Copyright

by

Elisheba Wairimu Kiru

2018

The Dissertation Committee for Elisheba Wairimu Kiru Certifies that this is the approved version of the following dissertation:

Mathematics Instruction with Information and Communication Technologies: An

International Comparison Using the TALIS Dataset

Committee:

Audrey M. Sorrells, Supervisor

North A. Cooc

Christian T. Doabler

Joan E. Hughes

Mathematics Instruction with Information and Communication Technologies: An International Comparison Using the TALIS Dataset

by

Elisheba Wairimu Kiru

Dissertation

Presented to the Faculty of the Graduate School of

The University of Texas at Austin

in Partial Fulfillment

of the Requirements

for the Degree of

Doctor of Philosophy

The University of Texas at Austin August, 2018

Dedication

To my wonderful and loving family, to all who believe in me, and to him who is

able to do immeasurably more than I can ask or imagine.

v

Acknowledgements

I would like to express my sincere appreciation to my advisor, Dr. Audrey Sorrells

for her willingness to join me in my learning adventures. Her support and openness to my

research interests and ultimately my dissertation study, as well as her feedback and

guidance have been invaluable. Dr. Sorrells, thank you for your mentorship and advocacy.

In addition, I wish to acknowledge the scholastic contributions and support of my

dissertation committee members Dr. North Cooc, Dr. Christian Doabler, and Dr. Joan

Hughes.

I would like to express my deepest appreciation to Dr. Cooc for cultivating in me

an appreciation for large-scale datasets as valuable resources for answering critical

questions. The inception of my dissertation study took place in our class on using datasets

to answer research questions in special education. Dr. Cooc exposed me to systematic ways

of developing research questions, conducting statistical analyses, reporting, and

interpreting findings. Moreover, I was able to apply and hone previously learned statistical

skills in analyzing data to better consume research literature. Thank you Dr. Cooc for your

availability, feedback, patience, and guidance throughout my study as I grew confident in

my statistical and writing skills as a scholar.

I would also like to express my sincere gratitude to Dr. Doabler for his guidance,

thoughtful feedback, and positive support. Dr. Doabler, thank you for your genuine

investment in my work, accommodating nature, affirmation, and your keen interest in my

success and well-being. I would like to express my appreciation to Dr. Joan Hughes for her

insightful feedback and course on technology, teacher learning and school change that

deepened my knowledge about technology and allowed me to think critically about

technology integration in schools. The Making Change HappenÔ board game was apt and

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one that equipped me with nuanced understandings of educational change processes. Dr.

Hughes, thank you for your willingness to be part of my dissertation committee.

I am grateful to my family for their unwavering support. I also want to acknowledge

colleagues and friends who offered feedback on different portions of this manuscript and

support in the process: Dr. Arati Maleku (Ohio State University); Dr. David Okech

(University of Georgia); Dr. Kelly Williams (University of Texas at Austin); Dr. Ray

Ostendorf (Western New England University); and Christine Kieti (Athens, Georgia).

Additionally, I appreciate the consultants at the University of Texas at Austin Writing

Center for providing their invaluable services. Lastly, I am grateful to the SMARTER

Consulting team at The University of Texas at Austin, Dr. Adam Sales and Danny Swan

for offering their statistical expertise.

vii

Mathematics Instruction with Information and Communication Technologies: An International Comparison Using the TALIS Dataset

Elisheba Wairimu Kiru, Ph.D.

The University of Texas at Austin, 2018

Supervisor: Audrey Sorrells

Countries around the world continue to invest in Information Communication

Technologies (ICT) for education and this has led to increased attention from stakeholders

(e.g., policymakers, educators, private sector, curriculum developers) (Trucano, 2017).

ICT has affordances that may facilitate students’ development of problem-solving skills,

analytical skills, and critical thinking needed in the 21st century. However, stakeholders

assume the presence of ICT in the classroom will lead to changes in teachers’ instructional

practices and enhance student learning in critical subjects (e.g., mathematics). Examining

the different relationships among key stakeholders (e.g., students, teachers, school leaders)

in a learning environment uncovers assumptions about ICT and provides insights into

effective and sustained ICT integration (Fullan, 2016). These relationships can explain

factors that contribute to the varied ways that teachers use ICT in instruction. With the

documented underutilization of ICT in the U.S., a comparative study can provide a global

outlook of teachers’ ICT use that may help contextualize this discrepancy from an

international lens. Furthermore, a study investigating how mathematics teachers use ICT

viii

in their classrooms can shed light on areas that need continued research and subsequently

enhance students’ learning. To that end, using data from the Teaching and Learning

International Survey [TALIS] (OECD, 2013) survey, this study focused on eight countries

(Australia, Finland, Latvia, Mexico, Portugal, Romania, Singapore, and Spain) to

investigate (a) To what extent do teachers use ICT in mathematics instruction? (b) What is

the relationship between professional qualifications (e.g., technology training) and

teachers’ ICT use of ICT? (c) What is the relationship between teachers’ instructional

approaches (e.g., constructivist approaches), beliefs (e.g., self-efficacy) and ICT use in

instruction? (d) Do teachers use ICT differently in mathematics instruction among students

with different characteristics (mathematics achievement levels, special needs status)? (e)

How do school contexts predict teachers’ ICT use? Results show that teachers’ education

levels, mathematics self-efficacy, constructivist practices and cooperation amongst

educators are associated with ICT use in instruction. Also, mathematics teachers are most

likely to incorporate ICT in classes with students with low socioeconomic status.

Administrative support and teachers’ constructivist beliefs are not associated with teachers’

ICT use in mathematics instruction. Implications for practice and future research of these

findings are discussed.

ix

Table of Contents

List of Tables ........................................................................................................ xii

List of Figures ...................................................................................................... xiii

CHAPTER ONE ......................................................................................................1

Introduction ..............................................................................................................1Statement of the Problem ................................................................................3Current Landscape ..........................................................................................7

Mathematics ...........................................................................................7ICT and Mathematics .............................................................................9ICT and Special Education ..................................................................10ICT and Socioeconomic Status ............................................................12Meeting the 1:X Challenge ..................................................................14

Significance of the Study ..............................................................................15Purpose of the Study .....................................................................................19

Definition of Key Terms ...............................................................................20

CHAPTER TWO ...................................................................................................22

Review of Literature ..............................................................................................22ICT and Teaching ................................................................................22International Trends in ICT .................................................................23

Theoretical Frameworks ...............................................................................27Theory of Diffusion of Innovations .....................................................27Sociocultural Theory ............................................................................28

ICT Integration in Schools ............................................................................31Active Ingredients of ICT Integration ...........................................................33

CHAPTER THREE ...............................................................................................48

Methodology ..........................................................................................................48Research Design ...................................................................................49

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Sources of Data and Data Collection ...................................................49Participants ...........................................................................................50Measures ..............................................................................................51Outcome Variables ...............................................................................56Data Analysis .......................................................................................57Hypotheses ...........................................................................................60

CHAPTER FOUR ..................................................................................................62

Results ....................................................................................................................62Research Question Two: Teachers’ Professional Qualifications and ICT

Use .......................................................................................................68Research Question Three: Teachers’ Instructional Approaches, Beliefs and

ICT Use ................................................................................................71Research Question Four: Teachers’ ICT Use and Students’

Characteristics ......................................................................................73Mathematics Levels .............................................................................73Socioeconomic Status ..........................................................................75Special Education Status ......................................................................76Mathematics Levels, Low Socioeconomic Status, Special Education

Status ...........................................................................................78Research Question Five: School Contexts and Teachers’ ICT use ...............81

CHAPTER FIVE ...................................................................................................83

Discussion ..............................................................................................................83

Key Findings ..........................................................................................................85Research Question One: ICT Use Among the Countries ..............................85Research Question Two: Teachers’ Professional Qualifications and ICT

Use .......................................................................................................87Research Question Three: Teachers’ Instructional Practices, Beliefs and

ICT Use ................................................................................................90Research Question Four: Students’ Characteristics and Teachers’ ICT

Use .......................................................................................................93Research Question Five: School Contexts and ICT Use ..............................95

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Limitations and Constraints ...................................................................................97

Implications for Practice ........................................................................................98

Conclusion ...........................................................................................................100

Appendix A: Variables Investigated and Sample Items ......................................103

Appendix B. Logistic Regression Results in Odds Ratios for Combined Model Including Teachers’ Professional Qualifications, Instructional Approaches, Beliefs, Classroom Students’ Characteristics, and School Context Predictors of Teachers’ ICT Use .................................................................106

References ............................................................................................................107

xii

List of Tables

Table 1: Similarities and Differences of the Countries ............................................3

Table 2. Sample Demographics: Descriptive Statistics .........................................63

Table 3. Means and Standard Deviations of Mathematics Teachers .....................64

Table 4. Probabilities of Overall ICT Use Across Countries .................................65

Table 5. Logistic Regression Results in Odds Ratios for Model Including

Teachers’ Professional Qualifications Predictors of ICT Use ..........70

Table 6. Logistic Regression Results in Odds Ratios for Model Including

Teachers’ Instructional Approaches and Beliefs Predictors of ICT

Use ....................................................................................................72

Table 7. Logistic Regression Results in Odds Ratios for Model Including

Students’ Characteristics as Predictors of Teachers’ ICT Use .........79

Table 8. Logistic Regression Results in Odds Ratios for Model Including

School Context Predictors of ICT Use ..............................................82

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List of Figures

Figure 1. Conceptual Framework for ICT use in Mathematics Instruction. ..........47

Figure 2. Means of Overall ICTUSE Across the Countries. ..................................66

Figure 3. Proportion of ICT Use Across Countries by Task. .................................68

Figure 4. Proportions of ICT Use for Students with Varying Mathematics

Achievement Levels. .........................................................................74

Figure 5. Proportions of ICT Use Among Students with Low Socioeconomic

Status. ................................................................................................76

Figure 6. Proportions of ICT Use Among Students with Special Education

Status. ................................................................................................77

1

CHAPTER ONE

Introduction

Technology advancements continue to alter and disrupt the traditional ways of

communicating, ways of engaging in commerce, and ways of sharing knowledge in our world.

Information and communication technologies (ICT) have become ubiquitous and integral in daily

living. ICT refers to electronic devices (e.g., laptops, chrome books), handheld devices (e.g.,

iPads®, iPods), interactive devices (e.g., interactive white boards), application software, and social

media platforms (e.g., Twitter, Facebook). In the United States, although schools and classrooms

have also experienced a proliferation of ICT, instructional practices using ICT remain largely

unchanged (Bain & Weston, 2012; Cuban, 2001, 2013; Peck et al., 2015). Scholars report that

instructional practices remain teacher-centered (Hughes & Read, 2018). It is safe to say that this

proliferation of ICT has not occurred in a vacuum.

The increase in ICT in classrooms exists in an educational landscape characterized by

education reform movements, high stakes testing, and teacher accountability, all efforts deemed to

improve teacher quality, increase student achievement, and prepare students for the 21st century.

Arguably, these shifts in the educational landscape are a product of the involvement of global

players such as the Organization for Economic Cooperation and Development (OECD), and the

World Bank influencing educational policy developments around the world (Akiba, 2017; Fullan,

2016; Pont, 2018). Some of these shifts require educators to prepare students for a knowledge-

based economy in an increasingly connected global environment (Darling-Hammond, 2010;

Friedman, 2005; OECD, 2012). Additionally, the results of international student performance

assessments (e.g., PISA) have increased the expectations of education systems and resulted in calls

for schools to innovate (Altrichter & Posch, 2009; Lingard, Martino, & Rezai-Rashti, 2013; Rizvi,

2

& Lingard, 2010). Investing in technology for teaching and learning is seen as a catalyst to spark

innovation and prepare global learners (McKnight et al., 2016). However, there is a need for a

focus beyond access to ICT to an examination of how teachers use ICT in instruction as well as

teachers’ views about ICT and learning (Ditzler, Hong, & Strudler, 2016). Although there is

emerging research on teacher ICT usage, the research on how teachers use ICT in teaching and

learning remains sparse (McKnight et al., 2016). Additionally, there is increased global attention

on inclusion where schools provide educational opportunities to all students including students

identified with special needs in the general education classroom (Alexiadou et al., 2016; OECD

2012).

To this end, the purpose of this dissertation study was to embrace a global outlook in

understanding education trends, with a special focus on teachers’ ICT use in mathematics

instruction. The study accomplished this objective by using data from the Teaching and Learning

International Survey (TALIS) (OECD, 2013). The TALIS (2013) survey was the first international

series of surveys with a major focus on the learning environment and working conditions of

teachers in schools. The survey examined pedagogical and professional practices of teachers as

well as how school level policies and practices shaped the working and learning environment. I

discuss the dataset further in Chapter 3.

Analyses from mathematics teachers in eight countries (i.e., Australia, Finland, Latvia,

Mexico, Portugal, Romania, Singapore, and Spain) provided an understanding of the teachers’

professional qualifications, instructional practices, and school contexts that contributed to

teachers’ ICT use. Additionally, this study examined whether classroom compositions with

varying students’ characteristics (i.e., mathematics achievement levels, special needs status,

socioeconomic status) impacted teachers’ ICT use to further develop insights on how classroom

3

composition of students may influence teaching practices and ICT use. This understanding

provides pertinent information and contributes to the research knowledge about effective and

strategic integration of ICT in teaching critical subjects such as mathematics. Table 1 shows some

similarities and differences of the countries. According to the World Bank (2016), upper middle-

income economies have a gross national income (GNI) per capita between $4,036 and $12,475,

whereas high-income economies have a GNI per capita of $12,476 or more. Table 1 shows that

75% of the countries in the analytic sample are high-income countries similar with the U.S. Most

of the countries are OECD member countries and all the countries participated in the 2012 PISA

mathematics assessment (OECD, 2012).

Table 1: Similarities and Differences of the Countries Countries OECD

Member Countries

OECD Partner Countries

PISA 2012 (math)

Upper Middle-Income

High-Income

European Non-European

Australia x x x x Finland x x x x Latvia x x x x Mexico x x x x Portugal x x x x Romania x x x x Singapore x x x x Spain x x x x

Note: Sources. OECD (2012); World Bank (2016); OECD = organization for economic cooperation and development; PISA = programme for international student achievement. Statement of the Problem

Despite declining technology costs, dynamic technology advancements, school

investments in ICT, teaching practices using ICT include automation of activities (e.g., grading,

taking attendance) and use in low-level tasks (e.g., practice skills). This peripheral and

underutilization of ICT has led scholars to explore reasons that contribute to ICT less than

transformative edge in teaching and learning where students experience enhanced learning with

ICT. Chief among potential reasons for the peripheral use of ICT and unclear linkages on student

4

performance has been attributed to inadequate and subpar teacher professional development

(Fullan, 2016). Zhao, Pugh, Sheldon, and Byers (2002) posited that most professional development

training for teachers tend to focus on the technical aspects of technology and neglect the social and

organizational aspects of ICT integration. Zhao et al. (2002) further argued that certain conditions

such as teachers’ awareness of available resources or compatibility of the ICT to existing

instructional and school practices are needed for successful ICT integration. For instance, teachers

with an understanding of the social culture in a school are more likely to negotiate some of the

barriers that get in the way of ICT integration such as existing school patterns, attitudes towards

ICT and learning or beliefs about integrating ICT in instruction. These teachers may negotiate the

barriers through collaborating with multiple parties in the school either to gain access to ICT

resources or to garner peer support. The organizational culture plays a critical role in ICT

integration. In essence, the organizational culture of a school refers to: (a) availability of

administrative or peer support that allows ICT integration; (b) availability of technical assistance;

(c) willingness of school officials to take risks, or (d) development of a vision for innovation.

These are actions that may facilitate innovative changes in teaching and learning. The

organizational culture cannot be underestimated because various stakeholders (e.g., teachers,

school leaders) may have different perspectives and attitudes towards using ICT in teaching,

student learning, or the role of the teacher in an ICT-rich environment (Zhao, Pugh, Sheldon, &

Byers, 2002).

Examining the different relationships among key stakeholders (e.g., teachers, students,

school leaders) in a learning environment can uncover assumptions about ICT and provide insights

into ICT integration. In addition, these relationships can explain the factors that contribute to the

varied ways that teachers use ICT in instruction, and the impact that ICT-enhanced instruction has

5

on both student academic and non-academic outcomes (e.g., motivation, engagement) (Mouza,

2008). Somekh (2007) highlighted the importance of understanding innovation from a

sociocultural perspective as this allows stakeholders to examine the different relationships and

components that exist in a learning environment. For instance, there is a need for careful

consideration of curriculum, pedagogy, professional development, teacher beliefs, teacher

training, and school leadership (Ertmer & Ottenbreit-Leftwich, 2010; Frank, Zhao, Penuel,

Ellefson, & Porter, 2011; Hughes, 2005). More importantly, there is a need for the development

of a shared meaning among multiple stakeholders (Fullan, 2016). To this end, Salomon and Perkins

(1998) highlighted the need for “metacognitive awareness” in organizations (e.g., schools) that can

facilitate discussions about invisible assumptions towards teaching practices or discussions

regarding changes in instructional practices (p. 20). This awareness may lead to deliberate

decision-making and discussions on problem solving that can contribute to improved learning for

the individual teacher and learning of the organization as a whole (Salomon & Perkins, 1998;

Somekh, 2007).

In the context of ICT integration, there is a need to build capacity and collaborative cultures

in schools (Fullan, 2016). Building capacity involves building skills and experiences that foster

the development of new experiences and increase motivation for teachers to engage in change

processes. Building capacity includes providing professional development for teachers, and

stakeholders engaging in discussions on ways to effectively and efficiently use available resources

(e.g., ICT). Fullan also posits that there is a need for “connected autonomy” that fosters

collaboration among teachers, allows for individual self-assessment as well as connections with

larger educational goals (Fullan, 2016, p. 262). These social and organizational aspects impact ICT

integration. Furthermore, it is imperative for stakeholders to critically examine current

6

perspectives in schools regarding ICT integration to ensure that students’ educational interests

transcend possible agendas from vested interests such as profit making from marketers of ICT.

Sewlyn (2012) challenged this underestimation of conflicting business interests (e.g., profit

motives) versus education interests (e.g., democratic values of education) that can potentially

widen the digital divide and increase inequality in society. Consequently, schools are faced with

the need to critically examine the motives that underpin ICT integration, yet, in most cases

motivations of ICT integration are overlooked in a haste to implement ICT or change at the expense

of limited resources and strategic planning.

Therefore, it is imperative that teachers, administrators, and policymakers understand how

ICT are actually used and how often they are used in the classroom to develop a nuanced

understanding of the relationship between teachers’ ICT use and students’ performance, or the

relationship between ICT use and instructional practices (McKnight et al., 2016; Overbay,

Patterson, Vasu, & Grable, 2010; Subramaniam, 2007). From an economic perspective, it is the

moral imperative for all stakeholders to understand the returns on investment from ICT on

students’ learning. Instead of stakeholders’ reliance on assumptions such as, the mere presence of

ICT in the classroom leads to effective ICT use in teaching and learning, this information can

provide nuanced insights for improving ICT integration. Additionally, this understanding can

inform professional development topics and activities on instructional practices that align with

ICT, and promote effective ICT use in teaching. These efforts can enhance instead of hinder

student learning especially in mathematics while ensuring efficient utilization of scarce resources.

7

Current Landscape

Mathematics

In the United States, low rankings in mathematical performance have fueled educational

reform practices especially in mathematics instruction to better equip students with the necessary

skills for a competitive global market (Turgut, 2013). In 2015, the U.S. ranked 38th on the

Programme for International Student Assessment (PISA) (OECD, 2012). PISA is an international

assessment administered every three years to assess the reading, mathematics or science skills of

15 year olds in participating OECD countries. Each subject area is assessed during every three-

year cycle; PISA 2012 focused on mathematics. In 2012, the OECD average was 494 while the

range among the countries was between 573 and 413, with Singapore ranking highest among all

the countries. The United States ranked 27th with a mean of 481. Out of the eight countries in the

analytic sample, two countries (Mexico and Romania) performed below the U.S. in the 2012 PISA

(OECD, 2012).

Low student mathematics performance in the U.S. persists despite increased spending in

K-12 education (Guglielimi & Brekke, 2017). In 2013-14, public schools spent about $11, 000 per

student, a 5% increase from the past decade (National Center for Educational Statistics [NCES],

2017). Furthermore, even with a growing demand for science, technology, engineering and

mathematics (STEM) skills, few students, only 16% of high school seniors pursue and graduate

from STEM fields (U.S. Department of Education, 2015). Additionally, racial and gender gaps

persist in STEM fields requiring strategic policy and practice efforts to promote equitable

employment opportunities that seek to close these gaps after graduation (Quinn & Cooc, 2015). In

2010, as part of the Educate to Innovate campaign, former U.S. president Barack Obama launched

the Change the Equation in STEM Education initiative and noted, “leadership tomorrow depends

8

on how we educate our students today-especially in science, technology, engineering and math”

(para. 2). This initiative demonstrated some of the interest and concerted efforts to improve

mathematics performance for all students.

According to NCES, 13% of all students in public schools are receiving special education

services in the U. S. (NCES, 2017). A close look at students’ performance shows that, for students

identified with special needs, mathematics performance is lower than that of their peers without

disabilities. For instance, in mathematics, the 2017 National Assessment of Educational Progress

(NAEP), shows that 80% of 4th grade students performed at the basic and above level and 60% at

the proficient level (NCES, 2017). At the 8th grade level, 70% performed at the basic or above

basic level and 34% at proficient level. For students with special needs, declining performance in

mathematics is even more pronounced: at the 4th grade level, 49% are performing at the basic or

above basic level in mathematics and only 16% are at proficient level. By 8th grade level,

approximately 31% of students with disabilities are performing at the basic or above basic level in

mathematics. Arguably, developing an understanding of the student composition in classrooms

(e.g., students’ performance levels, or percentage of students with special needs), and how teachers

use ICT among students can better inform professional development, teacher feedback, and policy

development on ways to harness available tools (e.g., ICT) to promote learning and meet students’

needs to ensure students achieve their potential.

Therefore, it is critical for researchers and various stakeholders to evaluate instructional

practices and tools used in teaching mathematics. This evaluation can provide information on

discrepancies with peripheral ICT use in instruction, differences in teachers’ ICT use among

students, and few graduates from STEM-related fields, that exist despite technological

advancements. It is possible that ICT can be incorporated into teaching practices as a leveraging

9

tool for students with varying achievement levels and needs to further develop their interests in

STEM-related fields. Also, examining ICT instructional practices in countries outside the United

States can provide insights on trends and patterns that can be modified and adapted to the United

States context to enhance ICT use in mathematics instruction.

ICT and Mathematics

Students need mathematics skills in various settings beyond the classroom to be successful

(Clarke, Doabler, & Nelson, 2014). The PISA 2012 results show that in the United States students

struggle with understanding real-life mathematics scenarios and developing mathematical models

when solving mathematical problems (OECD, 2012). ICT can provide students with real-life

examples, opportunities to develop these modeling skills through simulation, or graphing software

and connections with experts in different fields. However, it is unclear whether teachers make

these higher-order learning opportunities available to students especially using ICT. Available

literature addresses ICT use and student achievement with little attention to teachers’ ICT use

across content areas (Hennessy, Ruthven, & Brindley, 2005; Howard, Chan, & Caputi, 2015).

Furthermore, although examining student ICT use is critical, developing an understanding about

how teachers use ICT in mathematics instruction can inform researchers, teacher educators, and

policy makers on the existing gaps between ICT use in instruction and the impact of ICT use on

students’ learning. Few studies have examined how teachers used ICT in mathematics instruction

or whether mathematics teachers needed professional development in ICT to effectively integrate

ICT in teaching mathematics.

ICT can be used in mathematics instruction to promote students’ understanding of

mathematical skills and concepts, make conjectures, and develop high-order skills. Understanding

how teachers’ use ICT in mathematics instruction can provide a clearer picture on aspects and

10

affordances from ICT that improve mathematics learning. Furthermore, understanding the

different mathematical tasks incorporated in ICT use can also shed light on areas that need

continued research, such as examining mathematical concepts where teachers use ICT, and

examining the variation of tasks incorporated with ICT in instruction. Additionally, in an effort to

understand how teachers use ICT, and to examine the variation of ICT use in classrooms with

different student characteristics, for instance, students with varying achievement levels, it is

important to investigate the various factors (e.g., teacher beliefs about teaching and learning of

mathematics or beliefs about ICT in teaching and learning mathematics) that may impact ICT use.

An international comparison can provide a broader perspective on how mathematics teachers use

ICT, and factors that contribute to ICT use in mathematics instruction in global contexts. This

study analyzed how teachers’ used ICT specifically in mathematics instruction in eight countries

and provides important information on ways that teachers used ICT in mathematics instruction.

Additionally, the study examined teacher characteristics (e.g., epistemic beliefs, pedagogical

beliefs), student characteristics (e.g., mathematics levels, socioeconomic status, special education

status), and school context factors that influenced teachers’ ICT use.

ICT and Special Education

With the continued reform movements in mathematics and low student performance, it is

imperative for stakeholders (e.g., educators, policy makers, administrators and parents) to examine

the circumstances that contribute to student learning. As earlier stated, 13% of students in the U.S.

are identified with special needs—a large student population that requires intentional and strategic

focus to ensure the students are receiving appropriate and quality educational experiences. This

examination of learning environments, instructional practices, and students’ characteristics (e.g.,

11

interests, attitudes, knowledge) from researchers and stakeholders can provide insight on ways to

improve mathematics interest and develop skills needed for a knowledge-based economy.

Often, mathematics classrooms have unequal participation levels of students (Moss &

Beatty, 2010); this may be due to different levels of mathematics abilities or teacher perception of

students, and students’ self-perceptions as mathematics learners (Eynde, Corte, & Verschaffel,

2006; Malmivuori, 2006)). Yet this strong interest, internalization of tasks and engagement is

seldom the case for students with special education needs or low achieving students (Bottge, 2001).

ICT can provide students with affordances that can increase student motivation, provide authentic

environments, virtual tours (Bottge, Rueda, Grant, Stephens, & Laroque, 2010; Shapely, Sheehan,

Maloney, & Walker, 2011), provide engaging learning and collaborative opportunities (Howard et

al., 2015), provide personalized and small group learning (Basham, Smith, Greer, & Marino,

2013), and provide simulation activities (Bos, 2007). Even with available ICT affordances, it is

imperative for teachers, and school leaders to assess how teachers use ICT among students with

unique needs to avoid assumptions of uniform ICT use in instruction for all students. Additionally,

scholars (e.g., Basham, Smith, & Satter, 2016) there is a need for intentional consideration for

design features in ICT to ensure students identified with special needs are able to access the full

range of affordances through ICT. In particular, curriculum developers and designers of ICT for

education should ensure that they tap into the universal design for learning instructional framework

that emphasizes designing for people in the margins to facilitate the success of everyone. Teachers

should also be vigilant and ensure that programs offered through ICT incorporate evidence-based

practices documented to improve performance for students identified with special needs (e.g.,

feedback, scaffolding) (Rosenshine, 2012).

12

Varying ways of ICT use between students who are identified as high achieving and

students who are low achieving can lead to learning differences (Warschauer, Zheng, Niiya,

Cotton, & Farkas, 2014). For instance, Shapely, Sheehan, Maloney, and Walker (2011), reported

learning gains from ICT instruction only for students who were already high-achieving, showing

the importance of understanding teachers’ ICT use among students with differing achievement

levels and student performance within classrooms. This information can help in developing

professional development activities that strive to provide all students with a rich learning

experience with ICT.

ICT and Socioeconomic Status

Bourdieu (1986) argued that people engage in ways of doing and being depending on their

environments and socializations, and these practices comprises an individuals’ cultural capital

needed to participate in society. Bourdieu further argued that society values cultural capital

differentially. For instance, students will be more successful if they bring to the classroom cultural

capital that is valued in the learning environment (Nasir & Hand, 2006). Scholars (e.g., Tondeur,

Sinnaeve, van Houtte, & van Braak, 2011) have argued that in the 21st century, ICT represents a

form of cultural capital necessary for students to meet learning expectations. Yet, in the U.S. it is

estimated that up to 6.5 million students do not have high speed internet access and 23% of school

districts do not have sufficient bandwidth to meet the current needs for digital learning

(EducationSuperhighway, 2017). These statistics illuminate that even in the current landscape of

ICT ubiquity, large populations of students do not have ICT access in school or at home (Hughes

& Read, 2018; Schoology, 2017). This unequal availability of ICT leads to a digital divide

(Donahue, Finnegan, Lutkus, Allen, & Campbell, 2001). Furthermore, when some students have

access to ICT affordances they can leverage skills learned using ICT compared to students without

13

access to the affordances that ICT offers. This divide widens learning opportunities and

experiences between students limiting students’ edge to compete and fully participate in society.

The NCES (2017) report on children’s access and use of the internet shows that students

from high income backgrounds use the internet more so compared to students from low income

backgrounds. Moreover, research studies (e.g., DeBell & Chapman, 2006; Hughes & Read, 2018)

have documented variation in ICT use between students with low and high socioeconomic status

with these differences exacerbated with ICT implementation in schools. For instance, Warschauer,

Zheng, Niiya, Cotton, and Farkas (2014) reported the different foci of schools in ICT integration

where some schools invest in building a robust infrastructure and teacher professional

development, while some schools focus solely on acquiring the hardware and software, and neglect

the organizational and social factors. Schools with higher socioeconomic status focused on

building a robust infrastructure and providing teacher’ professional development were more

successful. Additionally, researchers (e.g., Reinhart, Thomas, & Toriskie, 2011) have documented

that students from low-socioeconomic environments use ICT for remediation or skills practice

more so compared to activities that are more cognitively challenging, for instance project-based

activities. This means that there is a need for an intentional and strategic consideration, such as

pedagogical approaches of teachers’ ICT use beyond technological factors especially in low

socioeconomic communities.

To summarize, with proliferation of ICT in schools and classrooms, critical decisions are

required from stakeholders (e.g., teachers, policy makers, school leaders) to ensure students from

low socioeconomic backgrounds, students identified with special needs, and students with low

mathematics achievement levels are not left further behind in accessing educational opportunities.

Ensuring all students are equipped with relevant 21st century skills and afforded an opportunity to

14

contribute to society is urgent. Therefore, pedagogical decisions on teachers’ ICT use in different

content areas), among students with varying achievement levels, students from low socioeconomic

backgrounds or students identified with special needs, need to be central in the discussion about

quality and equal access to digital learning opportunities for all students (Hughes & Read, 2018;

Warschauer et al., 2014). To this end, one benefit of ICT lies in providing a medium where a single

teacher can effectively meet the demands of students with varying strengths and needs.

Meeting the 1:X Challenge

Bain and Weston (2012) used the ratio 1:X to define the daunting challenge of a single

teacher to meet the needs of students in the classroom. Effective ICT use can facilitate and allow

effective use of a single teacher’s available resources in terms of time and cognitive load (Bain &

Weston, 2012; Feldon, 2007). Feldon (2007) adds that cognitive load reduces a teachers’ capacity

to deploy effective instructional practices leading to an overreliance of simple, unsophisticated

practices developed over time. ICT can reduce the cognitive load and potentially increase the

teacher’s capacity to meet diverse students’ needs through differentiating and individualizing

instruction (Bain & Weston, 2012; McCombs & Vakili, 2005; McKnight et al., 2016).

Differentiating instruction involves a teacher’s tailoring and delivery of instruction that places an

intentional focus on students’ strengths and needs; this can be particularly helpful for students

identified with special education needs (Hasselbring & Glaser, 2000). ICT affordances range from

teachers incorporating ICT in small group instruction, project-based learning activities, availability

of authentic learning environments, availability of simulation and interactive tools. Additionally,

ICT can provide teachers with real-time data on student progress that can inform instruction.

Moreover, scholars (e.g., Somekh, 2007) have argued that there is a considerable mismatch

between students use of ICT outside the school walls and in classrooms. Somekh posited that there

15

may be great potential in evaluating data on how students use ICT in these out of school spaces

that can be instrumental in informing ICT use in classrooms. With varying achievement levels and

students’ experiences with ICT, it is imperative for teachers to understand students’ repertoires as

they integrate ICT in instruction. ICT has affordances for providing personalized learning,

increased student autonomy, fostering engagement and motivation (McCombs & Vakili, 2005;

Shapely et al., 2011; Somekh, 2007).

Significance of the Study

The 21st century continues to experience shifts in the political, economic, social and global

fields. These shifts have impacted different sectors of society in unexpected ways, created tensions,

and uncertainty in unprecedented ways. In particular, the education field faces considerable jolting

and calls for transformation in order to meet the rhetoric of transforming schools or the calls for

schools to meet the needs of the labor market, as well as prepare democratic citizens (Wood, 2003).

With this backdrop, it is apt to take a step back and glean from Dewey’s (1899) work on

progressive education or Bruner’s (1977) on inquiry-based learning that can inform and help

situate the challenge of transforming schools in a historical context. In his work on progressive

education, over a century ago, Dewey argued that for schools to transform from the mechanical

industrial design that emphasized more of listening and less of doing, schools would need to

restructure their ways of using the space and the available time. In addition to this restructuring,

for progressive education to be a reality there would be a need for a change in learning activities

and instructional design (Waks, 2013). According to the World Economic Forum (2016) insight

report, 65% of students entering grade school will end up in jobs that do not exist. The need for

this progressive, experiential, inquiry-based learning could not be more urgent today with the

16

dynamic technology advancements brought by globalization, migration, and a need for knowledge-

based skills.

At the same time, Dewey acknowledged the challenge of developing schools from the

industrial design model to a model for providing students with experiential experiences or

democratic institutions that can bring about social transformation. This challenge is evident today.

Even with advancements in technology and information, pervasive educational reforms, school

structures and practices seem resistant or slow to change. Cuban (2013) uses the word ‘dynamic

conservatism’ to refer to ways that institutions incorporate changes but remain the same. Scholars

concur that to understand human behavior, in this case teaching practices, it is necessary to

understand knowledge from the context where the behavior is taking place and develop further

understanding about the tacit knowledge that drives the behavior (Somekh, 2007). Other scholars

further argue that understanding what actually takes place in schools and classrooms in particular

is key to unlocking lingering and persistent questions about school change and transformation

(Fullan, 2016). The following quote captures part of the tension prevalent in schools today, “What

reformers dead set on transforming traditional institutions often forget is that complex

organizations have plans for reformers as much as reformers do for organizations.” (Cuban, 2013,

p. 151). This means that even with educational reforms, schools change but perpetuate most of the

age-old practices. For a different perspective on understanding change in schools, Salomon and

Perkins (1998) argued that schools are performance systems and not learning systems, and this

leads to superficial solutions in an attempt to achieve multiple agendas and visions of multiple

stakeholders at any given time.

Subsequently, the tension, unpredictability, and uncertainty persists as school structures

and teaching practices are now faced with challenges, such as rapid development and mass

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dispersion of knowledge and information in global spaces due to technological advancements. For

students, these changes in global and local contexts warrant what Dewey referred to as fluid

intelligence, defined as acquired information and knowledge that can be adapted to novel scenarios

(Waks, 2013). Arguably, this fluid intelligence is embodied in the 21st century skills—problem

solving, analytical thinking, critical thinking, and collaboration skills required of all students. ICT

has potential to provide teachers with tools for teaching and facilitating the development of 21st

century skills.

As technology costs decrease and schools become wired up, challenges related to access

are quickly becoming less pronounced. However, further challenges exist when it comes to ICT

integration that transcends barriers related to access. Scholars have described first-order changes

as access to ICT hardware and software, limited planning or instruction time while second-order

changes include changes in existing structures, changes in beliefs about ICT in teaching and

learning, and rigidity of established routines (Cuban, 2013; Ertmer, 1999).

In theory, this may mean that after first order changes are accomplished, more resources

can be focused on achieving the second-order changes, and set the stage for the development of

Dewey’s ideas of progressive education, and enhance the development of fluid intelligence

transcending boundaries of space and time (e.g., physical classroom walls). However, second-

order changes continue to prove challenging to overcome as schools move beyond first-order

changes. Second order changes involve fundamental changes to educational foundations such as

identifying the role of schools in providing all students with educational opportunities, school

funding or school organization (Cuban, 2013). Furthermore, second-order changes can be personal

and may challenge beliefs about practice that may in turn require rethinking of ideas, practices and

roles (Ertmer, 1999).

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According to the EducationSuperhighway, an advocacy group for bandwidth access in

schools reported that in 2017, 39 million students and 74,000 schools have access to internet speeds

of up to 100 kilobits per hour and 97% of schools are connected through fiber (Herold, 2017).

These statistics show that access to the internet, a first-order change has been accomplished for

many U.S. schools. However, a recent survey with 2,846 teachers in the U.S. and across the globe

showed that the top five challenges teachers faced with integrating ICT in the classroom include,

teachers’ lack of time during the regular day, limited devices for students, ineffective professional

development and lack of a digitized curriculum (Schoology, 2017). This variation in obstacles and

barriers shows the complexity around ICT integration in teaching and learning across classrooms

and schools. Scholars have referred to these complexities with descriptive terms such as intense

sites of struggle (Sewyln, 2012, p. 127), and axes of tension (Wood, 2003, p. 30).

Examining individual schools’ experiences can also provide information on creative ways

that schools are integrating ICT and moving beyond existing barriers. Schools such as the High

Tech High Charter schools in California exemplify how ICT can be used as a vehicle to achieve a

progressive education that embraces personalization, adult learning, inquiry, and interdisciplinary

learning (Neumann, 2008; Pieratt, 2010). In Norway, Somekh (2007) noted that at one school,

teachers participated in action research, discussed the role of technology in teaching and learning,

and engaged each other in developing curriculum and assessment materials. Additionally, the

classrooms were rearranged to allow for different activities, such as independent study, group

participation, and arts related tasks. This transformation of school practices and structures

facilitated the development of the school’s understanding of ‘digital epistemology’ (Somekh,

2007, p. 21).

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Purpose of the Study

Given the increasing proliferation of ICT in classrooms and the responsibility for teachers

to prepare students to participate in a global world, the gap between increasing ICT in classrooms

and limited teachers’ ICT use in the United States has been documented in the literature.

Researchers continue to examine possible reasons for this puzzling discrepancy between increased

ICT access and limited ICT use in teaching. A comparative study is needed to examine how

teachers use ICT in mathematics classrooms outside the United States to develop a richer

understanding that could inform policy development, professional development programs and

harness the potential of ICT in teaching and learning.

To summarize, the purpose of this study therefore, was to understand the trends and

patterns of ICT use among mathematics teachers in eight countries. These trends and analyses

explain how teachers used ICT in mathematics. Moreover, the analyses examined whether teacher

professional qualifications, instructional approaches and beliefs, classroom composition of

students, and school contexts predicted ICT use. As such, this dissertation study addressed the

following research questions:

1. To what extent do teachers use ICT in mathematics instruction in eight countries (i.e.,

Australia, Finland, Latvia, Mexico, Portugal, Romania, Singapore, and Spain)?

2. What is the relationship between professional qualifications (e.g., technology training,

years of teaching experience) and teachers’ ICT use in mathematics instruction?

3. What is the relationship between teachers’ instructional approaches and beliefs (e.g.,

self-efficacy, constructivist beliefs, constructivist teacher practices) and ICT use

in mathematics instruction?

4. Do teachers use ICT differently for mathematics instruction in classrooms with students

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with different characteristics (mathematics achievement levels, special needs status,

socioeconomic levels)?

5. How do school contexts predict ICT use?

Definition of Key Terms

Information Communication Technology (ICT): This study uses ICT to refer to electronic

devices (e.g., laptops, chrome books), handheld devices (e.g., iPads®, iPods), interactive

devices (e.g., interactive white boards), application software, and social media platforms

(e.g., Twitter, Facebook). In schools, ICTs are used for teaching and learning and enable

connectedness among multiple parties (e.g., students, teachers).

Constructivism: This study defines constructivism as pedagogy characterized by co-

construction of knowledge between students and teachers through instructional practices

such as accessing a student’s background knowledge, facilitating connections with new

knowledge, the promotion of student inquiry, and active student participation (Ravitz,

Becker, & Wong, 2000).

Self-efficacy: This study uses Bandura’s (2006b) self-efficacy definition as, a person’s

belief in their potential to achieve tasks at hand.

Low socioeconomic status: This study defines students with low socioeconomic status as

students from homes lacking the basic necessities or advantages of life, such as adequate

housing, nutrition or medical care. (OECD, 2013, p. 84).

Special needs status: This study defines special needs status as students who have been

formally identified to receive additional resources to support their learning (OECD, 2013).

The International Standard Classification of Education (ISCED): ISCED is a framework

for classifying educational programmes into internationally agreed categories.

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o ISCED level 5a: This refers to the Community College level.

o ISCED level 6: This refers to the Bachelors level.

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CHAPTER TWO

Review of Literature

This chapter provides an overview of ICT and teaching, international trends in ICT,

theoretical frameworks, and review of the literature on active ingredients of ICT integration. I

conducted this study by examining two theoretical frameworks. They are: (a) theory of diffusion

of innovations (Rogers, 1995) and (b) sociocultural theory on teaching and learning (Vygotsky,

1978). I explore the frameworks in the next section.

ICT and Teaching

Through ICT, it is thought that students can benefit from rich authentic contexts,

simulations, virtual tours, and engage in remote social interactions with global audiences through

social networking websites (Shapely et al., 2011). However, despite the affordances of ICT,

scholars continue to report the peripheral use of ICT in classrooms and the slow change in teachers’

instructional practices using ICT. At face value, ICT affordances may seem to be an innovation.

What is an innovation? An innovation can be defined as something new and better, used to solve

problems or provide improved solutions (Magiera, 2016). It is important to note, the perceived

newness is relative depending on an individual’s attitudes, level of knowledge, interaction with

the innovation, making this perception specific to the individual (Magiera, 2016; Rogers, 1995).

Moreover, Fullan (2016) made the distinction between innovation and innovativeness and posited

that innovativeness refers to an organizations’ capacity to make improvements regularly. Beyond

ICT as an innovation, scholars (e.g., Kompf, 2005) have described ICT as leading to unpredictable

changes in well-established systems such as education. Education systems globally are

experiencing these changes brought about by globalization and technological advancements.

Therefore, understanding international trends in ICT use in schools and classrooms can provide

23

important information on key developments in ICT. Furthermore, much can be learned about the

role of ICT agencies (e.g., departments of education, ministries of education, semi-autonomous

bodies) in the development of ICT policies, trajectories of countries in their ICT integration, and

ways to overcome barriers in ICT integration. This can foster a richer understanding of the ICT

landscape globally. In the next section I discuss international trends in ICT use.

International Trends in ICT

Countries around the world have invested heavily in ICT for education and this has led to

increased attention from stakeholders compared to a decade ago, where discussions on ICT were

far limited (Trucano, 2017). Reasons for ICT investment range from the quest for schools and

countries to stay abreast with technology advancements, politicians’ zeal to appear innovative and

progressive, or an illusion of ICT as the solution to education problems. Arguably, the dynamic

nature of ICT offers affordances previously absent from the learning environment that provides

teachers and learners with tools that enhance creativity, collaboration and critical thinking.

Whatever the motivation, countries continue to roll out ICT at a rapid pace (Trucano & Dykes,

2017). One trend that continues to emerge with ICT development and expansion is the growth of

public-private partnerships where private companies continue to aggressively market ICT products

to schools as solutions to improve student achievement or develop students’ 21st century skills.

Some of these public-private partnerships, have led to the development of robust infrastructures of

ICT beyond the classroom, as well as collaboration with educators in developing instructional

materials. Public-private partnerships also allow schools, often seen as risk averse to take ventures

that may be risky, as the risk is distributed among multiple stakeholders.

Second, proliferation of ICT in classrooms has taken different forms as technology costs

have decreased. Schools around the world are implementing 1:1 initiatives where schools provide

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each student with a device (e.g., laptop, chrome books). These 1:1 initiatives have been

implemented in high, middle, and low-income countries, including countries such as Kenya,

Uruguay, and Rwanda. In Peru and Uruguay, the one laptop per child initiative was implemented

in schools in the rural and low-income areas in attempts to increase access to educational content.

Third, the shift towards mobile learning has led to investments in tablets, iPadsâ and social media

platforms. With increased connectivity and the ubiquitous nature of ICT, learning is taking place

beyond traditional parameters (e.g., classroom walls, regular school day) and across geographical

locations. For instance, the one laptop per child initiative has been harnessed in Uruguay to provide

students with English lessons from the neighboring countries of Argentina, Philippines and the

United Kingdom (Trucano, 2016). Interestingly, Trucano adds that although mobile learning has

been understood from the perspective of portable devices, some countries have conceptualized

mobile learning through making internet accessible in buses or automobiles. However, even with

increased connectivity, a digital divide exists within countries either between urban and rural areas,

or between high and low-income schools (Chen, 2015).

Fourth, proliferation of ICT has also brought discussions about pedagogical innovations

and effects on learning outcomes to the fore. With ICT affordances such as personalized learning,

immediate feedback, and data-based decision making, it is argued that teaching approaches will

be more student-centered and less teacher-centered (El Yacoubi, 2013). However, the overall

effectiveness of ICT on learning outcomes remains mixed. In a survey conducted in Israel, Shamir-

Inbal and Ina Blau (2017) reported that the school ICT leaders expressed the reality of teachers

underutilizing the ICT in meaningful ways. The authors also reported that time availability

contributed to significant changes in ICT use, with increased collaborative activities within and

25

between schools, increased use of digital content and development of school, and family

communication.

Fifth, another growing trend with ICT use in the recent years has been an increased

emphasis on developing students’ repertoire in coding from the earlier focus on informational

literacy (Trucano, 2017). The coding emphasis has gained momentum globally as experts argue

that coding provides students with skills that may cultivate students’ interest in STEM fields and

also addresses the gender gap prevalent in STEM courses through the strong advocacy for girls to

participate in coding activities (International Society for Technology in Education [ISTE], 2016).

Sixth, in a World Bank report on lessons learned from different countries, in the Asia and

Pacific region, Trucano and Dykes (2017) reported that successes in ICT hinged on a number of

factors, such as ICT agencies focusing on teachers’ need, effective leadership, and changes in the

organizational structures, such as schools and classrooms. Growing investments in ICT have led

to the birth of technology agencies that exist separate from governmental agencies, tasked with

responsibilities ranging from procurement of ICT, developing ICT policies, and leading innovation

initiatives. Therefore, countries around the world have rolled out ICT differently either through

governmental bodies (e.g., Ministry of Education, Department of Education) or autonomous and

semi-autonomous agencies. For instance, over two decades ago, the Korea Education and Research

Information Service (KERIS), a semi-autonomous governmental agency tasked with ICT

integration, developed strategic, long-term initiatives that included implementing the ICT

infrastructure, teacher training, developing digital content, putting in place an ecosystem of ICT

between home and school, followed by the development of mobile learning in South Korea.

In contrast, in Malaysia, public-private relationships were influential in the strategic

development of nationwide ICT initiatives in schools (Lee & Thah, 2017). Collaboration included

26

partnerships with business organizations and communities through the development of the

Malaysian Smart School Initiative (MSSI). ICT integration included a pilot phase, and a broad

rollout undertaken over a period of 15 years. Lee and Thah (2017) further reported that challenges

found in the pilot phase such as infrastructure needs, teacher, and administrators’ training, and

technology obsolescence led to the development of a set of standards to monitor and evaluate ICT

integration. In addition, ICT competency standards were developed that informed and provided

training and professional development for teachers as well as guided curriculum planning. Schools

in the rural areas were provided with internet cafes to access information and monitor progress.

Research studies were also conducted to investigate student performance in the smart schools and

non-smart schools (Lee & Thah, 2017). In summary, with continued advancements in ICT,

countries are grappling with the dynamic changes of devices, the changing roles of governmental

agencies as well as ICT policies on effective ways to bring about improved student outcomes.

Against this backdrop, schools as well as collaborative efforts between public-private partnerships

have developed creative ways of building teacher capacity. For instance, in Kenya, the Solar

Powered Internet Schools program, sponsored by Samsung and KERIS, provided training to

teachers in Kenya who in turn trained their peers (Center for Education Innovations, 2015).

Overall, the dynamic nature of ICT continues to change educational experiences for

students in cities around the world even in places considered to be underdeveloped or isolated from

major cities. For instance, Learning Equality (2017), a nonprofit organization provides Khan

Academy educational services (e.g., video demonstrations of mathematical problem solving)

offline to users in different languages. Kolibri, also from Learning Equality, is an open source

platform designed for offline users with increased affordances, such as educational content, peer

to peer sharing, and opportunities for users to create customized content. These offline options

27

allow these ICT products to reach people in places such as refugee camps, orphanages, and prisons.

Clearly, these versatile and creative innovations, continue to enlarge the classroom environment

and take learning outside the brick and mortar settings to learners in places that would otherwise

be considered off the grid. In the next section, I discuss the two theoretical frameworks (i.e., theory

of diffusion of innovations (b) sociocultural theory) that guided my study.

Theoretical Frameworks

Theory of Diffusion of Innovations

In Everett Rogers’s (1995) theory of diffusion of innovations, Rogers argued that the rate

at which individuals adopt any innovation depends on the innovation’s compatibility with the

values, beliefs, and prior experiences of the individuals in that social environment. Rogers added

that diffusion of an innovation involves the communication about the innovation within the social

system in a systematic or spontaneous manner.

Rogers (1995) further discussed certain key elements of the theory of diffusion: (a) relative

advantage – the degree to which the innovation is seen as better and having affordances especially

compared to the tried and tested approaches, (b) compatibility – the level at which the innovation

fits with an individual’s current beliefs, knowledge levels, or experiences, (c) complexity – the

level of learning needed to comfortably adopt the innovation, (d) trialability – the level of

allowance for testing and learning about the innovation before adopting and adding the innovation

to an individuals’ repertoire, and (e) observability – the level to which the results from the

innovation are evident to people. In summary, innovations with a higher relative advantage,

compatibility, triability, observability, and lower complexity have a higher probability of adoption.

Two additional elements are critical in the diffusion of innovations. Rogers discusses the

nature of the social process involved in the diffusion of innovations and the element of time.

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Communication and dissemination of a new idea among people in a social system (e.g., teachers

in a school) plays a role in the adoption of an innovation and so does the variation in terms of the

length of time it takes for the diffusion to take place. Therefore, the adoption of an innovation

varies from individual to individual and takes time depending on the individual and the social

system. ICT in teaching is perceived as an innovation from previous use of non-technological

tools. Rogers’s theory of diffusion of innovations provides a critical foundation in understanding

the adoption process, the elements that influence this diffusion, and the different characteristics of

individuals during the adoption stages. This knowledge can provide stakeholders with important

information and insights that would inform professional development to promote effective and

systematic ICT integration through strategic communication, for instance, the pairing of

individuals with different perspectives towards ICT integration (Harris, 2008). Furthermore,

developing a nuanced understanding of teachers’ attitudes, experiences and beliefs towards ICT

can inform the development of high-impact and customized professional development that caters

to teachers at varying adoption stages.

Sociocultural Theory

Sociocultural theory argues that learning is socially and culturally located in daily activities

where an individual’s behavior is influenced by the interaction of other individuals (Lave &

Wenger, 1991; Moll, 1990; Rogoff, 1990; Somekh, 2007; Vygotsky, 1978). Stakeholders would

benefit from understanding the social system, and the inter-relationships (e.g., institutional,

historical or cultural) that exist in a given learning environment to understand the uptake of ICT

innovations in education (Borko, 2004; Somekh, 2007). Classrooms exist in larger contexts of

schools, and are part of complex macro systems within states and within countries. These larger

contexts, directly and indirectly, impact classroom practices. In summary, social and contextual

29

factors compound the challenges of effective ICT integration in teaching and learning (Koehler &

Mishra, 2009). Educational policies on ICT, teacher training on ICT use, or perceptions of ICT in

teaching and learning intersect to impact instructional practices.

Sociocultural theory using Vygotsky’s perspective about mediation of human behavior

through tools, symbols, and artifacts can also elucidate further insights on ICT integration in

teaching and learning. In Vygosky’s sociocultural theory, the teacher is the mediator of the

available tools, guiding students to become active and engaged with the tools in the social

environment during the learning process (Subramaniam, 2007). Technology in and of itself does

not lead to improved communication or knowledge creation, but human behavior and creativity

leads to innovative ways of using the technology (Somekh, 2007). Sociocultural theory, as

highlighted by Vygotsky, further addresses the idea of the zone of proximal development present

during learning. For instance, as teachers and students engage with tools, such as ICT, both the

teacher and the student experience learning of and with the tools in their zone of proximal

development and this subsequently impacts teachers and students use of ICT (Subramaniam,

2007). Moreover, in their study with teachers (i.e., preservice, practicing teachers) on professional

development for ICT integration and use, scholars Whipp, Eckman, and Kieboom (2005) reported

key elements that contributed to the successful integration of technology for the preservice and

practicing teachers. They reported that the teacher participants were at different levels of proximal

development and teachers included different activities in their teaching. The teachers also required

different supports in communities of practice with peers as they integrated technology in their

teaching practices. These findings show that there is a need for deeper understanding of teacher

characteristics, strengths and professional development needs for ICT in order to understand

teachers’ ICT use.

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Furthermore, sociocultural theory can provide a way to analyze the different relationships

at play in the school environment to better understand the change process whether it is at the

individual, classroom, or national levels (Somekh, 2007). These different levels are situated in

historical, cultural, and institutional contexts critical in understanding decisions regarding ICT and

learning as well as the process of ICT implementation (Warschauer et al., 2014). Education

systems are organizations situated in these contexts and include a number of people with different

roles. Therefore, it is critical to understand the different ways that people’s roles interact, impact

each other and how this influences processes of change such as ICT integration in instruction

(Adamy & Heinecke, 2005). Additionally, these stakeholders embody varying roles and powers

of influence. For instance, policymakers develop ICT policies, school leaders make decisions on

ICT procurement, whereas teachers are tasked with integrating ICT in instruction. On the other

hand of the ICT integration equation, instructional coaches and curriculum coordinators may work

closely with schools to ensure teachers are implementing the required curriculum standards.

During these interactions, different participants in the social system can benefit from developing

and sharing a common language about instructional practices, attitudes towards ICT use or teacher

beliefs. A common language is essential in these environments to ensure successful

accomplishment of teaching and learning goals. For instance, a common language is one where

the participants in the school social system clearly articulate the role of ICT in mathematics

learning, teachers articulate their goals and views about teaching mathematics and overall

expectations of ICT use with varying students’ unique characteristics (e.g., varying mathematics

achievement levels, socioeconomic status, special education status). This common language also

helps the participants develop coherence in ICT integration in the existing ecosystem.

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The development of coherent systems takes multiple stakeholders, iterations, time, a school

climate focused on continuous improvement and understanding of tacit knowledge present in these

environments (Fullan, 2016; Somekh, 2007). Therefore, the development of coherent systems,

involve levels of complexity. Sociocultural theory can provide a framework for analyzing ICT use

among student groups in the school setting, as well as beyond the school walls, and how this access

to ICT and use impacts students’ learning. For instance, students with access to ICT use at home

will likely have more leverage and develop further ICT skills compared to students with limited

or no access to ICT outside the school setting. Also, in the classroom, teachers’ ICT use may differ

depending on pedagogical approaches (student-centered vs. teacher-centered) or the classroom

composition of students and this may contribute to differential learning experiences with ICT for

students. Understanding these nuances can assist policy developers and school administrators to

enact measures to mitigate potential digital divide through quality instruction with ICT, and inform

the development of professional development activities that promote student-centered pedagogy

with an equity focus.

Lastly, as countries around the world embrace ICT, it is likely that tensions will arise

between global standards for ICT use and local perspectives on the role of ICT in teaching and

learning. Therefore, to avoid blind acceptance of foreign policies, that may not meet the needs of

the local populations or may overburden the existing systems and cause deleterious effects in

teaching and learning outcomes, it is imperative for countries to embed ICT and ICT related

policies with awareness of characteristics of local contexts (Sewlyn, 2012).

ICT Integration in Schools

Following the increased proliferation of technologies in schools, research shows a

mismatch between technology availability, access, the frequency of technology use, and the

32

instructional practices using ICT (Cuban, 2001, 2013). Researchers have explored the factors and

barriers that hinder ICT use in classrooms that already have access to ICT. Scholars in the field

have tackled some of the micro and macro factors that influence technology integration, such as

cultures of practice, educational policies on ICT, and school organization. Burns (2013) noted that

the lackluster results from technology in education could best be seen as a result of human actions,

perspectives or organizational barriers towards technology and not entirely due to the technology

itself. Similarly, in the theory of the diffusion of innovations, Rogers (1995) argued that the social

structure of an individual adopting an innovation impacts the integration of the innovation. Also,

successful and systematic ICT integration, as well as changes in teaching practices will depend on

the deeply held beliefs and cultures of practice within schools (Bain & Weston, 2009). Cultures of

practice or communities of practice involve individual and group participation in activities within

a particular environment, that demonstrate shared values, beliefs, and language based on prior or

current experiences (Somekh, 2007).

Unpacking the assumptions of technology integration is critical. It is important to

acknowledge the tensions that teachers experience between what they already know and do, and

the demand to learn and integrate technological innovations. For instance, in a study with a sample

of Korean teachers, Baek, Jung, and Kim (2008) found the strongest factor that influenced

technology use in the classrooms was merely the compliance to external requests and expectations

from external parties (e.g., requirements from the Ministry of Education, common perceptions that

good teachers use technology well). Additional barriers include prior experiences with ICT.

Górniak-Kocikowska (2008) asserted that when a number of people have negative experiences

towards a new technology, resistance towards this technology is likely to increase coupled with

the distrust regarding the potential of the technology.

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Intentional decisions about ICT and teacher professional development can foster new

experiences and facilitate the diffusion of innovations over time, leading to effective ICT

integration. For instance, according to Rogers’s (1995) theory of diffusion of innovations, teachers

who are advanced in their technology adoption, referred to as innovators, can benefit from

individualized and personalized learning, whereas early adopters would benefit from action

research with peers to try and test different technologies. This is similar to Frank, Zhao, Penuel,

Ellefson, and Porter’s (2011) finding that professional development is strongest for individuals at

the beginning stages of ICT implementation. The authors also highlighted the impact of high level

experience and pedagogy as contributors of transformational teaching and learning. This is

contrary to popular opinion that new and young teachers use technology in ways that facilitate

transformational instructional practices. Research studies show that when teachers engage

informally with peers there is increased likelihood of teacher buy-in for technology integration and

teacher learning that leads to innovative teaching and increased technology use (Frank et al., 2011;

Hughes, 2005; Zhao et al., 2002). In the next section, I discuss additional factors that impact ICT

integration.

Active Ingredients of ICT Integration

School Organization

The way schools are organized impacts the content, the curriculum, and the tools that

teachers use in instruction. Therefore, it is critical to think about school organization when

evaluating ICT use, especially in mathematics. When it comes to instructional practices, Cuban

(2001) concluded that some teachers change certain teaching practices while maintaining others.

The challenge here becomes understanding the driving forces and contexts that lead to these

changes or lack thereof. Zhao et al. (2002) acknowledged the complexity of the teaching

34

environment and elaborated on key conditions for classroom technology innovations that are often

overlooked. For instance, Zhao and colleagues highlighted the need to consider the distance of the

technology integration from (a) existing practices, (b) pedagogy, (c) available technology, and (d)

peer resources in a school setting. These contextual and organizational factors are significant in

determining ICT integration. The authors argued that often teachers’ or the schools’ leadership

lack of this understanding leads to suboptimal results and possibly negative experiences of ICT

use.

Loveless, DeVoogd, and Bohlin (2001) questioned the role of the teacher when they asked,

“What are teachers for in the Information Society?” (p. 63). An information society is characterized

by a knowledge-based economy where cognitive skills are valued as the real currency and ICT is

assumed to be necessary to prepare students for a global workforce. The role and the identity of

the teacher in the information society is constantly challenged. Additionally, teachers are faced

with unrelenting changes and policies that accompany education reforms that may impact their

teaching practices. With ICT integration, it is critical for stakeholders to involve teachers in the

decision-making process to ensure teachers’ input.

Another critical element in school organization pertains to school leadership. Principals as

school leaders set the tone of school culture and impact school climate (Burns, 2013; Fullan, 2016).

School leaders who provide teachers with transformational leadership foster a culture of

collaboration, risk-taking in innovative practices, develop trust, and seek to build on teachers’

expertise are all necessary in integrating ICT (Wong, Li, Choi, & Lee, 2008).

Cultures of Practice

Cultures of practice include beliefs about teaching and learning and the ways that

knowledge is constructed and disseminated to learners. Researchers such as Schussler, Poole,

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Whitlock, and Evertson (2007) noted that the minimal use and underwhelming results from ICT

use may signify a complex situation in and around the learning environment. Currently, even for

classrooms that have integrated ICT, changes from old practices to new practices revolve around

automation (e.g., taking attendance, grading, word processing) instead of embedding technology,

and creating richer learning experiences, where students learn and develop their creativity with

ICT (Hughes & Read, 2018). Tensions arise between the old and the new ways of practice and

could be a result of teachers’ resistance towards change coupled with the complex nature of

learning environments, which contribute to current lackluster results from ICT in classrooms

(Gorniak-Kocikowska, 2008).

Following the continued investment in ICT and the complexity of learning environments,

there is a need to evaluate current pedagogical approaches and continue to investigate pedagogies

that are effective when integrating ICT, by exploring pedagogical innovations that ultimately lead

to increased student achievement. However, even before making that leap that helps stakeholders

(e.g., researchers, policymakers) understand the pedagogical approaches, they need to understand

how teachers use ICT in instruction for subjects such as mathematics and the factors that promote

or hinder ICT use. Researchers have reported that teachers used ICT more in English instruction

compared to mathematics. For instance, in Singapore, Tay, Lim, Lim, and Koh (2012) reported

that teachers incorporated ICT differently in English than in mathematics. In mathematics, ICT

was for skills practice compared to English classes where students used ICT in knowledge

construction. Hughes and Read (2018) echoed these findings about limited teachers’ and students’

ICT use in mathematics compared to content areas, such as English language arts or science.

Therefore, changing teachers’ mindset and perceptions about technology is equally as important

36

as the technology; this premise accentuates the need to address the role of technology on beliefs

and attitudes in teaching (Choy 2013; Mueller, Wood, Willoughby, Ross, & Specht, 2008).

School Climate

Scholars have reported that school climate can impact ICT integration, implementation,

use, and management. In a nutshell, teachers’ instructional practices are contextualized by micro

and macro factors. The micro factors are factors within the classroom, including students’

characteristics, the classroom environment, and available resources. The macro factors are those

outside the classroom environment including school leadership, colleagues, and the community.

Therefore, teaching and learning in the classroom takes place in a larger context influenced by the

school climate. Consequently, innovations such as ICT integration must take a sociocultural

perspective that takes into account characteristics that influence human behavior (Borko, 2004;

Somekh, 2007). Sociocultural perspectives include the social and organizational factors and can

impact ICT integration. Stories of success with ICT integration where students have benefitted

from ICT (i.e., in conditions that this success would have been unlikely) have been characterized

by an existing robust infrastructure (Burns, 2013; Warschauer et al., 2014). A robust infrastructure

may include reliable internet access, curriculum, pedagogy, technical support, and teacher-buy in.

Shared Vision

The discrepancies between availability of technology infrastructure and the lack of

students’ transformative learning experiences highlight potential disconnect amongst stakeholders

(e.g., students vs. teachers) on the expected goals to be achieved from ICT integration (Bain &

Weston, 2009; Hughes & Read, 2018). Additionally, it is likely that multiple stakeholders espouse

multiple assumptions about ICT. For instance, ICT can foster the development of high-order

thinking or result in increased learning (Burns, 2013). A potential missing link from this equation

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is an understanding of how ICT are used in core subjects such as mathematics. Scholars have noted

that developing a shared vision around ICT integration is often overlooked (Burns, 2013). Fullan

(2016) challenged education reformers and stakeholders to develop a shared vision that involves

identifying assumptions, developing program coherence, discussing moral values, and identifying

the correct policies. Other scholars (e.g., Windschitl & Sahl, 2002) have argued that a vision for

ICT integration needs to focus on discussions of beliefs about students, clarification of meaningful

learning, and teachers’ responsibilities. Additionally, developing a shared vision involves

developing a language that multiple stakeholders use to communicate (Burns, 2013). In doing so,

stakeholders can engage in evaluating real change as opposed to superficial change that starts and

ends with ICT acquisition. Developing a shared vision may lead to focusing on the human element

in the change process. This shared vision espouses that change occurs at an individual level, and

it is a process, not a single one-time event (Burns, 2013; Fullan, 2016). In the classroom, teachers

are the channels through which this change can occur and therefore, teachers should be part of the

discourse on education change (Górniak-Kocikowska, 2008). Affirming teachers as individuals

with varied teaching philosophies, experiences, epistemologies, and capacities can open up

avenues that position teachers as innovators, designers, and active participants in decision-making

instead of mere consumers of innovations (Cuban, 2001; Hughes & Read, 2018; Saunders &

Somekh, 2009).

Therefore, effective technology integration will require some degree of change and this

change process is ultimately a personal experience (Ertmer & Ottenbreit-Leftwich, 2010; Fullan,

2016). The benefit of understanding change in practice allows individuals to develop clarity in the

change process and possibly work towards the same goals. The change process should involve all

the stakeholders, including students, teachers, parents, administrators and the community.

38

Relatedly, Fullan highlights the importance of all stakeholders realizing that they are ‘the system’

(p. 53) and realizing the change process is a lengthy engagement with individuals who possess

varying experiences, and unique characteristics. In so doing, a synergistic relationship can develop

with time as people feel like they have a stake in the decisions and outcomes of the change process.

Furthermore, this ownership of belonging to ‘the system’ can foster system coherence where all

the stakeholders develop a shared mindset and accept the urgency of the work at hand (Fullan,

Rincon-Gallardo, & Hargreaves, 2015). For teachers especially, change is about beliefs, identities,

materials, and practices (Fullan, 2016). In a school with a mission focused on pedagogy-driven

ICT accompanied with supportive school leadership, teachers may incorporate ICT differently,

and encounter few challenges related to contextual factors compared to schools where teachers use

ICT as the main driver of instruction (Ling Koh, Chai, & Tay, 2014).

In their discussion of teachers’ development of discursive identities and technology use,

Chronaki and Matos (2014) presented the process through which teachers came to appreciate using

technology in mathematics instruction. The teachers in the study expressed the real struggle in

locating their identity amidst the assumptions of technology innovations and contradicting

discourses that exist in education. The teachers perceived their students as targeted consumers of

a technology product in a globalized market. Other scholars (Sewlyn, 2012; Warschauer et al.,

2014) echoed these sentiments based on their findings that digital divides exist in implementation

and management of technology initiatives as a result of social, financial and pedagogical reasons.

In a nutshell, understanding how teachers use ICT to instruct students with different achievement

levels and learning strengths can help promote digital equity among all students.

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Professional Development

Professional development for teachers is another crucial component in technology

integration. Frank et al. (2011) presented an empirical study that revealed the different profiles of

teachers at different technology adoption stages. Understanding teachers’ repertoires (e.g., skills,

expertise) can provide insights in customizing professional development. This can lead to

exchanges of experiences amongst peers, and the development of richer experiences of ICT

integration. The development of new experiences, and shared meaning can foster reflective

practices that engage teachers in thinking about what they know and how they can integrate

technology to change instructional practices (Hughes, 2005; Hughes, Guion, Bruce, Horton, &

Prescott, 2011). This process of self-awareness can be developed during professional development

with the infusion of activities that facilitate teachers’ reflections on beliefs and real-time practices

(Hughes, 2005).

Blanchard, LeProvost, Tolin, and Gutierrez (2016) in their study on professional

development in rural, high poverty schools showed that the time element is crucial in determining

trends and evaluating the impact of professional development on teacher beliefs and attitudes.

Although the measures the authors used may not have captured the changes in teacher attitudes

and practices, the reported increases in students’ mathematics and science outcomes may be a

testament to the impact of innovative teaching tools as well as teachers’ reflective practices in the

change process. Educational technology professional development (ETPD) can have different foci,

such as curriculum goals, learning objectives or content material (Harris, 2008). More importantly,

ETPD should take into consideration the unique teacher characteristics to ensure professional

development maximizes the different profiles of innovation adopters that Rogers (1995) elaborated

on in the theory of diffusion of innovations.

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Similarly, Fullan (2016) advocated for systems such as schools to invest in building skills

that increase personal and organizational capacity that allow individuals to develop new

experiences. Additionally, the development of collaborative cultures can foster group learning and

increase group collaboration. Researchers have emphasized that knowledge, self-efficacy,

pedagogical beliefs, and school culture are critical variables that intersect to bring about changes

in instructional teacher practices and technology innovations (Ertmer & Ottenbreit-Leftwich,

2010; Zhao et al., 2002). The power and agency that impacts teachers’ technology use rests in the

teachers’ perceived relative advantage of the technology. Teachers need to find value in these new

experiences in order to include them in classroom instruction, making it imperative for

professional developers to provide teachers with relevant training in content-specific areas

(Hughes, 2005). Also, there is a need for professional development that embeds ICT with the

content area and provides practical examples of ways that teachers can use ICT tools in the

classroom (Ertmer & Ottenbreit-Leftwich, 2010; Hughes, 2004; Hughes & Read, 2018). In

addition, scholars reiterate the need for pre-service teachers to build a robust understanding of the

role of ICT in teaching and learning during their teacher training. Professional development also

needs to be ongoing to ensure that teachers receive adequate support, coaching, and resources

(Burns, 2013). Furthermore, school districts can tap into the professional capital of teachers, media

specialists, students, and instructional coaches in designing professional development workshops

that provide examples of ICT integration in specific content areas (Hughes & Read, 2018).

Constructivist Beliefs

Scholars have noted teachers’ ICT integration activities are usually closely connected to

their beliefs (Ertmer, Ottenbreit-Leftwich, Sadik, Sendurur, & Sendurur, 2012). A teacher with

constructivist beliefs will more likely engage with instructional practices and approaches that are

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student-centered, fostering creativity and dialogue (Wong et al., 2008). A student-centered

approach allows the student to explore, engage in critical thinking, and develop conjectures in the

learning process. In a study with teachers in Hong Kong and Singapore, Wong, Li, Choi, and Lee

(2008) found that teachers with constructivist beliefs had commonalities in inquiry and

collaboration. They also found ICT to be important in altering instructional practices that resulted

in new experiences. Additionally, scholars (e.g., Overbay et al., 2010) found constructivist beliefs

were a strong predictor in teachers’ use of ICT after taking into account teachers’ individual

variables (e.g., subject, level of experience) and school level variables (e.g., teachers’ perception

of the administration, school level of support) variables. Overbay et al. (2010) also found that the

teachers’ beliefs on how ICT can promote student-centered activities positively impacted their ICT

use.

However, researchers have also documented that teachers’ beliefs may be inconsistent with

their instructional practices. These inconsistencies limit the effectiveness of the teaching practices

(Ertmer, 2001, 2005). In the case of ICT integration, Ertmer (2001) found that teachers who

reported constructivist beliefs integrated ICT in ways that engaged learners through completing

projects and creative tasks (constructivist approach). At the same time, these teachers also used

ICT in ways that promoted low-level learning, such as drill and practice (traditional approach),

employing a hybrid approach when teaching with ICT. It is important to note that contextual

factors and the perceived role of the teacher in student learning are some factors that contributed

to these discrepancies. The challenge then becomes understanding these inconsistencies between

teacher beliefs and actual instructional practices, and further identifying the beliefs that may

contribute to certain instructional practices (du Plessis, 2016; Ertmer, 2001). It is also important to

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note that a teacher’s self-realization of espoused beliefs is an important first step towards learning

and changing teaching beliefs on ICT integration (Hughes, 2005).

In most cases, ICT is still mainly used for low-level tasks, such as drill and practice, and

seldom for high-level tasks such as problem-solving (Ertmer, 2005; Hughes & Read, 2018).

According to the NCES (2010), teachers in the U.S. reported computers for low-level tasks such

as drill and practice (49%), word processing (96%), spreadsheets and graphing (61%) and even

lower usage for problem solving (27%) or presentation (63%). ICT use differed between schools

and specifically varied according to the schools’ socioeconomic levels. For instance, 83% of

students in high-poverty schools used ICT to learn or practice basic skills compared to 61% in

low-poverty schools. Scholars have underscored these sentiments regarding the low use of ICT for

high-level tasks (Barron, Kemker, Harmes, & Kalaydjian, 2003; Zhao et al., 2002).

Self-Efficacy

Self-efficacy includes a teachers’ ability to articulate the benefits of ICT in teaching, ability

to demonstrate the skills and possession of confidence required for effectively integrating ICT.

Scholars have argued that teachers’ confidence contributes to their likelihood of incorporating ICT

in instruction (Bauer & Kenton, 2005). Although scholars also argue that confidence alone is

insufficient for sustainable and effective ICT use, it is important for teachers to have confidence

in using ICT for instruction (Ertmer & Ottenbreit-Leftwich 2010). In considering the potential of

ICT as a tool that can provide different ways of teaching and learning, Ertmer and Ottenbreit-

Leftwich (2010) asserted that effective teaching should include technology and further argued that

effective ICT integration should be considered be a quality of good teaching. Considering the

complexities of the classroom environment, structural changes are needed to allow successful

instruction delivery using ICT (Bain & Weston, 2002). For instance, Bain and Weston argued for

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the need to redefine the role of teachers and students in the classroom. This suggestion augurs well

with the development of student-centered learning and teachers as facilitators. ICT provides

affordances that can allow individualized learning, inquiry-based learning, feedback, simulated

activities, real-time data recommendations and progress monitoring. Also, the authors argued that

ICT lends itself to the extension of learning beyond the classroom walls, facilitated by the ubiquity

of technology in the 21st century. Also, changes in school policies and structures can allow teachers

to be creative in how they meet the diverse needs of students in the classroom using ICT by

fostering development of autonomy for teachers to be instructional designers using ICT (Heath,

2017; Wood, 2003). Specifically, teachers need to develop skills that capture the interactions

among different constructs (i.e., technology, pedagogy, and content) as shown in the technology,

pedagogy, content, and knowledge (TPACK) framework (Koehler & Mishra, 2009). Koehler and

Mishra argued that teachers need to demonstrate technology, pedagogical and content knowledge

to effectively integrate technology in instruction. Scholars have reported that teachers’ knowledge

levels impact their self-efficacy with TPACK (Koh, Chai, & Tsai, 2013).

Although the development of ICT is robust, the actual implementation of these innovations

continues to be a salient problem in education (Fullan, 2016). Fullan posits that the right drivers

for any meaningful change leads to development of intrinsic motivation, collaboration, and

continuous improvement. According to Fullan, the right policy drivers include: systemness,

pedagogy, collaboration, and capacity building. Therefore, it is imperative for stakeholders to

consider the local contexts and identify the right drivers of the innovations. However, as Selwyn

(2012) asserted, technology-integration continues to exist in an “empirical vacuum” (p.139). Other

scholars have noted that this dearth of information on the experimental effects of ICT has not

deterred further investment in ICT (Bain & Weston, 2012). Yet even if research studies on effects

44

of ICT existed, the change process involved in adopting innovations is subjective and individual

decisions seldom rely on research studies due to perceived loss of valuable time, negative

experiences, or limited benefits (Fullan, 2016; Rogers, 1995).

Taken together, different social and organizational factors are interconnected and influence

decisions on how resources are procured, integrated, and become part of the school culture. Fullan

(2016) emphasized the need for “connected autonomy” (p. 262), where people collaborate within

the school, or with other schools while linking to overarching educational policy priorities. These

connections and collaborations can lead to sharing and borrowing of ideas on ICT use, sharing of

expertise, and development of shared meaning that may lead to transformative teaching and

learning using ICT across schools and countries in critical subjects such as mathematics.

Pedagogies

Learning theories influence teaching practices, teaching approaches, and teacher beliefs

and in turn influence instructional design (Ertmer & Newby, 2013). Effective use of ICT in

instruction largely depends on the pedagogies teachers incorporate in teaching (Magiera, 2016). In

the era of educational reforms, Fullan (2016) argued that for schools to experience meaningful

change, it is critical for stakeholders to identify and implement policy drivers (e.g., pedagogy). In

other words, pedagogy should drive how teachers use technology. Also, instead of implementation

of isolated changes, these stakeholders should embrace a holistic approach that seeks cohesion of

the changes in the system. Next, I will discuss constructivism as a pedagogical approach.

Constructivism

Constructivist pedagogy emphasizes knowledge construction when students connect newly

learned concepts and prior knowledge with the teacher as a facilitator (Bruner, 1977; Ravitz, et al.,

2000; Vygotsky, 1978). With constructivist pedagogy, the learner is an active contributor and

45

participant in the meaning-making process (Ertmer & Newby 2013). Constructivism is further

defined as either cognitive or social; cognitive constructivism focuses on the mind during learning

and originated from Piaget. Conversely, social-constructivism focuses on ways that the

environment, especially the social environment contributes to learning and has been attributed to

Vygotsky (Schcolnik, Kol, & Abarbanel, 2016). In the classroom, teachers using constructivist

pedagogy tend to be flexible and willing to take risks in the teaching and learning process

(Schcolnik et al., 2016). In mathematics, teachers using a constructivist pedagogy use ICT tools to

guide learners to develop hypotheses, conjectures, test multiple strategies and explore mathematics

concepts (Fast & Hankes, 2010). Some studies have reported positive benefits of this instructional

approach. For instance, Li and Ma (2010) reported that technology had stronger effects on student

achievement where teachers used a constructivist approach compared to the traditional, teacher-

driven approach. The authors found technology boosted mathematics achievement for students

identified with special needs and elementary students compared to secondary students.

Socio-Constructivism

The socio-constructivist theory emphasizes the social environment in the learning process.

Wertsch (1991) argued that individuals’ actions and behaviors are socially situated in partnerships

or small groups, and beyond these settings, knowledge is situated in larger contexts of society (e.g.,

historical, institutional, cultural). Acknowledgment of the influence that these contexts play in the

language, actions, behaviors, and beliefs of individuals can provide nuanced understandings of the

different layers of influence in learning or decision-making. In the context of ICT integration, to

understand how teachers use ICT in the classroom, similar to Roger’s argument, it is important to

understand the teacher’s social environment. Moreover, it is imperative to understand a teacher’s

view on learning as this may impact a teacher’s epistemic belief. With a socio-constructivist

46

approach, a teacher’s integration of ICT may involve students learning in groups, engaging in

project-based activities, and peer-to-peer discussions.

Taken together, this dissertation research gleans international trends on teachers’ ICT use

in mathematics instruction and adds to the literature base. First, I examine the extent of ICT use

among mathematics teachers in eight countries. Second, I investigate teachers’ professional

characteristics that contribute to ICT use. Third, I examine instructional practices and beliefs that

impact ICT use. Fourth, I examine whether classroom compositions of students with varying

characteristics impact teachers’ ICT use. Lastly, I examine school contexts that predict ICT use.

Findings from this study inform researchers, policymakers, and multiple stakeholders on key

contributors of ICT use and expand research on the impact of the learning and working

environments of the diffusion of innovations, such as ICT. To accomplish these objectives, I use

the Organization for Economic Co-operation and Development (OECD), Teaching and Learning

International Survey (TALIS) (OECD, 2013) dataset and the conceptual framework shown in

Figure 1 to examine teacher’s ICT use in mathematics instruction.

47

Figure 1. Conceptual Framework for ICT use in Mathematics Instruction.

Teachers' ICT Use in

Mathematics Instruction

Professional Qualifications

InstructionalPracticesand

Beliefs

TeacherCooperation

Administrators'Support

CollaborativeCultures

MathematicsLevels

SocioeconomicStatus

SpecialEducationStatus

Classroom Composition of Students

Teachers’ Characteristics

School Contexts

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CHAPTER THREE

Methodology

As discussed in chapter 1 and 2, given the existing gaps between technology advancements

and the peripheral teacher use of ICT in instruction or limited use of ICT in high-level mathematics

tasks, I used two theoretical frameworks to conduct this study and understand the trends and

patterns of ICT use among mathematics teachers in eight countries to examine: how teachers use

ICT in mathematics across countries, whether teacher characteristics, beliefs and instructional

practices, and student characteristics influenced ICT use, and whether school contexts predicted

teachers’ ICT use. The frameworks are: (a) theory of diffusion of innovations (Rogers, 1995) and

(b) sociocultural theory on teaching and learning (Vygotsky, 1978). I also examined the

constructivist pedagogical approach to further inform the development of the study and guide the

analysis of the data and the findings.

The purpose of this study therefore, was to understand the trends and patterns of ICT use

among mathematics teachers in a global context. As such, this dissertation research used the

TALIS 2013 dataset to answer the following research questions:

1. To what extent do teachers use ICT in mathematics instruction in eight countries (i.e.,

Australia, Finland, Latvia, Mexico, Portugal, Romania, Singapore, and Spain)?

2. What is the relationship between teacher’ professional qualifications (e.g., education,

technology training, years of teaching experience) and teachers’ ICT use in

mathematics instruction?

3. How do teacher characteristics (e.g., self-efficacy, constructivist beliefs, constructivist

teacher practices) explain differences in ICT use?

4. Do teachers use ICT differently for mathematics instruction in classrooms with students

49

with different characteristics (mathematics achievement levels, special needs status,

socioeconomic levels)?

5. How do school contexts predict ICT use?

Research Design

I used a quantitative research design to analyze the TALIS 2013 dataset and address the

five research questions. I used descriptive statistics (frequency, mean, standard deviation,

probabilities) and logistic regression to conduct the analyses. I used the STATA version 14.0 in

the analysis and included survey weights to ensure the population estimates are effectively

adjusted.

Sources of Data and Data Collection

The Teaching and Learning International Survey (TALIS) 2013 dataset was obtained with

permission from the Office for Special Education Programs (OSEP). TALIS 2013 is one of the

largest international surveys with a main focus on the learning and teaching environment. The

survey drew school and teacher level data from 33 countries (24 OECD countries & 9 partner

countries). The data were collected from 10,000 schools and more than 170,000 teachers. The

international sampling plan was a stratified two-stage probability sampling design. Teachers were

randomly selected from randomly selected schools. Data collection included surveys with (a)

teachers from lower secondary schools (7th grade to 9th grade) and (b) surveys from principals. An

opportunity for participation was offered to primary (1st grade to 6th grade) and upper secondary

schools (10th grade to 12th grade). TALIS was conducted to collect data that would provide

directions in policy development. Some of the policy areas included type of feedback that teachers

received and the outcomes of this feedback on teaching practices. In addition, the feedback

provided information on: the amount and type of professional development, impact of professional

50

development; obstacles to opportunities for professional development; ways that school level

policies and practices influenced the teachers working conditions; and degree to which current

trends in school leadership impacted teachers learning and working conditions.

Participants

TALIS 2013 was mandatory for lower secondary (7th grade to 9th grade) schools and

optional for countries in upper secondary level (10th grade to 12th grade) and for countries that

completed the PISA 2012. The sample included mathematics teachers of 15-year old students

sampled from the sampled PISA school (OECD, 2013). The participants making up the analytic

sample in this study consists of 6,570 teachers from eight countries: Australia, Finland, Latvia,

Mexico, Portugal, Romania, Singapore, and Spain. The teachers from these sampled countries

participated in the 2012 PISA and completed the additional mathematics questionnaire. All

questionnaires were translated in the respective countries language and verified for linguistic

equivalence (OECD, 2013).

The TALIS-PISA link provided information on teaching practices at the classroom level.

It is important to note that the TALIS (2013) technical report states that the TALIS results will not

be used to interpret the students’ scores on the PISA but the results should instead be used to

understand teacher/principals’ responses about the learning and teaching environment (OECD,

2013). As an example, for this analysis the focus is on teachers’ ICT use in their mathematics

instruction as well as an investigation of school contexts and teacher and student characteristics

that contribute to ICT use across the different countries, but does not extend the findings to

students’ mathematics performance. However, the findings from this analysis can inform current

practices around ICT use in mathematics instruction in contexts such as the United States. A

51

proposal to Institutional Review Board (IRB) at the university was submitted to obtain permission

to conduct research with de-identified data.

Measures

Using the TALIS technical report (OECD, 2013), codes are established for the predictor

variables (such as mathematics self-efficacy, constructivist beliefs).

Demographic Variables a. Highest level of formal education. The response scale was from 1- 4, 1- below ISCED

level 5, 2- ISCED level 5B, 3- ISCED level 5A, 4 – ISCED level 6. I recoded this variable

to 0-1, (0 = ISCED level 5B or 5, 1= ISCED level 5A and 6)

b. Years of Experience. Number of years of teaching experience is an ordinal

variable with four categories. (recoded 1 = 0 - 17, 2 = 18 – 35, 3 = 36 – 50+; categorical

variable).

c. Teacher Training – coded as (1 = yes, 2 = no). I recoded this variable to 0-1, (0 = No,

1= Yes).

d. Technology Training – coded as (1 = yes, 2 = no). I recoded this variable to 0-1, (0 =

No, 1- Yes).

Independent Variables

The teacher level variables as outlined in Appendix I examined constructivist beliefs,

teacher co-operation, and professional development training in ICT skills. School level variables

include school climate such as teacher participation in decision-making, collaborative school

cultures and support from the administration. For student characteristics, I included student

achievement level in mathematics, socioeconomic status and special education status. The

literature base on ICT integration has shown that organizational and social factors impact ICT

52

integration. Additionally, collaborative school culture, teacher participation in decision making

and administrative support influence ICT integration in teaching and learning. The literature also

shows teachers’ varying use of ICT use depending on students’ achievement levels.

a. Teacher Mathematics Self-efficacy

According to the TALIS 2013 technical report (OECD, 2013), the teacher mathematics

self-efficacy scale was defined from three scales: efficacy in classroom management,

efficacy in instruction and efficacy in student engagement. The self-efficacy in

mathematics scale was measured by asking teachers questions about their mathematics

teaching characterized by asking students questions that get them to think deeply about

mathematics, ease of developing students’ interest in mathematics and ability to get

students to feel confident in mathematics. These questions making up the self-efficacy

composite variable align with the literature’s definition of self-efficacy, defined as an

individual’s perception to accomplish a task (Bandura, 2006b). This was a composite

variable with a Cronbach’s reliability of 0.7 for Australia, Portugal, Singapore and Spain

and a Cronbach’s reliability of 0.6 for Finland, Latvia, Mexico, Romania, as provided in

the TALIS technical report (OECD, 2013). The pooled alpha coefficient was close to 0.7

(OECD, 2013).

b. Constructivist Beliefs - This refers to a teacher’s ability to prioritize thinking and

reasoning processes, promote a student’s inquiry and problem-solving skills. It is a

composite variable with a Cronbach’s reliability of 0.7 as provided in TALIS technical

report (OECD, 2013). The 4 items with the response range from 1-4, 1- “strongly disagree”,

2- “disagree”, 3 – “agree”, 4 – “strongly agree”.

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c. Teacher Cooperation - This refers to teachers’ frequency of exchanging teaching

materials with colleagues, and frequency of engaging in discussions about the learning

development of specific students. This cooperation among teaching staff scale was

measured by eight items from two subscales on exchange and coordination for teaching

and professional collaboration. The items were measured with response range from 1-6, 1-

“never”, 2 – “once a year or less”, 3- 2-4 times a year, 4 – “5-10 times a year”, 5 “1-3 times

a month”, 6 – “once a week or more”. The teacher cooperation variable in the TALIS

dataset was computed as an average of the two subscales (i.e., exchange scale and

collaboration scale).

Educational Approaches Variables

a. Constructivist Teaching Practices Variable – This variable referred to a teachers’

incorporation of the following teaching practices. It is important to note that this variable

captured constructivist-teaching practices specifically in mathematics while the variable

provided in the TALIS dataset captured constructivist beliefs on general teaching and

learning.

a. Expect students to explain their thinking on complex problems.

b. Give students a choice of problems to solve.

c. Connect mathematics concepts to uses of concepts outside of school.

d. Encourage students to solve problems in more than one way.

e. Require students to provide written explanations of how they solve problems.

f. Require students to work on mathematics projects that take more than a single class

period to complete.

g. Encourage students to work together to solve problems.

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The 7 items with the response range from 1-4, 1- “never or almost never”, 2-

“occasionally”, 3- “frequently”, 4- “in all or nearly all lessons”. Out of the 10 items, I

identified 7 times that aligned with constructivist pedagogies, characterized by student-

centered teaching and learning (Overbay et al., 2010). I created a composite variable,

consapp that captures teaching practices that demonstrate constructivist pedagogies. This

composite variable has a Cronbach’s reliability of 0.7.

b. Goals about Mathematics Variables - This refers to the following teacher’s goals and

views about teaching mathematics (i.e., teachers’ pedagogical beliefs about mathematics).

a. The goal of mathematics is to help students use mathematics to solve real-word

problems.

b. I want to see my students to see the structure of the number system and the logic of

mathematics.

c. Explaining why an answer is correct is just as important as getting a correct answer.

d. Students should be able to figure out for themselves whether they have solved a

mathematics problem correctly.

e. Asking students to solve difficult problems in class helps them become good problem

solvers.

f. I’d rather have my students solve a few complex problems than a lot of relatively easy

ones.

g. An important reason for teaching mathematics is to help students become more logical.

h. Graphic calculators and computers can be used to help students see mathematics

concepts in new and different ways.

i. Doing mathematics requires hypothesising, estimating, and creative thinking.

55

Out of the 12 items, I identified 9 times that aligned with constructivist pedagogies. The 9

items with the response range from 1-4, 1- “strongly disagree”, 2 - “disagree”, 3 – “agree”,

4 – “strongly agree”. I created a composite variable, gvcons that captures goals and views

about mathematics that align with constructivist pedagogies. This composite variable has

a Cronbach’s reliability of 0.73.

Professional Development Variables

a. Training in ICT Skills for teaching - This refers to a teachers’ participation in

professional development activities covering ICT skills during the last 12 months. The 2

items with the response range from 1-2, 1- “Yes”, 2- “No”. I recoded this variable to 0-1,

(0 = No, 1 = Yes).

Student Variables

a. Student Achievement Level - This classroom level variable referred to a teacher’s best

description of the students’ achievement levels in their class. The 4 items with the

response range from 1-4, 1- “mostly high achieving students in mathematics”, 2-

“mostly average students in mathematics”, 3 – “mostly low achieving students in

mathematics”, 4 – “approximately equal numbers of high, average, and low

achievement students in mathematics”.

b. Special Needs Status – This classroom level variable referred to estimates of the

percentage of students with special needs status in the target class.The 5 items with the

response range from 1-5, 1- “none”, 2- “1% to 10%”, 3 – “11% to 30%”, 4 – “31% to

60%”, 5 – “More than 60%”.

c. Socioeconomic Status - This classroom level variable referred to estimates of the

percentage of students with socioeconomic status in the target class.The 5 items with

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the response range from 1-5, 1- “none”, 2- “1% to 10%”, 3 – “11% to 30%”, 4 – “31%

to 60%”, 5 – “More than 60%”.

School Climate Variables

a. Participation in decision making– This refers to the availability of opportunities for staff

to actively participate in school decisions. The 4 items with the response range from 1-4,

1- “strongly disagree”, 2- “disagree”, 3 – “agree”, 4 – “strongly agree”. (0 = strongly

disagree and disagree, 1 = agree and strongly agree).

b. Collaborative school culture – This refers to the existence of a collaborative school

culture which is characterized by mutual support. The 4 items with the response range

from 1-4, 1- “strongly disagree”, 2- “disagree”, 3 – “agree”, 4 – “strongly agree”. (0 =

strongly disagree and disagree, 1 = agree and strongly agree).

c. Administrative support – This refers to availability of administrative support for teachers

at the school. The 4 items with the response range from 1-4, 1- “strongly disagree”, 2-

“disagree”, 3 – “agree”, 4 – “strongly agree”. I recoded this variable, to 0-1, (0 = strongly

disagree and disagree, 1 = agree and strongly agree).

Outcome Variables

a. This outcome variable refers to how teachers used ICT in mathematics instruction. This

question had four sub questions asked teachers how frequently they used ICT for drill

and practice, topic specific activities, data analysis, assessing student progress, and ICT

for Internet resources. The original response scale range was from 1- 4, 1- never or

almost never, 2- occasionally, 3 - frequently or 4 - always or almost always. I

developed a variable that captured overall ICT use.

57

b. This outcome variable refers to how teachers used ICT in mathematics instruction for

each task (drill and practice, topic-specific activities, spreadsheets/data-analysis,

assessment, internet resources). The original response scale range was from 1- 4, 1-

never or almost never, 2- occasionally, 3 - frequently or 4 - always or almost always. I

recoded the variable to 0 -1, 0 = never or almost never/occasionally, 1 =

frequently/always or almost always.

Data Analysis

All statistical analyses were conducted using STATA version 14. Prior to conducting the

various data analyses, I screened the TALIS 2013 dataset for missing values and incorporated

survey weights in all the analyses. The following analyses were conducted to answer the five

research questions.

Descriptive Statistics

Research Question 1: To what extent do teachers use ICT in mathematics instruction

in eight countries (i.e., Australia, Finland, Latvia, Mexico, Portugal, Romania,

Singapore, and Spain)?

I conducted three descriptive statistics of overall ICT use across the eight countries. First,

I examined the probabilities of overall ICTUSE. To generate the probabilities examining

the likelihood of teachers’ ICT use in instruction, I conducted a logistic regression analysis

using the margins command in STATA. I included the country identifier (idcntry) as the

categorical variable and ICTUSESUM variable as the continuous variable. This

ICTUSESUM variable is a binary variable that represented whether teachers incorporated

ICT in mathematics instruction frequently/almost always or occasionally or never. Second,

I examined the means of the overall ICTUSE in mathematics instruction among the eight

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countries. Third, I conducted a Chi-square test of independence to examine the proportions

of ICT use across the countries by ICT type.

Relationships

Research Question 2: What is the relationship between teacher’ professional

qualifications (e.g., education, technology training, years of teaching experience) and

ICT use in mathematics instruction?

I conducted logistic regression analyses between teacher’ professional qualifications and

teachers’ use of the five types of ICT and fitted the following regression model for each

ICT type:

𝑙𝑜𝑔𝑖𝑡 𝑝'( = 𝛽+( + 𝛾𝑋'(

I fitted separate models for each outcome variable (i.e., each ICT type). In the teacher

model, 𝑝'( is the probability of mathematics teachers’ ICT use in instruction for teacher i

in school j; 𝑋'( represents a vector of teacher’ professional qualifications with 𝛾 as a vector

of the associated coefficients.

Research Question 3: What is the relationship between teachers’ instructional

approaches and beliefs (e.g., self-efficacy, constructivist beliefs, constructivist teacher

practices) and ICT use in mathematics instruction?

I conducted logistic regression analyses between teachers’ instructional approaches and

beliefs and teachers’ use of the five types of ICT and fitted the following regression model:

𝑙𝑜𝑔𝑖𝑡 𝑝'( = 𝛽+( + 𝛾𝐴'(

I fitted separate models for each outcome variable (i.e., each ICT type). In the teacher

model, 𝑝'( is the probability of mathematics teachers’ ICT use in instruction for teacher i

59

in school j; 𝐴'( represents a vector of teacher instructional approaches and beliefs with 𝛾

as a vector of the associated coefficients.

Research Question 4: Do teachers use ICT differently for mathematics instruction in

classrooms with students with different characteristics (mathematics achievement

levels, special needs status, socioeconomic levels)?

I conducted logistic regression analyses between teachers’ instructional approaches and

beliefs and teachers’ use of the five types of ICT and fitted the following regression model:

𝑙𝑜𝑔𝑖𝑡 𝑝'( = 𝛽+( + 𝛾𝑆'(

I fitted separate models for each outcome variable (i.e., each ICT type). In the teacher

model, 𝑝'( is the probability of mathematics teachers’ ICT use in instruction for teacher i

in school j; 𝑆'( represents a vector of students’ characteristics with 𝛾 as a vector of the

associated coefficients.

Research Question 5: How do school contexts (e.g., teacher cooperation, collaborative

culture, administrative support) predict ICT use?

I conducted logistic regression analyses between school climate and teachers’ use of ICT

and fitted the following regression model:

𝑙𝑜𝑔𝑖𝑡 𝑝'( = 𝛽+( + 𝜔𝐶'(

I fitted separate models for each outcome variable (i.e., each ICT type). In the teacher

model, 𝑝'( is the probability of mathematics teachers’ ICT use in instruction for teacher i

in school j; 𝐶'( represents a vector of school context variables with 𝜔as a vector of the

associated coefficients.

60

Hypotheses

I developed five hypotheses for the dissertation study based on the available

research literature.

Research Question 1: To what extent do teachers use ICT in mathematics instruction

in eight countries (i.e., Australia, Finland, Latvia, Mexico, Portugal, Romania,

Singapore, and Spain)?

Hypothesis: The hypothesis is that ICT use in mathematics instruction is limited

and teachers use ICT in different ways.

Research Question 2: What is the relationship between teacher’ professional

qualifications (e.g., education, technology training, years of teaching experience) and

ICT use in mathematics instruction?

Hypothesis: I hypothesized that there was a strong positive relationship between

years of teaching experience and ICT use. Also, years of teaching experience have

statistically significant effects on teachers’ ICT use in mathematics instruction.

Teachers with more years of teaching experience use ICT more frequently than

teachers with fewer years of teaching experience. Also, teachers with more teaching

experience use ICT for high-level tasks (e.g., use of topic-specific software,

spreadsheets/data analysis software) tasks during mathematics instruction while

teachers with fewer years of teaching experience use ICT for low-level tasks (e.g.,

drill and practice) in mathematics instruction.

Research Question 3: What teacher characteristics (e.g., self-efficacy, constructivist

beliefs, constructivist teacher practices) explain differences in ICT use?

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Hypothesis: I hypothesized that teachers’ mathematics self-efficacy is a strong

predictor of ICT use. Teachers with higher mathematics self-efficacy use more

ICTs in high-level (e.g., use of topic-specific software, spreadsheets/data analysis

software) tasks during mathematics instruction while teachers with lower

mathematics self-efficacy used ICT for low-level tasks (e.g., drill and practice).

Also, constructivist beliefs may result in mixed results, that is, constructivist beliefs

may contribute to high ICT use for some ICT activities and at the same time may

contribute to low ICT use for different ICT tasks.

Research Question 4: Do teachers use ICT differently in mathematics instruction

in classrooms with students with different characteristics (mathematics

achievement levels, special needs status, socioeconomic levels)?

Hypothesis: I hypothesized that mathematics teachers use ICT differently

depending on classroom composition (i.e., students’ mathematics achievement

levels, students with special needs, socio-economic status). Teachers were likely to

use ICT for topic-specific activities and for spreadsheets/data analysis activities in

classrooms with students who were high achieving in mathematics. In contrast,

teachers used ICT for low-level tasks (e.g., drill and practice) in classrooms with

students who were low achieving, students identified with special needs and

students with low socioeconomic status.

Research Question 5: How do school contexts (e.g., teacher cooperation,

collaborative culture, administrative support) predict ICT use?

Hypothesis: I hypothesized that school climate (e.g., teacher cooperation,

collaborative culture and administrators’ support) is a strong predictor of ICT use.

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CHAPTER FOUR

Results

This chapter presents findings of descriptive statistics and logistic regression analyses on

mathematics teachers’ ICT use in instruction. To answer the five research questions, I used data

from the TALIS 2013 survey and analyzed the analytic sample that included mathematics teachers

from eight countries: Australia, Finland, Latvia, Mexico, Portugal, Romania, Singapore, and

Spain. The teachers from these sampled countries participated in the 2012 PISA and completed

the additional mathematics questionnaire.

With the current peripheral ICT use in instruction in the United States, the purpose of this

study was to embrace a global outlook in understanding education trends, with a special focus on

teachers’ ICT use in mathematics instruction in the eight countries. The present study examined

teachers’ professional qualifications, professional development, instructional practices and school

contexts as predictors of teachers’ ICT use in mathematics instruction. Additionally, this present

study examined whether students’ characteristics (i.e., mathematics achievement levels, special

needs, low socioeconomic status) impacted teachers’ ICT use to develop further insights on how

classroom composition of students may influence teaching practices and ICT use. To achieve these

objectives, I fitted linear regression models at the teacher and school level to examine the factors

associated with mathematics teachers’ ICT use. In all descriptive analyses and regression models,

I incorporated the TALIS survey weights to account for the survey design.

Descriptive Characteristics of Mathematics Teachers

Table 2 shows a descriptive summary of the mathematics teachers in the analytic sample.

Spain had the largest representation of teachers making up 21% of the sample, while Mexico and

Romania both had a close representation of teachers at 8%. It is also evident from Table 2 that the

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teachers were mainly from European countries (i.e., Finland, Latvia, Portugal, Spain, & Romania).

Almost 50% of the teachers were in the 36 and 51 years age bracket and more than 50% of the

teachers had at least 17 years of teaching experience.

Table 2. Sample Demographics: Descriptive Statistics Variable N % Countries Australia Finland

859 1027

13.1 15.6

Latvia Mexico

334 511

5.1 7.8

Portugal Singapore

683 1176

10.4 17.9

Spain Romania

1428 522

21.7 8.4

Age 20 - 35

1674

25.5

36 – 51 52 – 67 68+

3248 1631 13

49.5 24.8 0.2

Teaching Experience 0 – 17 18 – 35 36 – 50+

3654 2423 272

57.6 38.0 4.0

Table 2 displays a descriptive summary (i.e., means and standard deviations) of the

variables used in the analyses. For professional qualifications, majority of the mathematics

teachers had beyond a tertiary level (ISCED 5A or 6) of education but a lower percentage of the

teachers had received teacher education or teacher training. According to the TALIS technical

report, a score above ten on the mathematics teacher self-efficacy scale and the constructivist

beliefs scale shows agreement on the included items for these two measures. The self-efficacy

scale included items on teachers’ ability to get students to feel confident about mathematics. The

constructivist belief scale included items on teachers’ role as a facilitator of students’ own inquiry.

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Table 3. Means and Standard Deviations of Mathematics Teachers Variable M SE Professional Qualifications

Teaching Experience 16.56 0.39 Formal Education (ISCED 5a/6) 0.96 0.01 Teacher Training 0.68 0.02

Training in Technology During In-service 0.75 0.02

Instructional Practices/Beliefs

Mathematics Self-efficacy 11.06 0.09 Constructivist Beliefs 13.24 0.08 Constructivist Teacher Practices 2.70 0.02 Constructivist Goals and Views 3.22 0.02

School Environment

Collaborative Culture 0.70 0.02 Administrators’ Support 0.77 0.02 Teacher Cooperation 10.08 0.09 Participation in Decision Making 0.63 0.02

Teachers’ ICT Use Drill and Practice Software 1.73 0.03 Topic-Specific Software 1.85 0.03 Spreadsheets/Data Analysis 1.66 0.03 Assessing Student Learning 1.63 0.03 Internet Resources 2.38 0.04

Note. ICT = information communication technology; ISCED = international standard classification of education.

Therefore, Table 3 shows that on average, mathematics teachers demonstrated mathematics

self-efficacy and constructivist beliefs. Constructivist teacher practices and goals and views about

mathematics were both on a 1 - 4 points scale and similarly, the teachers scored above average.

Collaborative culture, administrators’ support and teacher participation in decision making were

all on a 0 to 1 scale. More mathematics teachers acknowledged receiving administrators support

compared to participating in decision making. According to the TALIS technical report, the teacher

cooperation measured the frequency of teachers’ exchanging materials with colleagues, observing

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peers’ classes and giving feedback. A score above ten shows consistent repetition of activities

included in the teacher cooperation scale. Table 3 shows that teachers in the sample participated

in activities that promoted teacher cooperation. Finally, the teachers’ ICT use variable was on a 1

- 4 points scale and measured the frequency of teachers’ ICT use in instruction during the school

year. Table 3 shows that teachers mostly incorporated internet resources compared to ICT for drill

and practice, topic-specific activities, spreadsheets/data analysis or ICT for assessment purposes.

Research Question One: ICT Use Among the Countries

Understanding the effectiveness of ICT in learning begins with developing an overview of

teachers’ overall ICT use. To provide this big picture on mathematics teachers’ ICT use in the

eight countries, first, I examined the probabilities of teachers’ ICT use for the five ICT tasks (drill

and practice, topic-specific, spreadsheets/data-analysis, assessment and internet resources).

Table 4. Probabilities of Overall ICT Use Across Countries ICT Use Probabilities Confidence Intervals Australia 0.549*** [0.502, 0.595] Finland 0.183*** [0.147, 0.218] Latvia 0.655*** [0.567, 0.743] Mexico 0.695*** [0.615, 0.775] Portugal 0.694*** [0.647, 0.741] Singapore 0.454*** [0.419, 0.488] Spain 0.412*** [0.369, 0.455] Romania 0.355*** [0.232, 0.478] N 5196

Note. 95% confidence intervals in brackets. * p < 0.05, ** p < 0.01, *** p < 0.001.

Then, I generated the means of the overall ICT use and lastly, I examined the proportions

of ICT use for the five ICT tasks. Table 4 displays the predicted probabilities and 95% confidence

intervals of overall ICTUSE across the countries. Notably, teachers in Mexico and Portugal had a

69% probability of using ICT in mathematics instruction. Teachers in Latvia and Australia

followed closely with 66% and 55% probabilities of ICT usage in mathematics instruction

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respectively. Teachers in Finland showed an 18% probability of using ICT in instruction which

was the lowest probability out of the eight countries. After examining the probabilities of ICT use,

I generated the overall mean for ICT use for the eight countries, M = 1.05, SE = 0.05. Figure 2

shows the means for overall ICT use across the eight countries.

Figure 2. Means of Overall ICTUSE Across the Countries.

To further examine how teachers used ICT in mathematics instruction, I examined the

proportions of mathematics teachers’ ICT use of the five ICT tasks across the eight countries as

shown on Figure 3. With the exception of Singapore, the highest proportion of mathematics

teachers in seven out of the eight countries reported using internet resources compared to

incorporating ICT for drill and practice activities, topic-specific activities, spreadsheets/data

analysis activities, and assessments. Most of the mathematics teachers in Singapore reported

incorporating ICT for drill and practice followed by teachers in Australia, while teachers in Finland

had the lowest proportion using drill and practice and lowest proportions of using ICT across all

the tasks.

0 0.5 1 1.5 2

PortugalLatvia

MexicoAustralia

SingaporeSpain

RomaniaFinland

Means

ICTUSESUM

ICTUSESUM

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Figure 3 shows teachers in Spain and Australia reported almost equivalent ICT use for

topic-specific activities and drill and practice in mathematics instruction in each country. Teachers

in Portugal reported the highest proportion of ICT for topic-specific activities, two times as much

as teachers’ in Australia (21%) and Mexico (20%). More teachers in Portugal reported using ICT

for spreadsheets/data analysis and assessment purposes in instruction compared to fewer teachers

in Romania and Finland. Overall, there was variability in teachers’ ICT use across the countries

and across the various ICT tasks. The Chi-square tests showed the general relationship between

ICT task and country was statistically significant (p < 0.001) for drill and practice, topic-specific

activities, assessment and internet resources, and also significant for spreadsheets/data analysis

activities (p < 0.01).

No apparent trends emerged from countries (i.e., Australia, Latvia, Mexico and Portugal)

reporting higher overall ICT use in mathematics instruction. Overall, teachers used ICT across and

within the four countries in different ways. For example, in Mexico, the proportions of ICT use

for internet resources was four times the usage for drill and practice, spreadsheets/data analysis

and assessment but three times that of topic-specific activities. Comparatively, mathematics

teachers in Australia reported similar proportions of ICT use for drill and practice and topic-

specific activities. Teachers in Australia also reported using internet resources two times as much

as ICT for spreadsheet/data-analysis activities. This shows that ICT use can vary with context and

task, and among teachers. In summary, in examining the proportions of ICT use, besides teachers’

use of internet resources, the findings confirmed my hypothesis about limited ICT use in

mathematics instruction. This also confirms the literature that ICT continues to remain in the

periphery in instruction for critical subjects such as mathematics.

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Figure 3. Proportion of ICT Use Across Countries by Task.

Additionally, I hypothesized that the frequency of ICT use would vary among the countries and

this was also confirmed in the study. There were mean differences in overall ICT use among the

countries some countries reporting higher overall means of ICT use. For instance, teachers in

Finland had the lowest mean while teachers in Portugal had the highest mean. Comparatively,

teachers in Mexico and Latvia showed similar levels of ICT use. These differences in ICT use

could be attributed to policies about ICT in teaching, teachers’ professional qualifications,

instructional practices, beliefs, and attitudes towards ICT use.

Research Question Two: Teachers’ Professional Qualifications and ICT Use

Scholars have attributed ICT use to teacher’ characteristics, such as professional

qualifications (e.g., education levels, teacher training). To examine the teachers’ professional

qualifications that predicted ICT use, I fitted a separate model for each ICT task and conducted

0

10

20

30

40

50

60

70

%

Proportion of ICT Use

Drill & Practice

Topic-specific

Spreadsheets or Data analysis

Assessment

Internet Resources

69

logistic regression analyses. An odds ratio of one represents the probability of teachers’ ICT use

compared to the reference category for each predictor. For instance, the reference category for

teacher training includes teachers who did not receive teacher training. Table 5 shows the teachers’

professional qualifications as predictors and the odds ratios of ICT use across all the five ICT tasks.

Education level was a significant predictor for ICT in drill and practice, spreadsheets/data analysis

activities, and assessment. Mathematics teachers with ISCED education levels 5a and 6 had

statistically significant higher odds of using ICT in these activities compared to mathematics

teachers with ISCED education levels of 5b or lower (p < 0.05). In contrast, teachers with more

than 10 years teaching experience had lower odds of using ICT for drill and practice and

assessment purposes in mathematics instruction. Teachers with teacher training and five to ten

years of teaching experience had higher odds of using ICT for drill and practice and topic-specific

activities compared to mathematics teachers without teacher training and less than five years of

teaching experience respectively. These odds were not statistically significant (p > 0.05) as shown

in Table 5.

Professional qualifications were not associated with teachers’ ICT use for topic-specific

activities. Teachers without teacher training had higher odds of using spreadsheets/data analysis

activities in instruction (p < 0.001). However, mathematics teachers with more than ten years of

teaching experience and technology training had higher odds of using spreadsheets/data analysis

activities but the odds were not statistically significant (p > 0.05). Also, mathematics teachers with

ISCED education levels 5a and 6 had statistically higher odds of using ICT for assessment

purposes compared to teachers with ISCED education levels of 5b or lower (p < 0.05). However,

the odds of using ICT for assessment purposes among teachers based on teaching experience,

teacher training, and technology training were non-significant (p > 0.05).

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Table 5. Logistic Regression Results in Odds Ratios for Model Including Teachers’ Professional Qualifications Predictors of ICT Use Drill and

practice activities

Topic specific

activities

Spreadsheets/Data analysis activities

Assessment activities

Internet Resources

Constant 0.121** 0.398 0.169** 0.189** 0.624 Education 2.685* 0.446 4.413** 3.292* 1.234 (5a or 6) Teacher training 1.127 1.887 0.239*** 0.554 1.002 Teaching exp. (ref = less than 5 years)

5-10 years 1.209 1.021 0.825 0.622 0.534* More than 10 years

0.739 0.938 1.045 0.613 0.656*

Technology training

0.713 0.645 1.252 0.968 1.117

N 4934 4936 4923 4930 4941

Note. All models controlled for country effects. * p < 0.05, ** p < 0.01, *** p < 0.001. Overall, only education levels were associated with teachers’ ICT use for assessment

purposes. Comparatively, mathematics teachers with ISCED education levels (i.e., 5a and 6) and

technology training had higher non-significant odds of using ICT for internet resources compared

to mathematics teachers with lower education levels without technology training (p > 0.05). Lastly,

teachers with five or more years of teaching experience had statistically significant lower odds of

using internet resources in mathematics instruction (p < 0.05). In contrast, in the full model (see

Appendix B), teachers’ education levels were a significant predictor for teachers’ use of ICT for

topic-specific activities, spreadsheets/data-analysis, and use of internet resources. The findings

about years of teaching experience and ICT use did not confirm my hypothesis that years of

teaching experience contributes to teachers’ ICT use.

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In summary, education levels were associated with mathematics teachers’ use of ICT for

drill and practice, spreadsheets/data analysis, and assessment in instruction. Additionally, teachers

with more than ten years of teaching experience had lower odds of using ICT for internet resources

in mathematics instruction compared to teachers with less than five years of teaching experience

(p < 0.05). Lastly, teacher training and technology training was not associated with teachers’ ICT

use across the five tasks (i.e., drill and practice, topic-specific, spreadsheets/data analysis,

assessment, internet resources).

Research Question Three: Teachers’ Instructional Approaches, Beliefs and ICT Use

In addition to teacher professional qualifications, scholars in the field have highlighted that

teaching practices such as constructivist approaches and beliefs may contribute to high ICT use.

To examine teacher instructional approaches and beliefs associated with mathematics teachers’

ICT use in instruction, I fitted a separate model for each ICT task and conducted logistic regression

analyses. Table 6 shows teachers’ instructional practices and beliefs (i.e., mathematics self-

efficacy, constructivist goals and views about mathematics) predictors for ICT use and the odds

ratios from the regression analyses.

On average, higher teachers’ mathematics self-efficacy predicted high odds of teachers’

ICT use in instruction for spreadsheets/data analysis activities as shown on Table 6 (p < 0.05).

This finding confirmed my hypothesis that teachers’ mathematics self-efficacy contributes

positively to teachers’ ICT use. However, after controlling for all the variables in the full model

(see Appendix B), self-efficacy was not a significant predictor of teachers’ ICT use for any of the

tasks. I also hypothesized that teachers with high self-efficacy in mathematics used ICT for high-

level (e.g., topic-specific activities, spreadsheets/data analysis) tasks during mathematics

instruction while teachers with lower mathematics self-efficacy used ICT for low-level tasks (e.g.,

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drill and practice). This hypothesis was not supported in the findings as teachers’ mathematics

self-efficacy was only associated with teachers’ ICT use of spreadsheets/data-analysis in

instruction. Teachers’ constructivist beliefs were not associated with teachers’ use of ICT in

instruction as shown on Table 6. These findings partially supported my hypotheses that

constructivist beliefs may result in mixed findings predicting both high and low odds of teachers’

ICT use in instruction. The results were different for constructivist teaching practices. Teachers

who incorporated constructivist teaching practices had higher odds of using ICT across in four out

the five tasks (p < 0.05).

Table 6. Logistic Regression Results in Odds Ratios for Model Including Teachers’ Instructional Approaches and Beliefs Predictors of ICT Use Drill and

practice activities

Topic specific

activities

Spreadsheets/data analysis activities

Assessment activities

Internet Resources

Constant 0.003*** 0.002*** 0.379 0.048** 0.170 Math self-efficacy

1.069 1.007 1.121* 1.072 1.043

Constructivist beliefs

0.991 0.991 1.040 1.069 1.053

Constructivist teacher practices

4.322*** 2.696*** 2.352 4.120*** 2.086**

Constructivist goals and views about math

0.995 1.887 0.226** 0.300* 0.552

N 5119 5119 5108 5116 5128

Note. All models controlled for country effects. * p < 0.05, ** p < 0.01, *** p < 0.001.

Interestingly, stronger constructivist teaching practices predicted up to four times the odds

of teachers’ ICT use for drill and practice and assessment purposes, and more than two times the

odds of teachers’ ICT use for topic-specific activities and internet resources. In the full model (see

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Appendix B), constructivist teaching practices were associated with teachers’ ICT use across the

five tasks. In this model, teachers’ constructivist goals and views about mathematics was not

associated with teachers’ use of ICT for topic-specific activities. However, in the full model when

controlling for all the other variables the odds were statistically significant (p < 0.05).

Research Question Four: Teachers’ ICT Use and Students’ Characteristics

Classrooms have diverse students characterized with different achievement levels,

strengths, and backgrounds. This student diversity may result in varying demands on available

resources (e.g., ICT), and may impact teaching practices. Teachers may use tools such as ICT

differentially among students. For instance, researchers have noted that schools with large numbers

of students from low socioeconomic communities use ICT for low cognitive tasks (e.g., skills

practice) compared to high cognitive tasks (e.g., creativity) in schools with large numbers of

students from high socioeconomic communities (Reinhart et al., 2011). To this end, I examined

teachers’ ICT use in classrooms with different students’ characteristics (i.e., mathematics

achievement levels, low socioeconomic status, special education needs). First, I examined

descriptive statistics of proportions of ICT use and reported the unadjusted results without any

controls. This included teachers’ proportions of ICT use across the five ICT tasks for each student

characteristic. Second, to examine the students’ characteristics associated with teachers’ ICT use

at the classroom level, I fitted a separate model for each ICT task in my logistic regression analyses

and included the students’ characteristics as predictors.

Mathematics Levels

To begin, I examined proportions of ICT use across the five ICT tasks among the four

categories of classes with students varying mathematics achievement levels using the Chi-square

test of independence. The four categories were (a) mostly high achieving, (b) mostly average

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achieving, (c) mostly low achieving, (d) approximately equal numbers of high, low and average

achieving. These categories represent the percentage of students in the different mathematics

achievement levels in the classroom.

I found that 17% of the teachers reported incorporating drill and practice in similar

proportions in classes with students who were mostly high and average achieving as shown in

Figure 4.

Figure 4. Proportions of ICT Use for Students with Varying Mathematics Achievement Levels.

This pattern was also evident for classes with students that were mostly low-achieving, and

classes with approximately equal numbers of students who were high, average and low achieving.

In summary, on average, most teachers incorporated internet resources across the four categories

of students with varying mathematics achievement levels followed by ICT for topic-specific

0

10

20

30

40

50

60

Drill & Practice Topic-specific Spreadsheets or Data analysis

Assessment Internet Resources

%

Proportion of Teachers' ICT Use Across Math Levels

Mostly high achieving

Mostly average achieving

Mostly low achieving

Approx. equal numbers of high, average, and low achieving

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activities. The relationship between students’ mathematics levels and teachers’ ICT use was only

statistically significant for students’ assessments, 𝜒2(3) = 66.17, p < 0.001.

Socioeconomic Status

Using the Chi-square test of independence, I examined proportions of ICT use across the

five tasks. The five categories were (a) none, (b) 1% to 10%, (c) 11% to 30%, (d) 31% to 60%, (e)

more than 60%. These categories represent the percentage of students in the classroom with

socioeconomic status.

Mathematics teachers incorporated ICT more frequently in classes with students with low

socioeconomic status. This pattern was evident across the five tasks as shown on Figure 5. For

topic-specific activities, teachers reported almost similar proportions in classes with students

across four categories. Teachers in classes with 31% to 60% of students with low socioeconomic

status incorporated ICT for mathematics instruction across the various tasks most frequently.

Additionally, on average, compared to ICT for drill and practice, topic-specific activities,

spreadsheets/data analysis and assessment, more teachers incorporated ICT when using internet

resources across the four low socioeconomic categories. Notably, the frequency of use of internet

resources increased with increasing percentages of students with low socioeconomic status. Fewer

teachers used ICT for all the tasks in classes without students with socioeconomic status (i.e. high

socioeconomic status), where 8% incorporated drill and practice, 16% incorporated ICT for topic-

specific activities, 6% incorporated ICT for spreadsheets/data analysis, 10% incorporated ICT for

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Figure 5. Proportions of ICT Use Among Students with Low Socioeconomic Status.

assessment purposes, and 35% incorporated internet resources. I examined the Chi-square results

and found that in general, the relationship between students’ socioeconomic status and teachers’

ICT use was statistically significant for drill and practice, spreadsheets/data analysis, and internet

resources (p < 0.05).

Special Education Status

Further, I examined proportions of teachers’ ICT use across the five tasks among classes

with varying percentages of students identified with special education needs using the Chi-square

test of independence. The five categories were (a) none, (b) 1% to 10%, (c) 11% to 30%, (d) 31%

to 60% (e) more than 60%. These categories represented the percentage of students identified with

special education needs in the classroom.

0

10

20

30

40

50

60

Drill & Practice Topic-specific Spreadsheets or Data analysis

Assessment Internet Resources

%

Proportion of Teachers' ICT Use Across Percentages of Students with Socioeconomic Status

None

1% to 10%

11% to 30%

31% to 60%

more than 60%

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Figure 6. Proportions of ICT Use Among Students with Special Education Status.

On average, besides internet resources, less than 20% of the mathematics teachers

incorporated ICT in instruction across the four categories of students with special education status,

and this frequency was similar in classes without students with special education status. Also, as

shown on Figure 6, more teachers incorporated ICT when using internet resources across the five

categories of students with special education status compared to ICT for drill and practice, topic-

specific activities, spreadsheets/data analysis and assessments.

Except for assessment purposes, fewer teachers used ICT in instruction in classes where

more than 60% of students were identified with special education needs. I examined the Chi-square

test of independence results and found that in general, the relationship between the students’

special education status and teachers’ ICT use was statistically significant for drill and practice,

spreadsheets/data analysis and internet resources (p < 0.05). Next, I report the odds ratios from the

0

10

20

30

40

50

60

70

Drill & Practice Topic-specific Spreadsheets or Data analysis

Assessment Internet Resources

%

Proportion of Teachers' ICT Use Across Percentages of Students with Special Education Status

None

1% to 10%

11% to 30%

31% to 60%

more than 60%

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logistic regression analyses examining the relationship between students’ characteristics and

mathematics teachers’ ICT use across the five tasks.

Mathematics Levels, Low Socioeconomic Status, Special Education Status

For a more sophisticated analysis of students’ characteristic predictors of mathematics

teachers’ ICT use, I fitted a separate model for each task (drill and practice, topic-specific,

spreadsheets/data analysis, assessment and internet resources) and conducted logistic regression

analyses for the five tasks. These logistic regression analyses examined whether classroom

composition of students predicted teachers’ ICT use in mathematics instruction. The classroom

level variables for student characteristics included mathematics achievement levels, special needs

status, and socioeconomic status.

To examine teachers’ ICT use, I controlled for students’ mathematics levels, special needs

status, and socioeconomic status. On average, controlling for socioeconomic and special education

status, teachers had lower odds of incorporating ICT for drill and practice in classes where students

were low achieving, and in classes with approximately equal numbers of students who were high,

low, and average achieving in mathematics. The odds were lowest but not statistically significantly

different in classes where students were mostly low achieving in mathematics (p > 0.05). This

finding was not consistent with my hypothesis that teachers were more likely to use ICT for drill

and practice in classes with students who were low achieving in mathematics. However, the

findings were consistent with my hypothesis that teachers had higher odds of using ICT for high-

level tasks (e.g., topic-specific) with students who were high achieving as shown in Table 7.

Comparatively, teachers incorporated ICT for drill and practice in classes where more than

30% of the students had low socioeconomic status compared to classes having students with high

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socioeconomic status as shown in Table 7 (p < 0.01). These trends were similar with topic-specific

activities, except in this case, the odds were not statistically significant (p > 0.05).

Table 7. Logistic Regression Results in Odds Ratios for Model Including Students’ Characteristics as Predictors of Teachers’ ICT Use Drill and

practice activities

Topic specific

activities

Spreadsheets/data analysis activities

Assessment activities

Internet Resources

Constant 0.179*** 0.303*** 0.129*** 0.140*** 0.416*** Math Levels (ref = High)

Mostly average

1.170 0.534** 0.866 1.316 0.928

Mostly L. A 0.645 0.299*** 0.551 0.541* 0.623* Approx. H. A, Av., & L. A

0.700 0.617 0.374* 0.768 0.957

Socioeconomic (ref = High)

1% to 10%

1.799** 1.514 2.601* 1.589 1.429

11% to 30%

1.797* 1.697 1.099 1.375 1.880*

More than 30% 2.926*** 2.673 2.503* 2.399* 1.648 Special Ed. (ref = None)

1% to 10%

1.047 1.052 1.242 1.429 1.197

11% to 30%

0.742 0.984 0.839 1.030 0.926

More than 30% 1.192 0.435 0.542 1.298 1.502 N 5064 5066 5053 5062 5071

Note. All models controlled for country effects. L.A = low achieving; H.A = high achieving; Av. = average achieving; Special Ed. = special education. * p < 0.05, ** p < 0.01, *** p < 0.001.

These findings confirmed my hypothesis that having students with low socioeconomic

status in classrooms was associated with teachers’ ICT use for low-level tasks (e.g., drill and

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practice). However, the findings did not confirm my hypothesis that having students identified for

special education in classrooms was associated with teachers’ ICT use for similar tasks.

The findings from the logistic regression analyses provided nuanced details of teachers’

ICT use across the five tasks and in classes with student heterogeneity. For instance, mathematics

teachers had lower odds of using ICT for spreadsheets/data analysis and internet resources in

classes composed of students with varying mathematics levels compared to classes where students

were mostly high achieving in mathematics. In contrast, controlling for students’ mathematics

levels, teachers had statistically higher odds of incorporating ICT for spreadsheets/data analysis in

classes with students with low socioeconomic status. Specifically, having classrooms with 1% to

10% of students with low socioeconomic status was associated with teachers’ ICT use for

spreadsheets/data analysis (p < 0.05). Also, this was the case with more than 30% of the students

with low socioeconomic status (p < 0.01).

These trends were similar with teachers incorporating ICT for student assessment as shown

in Table 7. However, only classrooms with more than 30% of the students with low socioeconomic

status was associated with teachers’ ICT use for assessment. For internet resources, controlling for

mathematics levels, teachers had statistically higher odds of incorporating internet resources in

instruction in classrooms where 11% to 30% of the students had socioeconomic status compared

to classes without students with socioeconomic status (p < 0.001). In the full model (see Appendix

B), some of the trends were similar, for instance, controlling for all the variables, (a) Having 1%

to 10% of students identified with low socioeconomic status was associated with teachers’ ICT

use for drill and practice (p < 0.05), (b) Having more than 30% of the students identified with low

socioeconomic status (p < 0.05) was associated with teachers’ ICT use for drill and practice, topic-

specific activities and assessments.

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In summary, controlling for mathematics levels and special education status, low

socioeconomic status was associated with mathematics teachers’ ICT use across three out of the

five tasks (p < 0.05). Also, teachers had high odds of ICT use mainly for classes where 1% to 10%

of the students had special education status. Additionally, teachers had higher odds of using ICT

for drill and practice activities, assessment purposes, and internet resources in classes with more

than 30% of the students identified with special education status. However, having students with

special education needs in classrooms is not associated with teachers’ ICT use in instruction (p >

0.05).

Research Question Five: School Contexts and Teachers’ ICT use

Teachers work in classrooms that exist in a larger school context. Furthermore, the

sociocultural environment that includes multiple stakeholders (e.g., administrators, colleagues,

policy makers) impacts teaching practices and requires consideration when analyzing teachers’

ICT use. To examine how school contexts predicted mathematics teachers’ ICT use for drill and

practice activities, topic-specific activities, spreadsheets/data analysis, assessment purposes and

internet resources in instruction, I conducted logistic regression analyses to examine school context

predictors (i.e., teacher cooperation, participation in decision making, collaborative culture,

administrative support) on teachers’ ICT use. The results in odds ratios are shown on Table 8.

Teacher cooperation was associated with ICT use in mathematics instruction in three out

of the five ICT tasks (p < 0.05). Comparatively, teachers’ participation in school decision making,

presence of a collaborative culture, and administrative support were not associated with teachers’

ICT use as shown in Table 8 (p > 0.05). Although these results confirmed my hypothesis that

school climate (e.g., teacher cooperation) is a strong predictor of teachers’ ICT use, the results

show that different factors attributed to school climate may increase or decrease the odds of

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teachers’ ICT use in mathematics instruction. These findings on school contexts and teachers’ ICT

use were closely mirrored in the full model (see Appendix B), where teacher cooperation was

associated with teachers’ ICT use for two out of the five ICT tasks (i.e., drill and practice, internet

resources).

Table 8. Logistic Regression Results in Odds Ratios for Model Including School Context Predictors of ICT Use Drill and

practice activities

Topic specific

activities

Spreadsheets/data analysis activities

Assessment activities

Internet Resources

Constant 0.032*** 0.142** 0.056** 0.026*** 0.137* Teacher Cooperation

1.222* 1.130 1.093 1.205* 1.189**

Decision Making

1.377 1.292 1.982 1.493 1.397

Collaborative Culture

0.808 1.018 0.890 0.576 1.082

Administrative Support

0.934 0.358** 0.883 1.517 0.427**

N 5052 5053 5043 5051 5061

Note. All models controlled for country effects. * p < 0.05, ** p < 0.01, *** p < 0.001.

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CHAPTER FIVE

Discussion

This dissertation study includes teachers from eight countries: Australia, Finland, Latvia,

Mexico, Portugal, Romania, Singapore, and Spain. Similar to the United States, six of the eight

countries are considered high-income countries and ranked higher on the PISA 2012 mathematics

assessment. Mexico and Romania are considered low-income and ranked lower on the same

assessment (OECD 2012; World Bank, 2012). For the study design, the PISA requirement for

sample sizes is 150 schools for each country. Therefore, the number of schools sampled in each

country for the TALIS-PISA link (i.e., including the mathematics module) ranged from 150 to

316. In Finland, Latvia, and Romania, 150 schools were sampled whereas 316 schools were

sampled in Spain. The purpose of the mathematics questionnaire in the TALIS-PISA was to

provide information about teaching practices at the classroom level. To complete the mathematics

questionnaire, participating teachers identified a target class of 15-year-olds as a reference.

The analytic sample is comprised of teachers from schools based on geography (rural,

urban), source of funding (private, public), type of educational programme, and school size

(OECD, 2013). Specifically, participating teachers in Australia were sampled from schools in six

states, two territories and three sectors (Catholic, government, Independent). Participating teachers

in Finland were sampled from schools in four regions (South, North, East, Swedish speaking).

Participating teachers in Latvia were sampled from three school types (basic – grades 1-6 or 1-9;

secondary – grades 1-12; gymnasium – grades 1-12 or 7 -12), and urbanizations (Riga, cities,

towns, rural). Participating teachers in Mexico were sampled from schools based on two sources

of funding (private, public), and two streams (general, technical). Participating teachers in

Romania were sampled from schools in two urbanizations (rural, urban). Participating teachers in

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Portugal were sampled from schools based on sources of funding (public, private), and four regions

within public schools. Participating teachers in Singapore were sampled from public schools.

Participating teachers in Spain were sampled from 18 autonomous communities.

This study examined how teachers used ICT in mathematics instruction across eight

countries to develop a richer understanding about ICT use that informs policy development, and

professional development programs in similar contexts. All over the globe, from the African

continent to the shores of South America, investments in technology have led to the proliferation

of ICT in classrooms. These developments are not surprising as ICT continues to revolutionize

daily living whether it is engagement in commerce, learning, or communication. Specifically, ICT

in the field of education has long been championed by many (e.g., politicians, administrators,

private investors) as a viable channel to revitalize and transform teaching and learning (Murphy,

2016). However, the peripheral use of ICT in classrooms and the documented digital divide related

with ICT use are two issues that warrant an intentional evaluation of teachers’ ICT use in

instruction (Cuban, 2013; Warschauer, 2007).

Beyond access to ICT, superficially defined as the presence of hardware in the classroom,

it is imperative for all stakeholders (e.g., policymakers, teachers, administrators, parents) to

develop a rich understanding on how teachers incorporate ICT in teaching to better evaluate the

role of ICT on student learning. Over the years, the number of computers in the classroom was

tantamount to successful technology integration (Fullan, 2016). However, considering the

significant financial investments in ICT, a shift is needed towards assessing the impact of ICT on

student learning experiences. Prior to making this assessment, it is imperative for stakeholders to

examine how teachers use ICT in teaching, and specifically in critical content areas such as

mathematics. Furthermore, understanding the ways that teachers use ICT in instruction and the

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predictors that impact ICT use can provide insights into policy development. Scholars in the field

have identified and documented factors that predict teachers’ ICT usage. These include teacher

professional development, social and organizational culture, teacher beliefs, and school leadership

(Ertmer & Ottenbreit-Leftwich, 2010, Frank et al., 2011, Fullan, 2016, Hughes, 2005).

Additionally, elements such as compatibility with current practices and beliefs, complexity with

adopting innovative practices (e.g., ICT), affordances from the innovation and the role of time in

implementing the innovation are all factors that impact the uptake of innovations (Rogers, 1995).

Key Findings

Research Question One: ICT Use Among the Countries

The results show that teachers in the eight countries used ICT in mathematics instruction

with varying frequency. Given the likely differences among the countries on aspects such as role

of ICT on teaching and learning or governments’ initiatives in different countries, such differences

in teachers’ ICT use are plausible. For instance, although teachers in Finland and Singapore did

not show high overall ICT use, they continue to rank highly and show excellent performance on

international assessments such as the PISA (OECD, 2012). On the other hand, teachers in countries

further down the international rankings on the PISA, for instance, Latvia, Mexico, and Portugal

reported higher ICT use. Taking Portugal as an example, in 2008, the government distributed about

half a million computers to schools, free educational materials, and enhanced internet connection

in an attempt to increase educational opportunities, particularly in developing computer literacy

for all students (Bugge, 2008). The project referred to as the Magellan project was heralded as one

that would transform learning along with long term benefits for society (Loureiro, Linhares, &

Ramos, 2012, September; Trucano, 2012). Trucano further highlighted that the Portuguese

government tapped into private-public partnerships to scale this project and distribute potential

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risks. Overall, a large vision and the ecosystem approach that involved stakeholders beyond the

classroom provided great leverage for ICT integration in Portugal (Trucano, 2014). Without

drawing any causal inferences, these initiatives can impact teachers’ ICT use and contribute to

differences among countries. However, what remains unclear is whether the proliferation of ICT

in classrooms, for instance in Portugal, subsequently contributed to transformational instructional

practices using ICT (Loureiro et al., 2012).

Regarding specific ICT tasks, mathematics teachers used internet resources more than ICT

for drill and practice, topic-specific activities, spreadsheets/data analysis and assessment purposes

in mathematics instruction. This could be attributed to the generic nature of internet resources, that

would provide teachers with versatile options when using ICT in mathematics instruction. Internet

resources could provide students with opportunities and tools to search for information, translate

unfamiliar languages, look up and view video clips demonstrating mathematics problem solving

or collaboration with experts and students in local and global settings. These unlimited affordances

may explain high usage of internet resources in mathematics instruction across all the eight

countries.

Second to internet resources, mathematics teachers in the eight countries frequently

incorporated ICT for topic-specific activities in instruction and assessment. Topic-specific

software can provide teachers and students with appropriate technological tools when learning

concepts such as algebra, making the learning more interactive and visual. ICT can offer a variety

of assessments that provide students and teachers with real-time feedback that can inform teaching

practices. Therefore, it is encouraging to see that mathematics teachers used ICT for student

assessment because it is imperative for teachers to evaluate student progress especially in

mathematics.

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Research Question Two: Teachers’ Professional Qualifications and ICT Use

The one professional qualification that predicted high ICT use was teachers’ education

level. High education levels (i.e., 5a or 6) was associated with teachers’ ICT use except for topic-

specific activities and use of internet resources. One plausible reason that may have contributed to

education levels’ impact on teachers’ ICT use is exposure to technology during the education

experience. Additionally, teachers with higher education levels may have incorporated ICT in

instruction as a result of the relative advantages offered through ICT (Rogers, 1995). These

advantages include providing students with opportunities to practice earlier learned mathematics

concepts using a medium other than paper and pencil, organizing statistical information using

spreadsheets and visually conducting data analysis. Additionally, students can complete formative

assessments that provide teachers with feedback on student progress. Higher education levels were

not associated with teachers’ use of internet resources. Arguably, incorporating internet resources

may not require teachers to have different education levels to accomplish teaching and learning

objectives. However, mathematics teachers with lower education levels had higher odds of

incorporating topic-specific software in instruction. A connection between education levels and

teachers’ ICT use for topic-specific activities would seem to be directly related, that is, higher

education levels would increase the teachers’ odds of using ICT for certain mathematical topics;

this was not the case in the current study. This could be because teachers with education levels of

5a and below may have acquired information about incorporating ICT in these activities from

different sources or through personal exploration and learning.

It was unsurprising that teacher training was not associated with teachers’ ICT use. This

finding is plausible especially if the training is not focused on teachers integrating ICT in

mathematics instruction. Scholars (e.g., Ertmer & Ottenbreit-Leftwich, 2010; Hughes 2005) have

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argued that teachers may require awareness of one’s beliefs about teaching and learning before

any change can occur. Also, teachers may need specific knowledge to successfully incorporate

ICT in teaching. Scholars, Koehler and Mishra (2009) argued that often teachers conceptualize

technology knowledge separate from the content and pedagogy knowledge and this subsequently

leads to a fragmented understanding and practice of technology knowledge instead of the

combination of technology knowledge, content knowledge and pedagogy knowledge

synergistically. To effectively and strategically integrate ICT in teaching and learning, teachers

can benefit from professional development that meshes content and pedagogy using ICT where

pedagogy and content drives ICT use.

Relatedly, the findings show that technology training predicted higher odds of mathematics

teachers’ ICT use for spreadsheets/data analysis, and internet resources. A plausible reason for the

higher likelihood of teachers’ use of ICT for spreadsheets/data analysis is the specialized nature of

this ICT task requiring a certain level of technology training. On the other hand, the versatility

provided through internet resources may have contributed to the increased likelihood of teachers’

using these internet resources in instruction. However, the findings showed that these odds were

not different from teachers without technology training. Overall, this study shows education levels

as the only professional qualification associated with teachers’ ICT use in mathematics instruction.

Researchers have documented that teachers with teaching experience and a strong grasp of

content pedagogy are more likely to use ICT in ways that provide students with opportunities to

participate in high-level tasks (Hughes, 2005). However, the findings in this study show that years

of teaching experience was not associated with teachers’ use of ICT in instruction. This finding

could be attributed to the experiences teachers had when using ICT in the past. Considering that

comparatively teachers with fewer than five years of teaching experience had higher odds of

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incorporating ICT in instruction for spreadsheets/data-analysis, assessment and internet resources,

the novelty of ICT use may incentivize these teachers to incorporate ICT more, compared with

teachers with more years of teaching experience.

In the technology, pedagogy, content, and knowledge (TPACK) framework, Koehler and

Mishra (2009) provide the various kinds of teacher knowledge required for effective ICT

integration. These include content knowledge (CK), pedagogical knowledge (PK), pedagogical

content knowledge (PCK), technology knowledge (TK), technology pedagogical knowledge

(TPK), and technology content knowledge (TCK). Examining teachers’ ICT use across the various

tasks using the TPACK framework can provide further insights on knowledge skills that teachers

are likely to demonstrate in mathematics instruction. According to Koehler and Mishra, TK refers

to knowledge about the technology and its affordances; CK refers to knowledge about the subject

matter; PK refers to a teacher’s understanding of students’ thinking and learning; PCK refers to a

teachers’ understanding of the interaction between the content and the methods of teaching the

content; TPK refers to a teacher’s understanding of ways of using the technology with specific

content while integrating appropriate methods to promote learning; TPACK refers to a deeper

understanding of the interactions, and constraints among the three domains of knowledge in order

to effectively integrate, and harness ICT in instruction. The TPACK framework can provide a lens

to examine ways in which the different constructs interact in instructional practices (Agyei &

Voogt, 2012; Graham, Borup, & Smith, 2012). Scholars (e.g., Bos, 2011) have argued that when

integrating ICT in mathematics instruction, teachers require cognitive awareness of TPACK as a

result of the cognitive demands required, for instance in teaching abstract mathematical concepts

with ICT.

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In this study, out of the five ICT tasks, certain tasks could be considered more content-

specific than others, requiring specific teacher knowledge for effective integration in instruction.

For instance, teachers who incorporated ICT for topic-specific activities and spreadsheets/data-

analysis, which relate directly to the mathematics content area, and involve making abstract

mathematical concepts concrete, required TPACK. On the hand TK is required when teachers

incorporate ICT for drill and practice, or assessment activities because the primary focus was on

students practicing or demonstrating learned skills (Graham et al., 2012; Kaput, Hegedus, & Lesh,

2007). When incorporating ICT for assessment purposes, teachers may have required PK as they

used the assessment data to evaluate student progress. Lastly, in this study, teachers used internet

resources most frequently mainly due to the breadth of affordances (e.g., drill and practice,

assessment, interactive activities) available through the internet. It is likely that teachers

demonstrated TPK when using internet resources as this construct captures a wide range of

knowledge skills and general teaching strategies (Graham et al., 2012; Hughes, 2013).

Research Question Three: Teachers’ Instructional Practices, Beliefs and ICT Use

Mathematics self-efficacy and constructivist teaching practices were significant predictors

of ICT use. In Overbay et al. (2010) teachers who reported constructivist teaching practices also

reported high ICT use. Additionally, research studies have documented that self-efficacy

contributes to teachers’ ICT use in instruction (Bauer & Kenton, 2005; Letwinsky, 2017; Wozney,

Venkatesh, & Abrami, 2006). Therefore, it is plausible that teachers with a strong mathematics

self-efficacy had confidence in using ICT and were willing to take risks in using ICT for

mathematics instruction and transferred this risk taking to their students, allowing students to use

ICT in their learning, for instance in spreadsheets/data analysis activities. One can argue that

mathematics self-efficacy and constructivist teaching practices share overlapping attributes by

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virtue of the fact that the teacher is engaging the student in learning and facilitating the construction

and development of mathematics knowledge.

Also, constructivist teaching practices were associated with mathematics teachers’ ICT use

in four out of the five tasks, but constructivist beliefs were not associated with teachers’ ICT use.

Scholars in the field have highlighted discrepancies between teachers’ constructivist beliefs and

corresponding teaching practices using ICT. Evidently, results from this study showed that

constructivist beliefs predicted higher odds for teachers’ ICT use in instruction for

spreadsheets/data-analysis, assessment activities and use of internet resources but these odds were

statistically non-significant. So, although the teachers reported having constructivist beliefs and

had higher odds of incorporating ICT in these activities, these odds could be attributed to chance

and not as a product of teachers’ constructivist beliefs.

In contrast, teachers’ constructivist teaching practices (i.e., (a) expected students to explain

their thinking on complex problems, (b) connected mathematics concepts from the classroom to

uses of concepts outside of school, (c) encouraged students to solve problems in more than one

way, and (d) required students to provide written explanations of how they solve problems), was

associated with ICT use across four of the five ICT tasks. Rogers (1995) defined compatibility as

the level at which the innovation fits with an individual’s current beliefs, knowledge levels, or

experiences. It may be that teachers who incorporated constructivist teaching practices found

compatibility with using ICT in instruction (Ertmer et al., 2012). In addition to compatibility, a

teachers’ perception of the usefulness of the ICT can lead to incorporation of the ICT in instruction

(Overbay et al., 2010). At the same time, a closer look at the odds ratios shows that teachers’

constructivist practices were associated with teachers’ use of ICT for drill and practice, topic-

specific activities, assessment, and internet resources. This finding closely mirrors Lim and Chai’s

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(2008) finding where teachers with constructivist beliefs incorporated ICT for drill and practice

when preparing students for high-stakes examination as students practiced already learned skills.

Additionally, this finding suggests that mathematics teachers incorporated ICT for various tasks,

including, promoting practice opportunities, and monitoring student progress, both important

components of mathematics instruction. However, teachers’ use of ICT for drill and practice

promotes rote learning, instead of providing students with opportunities to develop high-order

skills (Hughes & Read, 2018). Therefore, it is necessary for teachers to intentionally examine their

use of ICT to ensure that students have rich learning experiences that prepare them with 21st

century skills.

Scholars (e.g., Niess, 2013) reported that teachers with constructivist beliefs and

constructivist teaching practices incorporated spreadsheets in mathematics instruction in student-

centered activities and these teachers were more likely to have a solid development of TPACK.

Although in this study constructivist beliefs and teaching practices were not associated with

teachers’ ICT use for spreadsheets/data analysis activities, these constructivist practices were

associated with teachers’ ICT use for topic-specific activities. Teachers who incorporated ICT for

topic-specific activities more likely had strong TPACK knowledge and confidence in using ICT

in mathematical related tasks.

Similar to constructivist beliefs, teachers’ constructivist goals and views about

mathematics in teaching predicted both high and low odds of ICT use. Constructivist goals and

views about mathematics in teaching predicted lower odds of ICT use for all the ICT tasks except

topic-specific activities. This discrepancy between teachers’ constructivist goals and views about

learning and ICT use may be as a result of the lack of a direct relationship between the goals and

views about mathematics and teachers using ICT. This heightened the discrepancy already present

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between constructivist beliefs and instructional practices with ICT. It is important to note that these

factors do not work in isolation but may be simultaneously present in a teachers’ repertoire and a

teacher may activate the beliefs and practices differentially depending on the content, context, and

the type of ICT. Additionally, scholars, Ling Koh, Chai, and Tay (2014) emphasized that teachers’

capacity for designing lessons using ICT as they navigate the teaching environment hinges on

teachers’ TPACK skills. The scholars further added that when contextual variables manifest as

barriers of ICT integration in the classroom, such as logistical aspects of class time, teachers are

less focused on the pedagogical aspects of teaching with ICT, and this, in turn, impacts how

teachers’ use ICT.

Research Question Four: Students’ Characteristics and Teachers’ ICT Use

The classroom level variables for student characteristics (i.e., mathematics achievement

levels, low socioeconomic status, special education needs) provided data on teachers’ classroom

compositions of students in terms of these aforementioned characteristics. Findings from this study

showed that having classrooms with varying percentages of students with low socioeconomic

status was associated with teachers’ ICT use across four out of the five tasks (i.e., drill and practice,

topic-specific, spreadsheets/data analysis, assessments, internet resources). Teachers were likely

to use ICT in instruction for drill and practice in classes where most students had low

socioeconomic status. A plausible reason is that drill and practice activities offered the students

opportunities to practice their skills and met the students’ mathematical needs. It is likely that

students with low socioeconomic status had limited access to ICT resources outside the school

environment and the availability of ICT resources at school provided students with a different

medium for learning mathematics. Arguably, teachers probably found ICT as a viable tool that

would benefit these students when teaching mathematics. However, teachers’ overreliance of drill

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and practice activities limits the students’ development of rigorous skills and exposure to creative

activities necessary to adequately participate in society, widening the digital divide (Hughes &

Read, 2018). Warschauer, Knobel, and Stone (2004) also argued that teachers in classrooms with

a large percentage of students with low socioeconomic status may experience requirements to

prepare students for high-stakes examinations by providing consistency in practice. This focus on

high-stakes examinations emphasizes low-cognitive, repetitive activities that stifle students’

development of deeper learning.

Having classrooms with students with low socioeconomic status was not associated with

mathematics teachers’ ICT use for topic-specific activities. This shows that teachers did not use

ICT for topic-specific activities with these students, potentially limiting mathematical related

opportunities that benefit students, specifically more rigorous than drill and practice. On the

contrary, it is encouraging that teachers used ICT for spreadsheets/data analysis, assessment, and

internet resources with these students, illustrating the likelihood that some students with low

socioeconomic status were in classrooms where teachers used ICT beyond drill and practice.

The analyses show that teachers’ use of ICT was not associated with having students with

special education needs in classrooms. Despite the lack of significance, in some cases the odds of

teachers’ use of ICT were higher than in classes without students with special needs. For instance,

in classes where 1% to 10% of students had special education needs, mathematics teachers had

higher odds of using ICT across all the ICT tasks, but in classes with larger percentages (>30%)

of students with special education needs, the odds were high for drill and practice, assessment, and

internet resources. Conversely, teachers’ likelihood of using ICT for topic-specific activities, and

spreadsheets/data analysis were lower. This shows that teachers incorporated ICT mainly for

assessment more than spreadsheets and data analysis activities. It is possible that teachers used

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ICT differently among students with special needs due to a variety of reasons, such as

heterogeneity of students’ needs, in which case students completed different tasks using alternative

mediums, or the teachers did not find ICT to be appropriate for the students. Overall, having

students with special education needs is not associated with teachers’ ICT use.

Research Question Five: School Contexts and ICT Use

The school context included: teachers’ cooperation, participation in decision making, a

collaborative culture and administrative support. Teachers’ cooperation predicted higher odds of

teachers’ ICT use in mathematics instruction. These odds were statistically significant for teachers

use of ICT for drill and practice, assessment and internet resources. This finding can be

extrapolated to show that when teachers work together, they are likely to learn, and share ideas on

ways to integrate, and use ICT in mathematics instruction leading to increased frequency of ICT

use. Further, teacher cooperation can provide teachers with support as they become more willing

to take risks in using ICT (Overbay et al., 2010). Also, engaging in innovative teaching practices

such as integrating ICT can involve an element of risk taking and the literature documents the role

of peer-to-peer support as a vehicle that facilitates the diffusion of innovations. Therefore, although

teacher cooperation was associated with ICT use for three out five tasks, this shows potential in its

role on impacting greater ICT use in instruction. Peer-to-peer support can also provide teachers

with opportunities to develop TPACK as they develop confidence and interact with different ICT

in instruction (Galanouli, Murphy, & Gardner, 2004).

Overbay et al. (2010) reported that administrator support was not a significant predictor of

teachers’ ICT use. This current study showed administrator support, though statistically

significant, reduced the likelihood of teachers’ ICT use except for assessment purposes. This

finding can be explained by existing school policies on ICT use or an administrators’ leadership

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style. For instance, if a school administrator offers support but has a leadership style incompatible

with envisioning ICT as effective for teaching mathematics, the support may not be geared towards

ICT use in instruction and may not promote teachers’ ICT use.

Administrators play a critical role in determining school culture including a culture of

innovation such as using ICT for instruction (Fullan, 2016). In thinking about sociocultural theory,

school culture can result from teachers’ active participation in decision-making or collaboration

among stakeholders (Overbay et al., 2010;Somekh, 2007; Whipp, Eckman, & Kieboom, 2005).

However, participation in decision making and the presence of a collaborative culture was not

associated with teachers’ ICT use in mathematics instruction. This finding highlights the

possibility that teachers and stakeholders at a school may participate in decision making and

collaborate in tasks unrelated to ICT use, translating to limited effects on ICT use (Ertmer et al.,

2012).

A shared vision on the role of ICT in mathematics instruction can focus teacher

collaboration and participation in decision making towards effective use of ICT and may contribute

to increased use of ICT in the future. More importantly, beyond ICT, a shared vision may provide

stakeholders with opportunities to identify critical drivers of change, such as curricular and

pedagogical changes (Fullan, 2016; Warschauer et al., 2014). Similar to the U.S., countries in this

analytic sample showed limited overall ICT use in instruction. This may imply that the challenges

between countries facing ICT integration are similar and countries may benefit from learning from

each other about successes and challenges, with the hope of effective utilization of scarce resources

and transformed student learning experiences. In the next section, I discuss limitations and

implications for practice.

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Limitations and Constraints

There are a number of limitations in this dissertation study. First, although the study

includes a large number of participants, it was done with a specific population of teachers from

OECD member and partner countries. These were mainly teachers in high-income countries

participating in the PISA and limits the generalizability of the findings to wider populations of

teachers in different countries. However, the analytic sample also included some participants from

low-income countries.

Second, although the TALIS (2013) provided rich information about teachers’ learning and

working conditions, the questions in the survey may not be neutral. This means that, among the

participating teachers, the questions may have different interpretations, there may be differences

in the definition of words, concepts and starting points when assessing oneself or assessing school

contexts, and this may cause subjectivity in the information gathered. Additionally, the countries

in the sample may have varied policies on ICT or ICT resources that may impact teachers’ use of

ICT not captured in the data. Similarly, the level of integration of ICT in a school or country may

be different among these countries and this may impact ICT use.

Third, professional development for teachers on ICT skills is a critical factor that influences

technology integration in instruction. The inclusion of professional development training in the

survey was beneficial. However, the mere availability of professional development in ICT skills

may not lead to changes in how teachers use ICT in instruction. Professional development on ICT

skills in teaching may not be synonymous with developing adequate skills on technology-mediated

pedagogy where teachers develop rich experiences that provide a deeper understanding on how to

integrate ICT in mathematics instruction.

Finally, the survey included important questions about student characteristics (i.e.,

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mathematics levels, socioeconomic status, special education status). However, these student

characteristics can have varying definitions within schools and among teachers, making it

necessary to interpret the findings with this caveat in mind. For instance, teachers in the same

school or country may consider students to be average achieving, whereas different teachers in the

same school or country may consider the same students high achieving in mathematics.

Implications for Practice

Research: There is a need for continued qualitative and quantitative research on the ecological

perspectives of ICT in instruction to uncover, unlock and maximize the potential of ICT use in

instruction. Despite investments in technology, ICT remains on the periphery especially in

mathematics instruction. Researchers and practitioners may find it beneficial to collaborate in

action research in an attempt to understand potential barriers that are not captured through

questionnaires. Including students in these discussions can provide a student perspective and reveal

critical aspects of ICT use that are overlooked. Action research can be incorporated as a paradigm

for change as stakeholders (e.g., teachers, researchers) work together to uncover assumptions about

ICT in teaching and learning and most importantly, bring teacher voices and knowledge in the

development of policies around innovations such as ICT. I concur with scholars (e.g., Hughes &

Read, 2018; Somekh, 2007) that have argued that ICT cannot be integrated superficially in

teaching and learning in the existing educational systems. If qualitatively different results are

expected, such as transformative learning experiences and change, then radical and systematic

approaches are required. In the same token, it is critical for stakeholders to closely examine how

teachers use ICT in instruction to facilitate rich learning experiences instead of ICT use for low-

level cognitive tasks. Another implication is that, undoubtedly, questionnaires capture rich data

that inform researchers and policy makers on teaching and learning aspects, therefore,

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questionnaires should continuously be evaluated and tested for any kinds of biases in order to get

high quality information from respondents. Understandably, this charge may present a challenge

when trying to strike the right balance between length and quality of questions. However,

instrument developers should not compromise quality for quantity. Additionally, researchers

should explore creative ways to collect data that can supplement information from questionnaires

to counter not only verification challenges but also to provide a preponderance of data points. This

can allow for deeper analyses of instructional practices. For instance, including open-ended

questions can provide teachers with a space to add their input in an elaborate manner. Lastly,

evaluation of current ICT initiatives is needed. Evaluation research is a separate form of research

that could provide stakeholders with further guidance on topics such as, (a) alignment of initiatives

with curriculum requirements; (b) legal requirements for students identified with disabilities; (d)

considerations for universal design for learning, and; (e) sensitive topics about privacy of student

and teacher data. This is especially prudent considering the heavy financial investments in ICT in

education and the high stakes of improving students’ learning.

Policy: At the classroom level, the socioeconomic status of students predicted significant

differences in teachers’ ICT use. This finding may highlight potentially different learning

experiences among students with low and high socioeconomic statuses. Further examination is

needed to understand the scope of the digital divide and ways to provide students with equitable

learning opportunities. This examination of equitable access to quality educational opportunities

through ICT is especially important as ICT skills increasingly represent a form of cultural capital

that students require to fully participate in their learning experiences and beyond the classroom

walls. Teacher education programs will require a strategic and intentional focus on adequately

preparing teachers to effectively integrate ICT in instruction to ensure new teachers are equipped

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with relevant skills beyond the compartmentalized knowledge of content, technology, and

pedagogy. Teacher education programs can incorporate courses on design thinking that actively

involve preservice teachers in the development of context-specific activities that can boost their

development of TPACK and exposure to a range of ICT affordances and constraints. Teachers can

also benefit from developing skills for designing learning environments that incorporate pedagogy-

based ICT activities in mathematics instruction that promote student engagement, interest,

authentic learning, and growth (Fullan & Hargreaves, 2016). Overall, this analysis provides

evidence of the need for context specific ICT training for teachers. Both micro and macro factors

may hinder teachers from integrating ICT in instruction. Also, school leaders may benefit from

developing a shared vision for ICT in instruction, building capacities, collaborative cultures and

communities of practice to promote teacher cooperation and collaboration. Policy makers and

curriculum developers can consider aligning mathematics curriculum with ICT goals to provide a

clearer path for teachers on the role of ICT in teaching and learning. This alignment of the

curriculum, goals, and the role of ICT in teaching and learning, together with the development of

a shared vision, can facilitate coherence within complex educational systems (Fullan et al., 2015).

Conclusion

This analysis adds to the knowledge base explaining the extent to which teachers in the

TALIS-PISA (2013) link incorporated ICT in mathematics instruction. Researchers have

investigated the relationship between the quantity and quality of ICT use on student achievement,

as measured on standardized assessments such as PISA. ICT use has been found to result in mixed

results. Researchers that have found positive results of ICT use on student achievement have

attributed these results to the quality of ICT use, while others have found that the number of ICT

activities have a small and positive effect on student achievement. This is not surprising

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considering the varying ICTs available and how teachers’ use ICT in instruction.

Instead, this study sought to examine how teachers used ICT in mathematics instruction to

provide information that can allow stakeholders (e.g., policy makers, curriculum developers,

teachers, researchers) to understand whether teachers actually incorporate ICT in mathematics

instruction. Additionally, the study investigated potential factors that predicted ICT use in

mathematics instruction. These factors provide critical information on active ingredients that

promote effective ICT use or barriers that impact ICT use in mathematics instruction and lead to

research on ways to promote effective ICT use and improved returns on investment, especially on

student learning. If stakeholders care about the return on investment from ICT in schools and

largely how ICT affect students’ learning, understanding the usage and the predicting factors that

influence teachers’ ICT use can allow administrators, professional developers, and other

stakeholders to target policies that promote effective ICT use. Teacher education levels,

constructivist teachers’ practices, and teacher cooperation predicted high odds of ICT use.

Additionally, students’ socioeconomic status at the classroom level predicted teachers’ ICT use

compared to students’ mathematics levels and special need status. This warrants attention to

alleviate further widening gaps in students’ learning experience due to the digital divide.

Teachers are faced with increased student diversity and class size, as well as demands and

pressures to demonstrate high student performance in the classroom. These demands place a huge

cognitive load on teachers as they are expected to meet the diverse needs of all the learners in their

classrooms. This requirement often places undue burden on teachers’ capacity to provide

individualized instruction to all students while also providing contextual and multiple instructional

support in the face of daily time constraints on the teachers. Technological innovations and

learning environments may provide spaces that promote students’ (and teachers) development of

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new identities such as teachers as facilitators and students at the center of their learning. In

summary, this analysis shows that much work is still needed to investigate the mechanisms

underlying effective ICT integration in mathematics instruction. A strategic focus on

understanding the tensions present in the daily realities of teachers, juxtaposed with the demands,

and shifts in the learning environment brought about by educational reforms, classroom diversity,

and dynamic technological advancements can inform ICT policies and professional development.

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Appendix A: Variables Investigated and Sample Items

Variable Domain Description Sample Item Constructivist beliefs α =0.7

Teaching 4 items with the response range from 1-4, 1- Strongly disagree, 2- Disagree, 3 –Agree, 4 - Strongly agree

My role as a teacher is to facilitate students’ own inquiry; Students learn best by finding solutions to problems on their own; Thinking and reasoning processes are more important than specific curriculum

Mathematics self-efficacy α=0.7

Teaching

6 items with the response range from 1-4, 1- Strongly disagree, 2- Disagree, 3 –Agree, 4 - Strongly agree

I am able to ask questions that get students to think deeply about mathematics; I have a hard time getting students interested in mathematics; I am able to get my students to feel confident in mathematics

Teacher cooperation

School climate

6 items with the response range from 1-6, 1- never, 2- once a year or less, 3 –2 times a year, 4 – 5-10 times a year, 5- 1-3 times a month, 6-once a week or more

How often do you? Exchange teaching materials with colleagues; Engage in discussions about the learning development of specific students

Teacher participation in decision making

School climate

The scale ranged from 1- 4, 1- strongly disagree, 2- disagree, 3- agree, 4 – strongly agree.

This school provides staff with opportunities to actively participate in school decisions

Professional Development in ICT skills

Teacher professional development

The response scale was yes or no

Did the professional development activities you participated in during the last 12 months cover the following topic (ICT skills)?

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Variable Domain Description Sample Item ICTUSE

Teaching

The scale ranged from 1- 4, 1- never or almost never, 2- occasionally, 3- frequently, 4 – always or almost always

Over the course of the school year, how frequently do you use ICT resources in teaching?

Math Achievement Level

Classroom

The scale ranged from 1- 4, 1- mostly high achieving students in mathematics, 2- mostly average achieving students in mathematics, 3- mostly low achieving students in mathematics, 4 – approximately equal numbers of high, average, and low achievement students in mathematics

Which of the following best describes the achievement of students in the class?

Special Education Needs

Classroom The scale ranged from 1- 5, 1- none, 2- 1% to 10%, 3- 11% to 30%, 4 – 31% to 60%, and 5- More than 60%.

Please estimate the broad percentage of students who have the following characteristics.

Socioeconomic Status

Classroom The scale ranged from 1- 5, 1- none, 2- 1% to 10%, 3- 11% to 30%, 4 – 31% to 60%, and 5- More than 60%.

Please estimate the broad percentage of students who have the following characteristics.

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Constructivist Teaching Practices

Teaching

7 items with the response range from 1- 4, 1- never or almost never, 2- occasionally, 3- frequently, 4 – in all or nearly all lessons

How often do you employ the following teaching practices? I give students a choice of problems to solve; I encourage students to work together to solve problems; I connect mathematics concepts I teach to uses of these concepts outside of school.

Constructivist Goals and Views about mathematics

Teaching

9 items with the response scale from 1- 4, 1- strongly disagree, 2- disagree, 3- agree, 4 – strongly agree.

How much do you agree or disagree with the following goals for and views about teaching mathematics? The goal of mathematics is to help students use mathematics to solve real-world problems; doing mathematics requires hypothesizing, estimating, and creative thinking.

Teacher Training Formal Education Years of Experience

Background Background Background

The response scale was 1 = “yes” or 2 = “no”. The response scale from 1- 4, 1- below ISCED level 5, 2- ISCED level 5B, 3- ISCED level 5A, 4 – ISCED level 6. Teachers provided the number of years

Did you complete a teacher education or training programme? What is the highest level of formal education you have completed? How many years of experience do you have working as a teacher in total?

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Appendix B. Logistic Regression Results in Odds Ratios for Combined Model Including Teachers’ Professional Qualifications, Instructional Approaches, Beliefs, Classroom Students’ Characteristics, and School Context Predictors of Teachers’ ICT Use Drill and

practice activities

Topic specific activities

Spreadsheets /data analysis activities

Assessment activities

Internet Resources

Constant 0.000*** 0.000*** 0.085 0.011** 0.026** Education (5a or 6) 1.872 2.921* 3.627* 2.226 2.560* Teacher Training 1.235 1.530 0.243*** 0.589 0.836 Teaching Exp. (ref = Less than 5 years)

5-10 years 1.071 0.843 0.812 0.528 0.504* More than 10 years 0.695 0.843 1.038 0.568* 0.594* Technology Training 0.675 0.661 1.183 0.895 1.077 Math self-efficacy 0.996 1.015 1.036 1.035 0.989 Constructivist beliefs 1.001 0.983 1.094 1.049 1.070 Constructivist teacher practices 4.695*** 3.357*** 3.221*** 3.553** 2.705*** Constructivist goals and views 1.117 1.814* 0.236*** 0.330** 0.616 Math Levels (ref = High) Mostly average 1.217 0.623 0.793 1.231 0.931 Mostly L. A 1.045 0.469** 0.470* 0.681 0.634 Approx. H. A, Av., & L. A 0.956 0.716 0.480 0.852 1.353 Socioeconomic (ref = High) 1% to 10% 1.723* 1.490 1.815 1.396 1.207 11% to 30% 1.699 1.887* 1.111 1.217 1.649 More than 30% 2.407* 2.120* 3.477 2.343* 1.422 Special Ed. (ref = None) 1% to 10% 1.153 0.843 1.623 1.755* 1.134 11% to 30% 0.811 0.896 0.917 1.196 0.948 More than 30% 1.680 0.524 0.444 1.468 1.231 School Context Teacher Cooperation 1.139* 1.052 0.991 1.134 1.131* Decision Making 1.108 1.500 1.264 1.309 1.353 Collaborative culture 0.959 0.579* 1.331 0.744 0.856 Admin. Support 0.684 0.477** 1.110 1.554 0.411** N 4761 4765 4755 4761 4769

Note. L. A = low achieving; H. A = high achieving; Av. = average achieving; special ed. = special education; admin. support = administration support. * p < 0.05, ** p < 0.01, *** p < 0.001.

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