toward technology (ATT). Attitude toward technology (ATT) had a highly significant
relationship toward intention to use technology (IU), and intention to use technology (IU) had a
highly significant relationship toward actual use of technology (AU).

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CHAPTER 5: RECOMMENDATIONS
This chapter discusses the study’s findings within the context of previous research that
applied the TAM or extended TAM model in the K-12 educational settings. This study also
proposes a recommendation for the Beacon of Light Foundation to strengthen teacher attitudes,
intentions, and actual use of technology among its group of teachers. Finally, this chapter reviews
the strengths and limitations of the study, implications for practice, and future research.
Discussion of Findings
This section discusses two significant findings based on the survey results and analysis.
The first significant finding was that teachers under the Beacon of Light Foundation reported
high levels of agreement on all the survey statements, with little variation across the responses.
The second significant finding was that perceived usefulness (PU) and perceived ease of use
(PEU) stand out as two predictive variables toward teachers’ use of technology. Both findings
have significant implications for school recommendations and future research.
Positive Perception Toward Technology
This study found that the teachers who participated in the survey embraced an overall
positive perception toward technology. Teachers perceived technology to be useful and easy to
use, and they also reported to have a positive attitude, intention, and actual use of technology.
Furthermore, teachers were relatively confident in their individual ability to use technology, as
reflected in their self-efficacy scores. The results showed little variability among the responses,
with over 90% of teachers indicating that they “agreed” or “strongly agreed” with the positive
survey statements.
There are several contextual possibilities that contribute to the overall positive responses
of teachers toward technology. First, the survey was conducted only four months after the end of
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pandemic-induced online learning where schools fully relied on technology as a medium of
teaching and learning. For this reason, teachers may have felt a high level of self-efficacy in their
ability to teach through technology, having heavily relied on technology for the past 28 months.
Teachers may have also had a high level of perceived usefulness toward technology, as
technology had been the only possible medium of learning during pandemic learning. The
reliance and habitual use of technology may have led teachers to have a positive attitude,
intention, and actual use of technology. These possibilities create scope for future longitudinal
research focusing on the same group of teachers, to reconfirm whether this study’s findings hold
true across time.
The high level of agreement toward educational technology may have also been affected
by Indonesia’s cultural phenomenon. To begin, BLF is a top-down, Board driven organization
where vision, mission, hiring, curricular decisions are determined at the Foundation or
headquarter level. Because of this situation, the schools may have recruited teachers who have a
higher likelihood to agree with its policies, and vice versa, that the teachers interested in joining
BLF may also have a higher level of agreement with BLF’s policies.
Another by-product of BLF’s approach as a top-down organization is the centralization of
curriculum and curricular policies. As a result, teachers have less autonomy to shape the school
curriculum. This is not an uncommon practice across schools or Foundations in Indonesia. One
case-based study by Too & Saimima (2019) also found that teachers in Eastern Indonesia
experienced tensions between conformity and practice while teaching within a school-based
curriculum. In their study, the researchers found that teacher autonomy encompassed classroom
management and teaching strategies but did not extend to the selection of teaching materials and
activities. One theme that the Too & Saimima (2019) picked up was the phenomenon of
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conformity in practice, where teachers did not have control over curriculum, but simply followed
the school leaders’ direction, to shape their classroom teaching and learning practices.
Similarly, the culture of conformity within the schools under the Beacon of Light
Foundation may have led to a positive attitude toward technology. It is possible that the Beacon
of Light Foundation teachers believed that their responsibility is to align to the overall
institutional goals that have been determined at the head office level, therefore leading to less
variation in responses. It was interesting to note that teachers’ positive attitude toward
technology still held true even when teachers reported slightly lower levels of self-efficacy. This
might point to the conclusion that teachers were determined to embrace technology regardless of
their own confidence in their abilities to use technology.
Perceived Usefulness and Ease of Use as Predictive Variables Toward Technology Acceptance
Among Teachers
This study found that perceived usefulness and perceived ease of use were two variables
that continued to emerge as significant predictors of technology acceptance among teachers. To
begin, the second research question used a correlations matrix to demonstrate that perceived
usefulness, perceived ease of use, and attitudes, intentions, and actual use of technology were
constructs that were highly correlated with one another.
Furthermore, this study’s regression analysis also confirmed that perceived usefulness
and perceived ease of use were significant predictors of attitudes, intentions, and actual use of
technology. Several regression models were used according to a path analysis framework to
arrive at findings, using various regression statistical analyses to evaluate causal models between
dependent and independent variables. This framework is depicted below in Figure 6.
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Figure 6
Conceptual Framework with Regression Pathways.

The first regression model (R1) examined the relationship between self-efficacy (SE),
pedagogical beliefs (PB), perceived usefulness (PU), and perceived ease of use (PEU) with
attitude toward technology (ATT). The second regression model (R2) examined the relationship
between self-efficacy (SE), pedagogical beliefs (PB), perceived usefulness (PU), and perceived
ease of use (PEU) with intention to use technology (IU). The third regression model (R3)
examined the relationship between attitude toward technology (ATT) and intention to use
technology (IU). The fourth regression model (R4) examined the relationship between intention
to use technology (IU) and actual use of technology (AU).
Using a series of regression analyses, this study found that perceived usefulness (PU) and
perceived ease of use (PEU) could predict attitude toward technology (ATT) and intention to use
technology (IU). In addition, constructivist pedagogical beliefs (PB-C) were predictive of
attitude toward technology (ATT). The intention to use technology (IU) also predicted actual use
of technology (AU). Figure 7 illustrates the final conceptual model showing the significant
relationships.
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The correlation between perceived usefulness (PU), perceived ease of use (PEU), attitude
toward technology (ATT), intention to use technology (IU), and actual use of technology (AU)
confirms the transferability of the original Technology Acceptance Model (TAM) to the context
of this study. These findings confirm that the hypothesis of the original TAM model, which
purported that perceived usefulness (PU) and perceived ease of use (PEU) predicted attitude
toward technology (ATT), intention to use technology (IU), and actual use of technology (AU).
Figure 7
Conceptual Model Depicting the Regression Findings.
Many studies have used the TAM model in education to examine the relationship
between perceived usefulness (PU), perceived ease of use (PEU), attitude toward technology
(ATT), intention to use technology (IU), and actual use of technology (AU). Previous studies
that used the TAM model in educational research found that perceived usefulness (PU) was a
major determinant of intention to use technology (IU) (Scherer et al., 2015; Alharbi & Drew,
2014; Hanafi, 2021; Callum et al., 2014; Chang et al., 2012). When teachers understood a
technology to be useful, they were also more likely to incorporate the technology as part of their
teaching and learning activities.

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Recommendations for Practice
As mentioned in the literature review, technology is beneficial in education mainly for
utilitarian purposes – namely, as a tool to improve student outcomes. This study focused on
understanding teachers’ perceptions on technology use because teachers are the most important
determinants on whether technology ends up being used in the classroom. This study found that
perceived usefulness (PU) and perceived ease of use (PEU) are highly predictive of attitudes,
intentions, and actual use of technology. In addition, constructivist pedagogical beliefs (PB-C)
also predicted attitude toward technology (ATT).
The next section of this chapter will provide tangible recommendations on how schools
can strengthen teachers’ perceptions on technology’s usefulness, ease of use, and level of
constructivist pedagogical beliefs, to improve overall attitude, intention, and actual use of
technology. As this study revealed, teachers have a high level of buy-in when it comes to
technology adoption. However, since technology implementation requires resources and funding,
it is important for school leaders to demonstrate the value of technology as a tool to improve
teaching and learning and the school’s overall reputation and standing within the community.
Board Buy-In
The Board is responsible for setting the strategic direction to help the Foundation achieve
its missional purpose. Furthermore, the Board is also responsible for providing the financial
resources and external expertise to ensure that technology can be implemented well at the school
level. School leaders must first show how technology investments lead to a return of investment
(ROI) from the tangible and non-tangible perspectives. Table 34 provides an example of
justifying the deployment of technology resources to the Board.

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Table 34
Example of ROI justification to the Board.
Goals Key Indicators Value
Academic Improve student
learning outcomes
Improved NWEA MAP
Scores in Literacy and
Numeracy
School academic
reputation;
attractiveness to current
and prospective parents
Financial Generate additional
revenue
Technology fee Total revenue collected
Marketing and
Admissions
Parent and student
satisfaction
Improved net promoter
scores (NPS) and parent
feedback
School academic
reputation; word of
mouth
Operational Teacher satisfaction Improved teacher net
promoter score (NPS);
teacher retention rate
Improve teacher
effectiveness and use of
time by simplifying
certain tasks

In addition, school leaders must be able to create a summary of resources that are needed
to run school-level initiatives that aim to improve technology adoption. Resources may include
additional requests for human resources or stipends for existing employees. It may also include
financial funding for programs and technology investments.
School-Level Initiatives
When schools have secured the required resources to run school-level initiatives, this
study proposes school-level initiatives summarized in Table 35. The first recommendation
focuses on training a group of expert teachers through a train-the-trainer model, and the second
recommendation focuses on a peer-to-peer approach to teacher development.
Table 35
Summary of Recommendations for BLF Schools.
Recommendation Approach Domain Core Features
1 Train-the-trainer 1-2 in-person
sessions
Krathwohl (2002)
Revised Bloom's
Taxonomy
Factual Knowledge Content-focused
Conceptual
Knowledge
Content-focused
Single workshop Procedural
Knowledge
Skills-focused
Discussion forum Metacognitive
Knowledge
Discussion-based
(facilitator-led)
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2 Peer-to-peer
program (led by
expert teachers)
One-to-few
mentorship
Bandura (1977)
Social Cognitive
Theory
Observation,
imitation, and
modeling
Meetings and
mentorship
Classroom
Observation
Observation of others'
teaching practices (in-
class or demo)
Professional
Learning
Communities
On-the-job learning,
structured reflection in
community

Recommendation 1: Schools should have a train-the-trainer program for a core
group of teachers that delivers the key knowledge and competencies required to operate a
specific technology.
Professional development is one channel that can be used to shape teacher perceptions of
technology. A train-the-trainer program, which is a common intervention used in schools to
improve the quality of teaching (Popova et al., 2016). According to Borko (2004), teacher
professional training should incorporate both cognitive and social aspects of learning. Cognitive
learning focuses on changing beliefs or knowledge, while social learning focuses on learning
acquired through participation (Watson, 2014).
A successful train-the-trainer approach should begin by the provision of cognitive
learning to a small group of teacher participants who already demonstrate a reasonably strong
buy-in toward technology. This is done with the intention of turning that group of teachers into
subject matter experts, equipped with the necessary skills and knowledge to become role models
and trainers within the community.
School leaders will begin by setting clear goals for the core group. These goals should
target as many knowledge areas as possible according to Krathwohl’s (2002) framework, where
he outlines the factual, conceptual, procedural, and metacognitive domains of knowledge.
Concretely applied, this may include the ability to articulate the technology’s functionalities
(declarative), how it fits into the overall picture of teaching and learning (conceptual
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knowledge), the technical know-how to operate the system based on various classroom
requirements (procedural knowledge), and the self-knowledge to understand what the individual
finds difficult and how to overcome those obstacles (metacognitive knowledge).
To use one real example at the school-level, a group of teachers can be trained to be
subject matter experts on the school’s e-Library platform usage. The teachers can then be trained
to master the general knowledge of the platform’s capabilities, how this platform can assist in the
delivery of key learning outcomes in literacy, how to incorporate the platform into in-class
teaching and pedagogy, how to use the student dashboard analytics to inform them of the
student’s reading progress, and how to troubleshoot for help when needed.
The teachers will also be given training around instructional expertise, as they will be
assigned five other teachers each to individually mentor over the next six months. Therefore,
they will need to know how to convey the information to other teachers. Instructional expertise
begins with the ability to convey technical information about the product. It will also include the
ability to model the specific technology uses in the classroom for other teachers to observe
through in-classroom observations. Modeling is important because according to Bandura’s
Social Cognitive Theory (1977), people learn through observing, imitating, and modeling others’
behavior. Finally, instructional expertise will involve the ability to facilitate ongoing discussions
between teachers so they can learn from one another and share best practices.
Recommendation 2: Facilitate peer-to-peer (or teacher-to-teacher) programs where teacher
experts can lead other teachers toward positive perceptions of technology.
After a core group of teachers are trained to be subject matter experts, schools can
proceed to the second step of adopting a peer-to-peer training approach where expert teachers
teach, train, mentor, and facilitate discussions across professional groups. This can be
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accomplished through a combination of meetings, workshops, demo, and observation
opportunities.
Giving teachers the opportunity to learn from other teachers will strengthen their ideas
about technology’s ease of use and usefulness, especially when the interactions are facilitated by
expert teachers who function as role models toward technology use. Furthermore, having
teachers train other teachers is important because Popova et al. (2016) found that school-based
interventions that used educational professionals as trainers tended to have better outcomes than
those that used non-educational professionals as trainers. Furthermore, peer-to-peer learning
transforms passive activities to become actively reinforced in the context of community, where
technology implementation is continually analyzed, evaluated, and improved (Stewart, 2014).
Outside the expert teacher mentorship initiative, another common approach in schools is
to facilitate peer-to-peer observations, which is a structured program where experienced teachers
demonstrate the use of technology to other teachers, both as controlled demonstrations and
within the classroom setting (Hamilton, 2012). Having other teachers demonstrate technology
use will also allow the observing teacher to understand and assess the technology’s usefulness
and ease of use.
Peer-to-peer programs can also include creating professional learning communities where
teachers can collaborate and support one another. The core activity of professional learning
communities would be on-the-job professional development within a community of practice,
where learning takes place through experience and reflective action (Stewart, 2014). Using the
example of the e-Library platform adoption, schools can create a cross-school community of first
and second-grade teachers who teach English, where they could use the platform in classrooms
and bring back observation notes into the peer groups to discuss successes and struggles.
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Limitations and Delimitations
This study had several purposeful limitations and delimitations. First, the study sought to
understand technology adoption from a teacher’s perspective, recognizing that teachers are the
agents at the center of the teaching and learning process. However, the researcher also recognizes
that technology adoption involves multiple stakeholders, not any less the school board and
administrative leaders who decide on investments based on the direction they set for the school.
Furthermore, successful technology adoption also depends on the readiness of the students in the
classroom, which is an area that has also been extensively studied by researchers.
Second, this study focused on understanding technology adoption in 14 private schools
under the Beacon of Light Foundation in Indonesia. By narrowing down the number of schools,
the research was able to gain in-depth insight into a relatively small homogenous group of
teachers. However, data by the World Bank Group (2020) shows that only 22.85% of school
enrolments at the primary level were in private schools, as compared to the remaining enrolment
in government schools. Therefore, the findings of this study cannot be generalized to represent
elementary schoolteachers across Indonesia.
Thirdly, this study examined four constructs – self-efficacy, pedagogical beliefs,
perceived usefulness, and perceived ease of use – as they relate to teachers’ behavioral intention
to adopt technology in teaching. Ideally, the research would have benefitted from follow-up
interviews with the teachers to gain a deeper insight into what the quantitative data presents.
Furthermore, there were several other constructs that were excluded from the study, which the
researcher would have liked to have had included. However, these additional scopes were not
possible due to constraints in the dissertation’s timeline and scope.
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Finally, this study was conducted during the COVID-19 pandemic, which may have
impacted the responses of teachers. During the pandemic, Indonesian teachers were suddenly
thrusted into over two years of teaching online. The speed of change was also abrupt and may
have led to the development of certain sentiments toward technology that would normalize over
time. In hindsight, it is known that these arrangements do not truly represent the steady state of
teaching and learning arrangements that have largely returned on premises. Since this study was
done only three months after the return of face-to-face learning, the study’s results may only
portray teacher perceptions at a specific moment in time and may not be generalizable into the
future even for the Beacon of Light Schools.
Recommendations for Future Research
This study served as an initial study to understand teacher perceptions across four
constructs – namely self-efficacy, pedagogical beliefs, perceived usefulness, perceived ease of
use – as they relate to the teacher’s intentions to use technology in the classroom. Further studies
can be done to broaden, deepen, and reconfirm the findings of this research study.
To begin, future studies can broaden the scope of this study by examining constructs that
were not covered. Many studies using the extended TAM model often include knowledge –
using the TPACK model – as a predictive factor in behavioral intention to use technology (Yang
& Wang, 2019; Mailizar et al., 2021b; Thohir et al., 2023). Other constructs that are less
commonly studied include enjoyment (Wang et al., 2019; Almula, 2021), prior experience
(Mailizar et al., 2021a), subjective norm (Almula, 2021), and physical or digital infrastructure
(Sukendro et al., 2020).
From the perspective of depth, the constructs in this study were presented as
unidimensional concepts to survey teachers at 14 schools under the Beacon of Light Foundation.
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Furthermore, this study was specifically delimited to a broad definition of technology to refer to
“the use of tools, technologies, processes, procedures, resources, and strategies to improve
learning experiences in a variety of settings, such as formal learning, informal learning, non-
formal learning, lifelong learning, learning on demand, workplace learning, and just-in-time
learning” (Huang et al., 2019). However, in the actual day-to-day school operations, every
school, subset of teachers, and grade levels have specific goals for which they are attempting to
use technology. In addition, there is also a wide range of technologies that have different levels
and kinds of complexities, with unique value propositions to the teacher. Therefore, this
dissertation recommends further studies to understand technology adoption according to specific
purposes and teaching and learning goals that are present within the schools.
Future research can also examine the constructs in this dissertation using different survey
instruments or methodologies. For example, the researcher questioned why the survey’s self-
efficacy scores were high, and why contrary to expectations, it had no correlation with any of the
other constructs. One reason might be because the survey questions were very general, possibly
because the survey was adopted from a study conducted in 2011 by Holden and Rada, where
many technological changes have happened from 2011 to 2023. As a result, further research can
examine whether the findings around self-efficacy still hold true with a more recently created
survey instrument. Furthermore, this study’s research findings around self-efficacy can be
reconfirmed by employing a qualitative study using an interview format, therefore capturing
nuances that may have been missed using a quantitative approach.
The researcher questioned whether the survey results might be biased as a result of
teachers’ recent experiences in teaching during the pandemic. During the pandemic, Indonesian
teachers were forced to teach using technology as government regulations prohibited face-to-face
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learning. These experiences may have led teachers to embrace technology, given that there were
no other alternatives. Further research can be conducted several years from now to reconfirm
whether teachers still hold the same positive perception toward technology.
Finally, this study intentionally focused on factors that affect teachers’ behavioral
intention to use technology, without going further to understand whether technology is benefiting
the learning process as shown through objective measures of student outcomes. Further studies
should explore whether the investments of technology bring a return of investment from the
perspective of student outcomes.
Conclusion
The purpose of this project was to identify factors that affected teachers’ use of
technology in the classroom within the specific context of schools under the Indonesia-based
Beacon of Light Foundation. External factors, such as the COVID-19 pandemic and the
Indonesian government, have pressured Indonesian schools to take on technology tools as part of
teaching and learning. The focus was often greater on having technological tools available, as
compared to the readiness of the teachers in using the technology to improve teaching and
learning outcomes. This study sought to understand how to increase teachers’ adoption of
technology by understanding factors that played a role in their decision-making process.
This study found that two variables consistently played a large role in the teacher’s
decision to adopt technology, namely technology’s usefulness and ease of use. This study also
found that a constructivist pedagogical belief affected teachers’ attitudes toward technology.
Several areas emerged in the actual use of technology where teachers found technology
particularly helpful, primarily in teaching preparation and teaching delivery.
Indonesian teachers’ adoption of technology in the K-12 classroom
106

The findings of this study are important for the Beacon of Light Schools because
technology’s role in schools will only be ever increasing, and this study provides a broad
direction on how the Beacon of Light Schools can improve teacher buy-in for technology
adoption. Technology holds the potential to transform the student experience and learning
outcomes, as well as bring greater equity to the inequality in educational opportunities. Schools
that fail to embrace these changes run the risk of being obsolete and left behind. Therefore, this
study should serve as a catalyst for schools to understand how they can better support teachers
for 21
st
century learning.

Indonesian teachers’ adoption of technology in the K-12 classroom
107

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APPENDICES
Appendix A: Survey Questionnaire

Educational Technology in the Classroom Survey
The TAM questionnaire is made up of 6 demographic questions and 47 survey items - 10 items on
Self-Efficacy (SE), 14 items on Pedagogical Beliefs (PB), 7 items on Perceived Usefulness (PU), 6
items on Perceived Ease of Use (PEU), 5 items on Attitude Toward Technology (ATT), 3 items on
Intention to Use Technology (IU), and 2 items on Actual Use of Technology (AU).
Part I: Demographic Information
Instruction: Please indicate your response to the following questions by ticking the appropriate box.
1 Gender Male Female Others
Prefer not
to answer

2 Age 18-24 25-34 35-44 45+
Prefer not to
answer
3
Teaching
Experience (years)
0-5 years 6-10 years
11-15
years
>15 years
Prefer not to
answer
4
Academic
Qualifications
Under-
graduate -
Education
Under-
graduate -
Non-
Education
Master’s Doctorate
Others/Prefe
r not to
answer
5 Teaching Position
Kindergarte
n
Elementary
- General
Elementary
- Specialist
Secondary
Others/Prefe
r not to
answer
6 Leadership Position Principal
Subject
Coordinator
Year-Level
Lead
Others
None of the
above
Part II: Views on technology in the classroom
Instruction: Please indicate your level of agreement with the following statements where 1 =
Strongly Disagree and 4 = Strongly Agree.
Question Items
1 2 3 4
Strongly
disagree
Disagree Agree
Strongly
Agree
Self-Efficacy (SE)
Statement: In general, I could complete
any desired task using any technological
application if…
A1
… There was no one around to tell
me what to do as I go.

Indonesian teachers’ adoption of technology in the K-12 classroom
140

A2
… I had never used a technology
like it before.

A3
… I had only the manuals for
reference.

A4
… I had seen someone else using it
before trying it myself.

A5
… I could call someone for help if
I got stuck.

A6
… Someone else had helped me
get started.

A7
… I had a lot of time to complete
the task for which the technology
was provided.

A8
… I had just the built-in help
facility for assistance.

A9
… Someone showed me how to do
it first.

A1
0
… I had used similar technologies
before this one to do the same task.

Pedagogical Beliefs (PB)
B1
I believe that expanding on
students' ideas is an effective way
to build my curriculum.

B2
I prefer to cluster students' desks or
use tables so they can work
together.

B3
I invite students to create many of
my bulletin boards or other
collaborative spaces.

B4
I like to make curriculum choices
for students because they can't
know what they need to learn.

B5
I base student grades primarily on
homework, quizzes, and tests.

B6
To be sure that I teach students all
necessary content and skills, I
follow a textbook or workbook.

B7
I teach subjects separately,
although I am aware of the overlap
of content and skills

Indonesian teachers’ adoption of technology in the K-12 classroom
141

B8
I involve students in evaluating
their own work and setting their
own goals.

B9
I make it a priority in my
classroom to give students time to
work together when I am not
directing them.

B10
My interest in what students can
do independently is mainly for
mandatory assessment purposes.

B11
I generally use a teacher's guide
that is given to me, to lead class
discussions of a story or text.

B12
I prefer to assess students
informally through observations
and conferences.

B13
I find that textbooks and other
published materials are the best
sources for creating my
curriculum.

B14
I often create thematic units based
on the students' interest and ideas.

Perceived Usefulness (PU)
C1
Using technology in the classroom
allows for tasks to be
accomplished more quickly.

C2
Using technology in the classroom
improves my teaching
performance.

C3
Using technology in the classroom
increases my productivity.

C4
Using technology in the classroom
enhances teacher effectiveness.

C5
Using technology in the classroom
makes it easier to do my job.

C6
I find educational technologies
useful in my class.

C7
Using educational technologies
makes it easier to catch individual
student needs.

Perceived Ease-of-Use (PEU)
D1
I find it easy to operate educational
technologies in the classroom.

Indonesian teachers’ adoption of technology in the K-12 classroom
142

D2
I find it easy to get technology to
do what I want it to do.

D3
My interaction with technology is
clear and understandable.

D4
I find educational technologies
flexible to interact with.

D5
It is easy for me to become skillful
at using educational technologies.

D6
I would find educational
technologies easy to use.

Attitude Toward Technology (ATT)
E1
Using technology to support
classroom learning is good.
E2
Using technology to support
classroom learning is favorable.
E3
Using technology in class
enhances student learning
outcomes and interests.

E4
I would love to use educational
technology in my class.

E5
Using technology in class helps me
in my pedagogical approach.

Intention to Use (IU)
F1
I intend to use technology during
this semester.

F2
I intend to increase the occurrence
of technology use in my
classroom.

F3
I intend to use technology to
diversify approaches to learning.

Actual Use of Technology (AU)
G1
On average, I
educational
technologies in my
classroom…
0 lessons per
week
1-5 lessons
per week
6-10
lessons per
week
11-15
lessons per
week
Above 16
lessons per
week
G2
I use technology in
the classroom for
the following
purposes (tick all
that apply):
Deliver
learning
materials
Enhance
teaching
strategies
Manage
coursewor
k materials
Homework
assignment
s
Others: ____

Abstract (if available)

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Asset Metadata

Creator Riady, Stephanie (author)

Core Title Indonesian teachers' adoption of technology in the K-12 classroom: a TAM-based quantitative study

School Rossier School of Education

Degree Doctor of Education

Degree Program Organizational Change and Leadership (On Line)

Degree Conferral Date 2023-08

Publication Date 07/25/2023

Defense Date 07/11/2023

Publisher University of Southern California. Libraries (digital)

Tag actual use of technology,attitudes toward technology,Educational Technology,intention to use technology,K-12 education,OAI-PMH Harvest,pedagogical beliefs,perceived ease of use,perceived usefulness,self-efficacy,Teachers,technology acceptance model,technology adoption

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Malloy, Courtney (committee chair), Hirabayashi, Kimberly (committee member), Stowe, Kathy (committee member)

Creator Email sriady@gmail.com,sriady@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC113288973

Unique identifier UC113288973

Identifier etd-RiadySteph-12140.pdf (filename)

Legacy Identifier etd-RiadySteph-12140

Document Type Dissertation

Rights Riady, Stephanie

Internet Media Type application/pdf

Type texts

Source 20230725-usctheses-batch-1073 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus, Los Angeles, California 90089, USA

Repository Email cisadmin@lib.usc.edu