digital.library.unt.edu/67531/metadc862855/m2/1/high_res_d/GILES... · THE IMPACT OF A PAIRED GROUPING PRE‐SERVICE TECHNOLOGY INTEGRATION COURSE ON STUDENT PARTICIPANT ATTITUDES,

THE IMPACT OF A PAIRED GROUPING PRE‐SERVICE TECHNOLOGY INTEGRATION COURSE ON

STUDENT PARTICIPANT ATTITUDES, PROFICIENCY, AND TECHNOLOGICAL KNOWLEDGE

TOWARD TECHNOLOGY

Linda Michelle Giles, B.S., M.S., M.Ed.

Dissertation Prepared for the Degree of

DOCTOR OF PHILOSOPHY

UNIVERSITY OF NORTH TEXAS

August 2016

APPROVED:

Tandra Tyler‐Wood, Major Professor Gerald Knezek, Committee Member Lin Lin, Committee Member Jana M. Willis, Committee Member Cathie Norris, Interim Chair of the Department

of Learning Technologies Victor Prybutok, Interim Dean of the College of

Information and Vice Provost of the Toulouse Graduate School

Giles, Linda Michelle. The Impact of a Paired Grouping Pre-Service Technology

Integration Course on Student Participant Attitudes, Proficiency, and Technological Knowledge

toward Technology. Doctor of Philosophy (Learning Technologies), August 2016, 117 pp., 18

tables, references, 56 titles.

The purpose of this case study with supporting quantitative data was to investigate the

influence of paired grouping on student participants’ perceived attitudes toward technology,

perceived proficiency with technology, and perceived technological knowledge after

completing a required educational technology course. Additionally, student participants’

perceptions regarding the use of paired grouping on their attitudes, proficiency, and

technological knowledge with regard to technology was also investigated. To measure the

difference between perceived attitudes toward technology, perceived proficiency with

technology, and perceived technological knowledge after completing a required educational

technology course, 83 student participants enrolled in a required educational technology

course at a suburban midsized Gulf Coast university in the southern United States, completed

the Attitude Toward Technology Scale (ATTS), Technology Proficiency Self-Assessment for 21st

Century Learning (TPSA C21), and Technological Knowledge Tool (TK). Additionally, 24 student

participants participated in semi-structured interviews.

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Copyright 2016

by

Linda Michelle Giles

iii

ACKNOWLEDGEMENTS

First and foremost, I would like to acknowledge that without my lord and savior this

journey would not have been possible. Second, there are not enough words to express how

grateful I am to my father for his continued support, encouragement, and love. If it wasn’t for

him, I may never have started this journey much less finished it. I feel truly blessed to have such

a wonderful role model in my life. Thank you for always being there reminding me that

anything is possible. I would also like to thank my mom for all her love and support throughout

this journey. Every time I felt I wanted to give up, she encouraged me to continue. Next, I would

like to thank the rest of my family for their unconditional love and support. I would not be

where I am today without all of you.

I would like to thank my sister‐friend, Rhonda Ritter, for taking this journey with me. I

will be forever grateful for the many late night therapy sessions and support that you provided.

Every time I felt too overwhelmed to continue, you reminded me that I could. I would also like

to thank my dear friend, Tim Carrizal, for his support through this tiring process. Next, I would

like to thank my wonderful colleagues, Dr. Peters, Dr. Weiser, and Dr. Grigsby, for their support

and encouragement.

I would like to thank my committee members, Dr. Knezek and Dr. Lin, for taking the time

to be on my committee. I would like to thank Dr. Willis for being my mentor and my rock. She

kept me grounded through this process and I would not have been able to accomplish all that I

have without her. I thank her for always believing in me. I will be forever grateful for her and to

her. Lastly, I would like to thank my chair, Dr. Tyler‐Wood, for keeping me on track and guiding

me through this journey. I am truly grateful for all you have done for me.

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TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS………………………………………………………………………….……….…………………………iii

LIST OF TABLES…………………………………………………………………………………………………………………………vii

CHAPTER 1 INTRODUCTION ............................................................................................................ 1

Research Problem ...................................................................................................................... 2

Significance of the Study ............................................................................................................ 3

Theoretical Framework .............................................................................................................. 3

Research Purpose and Questions .............................................................................................. 4

Limitations and Delimitations .................................................................................................... 5

Definition of Key Terms .............................................................................................................. 6

Summary .................................................................................................................................... 7

CHAPTER 2 REVIEW OF THE LITERATURE ....................................................................................... 9

Technology Integration .............................................................................................................. 9

Attitudes Toward Technology .................................................................................................. 11

Teacher Beliefs .................................................................................................................... 13

Self‐Efficacy ......................................................................................................................... 16

Technology Self‐Efficacy ...................................................................................................... 18

Proficiency with Technology .................................................................................................... 20

Technological Knowledge ........................................................................................................ 22

Summary .................................................................................................................................. 30

CHAPTER 3 METHODOLOGY ......................................................................................................... 32

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Overview of Research Problem ................................................................................................ 32

Operationalization of Theoretical Constructs .......................................................................... 33

Research Purpose and Questions ............................................................................................ 34

Research Design ....................................................................................................................... 35

The Course ............................................................................................................................... 36

Population and Sample ............................................................................................................ 37

Pairings ..................................................................................................................................... 39

Instrumentation ....................................................................................................................... 40

Attitude Toward Technology Scale ..................................................................................... 40

Technology Proficiency Self‐Assessment for 21st Century Learning ................................... 41

Technological Knowledge Survey ........................................................................................ 42

Data Collection Procedures ...................................................................................................... 43

Data Analysis ............................................................................................................................ 44

Quantitative ......................................................................................................................... 44

Qualitative ........................................................................................................................... 46

Privacy and Ethical Considerations .......................................................................................... 47

Research Design Limitations .................................................................................................... 47

Summary .................................................................................................................................. 48

CHAPTER 4 RESULTS ...................................................................................................................... 50

Demographic Characteristics of the Participants .................................................................... 50

Survey Participants .............................................................................................................. 50

Interview Participants .............................................................................................................. 52

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Instrument Reliability ............................................................................................................... 53

Research Question 1 ................................................................................................................ 54



Support. ........................................................................................................................... 85

Outcomes. ........................................................................................................................ 87

Pairing Insights. ................................................................................................................ 92

Summary of Findings ................................................................................................................ 94

CHAPTER 5 DISCUSSION ................................................................................................................ 95

Summary of the Study .............................................................................................................. 95

Discussion of the Findings ........................................................................................................ 95

Implications .............................................................................................................................. 98

Implications for Instructors of Pre‐Service Teachers ............................................................... 99

Implications for Professional Development of In‐Service Teachers ........................................ 99

Recommendations for Future Research ................................................................................ 100

Conclusion .............................................................................................................................. 101

APPENDIX .................................................................................................................................... 103

REFERENCES ................................................................................................................................ 105

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LIST OF TABLES

Page

Table 1 Student Demographics of University .............................................................................. 38

Table 2 Student Demographics of School of Education ............................................................... 39

Table 3 Paired Groupings by Class ............................................................................................... 40

Table 4 Student Participant Survey Demographics: Gender, Race/Ethnicity,

Age, Certification Level Seeking, and Certification Content Area Seeking ....................... 51

Table 5 Student Participant Interview Demographics: Gender, Race/Ethnicity,

Age, Certification Level Seeking, and Certification Content Area Seeking ....................... 53

Table 6 Reliability Coefficients for Instrumentation .................................................................... 54

Table 7 Paired t‐Test: Pre Attitude Scores and Post Attitude Scores .......................................... 55

Table 8 Attitudes Toward Technology Scale (ATTS) Instrument by

Pre and Post Survey Results for Student Participants ...................................................... 56

Table 9 Attitude Toward Technology Scale (ATTS) Instrument by

Pre and Post Survey Results for Student Participants Collapsed and

p ‐values Per Survey Item ................................................................................................. 60

Table 10 Descriptive Statistics for the ATTS Items ...................................................................... 64

Table 11 Paired t‐Test: Pre Proficiency Scores and Post Proficiency Scores ................................ 67

Table 12 Technology Proficiency (TPSA C21) Instrument by


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Table 13 Technology Proficiency Self‐Assessment for 21st Century Learning

(TPSA C21) Instrument by Pre and Post Survey Results for

Student Participants Collapsed and p ‐Values Per Survey Item ....................................... 72

Table 14 Descriptive Statistics for the TPSA C21 Items ............................................................... 76

Table 15 Paired t‐Test: Pre Technological Knowledge Scores and

Post Technological Knowledge Scores .............................................................................. 80

Table 16 Technological Knowledge (TK) Instrument by


Table 17 Technological Knowledge (TK) Instrument by

Pre and Post Survey Results for Student Participants Collapsed and p ‐Values Per Survey

Item ................................................................................................................................... 82

Table 18 Descriptive Statistics for the TK Items .......................................................................... 83

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CHAPTER 1

INTRODUCTION

Technology integration in EC‐12 classrooms is on the rise, leaving classroom teachers

with the responsibility of helping students acquire the skills needed to use 21st century

technology tools. This responsibility requires a teacher to have a deep understanding of the

related technology tools as well as significant proficiency with these tools. Most teacher

preparation programs are not constructed to strongly influence pre‐service teachers’

technology use (Belland, 2009; Hermans, Tondeur, van Braak, & Valcke, 2008; Kay, 2006).

However, immersing pre‐service teachers in experiences with various 21st century tools and

employing strategies that build confidence regarding technology provide an effective

opportunity to develop technological expertise to promote learning of 21st century skills (King,

2011).

Pre‐service teachers often enter their education program with perceived attitudes

toward technology, perceived proficiency with technology, and perceived technological

knowledge in using technology in their future classroom based on experiences as EC‐12

students. Ertmer (2005) points out that teacher early experiences with technology, “can shape

teacher subsequent encounters for years to come, despite great efforts to persuade them

differently” (p. 30). Therefore, it is important for pre‐service teachers to develop the confidence

they need to become effective technology users within their own classroom. Without this self‐

confidence, pre‐service teachers will most likely not incorporate technology into their

classroom and therefore the teacher’s students will not be exposed to technology. A problem

education preparation programs are facing today is how to increase pre‐service teachers’

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attitudes, proficiency, and technological knowledge in regard to technology so the pre‐service

teachers’ will be more likely to integrate technology when they have their own classroom. In

order for teacher educators to better understand pre‐service teachers’ beliefs about technology

integration, there is a need to investigate strategies, such as peer mentoring/paired grouping,

in regard to technology and its impact on pre‐service teachers’ beliefs. Benefits of peer

mentoring/paired grouping in pre‐service teacher education include providing a valuable tool

for collaboration, evaluation of teacher effectiveness, and improvement in teacher quality

(Marshall, 2005). For the purpose of this study peer mentoring will be defined as paired

grouping and paired grouping is defined as grouping students of varying abilities to meet

instructional needs.

Research Problem

Peer mentoring has been implemented into teacher education programs “as a means of

providing pre‐service teachers with additional feedback and collegial support and to promote

reflective practice during early field experiences” (Jenkins, Hamrick, & Todorovich, 2002, p. 48).

A number of studies have been conducted on peer mentoring in pre‐service teacher education

and these studies support that pre‐service teachers who participate in peer mentoring have

positive increases in attitude, self‐efficacy, and motivation (Goker, 2006; Lu, 2010; Woullard &

Coats, 2004). However, few studies link peer mentoring and pre‐service teachers’ attitudes

toward technology, perceived proficiency with technology, and perceived technological

knowledge in using technology. The goal of this research is to determine if student participants

engaged in paired grouping through technology knowledge acquisition and skill development

will experience significantly greater increases in their perceived attitudes toward technology,

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perceived proficiency with technology, and perceived technological knowledge in using

technology, therefore, resulting in an increase in motivation to use technology. Implications for

this research include improving technology training within teacher preparation programs and

improved preparation of pre‐service teachers that can ultimately translate into the

improvement of K‐12 technology integration.

Significance of the Study

A number of studies have been conducted on peer mentoring in pre‐service teacher

education. These studies investigated variables such as attitude, self‐efficacy, and motivation

and have been largely limited to early field experiences (Cullen & Green, 2011; Dragon et al.,

2012; Evans & Gunter, 2004; Laffey & Musser, 1998). However, few studies link peer mentoring

and pre‐service teachers’ attitude toward technology, proficiency with technology, and

technological knowledge in using technology. Thus, the outcome of this study can provide

needed information on the adoption of paired grouping as a strategy for improving pre‐service

and in‐service teacher technology training and provide feedback on pre‐service teachers’

perceptions of paired grouping.

Theoretical Framework

The theoretical framework for this study is based on a combination of Bandura’s Social

Cognitive Theory (1997) and the Zone of Proximal Development (ZPD) from Vygotsky (1978).

Bandura’s theory provides the fundamental understanding of self‐efficacy as it is applied to

teaching and technology (Bandura, 1997). The ZPD (Vygotsky, 1978) utilizes the view that

interactions with peers are an effective way of developing skills and strategies.

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Bandura’s social cognitive theory is known for the domain‐specific belief construct of

self‐efficacy where self‐efficacy describes an individual’s perceptions about their ability to

perform a specific function. In other words, self‐efficacy refers to one’s confidence to perform

certain tasks. Bandura (1989) stated, “self‐efficacy beliefs affect thought patterns that may be

self‐aiding or self‐hindering” (p. 1175). Bandura (1989) also identified four sources of

information used to judge self‐efficacy. The identified sources include: successful performance

attainment, observing the performances of others, verbal persuasion indicating that one

possesses certain capabilities and physiological states by which one judge’s capability, strength,

and vulnerability (Bandura, 1989).

Vygotsky’s ZPD is one approach that is widely used to ensure that students have the

opportunity to make a meaningful contribution within a community of learners. ZPD is

described as the gap between actual developmental levels as determined by independent

problem solving and under guidance or in collaboration with more capable peers (Vygotsky,

1978). Pope, Hare, and Howard (2002) go further to state that the expected uses of Vygotsky’s

theory in a classroom are: scaffolding, small groups, cooperative learning, group problem‐

solving, cross‐age tutoring, assisted learning and/or alternative assessment. Because Bandura’s

social learning theory and Vygotsky’s ZPD suggests that one can learn through observation of

others and increase self‐efficacy we could expect that paired grouping would improve student

participants attitudes, proficiency, and knowledge in regards to technology.

Research Purpose and Questions

The purpose of this study is to investigate the influence of paired grouping on student

participants’ perceived attitudes toward technology, perceived proficiency with technology,

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and perceived technological knowledge after completing an educational technology course

within their program of study at a suburban midsized Gulf Coast university in the southern

United States. The current study answers the following research questions:

1. Is there a statistically significant mean difference in student participants’ perceived

attitudes toward technology after completion of an educational technology course as measured

by the Attitude Toward Technology Scale (ATTS) when participants are grouped based on

paired grouping?


proficiency with technology after completion of an educational technology course as measured

by the Technology Proficiency Self‐Assessment for 21st Century Learning (TPSA C21) when

participants are grouped based on paired grouping?


technological knowledge in using technology after completion of an educational technology

course as measured by the Technological Knowledge Assessment Tool (TK), a component of the

TPACK Assessment Tool, when participants are grouped based on paired grouping?

4. How do student participant perceptions of the paired grouping influence their

attitudes, proficiency, and technological knowledge with regard to technology?

Limitations and Delimitations

The current study was limited by implementation in a single suburban midsized Gulf

Coast university in the southern United States. A larger sample size would yield more reliable

results. Other limitations included gender that was primarily female (84.3%). The paired groups

participated in the study as part of their course curriculum and survey responses were

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voluntary. The results of the surveys were based on self‐reported data and are limited to this

study; therefore, they are not generalizable to all pre‐service teachers. Delimitations imposed

by the researcher include selection and use of the instruments that measured student

participants’ attitude, proficiency, and technological knowledge as well as the course selected

for this study.

Definition of Key Terms

The following is a list of definitions of the key terms used throughout this dissertation.

Attitude is defined as an individual’s feelings about performing certain behaviors (Ajzen, 1991)

and in this study refers specifically to varying interactions with technology.

Attitude Toward Technology Scale (ATTS) is the 31‐item survey instrument used to evaluate a

teacher’s attitudes toward different pieces of technology and their use (Kajs,

Underwood, Coppenhaver, Driskell, & Crawford, 2001).

Belief is defined as “an understanding held by an individual that guides that individual’s

intensions for action” (Hancock & Gallard, 2004, p. 281).

Paired Grouping is defined as grouping students of varying abilities to meet instructional needs

and is an element of peer mentoring.

Paired Group Member is defined as another student who can serve as a resource, a helping

hand, a sounding board, and provide support, encouragement, and information to

another paired group member.

Peer Mentoring is defined as “complex social interactions that mentor teachers and student

teachers’ construct and negotiate for a variety of professional purposes and in response

to the contextual factors they encounter” (Fairbanks, Freedman and Kahn, 2000, p.103).

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Proficiency with Technology is defined as having the skills to figure out how to use technologies.

Teaching Efficacy is defined as an individual’s perceptions about his or her ability to perform a

specific function; in this case, classroom instruction (Bandura, 1997).

Technological Knowledge is defined as knowledge about various technologies, ranging from

low‐tech technologies, such as pencil and paper, to digital technologies such as the

Internet, digital video, interactive whiteboards, and software programs (Schmidt et al.,

2009).

Technological Knowledge Survey (TK), a component of the Technological Pedagogical Content

Knowledge (TPACK) Assessment Tool, is a six‐item survey used to measure pre‐service

teachers’ technological knowledge (TK) with technology.

Technology Efficacy refers to an individual’s judgment of their ability to use

computers/technology (Downey & Zeltman, 2009).

Technology Proficiency Self‐Assessment for 21st Century Learning (TPSA C21) is the 34‐item

survey instrument used to measure pre‐service and in‐service teacher confidence in

their competence based on a technology proficiency checklist and emerging

technologies (Christensen et al., 2015; Ropp, 1999).

Summary



and perceived technological knowledge after completing an educational technology course

within their program of study at a suburban midsized Gulf Coast university in the southern

United States. Chapter 1 presented the research problem, significance of study, theoretical

8

framework, research purpose and questions, limitations and delimitations, and operational

definitions of the study. Chapter 2 will review the research that is related to student

participants’ attitude towards technology, proficiency with technology, technological

knowledge in using technology, and peer mentoring/paired grouping and pre‐service teacher

education.

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CHAPTER 2

REVIEW OF THE LITERATURE



and perceived technological knowledge after completing an educational technology course. To

address these areas, this literature review will focus on: (a) technology integration, (b) attitudes

toward technology, (c) proficiency with technology, (d) technological knowledge, and (e) peer

mentoring/paired grouping and pre‐service teacher education.

Technology Integration

An issue within teacher education relates to best practices in preparing pre‐service

teachers to integrate technology into their future classrooms. Pre‐service teachers should be

proficient with technology, understand the advantages of technology use in the classroom, and

be able to improve EC‐12 instruction through technology integration (Anderson & Maninger,

2007; Wright & Wilson, 2005‐2006). Integrating technology into teacher preparation

coursework is an important component for teacher education programs (Anderson & Maninger,

2007; Pope et al., 2002; Wright & Wilson, 2005‐2006), however, many teacher educator

programs do not currently provide the comprehensive instruction that pre‐service teachers

need to become proficient at integrating technology in the classroom (Gunter, 2001; Horung &

Bronack, 2000). Moursund and Bielefeldt (1999) reported that a survey of 416 teacher

preparation programs indicated that formal technology coursework was not well correlated

with pre‐service teachers’ technology application and integration skills. Similarly, results of a

study conducted by Evans and Gunter (2004) concluded that despite being exposed to a variety

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of technology tools and applications in content courses and field experiences, pre‐service

teachers felt they needed more technology preparation to equip them with the skills they

needed to integrate technology into their future classroom.

In an attempt to understand pre‐service teachers’ perceptions of technology

integration, Rehmat and Bailey (2014), explored 15 elementary science methods students’

definitions of technology and their attitudes toward integrating technology into their teaching.

The study took place in a science methods course that was based on a constructivist approach

to teaching and learning science through science activities and class discussions, with an

emphasis on a teacher beliefs’ framework. Results of a qualitative analysis of an open‐ended

pre and post‐technology integration survey, lesson plans, and reflections on activities

conducted throughout the semester identified improvements in students’ technology

definitions, increased technology incorporation into science lesson plans, and favorable

attitudes toward technology integration in science teaching after instruction. These results

suggest that using a constructivist approach to teaching and learning can result in positive

changes in beliefs and behaviors relating to technology integration among pre‐service teachers

(Rehmat & Bailey, 2014).

To better understand the role of ability and usage in technology integration, Hsu (2010)

conducted a quantitative study using separate ability and usage scales with 3,729 Taiwan

teachers. The purpose of the study was to find the relationship between teachers’ technology

integration ability and usage. Results showed a positive correlation between teachers’

technology integration ability and usage. Furthermore, Structural Equation Modeling (SEM)

confirmed the structure of the scales and revealed a higher correlation between the two scales

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after adjusting for measurement. Based on the studies described, there is support for the

notion that educators should design educational programs that encompass various technology

use in EC‐12 classrooms to enhance student learning and possibly increase pre‐service teachers’

attitudes toward technology.

Attitudes Toward Technology

The influence of technology in EC‐12 classrooms must not be ignored. Students need

instruction that includes technology instruction in order to thrive in an increasingly

technological world. Ajzen (2005) reported that attitudes, that are positive and negative

judgments constructed out of our beliefs and experiences, are primary indicators of a person’s

intent to perform a task. Therefore, it would be expected that if pre‐service teachers have

negative attitudes toward technology, they would likely not integrate technology in their future

classrooms. For this reason, it is imperative that teacher education programs incorporate

experiences into the curriculum that help promote positive attitudes toward technology.

Studies have shown that one of the best predicators for successful technology

integration in the classroom relates to positive attitudes toward technology (Cullen & Green,

2011; Palak & Walls, 2009; Riza, 2000). For example, results of a study conducted by Ropp

(1999) indicated that teacher candidates who were confident in their ability to perform

computer tasks were also less anxious about using computers, held more positive attitudes

toward technology and computers, were more confident in their ability to perform tasks

related to teaching with technology, and used more computer coping strategies. Since teachers’

attitudes and self‐efficacy are predicators of teachers’ use of technology (Anderson &

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Maninger, 2007; Palak & Walls, 2009), there are important implications for research on pre‐

service teachers attitudes toward technology.

In a an investigation of major course changes of a stand‐alone educational technology

course redesigned around 21st century skill sets as opposed to technical skill development,

Lambert and Gong (2010), conducted a quantitative study with a random sample of 100 pre‐

service teachers enrolled in 11 sections of an education technology course. The instruments

used in the study consisted of a demographic questionnaire and a survey to measure attitude

toward computers, self‐perceived ability to integrate technology in the classroom, and

computer skills. Results found that even with course changes, pre‐service teachers became less

anxious about computers, their belief in the value of using technology to enhance teaching and

learning as well as their self‐efficacy toward integrating technology in the classroom

significantly improved. In addition, these pre‐service teachers became more advanced in their

technical skills and knowledge of how to apply these skills in the classroom. Results of this study

suggest that pre‐service teachers’ attitudes and self‐efficacy changed as a result of the focus on

21st century technology skills.

In a similar study that analyzed changing attitudes and beliefs towards technology, Guo

and Carey (2008) conducted a qualitative study on two classes of in‐service and pre‐service

teachers enrolled in second language education technology courses. The data consisted of

analyses of surveys, class and online assignments, class and online discussions, course

evaluations, questionnaires, and interviews. Results found that at the beginning of the course

pre‐service teachers attitudes toward the importance of mastering educational technology for

their language teaching careers were very mixed and varied from negative to neutral. However,

13

their attitudes toward technology changed during the course as pre‐service teachers became

convinced that technology could play an important role in enhancing student learning,

motivations, and outcomes (Guo & Carey, 2008). These results suggest that changes were due

to opportunities to actively participate in technology activities during the course.

In an examination of the impact of a technology course on in‐service teachers, Leh

(2000) conducted a study with 68 teachers enrolled in four sections of a technology course to

investigate the teachers’ comfort level, belief, confidence, and attitude toward the use of

technology. In addition, the impact of the course with different degrees of technology

integration was also researched. Cronbach’s Alpha, means, and t‐tests were used to analyze the

data. Findings indicated that the course increased students’ comfort level, confidence, and

attitude toward the use of technology. In addition, the findings also indicated that no significant

differences were found regarding students’ comfort level, confidence, and attitude between

the two groups who experienced different degrees of emphasis on technology integration (Leh,

2000). Results from previous studies showed significant differences in attitudes when

technology integration was emphasized in a course (Guo & Carey, 2008; Lambert & Gong,

2010). However, results of this study found there to be no differences in attitude when

technology integration was emphasized.

Teacher Beliefs

Just as attitudes can serve as predictors of behavior, beliefs can also serve as predictors

of behavior. Beliefs can be defined in many ways. According to Tobin, Tippins, and Gallard

(1994), beliefs include attitudes, confidence, motivation, self‐concept, and self‐esteem. Clark

(1988) identifies teachers’ beliefs as preconceptions and implicit theories. Clark noted that

14

these beliefs seemed to be “eclectic aggregations of cause‐effect propositions from many

sources, rules of thumb, generalizations drawn from personal experience, beliefs, values,

biases, and prejudices” (p. 5). For the purpose of this study, belief will be defined using Hancock

and Gallard’s (2004) definition as “an understanding held by an individual that guides that

individual’s intensions for action” (p. 281).

According to Nespor (1987), beliefs about teaching are powerful constructs that

mediate how pre‐service teachers make sense of their learning experiences as they prepare for

the beginning of their teaching careers. These beliefs are powerful because they not only

dictate how pre‐service teachers make sense of their experiences, but beliefs can also influence

what teachers do in their classrooms (Kagan, 1992). In order for teacher education preparation

programs to better understand pre‐service teachers’ beliefs about technology integration,

there is a need to investigate the prior perceptions and memories of teaching and learning pre‐

service teachers’ bring to their teacher training (Kearney & Hyle, 2004). If pre‐service teachers

themselves do not recognize and examine their beliefs about teaching with technology, they

may perpetuate the teacher‐centered methods that they experienced as students (Ertmer,

2005). Research suggests that teachers who perpetuate traditional, teacher‐centered methods

use technology for low‐level activities and those teachers with constructivist beliefs tend to use

technology to support higher‐level, student‐centered learning (Judson, 2006; Roehrig, Kruse &

Kern, 2007). Park and Ertmer (2007) concluded that in order to change teacher technology

integration, it is important that teachers embrace a more student‐centered pedagogy. Although

studies have been conducted in an effort to understand the impact of effective technology

integration and teacher beliefs, there is much less research on specific technology‐integrated

15

pedagogical strategies such as peer mentoring and the potential of peer mentoring to help pre‐

service teachers shift from a traditional instructional approach to a more constructivist,

student‐centered approach. Sandholtz, Ringstaff, and Dwyer (1997) report that many of the

interventions that have been developed to assist pre‐service teachers in using technology are

based on earlier studies focused on in‐service teachers. Pre‐service teachers however, have

much less experience than that of in‐service teachers and therefore have different needs

(Sandholtz et al., 1997).

Pope et al. (2002) suggests that pre‐service teachers base much of their thinking on

their own learning experiences and, after several methods courses, may incorporate

experiences obtained in the methods courses into their existing beliefs about teaching. In order

for pre‐service teachers to envision themselves as teachers who integrate technology into their

future teaching and classroom, teacher educators must design instruction and interventions

that assist in changing pre‐service teachers’ beliefs. Therefore, it is important that teacher

educators understand pre‐service teachers’ beliefs about teaching and how these beliefs might

impact the pre‐service teachers’ vision of their future classrooms.

Laffey and Musser (1998) conducted a study of 69 students entering an undergraduate

teacher education program to identify attitudes of pre‐service teachers that influence learning

to teach with technology. Results indicate that for many of these pre‐service teachers,

computing is viewed as stressful. In addition, these pre‐service teachers are anxious about

using computers in teaching. Results indicated that these pre‐service teachers feared that

computers would interfere with the teacher‐student relationship (Laffey & Musser, 1998).

Evans and Gunter (2004) conducted a study to determine whether or not pre‐service teachers

16

at the University of Central Florida received the training and support needed to achieve

technology proficiency. Specifically, this study focused on whether or not the teacher education

program could foster positive attitudes about integrating technology into pre‐service teachers’

future classroom (Evans & Gunter, 2004). Results of the study indicated that the majority of the

pre‐service teachers had positive attitudes about integrating technology into their future

classroom, which is a direct contradiction to Laffey and Musser’s findings.

Cullen and Green (2011) used the Theory of Planned Behavior and Self‐Determination

Theory to examine 67 pre‐service teachers’ beliefs, attitudes, and motivation about technology

integration. The researchers found that for these pre‐service teachers, the best single predictor

of both intrinsic and extrinsic motivation was positive attitudes toward technology use.

Furthermore, results demonstrated that these pre‐service teachers struggled to design

meaningful technology integration activities. Dragon et al. (2012) conducted a mixed‐methods

study exploring relationships associated with changes in 20 pre‐service teachers’ attitudes and

perceived proficiency with technology integration in a post‐secondary, Aboriginal, elementary

teacher education program over a two‐year time period. Results indicated significant increases

in attitude constructs as well as overall computer proficiency over the course of the study.

Furthermore, results revealed participants’ perception of technology integration as a

contributing factor in this positive change (Dragon et al., 2012).

Self‐Efficacy

In general, self‐efficacy can be defined as perception about one’s abilities to perform a

task. Bandura (1997) described perceived self‐efficacy as “beliefs in one’s capabilities to

organize and execute the courses of action required to produce given attainments” (p. 3). He

17

further explained that self‐efficacy beliefs influence many aspects of behavior, including the

choice of a course of action, the amount and duration of effort put forth, and the emotional

response to the success of an endeavor (Bandura, 1997, p. 3). Furthermore, Bandura (1989)

stated, “self‐efficacy beliefs affect thought patterns that may be self‐aiding or self‐hindering”

(p. 1175). Bandura (1997) also identified four sources of information used to judge self‐efficacy.

The identified sources include successful performance attainment, observing the performances

of others, verbal persuasion indicating that one possesses certain capabilities and physiological

states by which one judge’s capability, strength, and vulnerability (Bandura, 1989). Among

these four sources, performance attainment has been suggested as having the strongest

influence on self‐efficacy beliefs and thus a strong influence on behavior. The influence of these

performances on self‐efficacy will vary depending on whether or not success was achieved.

Performance attainment in which a person experiences success will lead to increased self‐

efficacy, provided these performance attainments are in an authentic environment and the task

requires “overcoming obstacles through perseverant effort” (Bandura, 1997, p. 80). However,

success that comes without effort is not likely to have a positive influence on self‐efficacy.

Furthermore, failures in authentic environments are likely to decrease self‐efficacy beliefs

(Bandura, 1997). Albion (2001) suggested that coursework in teacher education programs

“should be structured and taught using approaches that build the confidence of students in

their capacity for effective computer use” (p. 345).

Wang, Ertmer, and Newby (2004) conducted a 2 X 2 (Vicarious Experiences x Goal

Setting) mixed factorial design study to examine how vicarious experiences and goal setting

affected pre‐service teachers’ judgments of self‐efficacy for technology integration. Two

18

hundred and eighty students, enrolled in an introductory educational technology course,

participated in the study. The researchers found significant treatment effects for vicarious

experiences and goal setting on participants’ judgments of self‐efficacy for technology

integration. Furthermore, a significantly more powerful effect was found when vicarious

learning experiences and goal setting were both present compared to when only one of the two

factors was present (Wang et al., 2004). Results of this study indicate that incorporation of

vicarious learning experiences can increase pre‐service teachers’ confidence, which can

ultimately lead to technology integration. Additionally, Albion (1996) investigated student

teachers’ dispositions toward computers and their uses of computers in primary school

classrooms during a final‐year practicum. Results suggested that lack of confidence for teaching

with computers was an important factor influencing the levels of computer use by student

teachers (Albion, 1996). Together, these studies suggest that teachers’ beliefs and self‐efficacy

beliefs are indicators of levels of technology integration. Studies such as these provide reasons

for further investigations in this area, which could result in suggestions for improved teacher

education programs and professional development opportunities that could increase self‐

efficacy for teaching with technology.

Technology Self‐Efficacy

Technology self‐efficacy is an individual’s judgment of their ability to use

computers/technology (Downey & Zeltman, 2009). Furthermore, self‐efficacy beliefs toward

technology integration have been theorized to be a determining factor in how well a teacher is

able to effectively use technology to improve teaching and learning (Celik & Yesilyurt, 2013;

Wang et al., 2004). Perceived self‐efficacy with respect to computers has been found to be an

19

important factor in decisions about using computers (Hill, Smith, & Mann, 1987). In addition,

increased performance with computer related tasks have been found to be significantly related

to higher levels of computer self‐efficacy (Albion, 1999; Harrison, Rainer, Hochwarter, &

Thompson, 1997).

Founded in social cognitive theory, teachers' self‐efficacy beliefs have been associated

with positive teaching behaviors and student outcomes (Henson, 2003). For example, according

to Eachus and Cassidy (1999), “Self‐efficacy has repeatedly been reported as a major factor in

understanding the frequency and success with which individuals use computers” (p. 2). While

conducting a longitudinal study examining 394 participants over a one‐year interval to test the

influence of computer self‐efficacy beliefs, outcomes expectations, affect, and anxiety on

computer use, researchers found that computer self‐efficacy beliefs had a significant, positive

influence on computer use.

Goker (2006) explored the impact of peer coaching on self‐efficacy and instructional

skills in a Teaching English Foreign Language (TEFL) teacher education. The goal of Goker’s

study was to test whether student teachers trained using a peer‐coaching training program

after teaching practicum sessions in TEFL would demonstrate greater improvement in

instructional skills and self‐efficacy than those just receiving traditional supervisor visits. Results

of the study suggest that when pre‐service teachers engage in peer coaching it may play a

crucial role in improving the performance of pre‐service teachers and helps develop self‐

efficacy in pre‐service teachers. Koh and Frick (2009) conducted a study to determine how

instructor and student classroom interactions, during technology skills instruction, could

facilitate pre‐service teachers’ computer self‐efficacy. Participants included pre‐service

20

teachers enrolled in three sections of an educational technology course, taught by different

instructors. The researchers found that technology skills instruction conducted by the

instructors appeared to have a positive impact on the computer self‐efficacy of students. This

finding is consistent with previous studies on pre‐service teachers’ computer self‐efficacy

(Downey & Zeltmann, 2009; Wang et al., 2004). Celik and Yesilyurt (2013) investigated student

attitudes regarding technology, perceived computer self‐efficacy, and computer anxiety as

predictors of computer supported education. The researchers found that attitude regarding

technology, perceived computer self‐efficacy, and computer anxiety were important predictors

of pre‐service teachers' attitude toward using computer‐supported education. Based on the

studies described, there is a clear connection between pre‐service teachers’ beliefs, attitudes,

and self‐efficacy and increased likelihood to integrate technology in their future classrooms.

Proficiency with Technology

The Technology Proficiency Self‐Assessment (TPSA) questionnaire is a well‐established

instrument that has been used for a number of years in studies regarding technology

integration in the classroom. The instrument was developed by Ropp (1999) in an effort to

measure teacher confidence (self‐efficacy) when using technology for educational purposes.

The TPSA is a self‐rating measure with four subscales. The subscales measure proficiency on e‐

mail, World Wide Web, integrated applications, and teaching with technology. For the purpose

of this study proficiency with technology is defined as having the skills to figure out how to use

technologies.

In a study exploring the relationships among individual teacher characteristics that

might change through experience and instruction in pre‐service teacher education, Ropp (1999)

21

conducted a study with 53 teacher candidates enrolled in two sections of a teacher preparation

course. A set of surveys, designed to assess the aptitudes, experiences, and individual

characteristics associated with learning to use computers, were administered in a pretest‐

posttest design. Data was analyzed using Pearson correlation coefficients, correlational

analyses, and paired t‐tests. Both pretest and posttest data was correlated with five

characteristics as measured by the instruments used in the study, background variables, and

experiences. Findings revealed significant correlations among all but computer coping

strategies. However, significant improvements in technology proficiency, computer self‐

efficacy, and computer coping strategies occurred from the beginning to end of the course.

These results would suggest that students who are less competent and have the most to learn

are the most anxious about learning to use computers. Furthermore, previous research has

identified a gap that exists between the technological knowledge and skills pre‐service teachers

possess and their confidence in using this knowledge and skills to successfully integrate

technology in the classroom (Pope et al., 2005).

In a similar study that investigated whether a single educational technology course

could have an impact on perceived computer ability and attitudes toward technology, Lambert,

Gong, and Cuper (2008) utilized a pretest post‐test group design with 62 pre‐service teachers.

The instruments used in the study consisted of a demographic questionnaire, an instrument to

measure technology knowledge, skills and dispositions, and a questionnaire to measure

teachers’ attitudes toward computers. Data were analyzed using a one‐way analysis of variance

(ANOVA), independent‐samples t‐tests, paired‐samples t‐tests, and a univariate analysis of

covariance (ANCOVA). Results indicated that a single course greatly impacted perceived

22

computer ability but not general computer attitudes. In addition, course instruction as well as

prior technology experience was found to have a significant influence on pre‐service teachers’

ability to understand the usefulness of integrating technology in the classroom. Furthermore,

the researchers found that student outcomes were strongly related to the use of particular

instructional strategies that accommodate widely varying experience levels in learners (Lambert

et al., 2008).

In an attempt to explore in‐service teachers’ changing knowledge, skill, and dispositions

toward technology within a graduate teacher education program, Topper (2004) conducted a

study with three groups consisting of 53 in‐service teachers. A self‐assessment instrument was

administered in a pretest‐posttest design. Data collected from the self‐assessment instrument

along with artifacts from classes were examined and compared within and across groups.

Quantitative data analysis suggests that in‐service teachers enter graduate programs with the

same limited set of skills and knowledge that pre‐service teachers leave undergraduate

programs (Tooper, 2004). However, these skills can be upgraded when teachers are exposed to

a course in educational technology. Studies such as these suggest that while teacher education

programs are doing a decent job of preparing pre‐service teachers, more can be done in an

effort to increase pre‐service teachers’ technological knowledge and help them to begin to

integrate technology into their teaching practices.

Technological Knowledge

Technological knowledge can be defined as ones knowledge about various technologies,

ranging from low‐tech technologies, such as pencil and paper, to digital technologies such as

the Internet, digital video, interactive whiteboards, and software programs (Schmidt et al.,

23

2009). Niederhauser, Salem, and Fields (1999) reported “the trend in education today has

moved from the transmission of didactic pedagogy to a leaner‐centered constructivist

approach” (p. 154). Pope et al. (2005) stated, “this movement has led to the need of

strengthening the constructivist instructional methods in the classroom” (p. 574).

Constructivists believe that students acquire knowledge through active participation with the

learning environment. Studies support that using models of integration of technology in

teacher education programs can help to increase technological knowledge of pre‐service

teachers and ultimately influence their use of technology in the classroom (Collier, Weinburgh,

& Riveria, 2004; Doering, Hughes, & Huffman, 2003; Pope et al., 2005; Topper, 2004).

In a an investigation of how a model of technology instructional delivery impacted the

self‐reported confidence level and use of technology of pre‐service teachers, Pope et al. (2005),

conducted a time series analysis with a sample of 26 pre‐service teachers seeking certification

in an elementary education program. The instruments used in the study consisted of one survey

to measure the technology proficiency level of pre‐service teachers and a second survey to

measure the use of specific technology practices by pre‐service teachers during student

teaching. Findings indicated that the pre‐service teachers’ confidence level in integrating

specific technologies into their teaching practices increased during the duration of the study. In

addition, the pre‐service teachers demonstrated a higher use of the technologies in which they

had more confidence and with the technologies that their supervising teachers used in the

classroom (Pope et al., 2005).

In a somewhat similar study, Collier et al. (2004) conducted a mixed methods study to

assess the effectiveness of technology infusion in an initial certification program. The purpose

24

of the study was to determine what technology skills prospective teachers should develop prior

to student teaching. Thirteen undergraduate faculty members and 43 early

childhood/elementary education majors in their junior and senior years participated in the

study. Data was collected using information from faculty, course syllabi, and pre‐service teacher

self‐assessment surveys. Results of the study supports “the effectiveness of integrating

deliberately scaffolded hands‐experiences and increased modeling of technology to elevate

future teachers’ ability to select and use appropriate technologies in the instructional setting”

(Collier et al., 2004, p. 448).

In an attempt to understand how a group of pre‐service teachers envisioned the use of

technology within their future classroom before and after participating in an innovative

technology component of a teacher educator preparation program, Doering, Hughes, and

Huffman(2003), conducted a case study with a sample of 10 pre‐service teachers enrolled in a

master’s degree in education program. Data was collected from three focus group interviews

and written reflections. A constant comparative method (Glaser & Strauss, 1967) was used to

analyze the data. Research showed that pre‐service teachers developed, to a limited extent, “a

thinking with technology perspective” (Doering et al., 2003, p. 342). However, only one

participant was able to generate new ideas and purposes for using technology in their future

classroom. Furthermore, most of these pre‐service teachers expressed fear that they were not

experts in the use of technology and therefore resisted integrating any technology that they did

not extensively understand (Doering et al., 2003). Studies such as these provide support for

further investigations that could provide insight into strategies that teacher education

25

programs can implement to better prepare pre‐service teachers to integrate technology in their

future classrooms.

Peer Mentoring/Paired Grouping and Pre‐service Teacher Education

The concept of peer mentoring/paired grouping is grounded in Vygotsky’s notion of the

role of social interaction in supporting students’ development. Vygotsky (1978) contended that

an individual’s performance can be described in terms of two levels, a developmental level and

level of potential development. A student’s actual developmental level is indicative of what a

student can do independently at a given time, whereas the level of potential development

reflects what a student can do with support or assistance. It is said that the distance between

these two levels is described as the zone of proximal development. The ZPD is the space where

learning occurs. A goal of education is to keep learners in their own ZPD but with tasks that are

slightly more difficult than what they do alone (Roosevelt, 2008). Shabani, Khatib, and Ebadi

(2008) state “that after completing the task jointly, the learner will likely be able to complete

the same task individually next time, and through that process, the learner’s ZPD for that

particular task will have been raised” (p. 238). At times tasks assigned to learners will fall

outside of the ZPD that the learner can already do, or tasks that the learner would not able to

do even with help. Furthermore, Shabani et al. (2008) concluded “the focus of teaching is on

tasks inside the ZPD which the learner cannot do by him or herself but has the potential to

accomplish with the guidance of others” (p. 238). A teacher or more capable peer is able to

provide guidance that enables the learner to develop strategies or understanding that they

would not have been capable of on their own (Many, Dewberry, Taylor, & Coady, 2009).

26

It should be noted that there is little literature devoted to peer mentoring in the field of

education. Therefore, to gain a more comprehensive understanding of peer mentoring,

literature from other fields was also examined. Based on the literature there is no single

definition of peer mentoring. Even in the context of pre‐service teacher education, the

definitions vary greatly. Smith (2007) defines mentoring as “a particular mode of learning

wherein the mentor not only supports the mentee, but also challenges them productively so

that progress is made” (p.277). However, Fairbanks, Freedman and Kahn (2000) define

mentoring in teacher education as “complex social interactions that mentor teachers and

student teachers’ construct and negotiate for a variety of professional purposes and in

response to the contextual factors they encounter” (p.103).

Mentoring as described in the literature usually involves supporting and providing

feedback without judgment or criteria and can be identified by different approaches. One

approach, alternative mentoring, is a contemporary concept and includes several models of

mentoring relationships such as peer mentoring (Mullen, 2005). Draves and Koops (2011) state

“alternative mentoring is non‐hierarchical and centers on best practices that may vary from one

context to another” (p. 68). Alternative mentoring practices seek to meet the needs of the

group or individuals involved. Nonhierarchical mentoring relationships alleviate problems such

as isolation and self‐doubt (Mullen, 2005). Additionally, alternative mentoring allows for free

exchange of ideas among members (McCormack & West, 2006).

As defined by Mullen (2005), co‐mentoring is an alternative mentoring concept in which

mutual and reciprocal learning takes place. In a co‐mentoring relationship, each participant

embodies both teaching and learning roles (Mullen, 2005). One of the most common types of

27

co‐mentoring is known as peer coaching or peer mentoring (Mullen, 2005). In a peer mentoring

relationship, two individuals or a group engage in a “mutual, non‐evaluative relationship”

(Mullen, 2005, p.74). Within a peer mentoring relationship, “everyone acts as both mentor and

mentee and all participants share a similar status” (Draves & Koop, 2011, p. 68).

Peer mentoring in pre‐service education is a process in which teams of pre‐service

teachers regularly observe each other to provide assistance, suggestions, and support (Joyce &

Showers, 1980). Peer mentoring in education is sometimes referred to as peer coaching,

learner‐centered supervision, peer supervision, or cognitive coaching (Britton & Anderson,

2010). “Peer mentoring has been implemented into teacher education programs as a means of

providing pre‐service teachers with additional feedback and collegial support and to promote

reflective practice during early field experiences” (Jenkins et al., 2002, p. 48). Benefits of peer

mentorship in pre‐service teacher education include providing a valuable tool for collaboration,

evaluation of teacher effectiveness, and improvement in teacher quality (Marshall, 2005). For

the purpose of this study peer mentoring will be defined as paired grouping and paired

grouping is defined as grouping students of varying abilities to meet instructional needs. In

addition, a paired group member is another student who can serve as a resource, a helping

hand, a sounding board, and provide support, encouragement, and information to another

paired group member.

In a an investigation of a task‐centered, peer mentoring group initiated by a group of

female junior faculty to support one another toward tenure and work/life balance , Goeke et al.

(2011), conducted a qualitative study with six female tenure‐track assistant professors. The six

female tenure‐track faculty meet once a month for two hours in an attempt to provide support

28

and accountability for the completion of scholarly products. The meetings were audiotaped

with the agreement that the participants would write about the group process. Data for the

study was collected over the course of 12 monthly meetings and consisted of task contracts,

audio‐taped recordings of meetings, and participants’ written reflections. Data was analyzed

using the constant comparative analysis and a group coding process. Findings indicated that

strategic support and sisterhood were two aspects of peer mentoring practice that were

particularly valuable for achieving scholarly productivity and work/life balance.

In a somewhat similar study, Steele, Fisman and Davidson (2013) conducted a mixed

methods study designed to understand factors that may be barriers to recruitment and

retention of academic junior faculty. The purpose of the study was to explore the views of

junior faculty toward informing mentorship program development. One hundred seventy‐five

junior faculty members participated in the quantitative portion of the study and 27 junior

faculty participated in the qualitative portion of the study. Data was collected from

questionnaires, focus groups, and individual interviews. Results of the study indicate that

“having role models increased commitment to an academic career; mentorship experience

during residency training was a high incentive to pursue and academic career; and junior faculty

did have identifiable mentorship experiences” (Steele et al., 2013, p.e1130).

In an attempt to explore the impact of peer mentoring on the learning culture in

universities in Pakistan, Naseem (2013), conducted a pilot peer mentoring project with 80

mentors and 145 mentees from two universities. The purpose of the study was to investigate

students’ involvement in peer mentoring and see if peer mentoring could transform learning in

the institution and promote skills for lifelong learning and increased social cohesion. Data was

29

collected from questionnaires, focus groups, interviews, and informal discussions. Results

demonstrate that peer mentoring has a beneficial impact on improvement on results,

progression, and retention. In addition results demonstrate that peer mentoring has the

potential to enhance peer‐support between diverse groups within a university.

To determine if creating a college‐going culture through long‐term mentoring of

academically and economically at‐risk students would have a positive impact on students’

education, Radcliffe and Bos (2011), conducted a seven‐year longitudinal study employing a

quasi‐experimental design with 50 students enrolled in a rural school. Data sources included

surveys, interviews, written reflective statements, student projects, and student enrollment

and academic performance measures. Quantitative data were analyzed using SPSS (Statistical

Package for the Social Sciences) to determine mean scores and to analyze variation among

groups while qualitative data were reviewed by the researchers looking for repetition of terms

and statements. Findings suggest that improvements in students’ college perceptions, state

mandated test scores, and high school perseverance may be associated with mentor‐led

initiatives.

Although research on peer mentoring in pre‐service teacher education is sparse, the

research does support that pre‐service teachers’ who participate in peer mentoring or peer

coaching have positive increases in attitude, self‐efficacy, and motivation (Goker, 2006; Lu,

2010; Woullard & Coats, 2004). In an attempt to determine if a pre‐service teacher mentoring

program could affect changes in emotions, attitudes, and anxieties of students about the

teaching profession, Woullard and Coats (2004), conducted a quantitative study using a quasi‐

experimental research design with 60 education majors. The participants were divided into two

30

groups in which one group worked with a peer mentoring program and the other group did not.

An analysis of covariance (ANCOVA) was used to estimate the difference between the two

groups. Results indicated that while there was no statistically significant difference between

groups with respect to changes in emotions and anxiety, there was a statistically significant

difference between groups with respect to attitudinal changes (Woullard & Coats, 2004).

Lu (2010) conducted a literature review in an attempt to identify similarities and

differences in peer coaching and to examine its feasibility and challenges in pre‐service teacher

education. The researcher reviewed eight studies covering the years 1997 through 2007.

According to Lu (2010), results of the literature review revealed that “peer coaching appears to

possess unique advantages and have much value for pre‐service teacher candidates’ education”

(p.748). Furthermore, these advantages could sustain the feasibility and serve as a rationale for

the incorporation of peer coaching in pre‐service teacher education (Lu, 2010). Goker’s 2006

study exploring the impact of peer coaching on self‐efficacy and instructional skills in TEFL

teacher education suggested that peer coaching could be a vehicle to develop self‐efficacy. This

finding is consistent with results of previous studies on peer mentoring and pre‐service teacher

education (Draves & Koop, 2011; Joyce & Showers, 1980; Woullard & Coats, 2004). Although

there is limited research on peer mentoring in pre‐service teacher education, the literature that

is currently available indicates that peer mentoring is a valuable process that can have a

positive impact on pre‐service teachers.

Summary

The review of literature serves as a foundation to support the constructs of this study by

including information regarding: (a) technology integration, (b) attitudes toward technology, (c)

31

proficiency with technology, (d) technological knowledge, and (e) peer mentoring/paired

grouping and pre‐service teacher education. The following methodology chapter will explain

the exact procedures to be utilized by the researcher during the study. Chapter 3 includes an

overview of the research problem, operationalization of constructs, research purpose and

questions, research design, population and sample, instrumentation, data collection

procedures, data analysis, privacy and ethical considerations, and research design limitations

for this study.

32

CHAPTER 3

METHODOLOGY

The purpose of this study was to investigate the influence of paired grouping on student


and perceived technological knowledge after completing an educational technology course.

Additionally, student participants’ perceptions regarding the use of paired grouping on their

attitudes, proficiency, and technological knowledge with regard to technology was also

investigated. This study collected survey and interview data from a purposeful sample of

student participants enrolled in an educational technology course within a pre‐service teacher

education program at a suburban mid‐sized Gulf Coast university in the southern United States.

Quantitative data were analyzed using frequencies, percentages, means, standard deviations,

and two‐tailed paired t‐tests, while an inductive coding process was used to analyze the

qualitative data. This chapter presents an overview of the research problem, operationalization

of constructs, research purpose and questions, research design, population and sample,

instrumentation, data collection procedures, data analysis, privacy and ethical considerations,

and research design limitations for this study.

Overview of Research Problem

Technology integration in the EC‐12 classroom is on the rise placing demands on

classroom teachers to have positive attitudes toward technology, be proficient with technology,

and knowledgeable in using technology in order to help students acquire the skills needed to

use 21st century technology tools. One strategy that has been implemented into pre‐service

teacher education programs that has been used “as means of providing pre‐service teachers

33

with additional feedback and collegial support” (Jenkins et al., 2002, p. 48) is peer mentoring. A

number of studies have been conducted on peer mentoring and pre‐service teacher education

and these studies support that pre‐service teachers’ who participate in peer mentoring have

positive increases in attitude, self‐efficacy, and motivation (Goker, 2006; Lu, 2010; Wollard &

Coats, 2004). However, few studies link peer mentoring and pre‐service teachers’ attitudes

toward technology, proficiency with technology, and technological knowledge in using

technology. Therefore, there is a need to examine the influence of peer mentoring/paired

grouping on student participants’ perceived attitudes, perceived proficiency, and perceived

technological knowledge with regard to technology.

Operationalization of Theoretical Constructs

This study consists of three constructs: (a) attitudes toward technology, (b) proficiency

with technology, and (c) technological knowledge in using technology. Attitude toward

technology is defined as what teachers believe about using technology in the classroom and will

be measured using the Attitude Toward Technology Scale (ATTS) survey instrument. Proficiency

with technology is defined as having the skills to figure out how to use technologies and will be

measured using the Technology Proficiency Self‐Assessment (TPSA C21) survey instrument.

Technological knowledge in using technology is defined as knowledge about various

technologies, ranging from low‐tech technologies, such as pencil and paper, to digital

technologies, such as the Internet, digital video, interactive whiteboards, and software

programs and will be measured using the Technological Knowledge Assessment Tool (TK)

survey instrument.

34

Research Purpose and Questions



and perceived technological knowledge after completing an educational technology course. The

current study answers the following research questions:


attitudes toward technology after completion of an educational technology course

as measured by the Attitude Toward Technology Scale (ATTS) when participants are

grouped based on paired grouping?


proficiency with technology after completion of an educational technology course as

measured by the Technology Proficiency Self‐Assessment for 21st Century Learning

(TPSA C21) when participants are grouped based on paired grouping?


technological knowledge in using technology after completion of an educational

technology course as measured by the Technological Knowledge Assessment Tool

(TK), a component of the TPACK Assessment Tool, when participants are grouped

based on paired grouping?

4. How do student participants’ perceptions of the paired grouping influence their

attitudes, proficiency, and technological knowledge with regard to technology?

35

Research Design

For this study, a case study utilizing quantitative and qualitative data collection was

employed. Merriam (2009) defines a case study as “an in depth description and analysis of a

bounded system” (p. 43). According to Yin (1994), the form of the research question(s) provides

an important clue regarding the most relevant research strategy to use. Research questions

including “how” or “why” indicate that a case study is the most appropriate research method.

One purpose for a case study is to develop an understanding of a complex phenomenon in its

natural context and from the perspective of the participants involved in the phenomenon (Gall,

Gall, & Borg, 2003). Yin (2013) explains:

The classic case study consists of an in‐depth inquiry into a specific and complex

phenomenon (the ‘case’), set within its real‐world context. To arrive at a sound

understanding of the case, a case study should not be limited to the case in isolation but

should examine the likely interaction between the case and its context” (p. 321).

Case studies are appropriate when “concentrating on a single phenomenon or entity

(the case) and the researcher aims to uncover the interaction of significant factors

characteristic of the phenomenon” (Merriam, 2009, p. 43). Flyvbjerg (2006) states, “one can

often generalize on the basis of a single case, and the case study may be central to scientific

development via generalization as supplement or alternative to other methods” (p. 305). For

this study, the phenomena of interest is paired grouping and its influence on perceived

attitudes, perceived proficiency, and perceived technological knowledge in regard to

technology from the perspective of student participants enrolled in an educational technology

course. In this particular study, education students seeking certification are required to take

36

particular courses within their program of study. The particular course in this study is the only

course within the program of study that is utilizing paired grouping. Given that this course is the

only course utilizing paired grouping this would be considered a bound case.

A purposeful sample of student participants were solicited from three sections of an

undergraduate educational technology course taught at a suburban mid‐sized Gulf Coast

university in the southern United States. In the quantitative portion of the study, student

participants’ were paired individually and placed into one of four categories in each class based

on self‐reported attitudes toward technology, proficiency with technology, and technological

knowledge in using technology as measured using the ATTS, TPSA C21, and TK survey

instruments. For the qualitative portion of the study, a purposeful sample of participant’s

representative of each pairing, were selected from each class for interviews based on

participants’ responses to participate in the interviews. Quantitative data was analyzed using

descriptive statistics and a two‐tailed paired t‐test, while qualitative data was analyzed using an

inductive coding process.

The Course

The educational technology course for this study is a 15‐week required core

undergraduate‐level course and the researcher is the instructor of the course. The course was

developed to introduce pre‐service teachers to the tools and skills necessary to understand and

operate computers, navigate the World Wide Web, and utilize a variety of multimedia and web‐

based technology tools. The course includes educational applications of technologies that

promote the integration of technology into the EC‐12 classroom. Pre‐service teachers gain

37

experience in the educational use of such technologies as productivity tools, presentation

graphics, multimedia, and web‐based technologies.

Population and Sample

For this study, the population consisted of undergraduate students from the campus of

a suburban mid‐sized Gulf Coast university in the southern United States. For more than three

decades the university has been an academic resource specializing in upper‐level

undergraduate and graduate degree programs to meet the needs of a diverse population. Table

1 illustrates the university student populations including classification, gender, status, and

ethnicity. In the spring 2015 semester there, were a total of 8,331 students enrolled in the

university, that included 946 undergraduate and 497 graduate students from the School of

Education. Table 2 displays the university demographics for the School of Education including

classification, gender, status, and ethnicity. A purposeful sample of undergraduate students

enrolled in three sections of an educational technology course within the School of Education

were asked to complete the Attitudes toward Technology Scale (ATTS), Technology Proficiency

Self‐Assessment for 21st Century Learning (TPSA C21), and Technological Knowledge

Assessment Tool (TK) surveys and placed into one of four categories based on self‐reported

attitudes toward technology, proficiency with technology, and technological knowledge in using

technology (see p. 35 for more detail on the pairings). From the initial paired groupings, a

purposeful sample of participant’s representative of each pairing, were selected from each class

for semi‐structured interviews based on participants’ responses to participate in the interviews.

38

Table 1

Student Demographics of University

University

Students Frequency Percentage

Total 8,331 100.0

Classification

Undergraduate 5,242 62.9 Graduate 3,089 37.1

Gender Male 3,189 38.3

Female 5,142 61.7Status

Full Time 3,965 47.6 Part Time 4,366 52.4

Race/Ethnicity

White 3,182 38.2

Black 759 9.1

Hispanic 2,221 26.7

Asian 519 6.2

American Indian 25 0.3

International 1,312 15.7

Unknown 104 1.2

Hawaiian/Pacific Islander 6 0.1

Multiracial 203 2.4

39

Table 2

Student Demographics of School of Education

School of Education

Students Frequency Percentage

Total 1,443 100.0

Classification

Undergraduate 946 65.6

Graduate 497 34.4

Gender Male 166 11.5Female 1,277 88.5

Status

Full Time 469 32.5

Part Time 974 67.5

Race/Ethnicity

White 610 42.3

Black 171 11.9

Hispanic 568 39.4

Asian 49 3.4

American Indian 3 0.2

International 9 0.6

Unknown 10 0.7

Hawaiian/Pacific Islander 0 0.0

Multiracial 23 1.6

Pairings

For each class the student participants were individually paired and placed into one of

four categories (see Table 3). The pairings were based on self‐reported attitudes toward

technology, proficiency with technology, and technological knowledge in using technology as

measured using the ATTS, TPSA C21, and TK survey instruments. Pairings were not based on any

other criteria such as gender, race/ethnicity, or age. Scores for proficiency and knowledge were

collapsed into one composite score and titled proficiency. Median scores were calculated for

the attitude and proficiency composites and used to determine which category to place each

40

participant into. Participants who scored above the mean were considered to be in the top half

of the class and participants who scored below the mean were considered to be in the bottom

half of the class. Once participants were identified as either “top or bottom” for attitude and

proficiency, participants were then paired accordingly.

Table 3

Number of Paired Groupings by Class

Paired Groupings Class 1 Class 2 Class 3

Bottom Attitude/Bottom Proficiency 4 4 5

Top Attitude/Bottom Proficiency 3 2 2 Bottom Attitude/Top Proficiency 3 2 2 Top Attitude/Top Proficiency 4 5 4

Instrumentation

Attitude Toward Technology Scale

The Attitude Toward Technology Scale (ATTS) instrument was developed and piloted

during the evaluation of the teacher preparation program at the University of Houston–Clear

Lake (UHCL) by Kajs et al. (2001). The purpose of the instrument is to monitor teacher beliefs

about technology and how those beliefs change during teacher training. Items contained in the

survey have teachers express their attitudes toward the influences of technology in working

directly with students, as an evaluation tool, as an engagement strategy, and as an

organizational or presentation tool for teachers.

The ATTS instrument utilizes a 31‐item survey using a 5‐point Likert Scale to evaluate

teacher attitude with technology in the classroom (Kajs et al., 2001). Responses range from

1 = Strongly Disagree to 5 = Strongly Agree. The survey includes reverse coded items such as

41

“time spent incorporating technology could be better spent teaching the basics” and “the

benefits of technology to education are overrated” to increase the reliability of the survey in

confirming consistent responses from participants. Item numbers 3, 5, 8, 11, 19, 20, 24, 25, 26,

and 29 are reverse coded. It should be noted that the researcher reverse coded the items in the

analysis of this study for reliability purposes. Composite scores were calculated ranging from 31

to 155. Higher composite scores indicate a more positive attitude about technology use in the

classroom. The Cronbach’s alpha reliability coefficient was reported to be 0.98 (Kajs et al.,

2001).

Technology Proficiency Self‐Assessment for 21st Century Learning

The Technology Proficiency Self‐Assessment for 21st Century Learning (TPSA C21) is an

adapted version of the TPSA developed by Ropp (1999). The purpose of the newly revised TPSA

C21 was to include two new scales that focus on emerging technologies (teaching with

emerging technologies and emerging technology skills). Items contained in the survey have pre‐

service teachers indicate their attitude on items such as e‐mail, the World Wide Web (WWW),

integrated applications, and integrating technology into teaching.

The TPSA C21 instrument utilizes a 34–item survey using a five‐point Likert type scale to

measure pre‐service and in‐service teacher confidence in their competence based on a

technology proficiency checklist and emerging technologies (Christensen et al., 2015; Ropp,

1999). Responses range from 1 = Strongly Disagree to 5 = Strongly Agree. Composite scores

were calculated by totaling the scores for all individual responses producing values ranging

from 34 to 170. Higher composite scores indicate a more positive proficiency with using

technology. The TPSA C21 has been found to be a reliable and valid measurement, maintaining

42

“respectable reliability estimates ranging from .73 to .86, while the two new scales focusing on

emerging technologies yielded Cronbach’s Alpha internal consistency reliability estimates of .84

and .91” (Christensen et al., 2015, p. 1130).

Technological Knowledge Survey

The Technological Knowledge Survey (TK) is a component of the Technological

Pedagogical Content Knowledge (TPACK) Assessment Tool. The purpose of the TK Assessment

Tool is to measure pre‐service and in‐service teacher technology knowledge. According to

Harris, Mishra, and Koehler (2009):

Technological knowledge requires a deeper, more essential understanding and mastery

of technology for information processing, communication, and problem solving than

does the traditional definition of computer literacy. Also, this conceptualization of TK

does not posit an "end state," but rather assumes TK to be developmental, evolving

over a lifetime of generative interactions with multiple technologies (p. 398).

The TK instrument utilizes a six‐item survey using a five‐point Likert scale to measure

pre‐service teachers’ technological knowledge (TK) with technology. Responses range from

1 = Strongly Disagree to 5 = Strongly Agree. Composite scores were calculated by totaling the

scores for all individual responses producing values ranging from six to 30. Higher composite

scores indicate a more positive feeling about technological knowledge with technology. The

Cronbach’s alpha reliability coefficient for the TK subscale has been found to be .82 (Schmidt et

al., 2009). This value suggests high internal consistency.

43

Data Collection Procedures

After receiving Internal Review Board (IRB) approval from both the researcher’s

institution and the participating university, data were collected during one semester of a 15‐

week educational technology course. The ATTS, TPSA C21, and TK surveys were administered

online and responses collected through SurveyMonkey. The survey was available for a one‐

week period allowing participants sufficient time to complete the survey. Participants were

asked to complete the surveys during Week one of the course and again during Week 12 of the

15‐week course. Data was stored on a flash drive and the hard drive of the researcher’s laptop.

At all times, data was secured in a password‐protected folder on the researcher’s laptop and on

a flash‐drive located in the researcher’s personal residence.

Semi‐structured interviews were conducted during the second half of the course. All

participants that completed the surveys were invited to participate in the interviews. Of the 83

student participants, 68 volunteered to participate. A random sample of 24 of the 68

participants that volunteered were selected for interviews. Of the 24 interview participants,

eight per class were purposefully selected from each paired grouping category. After selecting

participants for interview, the researcher contacted the 24 volunteers individually via email.

Twenty‐four student participants responded to the email request; none declined participation.

Following their agreement to interview, meetings were scheduled at mutually agreeable

dates and times. All participants elected to interview during their scheduled class time. The

interviews were held individually in a conference room on the participating university campus.

Prior to interviewing, each participant was informed of the purpose of the study, approximate

time for the interview, and that participation was voluntary through a written informed consent

44

form. All interviews were completed during Week 14 of the course. Interviews ranged in length

from 15 to 25 minutes and were semi‐structured in format. A set of questions was developed

for use during the interviews based on a review of the literature (see Appendix A). Interview

sessions were audio recorded and transcribed by the researcher. Following the study’s

completion, the researcher will maintain the data for five years, the required time set forth by

IRB. Once the deadline has passed, the researcher will destroy all data files. Electronic versions

will be deleted and hardcopy versions will be shredded.

Data Analysis

Quantitative

Following data collection, pre and post‐survey responses were downloaded into an Excel

spreadsheet and transferred into Statistical Package for Social Sciences (SPSS) for statistical

analysis. To answer Research Question 1, Is there a statistically significant mean difference in

student participants’ perceived attitudes toward technology after completion of an educational

technology course as measured by the Attitude Toward Technology Scale (ATTS) when

participants are grouped based on paired grouping? a two‐tailed paired t‐test was conducted to

determine if there was a statistically significant mean difference between pre and post survey

scores. The independent variable was participation in a paired grouping experience, while the

independent variable or outcome was the change in pre to post survey scores. Cohen’s d and

coefficient of determination (r2) were utilized to calculate effect sizes (Cohen, 1988). In

addition, frequencies, percentages, means, and standard deviations were calculated on

participant responses to each survey item.

45

To answer Research Question 2, Is there a statistically significant mean difference in

student participants’ perceived proficiency with technology after completion of an educational

technology course as measured by the Technology Proficiency Self‐Assessment for 21st Century

Learning (TPSA C21) when participants are grouped based on paired grouping? a two‐tailed

paired t‐test was conducted to determine if there was a statistically significant mean difference

between pre and post survey scores. The independent variable was participation in a paired

grouping experience, while the independent variable or outcome was the change in pre to post

survey scores. Cohen’s d and coefficient of determination (r2) were utilized to calculate effect

sizes (Cohen, 1988). In addition, frequencies, percentages, means, and standard deviations

were calculated on participant responses to each survey item.

To answer Research Question 3, Is there a statistically significant mean difference in

student participants’ perceived technological knowledge in using technology after completion

of an educational technology course as measured by the TPACK Assessment Tool when

participants are grouped based on paired grouping? a two‐tailed paired t‐test was conducted to

determine if there was a statistically significant mean difference between pre and post survey

scores. The independent variable was participation in a paired grouping experience, while the

independent variable or outcome was the change in pre to post survey scores. Cohen’s d and

coefficient of determination (r2) were utilized to calculate effect sizes (Cohen, 1988). In

addition, frequencies, percentages, means, and standard deviations were calculated on

participant responses to each survey item.

46

Qualitative

To answer Research Question 4, How do student participant perceptions of the paired

grouping influence their attitudes, proficiency, and technological knowledge with regard to

technology? semi‐structured interviews were audio recorded and then later transcribed.

Qualitative interview data was analyzed using an inductive coding process – organizing the data

based upon common patterns or themes, thereby giving structure to conclusions based on the

data (Mertler, 2006). Saldana (2013) describes a code as “most often a word or phrase that

symbolically assigns a summative, salient, essence‐capturing, and evocative attribute for a

portion of language‐based or visual data” (p. 3). The interview transcripts were hand‐coded by

the researcher. Data were first organized into meaningful categories through coding (Coffey &

Atkinson, 1996). During this process relevant concepts were identified. The coding process

began by marking segments of the text, creating categories, and assigning codes. After

appropriate codes were identified, emphasis was placed on the search for themes and patterns

from the data (Coffey & Atkinson, 1996). Supporting quotes were then organized under each

theme. A narrative description of the findings was presented with a detailed discussion of the

participant’s perceptions. To ensure reliability a second coder was asked to work independently

to verify the codes and themes found by the researcher. This information was used in

conjunction with the quantitative data to provide a more comprehensive view of student

participant perceptions of paired grouping and what the connections were to the use of paired

grouping on their perceived attitudes, perceived proficiency, and perceived technological

knowledge with regard to technology. To ensure credibility and trustworthiness, all twenty‐four

participants interviewed by the researcher were given the opportunity to view their transcribed

47

interviews for accuracy. Of the twenty‐four participants, one did not respond to the request for

review. However, 23 agreed to read the transcript. Feedback confirmed the interpretation of

the respective views.

Privacy and Ethical Considerations

The researcher gained IRB approval from both the researcher’s institution and from the

university on which the study took place before data was collected. A cover letter and link to

the survey were provided in a discussion forum within the Blackboard Learning Management

System (LMS) at the participating university. The cover letter stated that participation was

voluntary, that the approximate timeframe to complete the survey was 20 minutes, and that

personal identifying information would be kept confidential. Similarly, participants in interviews

were provided information about the purpose of the study, approximate timeframe, and that

participation is voluntary through a written consent. Confidentiality was maintained through

the use of pseudonyms of interview participants. At all times, data was secured in the

researcher’s personal residence within a password‐protected folder on the researcher’s laptop

and on a flash drive. Upon completion of the study, data will be stored for five years and then

destroyed. Electronic versions will be deleted and hardcopy versions will be shredded.

Research Design Limitations

There were several design limitations in this study. First, the sample was derived from

student participants from only one university. Therefore, the generalizability of the findings is

limited to this university. Second, survey instruments were used to gather information

regarding student participant perceptions of attitude, proficiency, and technological knowledge

with regard to technology, so the data will only be as accurate as the honesty of the

48

respondents. In addition, student participants may have responded to questions based on their

prior experiences instead of their current preferences, which may have affected the accuracy of

the data. Third, each participant’s level of technology skill may vary based on the participant’s

prior experiences and training which may affect the participant’s responses on the surveys.

Fourth, the participants are at various points in their education, so responses may have been

reflective of prior experiences within their program. Fifth, the current study did not utilize a

control group, therefore it cannot be said affirmatively that the paired grouping resulted in the

changes in the participants’ perceived attitudes, perceived proficiency, and perceived

technological knowledge in regard to technology. The changes could be attributed to the

participants enrolling in and participating in the class. Finally, this study focused on the

perceptions of student participants enrolled in an educational technology course that is geared

toward pre‐service teachers, so participants’ who are not education majors may have

responded to questions based on their own experiences, which may have affected the data.

Summary

Chapter 3 discussed the research purpose, questions, and research design methods that

were employed in this study. The purpose of this study was to investigate the influence of

paired grouping on student participants’ perceived attitudes toward technology, perceived

proficiency with technology, and perceived technological knowledge after completing a

required educational technology course. Additionally, student participants’ perceptions

regarding the use of paired grouping on their attitudes, proficiency, and technological

knowledge with regard to technology was also investigated. The instruments used to collect

data were the Attitudes Toward Technology Scale (ATTS), the Technology Proficiency Self‐

49

Assessment for 21st Century Learning (TPSA C21), and the Technological Knowledge Survey, a

component of the TPACK Assessment Tool (TK). The qualitative component of Chapter 3 was

collected through semi‐structured interviews on student participants’ perceptions of paired

grouping’s influence on their perceived attitudes, perceived proficiency, and perceived

technological knowledge with regard to technology. Chapter 4 will report the findings of the

ATTS, TPSA C21, and TK as well as all of the interview data that was collected. Results will be

reported in the following order: demographic characteristics of the participants, instrument

reliability, results of data collection for all four of the research questions, and a summary of the

findings.

50

CHAPTER 4

RESULTS



and perceived technological knowledge after completing an educational technology course.

Additionally, student participants’ perceptions regarding the use of paired grouping on their

attitudes, proficiency, and technological knowledge in regard to technology was also

investigated. This chapter presents the results from the quantitative and qualitative data

analysis of this study. Survey results were analyzed comparing the pre and post responses.

Interview data was used to support the results of pre and post and survey comparisons. This

chapter begins with a presentation of participant demographics, instrument reliability, and data

analysis for each of the four research questions, concluding with a summary of findings.

Demographic Characteristics of the Participants

Survey Participants

The 83 student participants participating in the quantitative portion of the study see

Table 4) consisted of 84.3% female (n = 70) and 15.7% male (n = 13) student participants. With

regard to race/ethnicity, the participants fell into six groups. Three (3.6%) identified as Asian,

five (6.0%) identified as Black or African American, 39 (47.0%) identified as Hispanic or Latino,

one (1.2%) identified as Native Hawaiian or Pacific Islander, 33 (39.8%) identified as White or

Caucasian, and two (2.4%) identified as two or more races. Student participants ranged in age

from 18 to 54 years of age, with 59 (71.1%) of the 83 student participants ranging between 18

and 24, 14 (16.9%) between 25 and 34, eight (9.6%) between 35 and 44, and two (2.4%)

51

between 45 and 54 years of age. Certification levels sought by the student participants’

included 51 (61.4%) Early Childhood‐6th grade, seven (8.4%) 4th‐8th grade, 19 (22.9%) 8th‐12th

grade, three (3.6%) Early Childhood/Non‐certification, and three (3.6%) non‐education majors.

The certification content area of the student participants’ consisted of 32 (38.6%) Generalist, 15

(18.1%) Bilingual, eight (9.6%) English as a Second Language (ESL), eight (9.6%) Math, one

(1.2%) Science, six (7.2%) Social Studies, six (7.2%) Language Arts, 11 (13.3%) Special Education,

three (3.6%) Art, four (4.8%) Early Childhood, and four (4.8%) Non‐certification.

Table 4

Student Participant Survey Demographics: Gender, Race/Ethnicity, Age, Certification Level Seeking, and Certification Content Area Seeking

Frequency(n)

Percentage(%)

Gender Female 70 84.3Male 13 15.7Race/Ethnicity Asian 3 3.6Black or African American 5 6.0Hispanic or Latino 39 47.0Native Hawaiian or Pacific Islander 1 1.2White or Caucasian 33 39.8Two or More Races 2 2.4

Age 18 – 24 59 71.125 – 34 14 16.935 – 44 8 9.645 – 54 2 2.4

Certification Level Seeking Early Childhood‐6th grade 51 61.44th–8th grade 7 8.48th–12th grade 19 22.9Early Childhood‐Non‐Certification 3 3.6Non‐Education Major 3 3.6

(table continues)

52

Table 4 (continued).

Frequency(n)

Percentage (%)

Certification Content Area Seeking Generalist 32 38.6Bilingual 15 18.1ESL 8 9.6Math 8 9.6Science 1 1.2Social Studies 6 7.2Language Arts 6 7.2Special Education 11 13.3Art 3 3.6Early Childhood 4 4.8Non‐Education Major 4 4.8

Interview Participants

The student participants participating in the qualitative portion of this study (see

Table 5) consisted of 79.2% female (n = 19) and 20.8% male (n = 5) student participants. With

regard to race/ethnicity, the participants fell into two groups. Thirteen (54.2%) identified as

Hispanic or Latino and 11 (45.8%) identified as White or Caucasian. Student participants ranged

in age from 18 to 44 years of age, with 18 (75.0%) of the 24 student participants being between

18 and 24, five (20.8%) between 25 and 34, and one (4.2%) between 35 and 44 years of age.

Certification levels sought by the student participants’ included 15 (62.5%) Early Childhood‐6th

grade, three (12.5%) 4th‐8th grade, four (16.7%) 8th‐12th grade, one (4.2%) Early Childhood/Non‐

certification, and one (4.2%) Non‐Education major. The certification content area of the student

participants’ consisted of 10 (41.7%) Generalist, six (25.0%) Bilingual, two (8.3%) English as a

Second Language (ESL), one (4.2%) Math, two (8.3%) Social Studies, four (16.7%) Special

Education, one (4.2%) Art, one (4.2%) Early Childhood, and two (8.3%) Non‐certification.

53

Table 5

Student Participant Interview Demographics: Gender, Race/Ethnicity, Age, Certification Level Seeking, and Certification Content Area Seeking

Frequency(n)

Percentage (%)

Gender Female 19 79.2 Male 5 20.8

Race/EthnicityAsian 0 0.0 Black or African American 0 0.0 Hispanic or Latino 13 54.2 Native Hawaiian or Pacific Islander 0 0.0 White or Caucasian 11 45.8 Two or More Races 0 0.0

Age 18 – 24 18 75.0 25 – 34 5 20.8 35 – 44 1 4.2 45 – 54 0 0.0

Certification Level SeekingEarly Childhood–6th grade 15 62.5 4th‐8th grade 3 12.5 8th‐12th grade 4 16.7 Early Childhood‐Non‐Certification 1 4.2 Non‐Education Major 1 4.2

Certification Content Area SeekingGeneralist 10 41.7 Bilingual 6 25.0 ESL 2 8.3 Math 1 4.2 Science 0 0.0 Social Studies 2 8.3 Language Arts 0 0.0 Special Education 4 16.7 Art 1 4.2 Early Childhood 1 4.2 Non‐Education Major 2 8.3

Instrument Reliability

Cronbach’s alphas were calculated to assess the reliability or internal consistency of the

three surveys used in this study and compared in Table 6 to the reliability coefficients reported

by Kajs et al. (2001), Christensen et al. (2015), and Schmidt et al. (2009). According to DeVellis

54

(2012), Cronbach’s coefficient alpha is a widely used measure of reliability. Reliability

coefficients that are greater than .70 are considered acceptable (Fraenkel & Wallen, 2006).

Table 6

Reliability Coefficients for Instrumentation

Cronbach’s a Cronbach’s b

1. Attitude Toward Technology Scale .92 .98

2. Technology Proficiency Self‐Assessment for 21st CenturyLearning

.94 .91

Email Subscale .62 .73 WWW Subscale .80 .73 Integrated Applications Subscale .85 .79

Teaching with Technology .86 .86 Teaching with Emerging Technologies .90 .91 Emerging Technology Skills .82 .84

3. TK Assessment Tool .91 .82

Note. a = Giles (2015). b = Kajs et al. (2001); Christensen et al. (2015), Schmidt et al. (2010)

Research Question 1

Research Question1 states, “Is there a statistically significant mean difference in student

participants’ perceived attitudes toward technology after completion of an educational

technology course as measured by the ATTS when participants are grouped based on paired

grouping?”

Research Question 1 was answered by conducting a two‐tailed paired t‐test to

determine if there was a statistically significant mean difference in student participants’

perceived attitudes toward technology as measured by the ATTS before and after paired

grouping. Results indicate (see Table 7) that there was a statistically significant positive mean

difference between the pre and post attitude toward technology scores, t(82) = 3.072, p< .05, d

= .307 (small effect size), r2 = .023. The paired grouping had a small effect on attitude toward

55

technology of the student participants’ and 2.3% of the variance in those scores could be

attributed to the paired groupings. Mean attitude scores rose from 114.14 to 118.8.

Table 7

Paired t‐Test: Pre Attitude Scores and Post Attitude Scores for the ATTS

Paired Groupings N M SD t‐value df p‐value d‐value r2

Pre Attitude 83 114.4 14.7 3.072 82 .003* .307 .023

Post Attitude 83 118.8 14.0

Note. *Statistically significant (p < .05)

The frequencies/percentages of individual participant responses to the ATTS survey

instrument are shown in Table 8 grouped by pre‐ and post‐ survey responses. For the ATTS, the

highest increases in attitude all pertained to questions regarding technology integration in the

classroom as being useful and improving student learning. Prior to taking the course and

working with a paired group member, 48.1% of the participants selected Strongly Agree/Agree

to the prompt of technology in the classroom improves thinking in comparison to 61.5%

following course completion (13.4% increase). Prior to taking the course and working with a

paired group member, 77.1% of the students selected Strongly Agree/Agree to the prompt of

technology in the classroom helps students learn in comparison to 90.4% following course

completion (13.3% increase). Prior to taking the course and working with a paired group

member, 72.3% of the students selected Strongly Agree/Agree to the prompt of incorporating

technology into classroom activities is worth the effort required in comparison to 83.1%

following course completion (13.2% increase). Prior to taking the course and working with a

paired group member, 16.8% of the students selected Strongly Agree/Agree to the prompt of

technology should be part of all classroom assignments in comparison to 26.5% following

56

course completion (9.7% increase). Prior to taking the course and working with a paired group

member, 73.5% of the students selected Strongly Agree/Agree to the prompt of technology in

the classroom enhances student learning in comparison to 83.1% following course completion

(9.6% increase). These positive increases in attitude could be attributed to participants’

exposure to new technologies and learning how to integrate the new technologies into their

future classroom after working with their paired group member and completing the

educational technology course.

Table 8

Attitudes Toward Technology Scale (ATTS) Instrument by Pre and Post Survey Results for Student Participants

Survey ItemStrongly Disagree

Disagree Neither

Agree Nor Disagree

Agree Strongly Agree

1. Students learn better whentechnology is included in theiractivities.

Pre 0.0 (n = 0)

4.8 (n = 4)

18.1 (n = 15)

59.0 (n = 49)

18.1 (n = 15)

Post 0.0 (n = 0 )

0.0 (n = 0 )

25.3 (n = 21 )

55.4 (n = 46)

19.3 (n = 16 )

2. Technology can beincorporated into anyclassroom subject.

Pre 0.0 (n = 0)

2.4 (n = 2)

9.6 (n = 8)

54.2 (n = 45)

33.7 (n = 28)

Post 0.0 (n = 0)

1.2(n = 1)

7.2(n = 6)

53.0 (n = 44)

38.6(n = 32)

3. Time spent incorporatingtechnology could be betterspent teaching the basics.

Pre 1.2 (n = 1)

26.5 (n = 22)

53.0 (n = 44)

15.7 (n = 13)

3.6 (n = 3)

Post 3.6(n = 3)

31.3(n = 26)

45.8(n = 38)

12.0 (n = 10)

7.2(n = 6)

4. Technology allows a teacherto capture a student'sinterest.

Pre 0.0 (n = 0)

3.6 (n = 3)

9.6 (n = 8)

59.0 (n = 49)

27.7 (n = 23)

Post 0.0 (n = 0)

0.0 (n = 0)

7.2 (n = 6)

50.6 (n = 42)

42.2 (n = 35)

5. Technology costs schoolsmore than it's worth.

Pre 9.6 (n = 8)

41.0 (n = 34)

36.1 (n = 30)

10.8 (n = 9)

2.4 (n = 2)

Post 21.7 (n = 18)

32.5 (n = 27)

37.3 (n = 31)

7.2 (n = 6)

1.2 (n = 1)

(table continues)

57



Disagree Neither

Agree Nor Disagree


6. The use of technology in theclassroom improves education.

Pre 0.0 (n = 0)

6.0 (n = 5)

19.3 (n = 16)

55.4 (n = 46)

19.3 (n = 16)

Post 1.2 (n = 1)

0.0 (n = 0)

19.3 (n = 16)

50.6 (n = 42)

28.9 (n = 24)

7. Technology provides a usefulclassroom resource for teachers.

Pre 0.0 (n = 0)

0.0 (n = 0)

3.6 (n = 3)

54.2 (n = 45)

42.2 (n = 35)

Post 0.0 (n = 0)

1.2 (n = 1)

1.2 (n = 1)

54.2 (n = 45)

43.4 (n = 36)

8. Technology drains schoolresources that could be better used.

Pre 14.5 (n = 12)

50.6 (n = 42)

30.1 (n = 25)

4.8 (n = 4)

0.0 (n = 0)

Post 22.9 (n = 19)

42.2 (n = 35)

28.9 (n = 24)

4.8 (n = 4)

1.2 (n = 1)

9. Students get excited abouttechnology in the classroom.

Pre 0.0 (n = 0)

2.4 (n = 2)

1.2 (n = 1)

53.0 (n = 44)

43.4 (n = 36)

Post 0.0 (n = 0)

0.0 (n = 0)

4.8 (n = 4)

41.0 (n = 34)

54.2 (n = 45)

10. Technology encouragesstudents to learn on their own.

Pre 0.0 (n = 0)

7.2 (n = 6)

16.9 (n = 14)

47.0 (n = 39)

28.9 (n = 24)

Post 1.2 (n = 1)

2.4 (n = 2)

20.5 (n = 17)

51.8 (n = 43)

24.1 (n = 20)

11. There is too much emphasison technology in the classroom.

Pre 7.2 (n = 6)

34.9 (n = 29)

34.9 (n = 29)

18.1 (n = 15)

4.8 (n = 4)

Post 12.0 (n = 10)

31.3 (n = 26)

37.3 (n = 31)

15.7 (n = 13)

3.6 (n = 3)

12. Technology in the classroomenhances student learning.

Pre 0.0 (n = 0)

4.8 (n = 4)

21.7 (n = 18)

53.0 (n = 44)

20.5 (n = 17)

Post 0.0 (n = 0)

2.4 (n = 2)

14.5 (n = 12)

53.0 (n = 44)

30.1 (n = 25)

13. Incorporating technology intoclassroom activities is worth the effort required.

Pre 0.0 (n = 0)

2.4 (n = 2)

25.3 (n = 21)

50.6 (n = 42)

21.7 (n = 18)

Post 0.0 (n = 0)

1.2 (n = 1)

13.3 (n = 11)

59.0 (n = 49)

26.5 (n = 22)

14. Technology can solve manyclassroom problems.

Pre 1.2 (n = 1)

10.8 (n = 9)

33.7 (n = 28)

43.4 (n = 36)

10.8 (n = 9)

Post 0.0 (n = 0)

8.4 (n = 7)

37.3 (n = 31)

39.8 (n = 33)

14.5 (n = 12)

15. More technology in theclassroom is a good thing.

Pre 0.0 (n = 0)

13.3 (n = 11)

33.7 (n = 28)

50.6 (n = 42)

2.4 (n = 2)

Post 0.0 (n = 0)

6.0 (n = 5)

31.3 (n = 26)

47.0 (n = 39)

15.7 (n = 13)

(table continues)

58



Disagree Neither

Agree Nor Disagree


16. Technology in the classroomimproves thinking.

Pre 0.0 (n = 0)

24.1 (n = 20)

27.7 (n = 23)

37.3 (n = 31)

10.8 (n = 9)

Post 0.0 (n = 0)

9.6 (n = 8)

28.9 (n = 24)

41.0 (n = 34)

20.5 (n = 17)

17. Technology in the classroomhelps students learn.

Pre 0.0 (n = 0)

2.4 (n = 2)

20.5 (n = 17)

61.4 (n = 51)

15.7 (n = 13)

Post 0.0 (n = 0)

0.0 (n = 0)

9.6 (n = 8)

62.7 (n = 52)

27.7 (n = 23)

18. Technology improvesteaching.

Pre 2.4 (n = 2)

7.2 (n = 6)

27.7 (n = 23)

48.2 (n = 40)

14.5 (n = 12)

Post 0.0 (n = 0)

96 (n = 8)

20.5 (n = 17)

44.6 (n = 37)

25.3 (n = 21)

19. Teaching and technology donot belong together.

Pre 31.3 (n = 26)

49.4 (n = 41)

15.7 (n = 13)

3.6 (n = 3)

0.0 (n = 0)

Post 44.6 (n = 37)

37.3 (n = 31)

13.3 (n = 11)

4.8 (n = 4)

0.0 (n = 0)

20. Technology distracts fromlearning.

Pre 13.3 (n = 11)

39.8 (n = 33)

28.9 (n = 24)

16.9 (n = 14)

1.2 (n = 1)

Post 25.3 (n = 21)

39.8 (n = 33)

26.5 (n = 22)

8.4 (n = 7)

0.0 (n = 0)

21. Every classroom should make use of technology.

Pre 1.2 (n = 1)

9.6 (n = 8)

26.5 (n = 22)

45.8 (n = 38)

16.9 (n = 14)

Post 1.2 (n = 1)

8.4 (n = 7)

24.1 (n = 20)

45.8 (n = 38)

20.5 (n = 17)

22. Technology should be part ofall classroom assignments.

Pre 10.8 (n = 9)

37.3 (n = 31)

34.9 (n = 29)

10.8 (n = 9)

6.0 (n = 5)

Post 10.8 (n = 9)

31.3 (n = 26)

31.3 (n = 26)

16.9 (n = 14)

9.6 (n = 8)

23. Technology is makingclassrooms more appealing to students.

Pre 1.2 (n = 1)

3.6 (n = 3)

13.3 (n = 11)

59.0 (n = 49)

22.9 (n = 19)

Post 1.2 (n = 1)

2.4 (n = 2)

9.6 (n = 8)

60.2 (n = 50)

26.5 (n = 22)

24. Technology is a threat to"real" learning.

Pre 15.7 (n = 13)

42.2 (n = 35)

24.1 (n = 20)

16.9 (n = 14)

1.2 (n = 1)

Post 28.9 (n = 24)

38.6 (n = 32)

22.9 (n = 19)

8.4 (n = 7)

1.2 (n = 1)

25. The most important thingscannot be taught using technology.

Pre 7.2 (n = 6)

33.7 (n = 28)

37.3 (n = 31)

20.5 (n = 17)

1.2 (n = 1)

Post 14.5 (n = 12)

26.5 (n = 22)

38.6 (n = 32)

15.7 (n = 13)

4.8 (n = 4)

(table continues)

59



Disagree Neither

Agree Nor Disagree


26. Technology is only useful inteaching the most basic skills.

Pre 12.0 (n = 10)

59.0 (n = 49)

24.1 (n = 20)

3.6 (n = 3)

1.2 (n = 1)

Post 20.5 (n = 17)

41.0 (n = 34)

27.7 (n = 23)

10.8 (n = 9)

0.0 (n = 0)

27. The use of technology in theclassroom can revitalize education.

Pre 1.2 (n = 1)

2.4 (n = 2)

27.7 (n = 23)

49.4 (n = 41)

19.3 (n = 16)

Post 0.0 (n = 0)

0.0 (n = 0)

24.1 (n = 20)

60.2 (n = 50)

15.7 (n = 13)

28. The use of technology in theclassroom improves test scores.

Pre 1.2 (n = 1)

14.5 (n = 12)

50.6 (n = 42)

26.5 (n = 22)

7.2 (n = 6)

Post 0.0 (n = 0)

12.0 (n = 10)

49.4 (n = 41)

31.3 (n = 26)

7.2 (n = 6)

29. The benefits of technology toeducation are overrated.

Pre 10.8 (n = 9)

45.8 (n = 38)

32.5 (n = 27)

8.4 (n = 7)

2.4 (n = 2)

Post 14.5 (n = 12)

45.8 (n = 38)

32.5 (n = 27)

6.0 (n = 5)

1.2 (n = 1)

30. The use of technology in theclassroom can benefit all students.

Pre 2.4 (n = 2)

10.8 (n = 9)

18.1 (n = 15)

48.2 (n = 40)

20.5 (n = 17)

Post 1.2 (n = 1)

6.0 (n = 5)

19.3 (n = 16)

48.2 (n = 40)

25.3 (n = 21)

31. The use of technology in theclassroom improves teaching.

Pre 2.4 (n = 2)

7.2 (n = 6)

24.1 (n = 20)

49.4 (n = 41)

16.9 (n = 14)

Post 0.0 (n = 0)

14.5 (n = 12)

19.3 (n = 16)

50.6 (n = 42)

15.7 (n = 13)

For the ATTS survey instrument, the two questions with the highest decreases in

attitude pertained to questions regarding students learning better when technology is included

in their activities and students getting excited about technology in the classroom. Prior to

taking the course, 77.1% of the students selected Strongly Agree/Agree to the prompt of

students learn better when technology is included in their activities in comparison to 74.7%

following course completion (2.4% decrease). Prior to taking the course, 96.4% of the students

selected Strongly Agree/Agree to the prompts of students get excited about technology in the

classroom in comparison to 95.2% following course completion (1.2% decrease). The decreases

60

in attitude could be attributed to student participants’ lack of understanding in what

constitutes a technology‐based activity. The decrease could be supported by the contradictory

responses on the following two statements: “Technology should be part of all classroom

assignments,” and “Students learn better when technology is included in their activities.”

The frequencies/percentages of individual participant responses to the ATTS survey

instrument grouped by pre‐ and post‐ survey responses with end points collapsed and p ‐values

for individual items are shown in Table 9. Eleven of the 31 individual survey items were found

to be statistically significant from pre to post survey. The eleven items that were found to be

statistically significant all pertained to technology and its value, worth, or use in the classroom.

Table 9 Attitude Toward Technology Scale (ATTS) Instrument by Pre and Post Survey Results for Student Participants Collapsed and p ‐values Per Survey Item

Survey Item Strongly

Disagree/Agree

Neither Agree Nor Disagree

Strongly Agree/Agree

p ‐Value

1. Students learn better when technology is included in their activities.

Pre 4.8(n = 4)

18.1(n = 15)

77.1 (n = 64)

.652

Post 0.0(n = 0)

25.3(n = 21)

74.7 (n = 62)

2. Technology can be incorporated into any classroom subject

Pre 2.4(n = 2)

9.6(n = 8)

87.9 (n = 73)

.246

Post 1.2(n = 1)

7.2(n = 6)

91.6 (n = 76)

3. Time spent incorporating technology could be better spent teaching the basics.

Pre 27.7(n = 23)

53.0(n = 44)

19.3 (n = 16)

.690

Post 34.9(n = 29)

45.8(n = 38)

19.2 (n = 16)

4. Technology allows a teacher to capture a student's interest.

Pre

3.6(n = 3)

9.6(n = 8)

86.7 (n = 72)

.007*

Post 0.0(n = 0)

7.2(n = 6)

92.8 (n = 77)

(table continues)

61


Survey ItemStrongly

Disagree/Agree



p ‐Value

5. Technology costs schools morethan it's worth.

Pre 50.6(n = 42)

36.1(n = 30)

13.2 (n = 11)

.047*

Post 54.2(n = 45)

37.3(n = 31)

8.4 (n = 7)


Pre 6.0(n = 5)

19.3(n = 16)

74.7 (n = 62)

.027*

Post 1.2(n = 1)

19.3(n = 16)

79.5 (n = 66)

7. Technology provides a usefulclassroom resource for teachers.

Pre 0.0(n = 0)

3.6(n = 3)

96.4 (n = 80)

.866

Post 1.2(n = 1)

1.2(n = 1)

97.6 (n = 81)

8. Technology drains schoolresources that could be better used.

Pre 65.2(n = 54)

29.2(n = 25)

5.6 (n = 4)

.473

Post 65.1(n = 54)

28.9(n = 24)

6.0 (n = 5)


Pre 2.4(n = 2)

1.2(n = 1)

96.4 (n = 80)

.106

Post 0.0(n = 0)

4.8(n = 4)

95.2 (n = 79)


Pre 7.2(n = 6)

16.9(n = 14)

75.9 (n = 63)

.917

Post 3.6(n = 3)

20.5(n = 17)

75.9 (n = 63)

11. There is too much emphasis ontechnology in the classroom.

Pre 42.1(n = 35)

34.9(n = 29)

22.9 (n = 19)

.366

Post 43.3(n = 36)

37.3(n = 31)

19.3 (n = 16)

12. Technology in the classroomenhances student learning.

Pre 4.8(n = 4)

21.7(n = 18)

73.5 (n = 61)

.015*

Post 2.4(n = 2)

14.5(n = 12)

83.1 (n = 70)

13. Incorporating technology intoclassroom activities is worth the effort required.

Pre 2.4(n = 2)

25.3(n = 21)

72.3 (n = 60)

.029*

Post 1.2(n = 1)

13.3(n = 11)

85.5 (n = 71)

(table continues)

62


Survey ItemStrongly

Disagree/Agree



p ‐Value

14. Technology can solve manyclassroom problems.

Pre 12.0(n = 10)

33.7(n = 28)

54.2 (n = 45)

.443

Post 8.4(n = 7)

37.3(n = 31)

54.3 (n = 45)

15. More technology in theclassroom is a good thing.

Pre 13.3(n = 11)

33.7(n = 28)

53.0 (n = 44)

.003*

Post 6.0(n = 5)

31.3(n = 26)

62.7 (n = 52)

16. Technology in the classroomimproves thinking.

Pre 24.1(n = 20)

27.7(n = 23)

48.1 (n = 40)

.003*

Post 9.6(n = 8)

28.9(n = 24)

61.5 (n = 51)

17. Technology in the classroomhelps students learn.

Pre 2.4(n = 2)

20.5(n = 17)

77.1 (n = 64)

.001*

Post 0.0(n = 0)

9.6(n = 8)

90.4 (n = 75)

18. Technology improves teaching. Pre 9.6(n = 8)

27.7(n = 23)

62.7 (n = 52)

.054

Post 9.6(n = 8)

20.5(n = 17)

69.9 (n = 58)

19. Teaching and technology do notbelong together.

Pre 80.7(n = 67)

15.7(n = 13)

3.6 (n = 3)

<.001*

Post 81.9(n = 68)

13.3(n = 11)

4.8 (n = 4)

20. Technology distracts fromlearning.

Pre 53.1(n = 44)

28.9(n = 24)

18.1 (n = 15)

<.001*

Post 65.1(n = 54)

26.5(n = 22)

8.4 (n = 7)

21. Every classroom should makeuse of technology.

Pre 10.8(n = 9)

26.5(n = 22)

62.7 (n = 52)

.281

Post 9.6(n = 8)

24.1(n = 20)

66.3 (n = 55)

22. Technology should be part of allclassroom assignments.

Pre 48.1(n = 40)

34.9(n = 29)

16.8 (n = 14)

.111

Post 42.1(n = 35)

31.3(n = 26)

26.5 (n = 22)

(table continues)

63


Survey ItemStrongly

Disagree/Agree



p ‐Value

23. Technology is makingclassrooms more appealing to students

Pre 4.8(n = 4)

13.3(n = 11)

81.9 (n = 68)

.349

Post 3.6(n = 3)

9.6(n = 8)

86.7 (n = 72)

24. Technology is a threat to "real"learning.

Pre 57.9(n = 48)

24.1(n = 20)

18.1 (n = 15)

.026*

Post 67.5(n = 56)

22.9(n = 19)

9.6 (n = 8)

25. The most important thingscannot be taught using technology.

Pre 40.9(n = 34)

37.3(n = 31)

21.7 (n = 18)

.686

Post 41.0(n = 34)

38.6(n = 32)

20.5 (n = 17)

26. Technology is only useful inteaching the most basic skills.

Pre 71.0(n = 59)

24.1(n = 20)

4.8 (n = 4)

.512

Post 61.5(n = 51)

27.7(n = 23)

10.8 (n = 9)

27. The use of technology in theclassroom can revitalize education.

Pre 3.6(n = 3)

27.7(n = 23)

68.7 (n = 57)

.397

Post 0.0(n = 0)

24.1(n = 20)

75.9 (n = 63)

28. The use of technology in theclassroom improves test scores.

Pre 15.7(n = 13)

50.6(n = 42)

33.7 (n = 28)

.271

Post 12.0(n = 10)

49.4(n = 41)

38.5 (n = 32)

29. The benefits of technology toeducation are overrated.

Pre 56.6(n = 47)

32.5(n = 27)

10.8 (n = 9)

.168

Post 60.3(n = 50)

32.5(n = 27)

7.2 (n = 6)

30. The use of technology in theclassroom can benefit all students.

Pre 13.2(n = 11)

18.1(n = 15)

68.7 (n = 57)

.068

Post 7.2(n = 6)

19.3(n = 16)

73.5 (n = 61)

31. The use of technology in theclassroom improves teaching.

Pre 9.6(n = 8)

24.1(n = 20)

66.3 (n = 55)

.753

Post 14.5(n = 12)

19.3(n = 16)

66.3 (n = 55)


64

The Attitude Toward Technology subscale asked participants to rate their attitude

towards technology on topics such as students learn better when technology is included in the

student’s activities and the use of technology in the classroom improves teaching. Participants

reported mean increases in attitude towards technology greater than .25 in three out of the 31

items in this subscale. Mean increases in attitude towards technology ranged from ‐0.35 to .38.

Table 10 displays the descriptive statistics for this subscale.

Table 10

Descriptive Statistics for the ATTS Items

Attitude Toward Technology Mean

Pre Attitude

Standard Deviation

Pre Attitude

Mean Post Attitude

Standard Deviation

Post Attitude

Mean Difference

1. Students learn better whentechnology is included in their activities.

3.90 .74 3.93 .66 0.03

2. Technology can be incorporatedinto any classroom subject.

4.19 .70 4.28 .65 0.09

3. Time spent incorporatingtechnology could be better spent teaching the basics.

2.93 .78 2.87 .92 ‐0.06

4. Technology allows a teacher tocapture a student's interest.

4.10 .71 4.34 .61 0.24

5. Technology costs schools morethan it is worth.

2.55 .90 2.33 .94 ‐0.22


3.87 .78 4.06 .77 0.19

7. Technology provides a usefulclassroom resource for students.

4.38 .55 4.39 .58 0.01

8. Technology drains schoolresources that would be better used.

2.25 .76 2.19 .89 0.06


4.37 .63 4.49 .59 0.12


3.97 .86 3.95 .81 ‐0.02

(table continues)

65



Pre Attitude

Standard Deviation

Pre Attitude

Mean Post Attitude

Standard Deviation

Post Attitude

Mean Difference

11. There is too much emphasis on technology in the classroom.

2.78 .98 2.67 1.00 ‐0.11

12. Technology in the classroom enhances student learning.

3.89 .78 4.10 .73 0.21

13. Incorporating technology into classroom activities is worth the effort required.

3.91 .75 4.10 .66 0.19

14. Technology can solve many classroom problems.

3.51 .87 3.60 .84 0.09

15. More technology in the classroom is a good thing.

3.42 .75 3.72 .80 0.30

16. Technology in the classroom improves thinking.

3.34 .96 3.72 .90 0.38

17. Technology in the classroom helps students learn.

3.90 .67 4.18 .58 0.28

18. Technology improves teaching. 3.65 .90 3.85 .91 0.20 19. Teaching and technology do not belong together.

1.91 .78 1.78 .85 ‐0.13

20. Technology distracts from learning.

2.53 .96 2.18 .91 ‐0.35

21. Every classroom should make use of technology.

3.67 .91 3.75 .91 0.08

22. Technology should be part of all classroom assignments.

2.63 1.01 2.83 1.13 0.20

23. Technology is making classrooms more appealing to students.

3.98 .78 4.08 .75 0.10

24. Technology is a threat to "real" learning.

2.45 .99 2.14 .97 ‐0.31

25. The most important things can not be taught using technology.

2.74 .90 2.69 1.05 ‐0.05

26. Technology is only useful in teaching the most basic skills.

2.22 .75 2.28 .91 0.06

27. The use of technology in the classroom can revitalize education.

3.83 .80 3.91 .62 0.08

(table continues)

66



Pre Attitude

Standard Deviation

Pre Attitude

Mean Post Attitude

Standard Deviation

Post Attitude

Mean Difference

28. The use of technology in the classroom improves test scores.

3.24 .83 3.33 .78 0.09

29. The benefits of technology to education are overrated.

2.45 .88 2.33 .84 ‐0.12

30. The use of technology in them classroom can benefit all students.

3.73 .98 3.90 .89 0.17

31. The use of technology in the classroom improves teaching.

3.71 .91 3.67 .91 ‐0.04

The item with the largest mean increase was item 16 which stated that technology in

the classroom improves thinking. The mean score for this item rose from 3.34 to 3.72. The item

with largest decrease was item 20 which stated that technology distracts from learning. The

mean score of this item decreased from 2.53 to 2.18. The increase for item 16 and the decrease

for item 20 could be attributed to the student participants learning about different

technologies and how they can be used in the classroom.

Research Question 2

Research Question 2 states, “Is there a statistically significant mean difference in


technology course as measured by the TPSA C21 when participants are grouped based on

paired grouping?”



proficiency with technology as measured by the TPSA C21 before and after paired grouping.

67

Findings suggested (see Table 11) that there was a statistically significant positive mean

difference between the pre and post proficiency with technology scores, t(82) = 8.394, p < .05,

d = .892 (large effect size), r2 = .166. The paired grouping had a large effect on proficiency with

technology of the student participants and 16.6% of the variance in those scores could be

attributed to the paired groupings. Mean proficiency rose from 142.7 to 157.0.

Table 11

Paired t‐Test: Pre Proficiency Scores and Post Proficiency Scores

Paired Groupings N M SD t‐value df p ‐value d‐value r2

Pre Attitude 83 142.7 7.5 8.394 82 <.001* .892 .166



The frequencies/percentages of individual participant responses to the TPSA C21 survey

instrument are shown in Table 12 grouped by pre and post survey responses. For the TPSA C21,

the highest increases in proficiency all pertained to questions related to specific technologies

and content that were studied during the educational technology course. Prior to taking the

course and working with a paired group member, 26.5% of the students selected Strongly

Agree/Agree to the prompt of use a spreadsheet to create a bar graph of the proportions of the

different colors of chocolate candies in a bag in comparison to 71.1% following course

completion (44.6% increase). Prior to taking the course and working with a paired group

member, 22.9% of the students selected Strongly Agree/Agree to the prompt of describe five

software programs or apps that I would use in my teaching in comparison to 62.7% following

course completion (39.8% increase). Prior to taking the course and working with a paired group

member, 28.9% of the students selected Strongly Agree/Agree to the prompt of create a

68

newsletter with graphics in comparison to 66.3% following course completion (37.4% increase).

Prior to taking the course and working with a paired group member, 27.7% of the students

selected Strongly Agree/Agree to the prompt of create a lesson or unit that incorporates

subject matter software as an integral part in comparison to 63.9% following course completion

(36.2% increase). Prior to taking the course and working with a paired group member, 16.9% of

the students responded Strongly Agree/Agree to the prompt of create a database of

information about important authors in a subject matter field in comparison to 50.6% following

course completion (33.7% increase). During the educational technology course, participants

had to complete specific assignments on databases, graphing, and newsletters. The participants

created lesson plans that required the integration of these particular technologies. Throughout

the course, the participants were also exposed to many different software programs and apps.

This exposure could account for the positive increases in proficiency with these particular

items.

Table 12 Technology Proficiency (TPSA C21) Instrument by Pre and Post Survey Results for Student Participants

Survey Item Strongly Disagree

Disagree Neither

Agree Nor Disagree


1. ...send email to a friend. Pre 0.0 (n = 0)

0.0 (n = 0)

0.0 (n = 0)

20.5 (n = 17)

79.5 (n = 66)

Post 0.0 (n = 0 )

0.0 (n = 0 )

0.0 (n = 0)

13.3 (n = 11)

86.7 (n = 72)

2. ...subscribe to a discussion list. Pre 0.0

(n = 0) 4.8

(n = 4) 13.3

(n = 11) 31.3

(n = 26) 50.6

(n = 42) Post 0.0

(n = 0) 0.0

(n = 0) 4.8

(n = 4) 22.9

(n = 19) 72.3

(n = 60)

(table continues)

69



Disagree Neither

Agree Nor Disagree


3. ...create a "distribution list" tosend email to several people at once.

Pre 0.0 (n = 0)

4.8 (n = 4)

8.4 (n = 7)

31.3 (n = 26)

55.4 (n = 46)

Post 0.0 (n = 0)

0.0 (n = 0)

2.4 (n = 2)

20.5 (n = 17)

77.1 (n = 64)

4. ...send a document as anattachment to an email message.

Pre 0.0 (n = 0)

0.0 (n = 3)

0.0 (n = 0)

20.5 (n = 17)

79.5 (n = 66)

Post 0.0 (n = 0)

0.0 (n = 0)

1.2 (n = 1)

14.5 (n = 12)

84.3 (n = 70)

5. ...keep copies of outgoingmessages that I send to others.

Pre 0.0 (n = 0)

1.2 (n = 1)

8.4 (n = 7)

27.7 (n = 23)

62.7 (n = 52)

Post 0.0 (n = 0)

0.0 (n = 0)

6.0 (n = 5)

16.9 (n = 14)

77.1 (n = 64)

6. ...use an Internet search engine(e.g., Google) to find Web pages related to my subject matter interests.

Pre 0.0 (n = 0)

0.0 (n = 0)

1.2 (n = 1)

18.1 (n = 15)

80.7 (n = 67)

Post 0.0 (n = 0)

0.0 (n = 0)

1.2 (n = 1)

12.0 (n = 10)

86.7 (n = 72)

7. ...search for and find the Smithsonian Institution Web site.

Pre 0.0 (n = 0)

1.2 (n = 1)

6.0 (n = 5)

25.3 (n = 21)

67.5 (n = 56)

Post 1.2 (n = 1)

0.0 (n = 0)

7.2 (n = 6)

16.9 (n = 14)

74.7 (n = 62)

8. ...create my own web page. Pre 14.5 (n = 12)

24.1 (n = 20)

20.5 (n = 17)

24.1 (n = 20)

16.9 (n = 14)

Post 0.0 (n = 0)

9.6 (n = 8)

14.5 (n = 12)

27.7 (n = 23)

48.2 (n = 40)

9. ...keep track of Web sites I havevisited so that I can return to them later. (An example is using bookmarks.)

Pre 0.0 (n = 0)

1.2 (n = 1)

3.6 (n = 3)

25.3 (n = 21)

69.9 (n = 58)

Post 0.0 (n = 0)

1.2 (n = 1)

1.2 (n = 1)

18.1 (n = 15)

79.5 (n = 66)

10. ...find primary sources ofinformation on the Internet that I can use in my teaching.

Pre 0.0 (n = 0)

2.4 (n = 2)

4.8 (n = 4)

33.7 (n = 28)

59.0 (n = 49)

Post 1.2 (n = 1)

1.2 (n = 1)

3.6 (n = 3)

20.5 (n = 17)

73.5 (n = 61)

11. ...use a spreadsheet to create abar graph of the proportions of the different colors of M&Ms in a bag.

Pre 2.4 (n = 2)

10.8 (n = 9)

18.1 (n = 15)

42.2 (n = 35)

26.5 (n = 22)

Post 0.0 (n = 0)

2.4 (n = 2)

4.8 (n = 4)

21.7 (n = 18)

71.1 (n = 59)

12. ...create a newsletter withgraphics.

Pre 4.8 (n = 4)

16.9 (n = 14)

18.1 (n = 15)

31.3 (n = 26)

28.9 (n = 24)

Post 0.0 (n = 0)

0.0 (n = 0)

1.2 (n = 1)

32.5 (n = 27)

66.3 (n = 55)

(table continues)

70



Disagree Neither

Agree Nor Disagree


13. ...save documents in formats sothat others can read them if they have different word processing programs (eg.,saving Word, pdf, RTF, or text).

Pre 0.0 (n = 0)

7.2 (n = 6)

3.6 (n = 3)

42.2 (n = 35)

47.0 (n = 39)

Post 0.0 (n = 0)

0.0 (n = 0)

3.6 (n = 3)

20.5 (n = 17)

75.9 (n = 63)

14. ...use the computer to create aslideshow presentation.

Pre 0.0 (n = 0)

1.2 (n = 1)

0.0 (n = 0)

28.9 (n = 24)

69.9 (n = 58)

Post 0.0 (n = 0)

0.0 (n = 0)

1.2 (n = 1)

16.9 (n = 14)

81.9 (n = 68)

15. ...create a database ofinformation about important authors in a subject matter field.

Pre 7.2 (n = 6)

20.5 (n = 17)

26.5 (n = 22)

28.9 (n = 24)

16.9 (n = 14)

Post 0.0 (n = 0)

2.4 (n = 2)

19.3 (n = 16)

27.7 (n = 23)

50.6 (n = 42)

16. ...write an essay describing how Iwould use technology in my classroom.

Pre 0.0 (n = 0)

2.4 (n = 2)

3.6 (n = 3)

38.6 (n = 32)

55.4 (n = 46)

Post 0.0 (n = 0)

0.0 (n = 0)

1.2 (n = 1)

25.3 (n = 21)

73.5 (n = 61)

17. ...create a lesson or unit thatincorporates subject matter software as an integral part.

Pre 3.6 (n = 3)

10.8 (n = 9)

25.3 (n = 21)

32.5 (n = 27)

27.7 (n = 23)

Post 0.0 (n = 0)

0.0 (n = 0)

6.0 (n = 5)

30.1 (n = 25)

63.9 (n = 53)

18. ...use technology to collaboratewith teachers or students, who are distant from my classroom.

Pre 0.0 (n = 0)

1.2 (n = 1)

12.0 (n = 10)

44.6 (n = 37)

42.2 (n = 35)

Post 0.0 (n = 0)

0.0 (n = 0)

4.8 (n = 4)

25.3 (n = 21)

69.9 (n = 58)

19. ...describe 5 software programsor apps that I would use in my teaching.

Pre 0.0 (n = 0)

20.5 (n = 17)

21.7 (n =18)

34.9 (n = 29)

22.9 (n = 19)

Post 0.0 (n = 0)

0.0 (n = 0)

4.8 (n = 4)

32.5 (n = 27)

62.7 (n = 52)

20. ...write a plan with a budget tobuy technology for my classroom.

Pre 1.2 (n = 1)

21.7 (n = 18)

20.5 (n = 17)

33.7 (n = 28)

22.9 (n = 19)

Post 1.2 (n = 1)

6.0 (n = 5)

22.9 (n = 19)

26.5 (n = 22)

43.4 (n = 36)

21. ...integrate mobile technologiesinto my curriculum.

Pre 0.0 (n = 0)

8.4 (n = 7)

18.1 (n = 15)

45.8 (n = 38)

27.7 (n = 23)

Post 0.0 (n = 0)

0.0 (n = 0)

4.8 (n = 4)

43.4 (n = 36)

51.8 (n = 43)

22. ...use social media tools forinstruction in the classroom. (ex. Facebook, Twitter, etc,)

Pre 0.0 (n = 0)

9.6 (n = 8)

14.5 (n = 12)

37.3 (n = 31)

38.6 (n = 32)

Post 0.0 (n = 0)

1.2 (n = 1)

7.2 (n = 6)

20.5 (n = 17)

71.1 (n = 59)

(table continues)

71



Disagree Neither

Agree Nor Disagree


23. ...create a wiki or blog to havemy students collaborate.

Pre 2.4 (n = 2)

18.1 (n = 15)

21.7 (n = 18)

30.1 (n = 25)

27.7 (n = 23)

Post 0.0 (n = 0)

3.6 (n = 3)

6.0 (n = 5)

31.3 (n = 26)

59.0 (n = 49)

24. ...use online tools to teach mystudents from a distance.

Pre 0.0 (n = 0)

9.6 (n = 8)

12.0 (n = 10)

41.0 (n = 34)

37.3 (n = 31)

Post 0.0 (n = 0)

2.4 (n = 2)

3.6 (n = 3)

27.7 (n = 23)

66.3 (n = 55)

25. ...teach in a one‐to‐oneenvironment in which students have their own device.

Pre 0.0 (n = 0)

7.2 (n = 6)

12.0 (n = 10)

38.6 (n = 32)

42.2 (n = 35)

Post 0.0 (n = 0)

1.2 (n = 1)

4.8 (n = 4)

31.3 (n = 26)

62.7 (n = 52)

26. ...find a way to use a smartphonein my classroom for student responses.

Pre 0.0 (n = 0)

7.2 (n = 6)

12.0 (n = 10)

41.0 (n = 34)

39.8 (n = 33)

Post 0.0 (n = 0)

2.4 (n = 2)

4.8 (n = 4)

30.1 (n = 25)

62.7 (n = 52)

27. ...use mobile devices to connect toothers for my professional development.

Pre 0.0 (n = 0)

1.2 (n = 1)

7.2 (n = 6)

44.6 (n = 37)

47.0 (n = 39)

Post 0.0 (n = 0)

1.2 (n = 1)

4.8 (n = 4)

25.3 (n = 21)

68.7 (n = 57)

28. ...use mobile devices to have mystudent’s access leaning activities.

Pre 0.0 (n = 0)

1.2 (n = 1)

13.3 (n = 11)

43.4 (n = 36)

42.2 (n = 35)

Post 0.0 (n = 0)

0.0 (n = 0)

6.0 (n = 5)

28.9 (n = 24)

65.1 (n = 54)

29. ...download and listen topodcasts/audio books.

Pre 1.2 (n = 1)

4.8 (n = 4)

7.2 (n = 6)

38.6 (n = 32)

48.2 (n = 40)

Post 0.0 (n = 0)

1.2 (n = 1)

8.4 (n = 7)

22.9 (n = 19)

67.5 (n = 56)

30. ...download and read ebooks. Pre 1.2 (n = 1)

2.4 (n = 2)

4.8 (n = 4)

37.3 (n = 31)

54.2 (n = 45)

Post 0.0 (n = 0)

0.0 (n = 0)

3.6 (n = 3)

24.1 (n = 20)

72.3 (n = 60)

31. ...download and view streamingmovies/video clips.

Pre 0.0 (n = 0)

2.4 (n = 2)

4.8 (n = 4)

36.1 (n = 30)

56.6 (n = 47)

Post 0.0 (n = 0)

0.0 (n = 0)

4.8 (n = 4)

21.7 (n = 18)

73.5 (n = 61)

32. ...send and receive text messages. Pre 0.0 (n = 0)

0.0 (n = 0)

0.0 (n = 0)

25.3 (n = 21)

74.7 (n = 62)

Post 0.0 (n = 0)

0.0 (n = 0)

2.4 (n = 2)

14.5 (n = 12)

83.1 (n = 69)

33. ...transfer photos or other data viaa smartphone.

Pre 0.0 (n = 0)

2.4 (n = 2)

2.4 (n = 2)

24.1 (n = 20)

71.1 (n = 59)

Post 0.0 (n = 0)

0.0 (n = 0)

1.2 (n = 1)

16.9 (n = 14)

81.9 (n = 68)

(table continues)

72



Disagree Neither Agree Nor Disagree


34. ...save and retrieve files in a cloud‐based environment.

Pre 2.4 (n = 2)

10.8 (n = 9)

13.3 (n = 11)

33.7 (n = 28)

39.8 (n = 33)

Post 0.0 (n = 0)

2.4 (n = 2)

9.6 (n = 8)

21.7 (n = 18)

66.3 (n = 55)

The frequencies/percentages of individual participant responses to the CPSA C21 survey


for individual items are shown in Table 13. Twenty six of the 34 individual survey items were

found to be statistically significant from pre to post survey. Eight of the 34 individual items

were not found to be statistically significant. The eight items that were found not to be

statistically significant all pertained to things that were not covered during the duration of the

educational technology course such as sending an email attachment to several people at once,

keeping track of Web sites visited to return to later, and sending and receiving text messages.

Table 13

Technology Proficiency Self‐Assessment for 21st Century Learning (TPSA C21) Instrument by Pre and Post Survey Results for Student Participants Collapsed and p ‐Values Per Survey Item

Survey ItemStrongly Disagree/ Disagree


Agree/Strongly Agree

p ‐Value

1. ...send email to a friend. Pre 0.0 (n = 0)

0.0 (n = 0)

100.0 (n = 83)

.157

Post 0.0 (n = 0 )

0.0 (n = 0)

100.0 (n = 83)

2. ...subscribe to a discussion list. Pre 4.8 (n = 4)

13.3 (n = 11)

50.6 (n = 42)

<.001*

Post 0.0 (n = 0)

4.8 (n = 4)

72.3 (n = 60)

(table continues)

73


Survey Item Strongly Disagree/ Disagree



p ‐Value

3. ...create a "distribution list" to send email to several people at once.

Pre 27.7 (n = 23)

8.4 (n = 7)

55.4 (n = 46)

<.001*

Post 34.9 (n = 29)

2.4 (n = 2)

77.1 (n = 64)

4. ...send a document as an attachment to an email message.

Pre 3.6 (n = 3)

0.0 (n = 0)

79.5 (n = 66)

.491

Post 0.0 (n = 0)

1.2 (n = 1)

84.3 (n = 70)

5. ...keep copies of outgoing messages that I send to others.

Pre 50.6 (n = 42)

8.4 (n = 7)

62.7 (n = 52)

.007*

Post 54.2 (n = 45)

6.0 (n = 5)

77.1 (n = 64)

6. ...use an Internet search engine (e.g., Google) to find Web pages related to my subject matter interests.

Pre 6.0 (n = 5)

1.2 (n = 1)

80.7 (n = 67)

.255

Post 1.2 (n = 1)

1.2 (n = 1)

86.7 (n = 72)


Pre 0.0 (n = 0)

6.0 (n = 5)

67.5 (n = 56)

.348

Post 1.2 (n = 1)

7.2 (n = 6)

74.7 (n = 62)

8. ...create my own web page. Pre 65.1

(n = 54) 20.5

(n = 17) 4.8

(n = 4) <.001*

Post 65.1 (n = 54)

14.5 (n = 12)

6.0 (n = 5)

9. ...keep track of Web sites I have visited so that I can return to them later. (An example is using bookmarks.)

Pre 2.4 (n = 2)

1.2 (n = 1)

96.4 (n = 80)

.092

Post 0.0 (n = 0)

4.8 (n = 4)

95.2 (n = 79)

10. ...find primary sources of information on the Internet that I can use in my teaching.

Pre 0.0 (n = 0)

4.8 (n = 4)

59.0 (n = 49)

.053

Post 1.2 (n = 1)

3.6 (n = 3)

73.5 (n = 61)

11. ...use a spreadsheet to create a bar graph of the proportions of the different colors of M&Ms in a bag.

Pre 2.4 (n = 2)

18.1 (n = 15)

26.5 (n =22)

<.001*

Post 0.0 (n = 0)

4.8 (n = 4)

71.1 (n = 59)

12. ...create a newsletter with graphics. Pre 4.8

(n = 4) 18.1

(n = 15) 28.9

(n = 24) <.001*

Post 0.0 (n = 0)

1.2 (n = 1)

66.3 (n = 55)

(table continues)

74





p ‐Value

13. ...save documents in formats so that otherscan read them if they have different word processing programs (e.g., saving Word, pdf, RTF, or text).

Pre 0.0 (n = 0)

3.6 (n = 3)

47.0 (n = 39)

<.001*

Post 0.0 (n = 0)

3.6 (n = 3)

75.9 (n = 63)

14. ...use the computer to create a slideshowpresentation.

Pre 0.0 (n = 0)

0.0 (n = 0)

69.9 (n = 58)

.028*

Post 0.0 (n = 0)

1.2 (n = 1)

81.9 (n = 68)

15. ...create a database of information aboutimportant authors in a subject matter field.

Pre 7.2 (n = 6)

26.5 (n = 22)

16.9 (n = 14)

<.001*

Post 0.0 (n = 0)

19.3 (n = 16)

50.6 (n = 42)

16. ...write an essay describing how I woulduse technology in my classroom.

Pre 0.0 (n = 0)

3.6 (n = 3)

55.4 (n = 46)

.002*

Post 0.0 (n = 0)

1.2 (n = 1)

73.5 (n = 61)

17. ...create a lesson or unit that incorporatessubject matter software as an integral part.

Pre 3.6 (n = 3)

25.3 (n = 21)

27.7 (n = 23)

<.001*

Post 0.0 (n = 0)

6.0 (n = 5)

63.9 (n = 53)

18. ...use technology to collaborate withteachers or students, who are distant from my classroom.

Pre 0.0 (n = 0)

12.0 (n = 10)

42.2 (n = 35)

<.001*

Post 0.0 (n = 0)

4.8 (n = 4)

69.9 (n = 58)

19. ...describe 5 software programs or appsthat I would use in my teaching.

Pre 0.0 (n = 0)

21.7 (n = 18)

22.9 (n = 19)

<.001*

Post 0.0 (n = 0)

4.8 (n = 4)

62.7 (n = 52)

20. ...write a plan with a budget to buytechnology for my classroom.

Pre 1.2 (n = 1)

20.5 (n = 17)

22.9 (n = 19)

.001

Post 1.2 (n = 1)

22.9 (n = 19)

43.4 (n = 36)

21. ...integrate mobile technologies into mycurriculum.

Pre 0.0 (n = 0)

18.1 (n = 15)

27.7 (n = 23)

<.001*

Post 0.0 (n = 0)

4.8 (n = 4)

51.8 (n = 43)

22. ...use social media tools for instruction inthe classroom. (ex. Facebook, Twitter, etc.)

Pre 0.0 (n = 0)

14.5 (n = 12)

38.6 (n = 32)

<.001*

Post 0.0 (n = 0)

7.2 (n = 6)

71.1 (n = 59)

(table continues)

75





p ‐Value

23. ...create a wiki or blog to have my studentscollaborate.

Pre 2.4 (n = 2)

21.7 (n = 18)

27.7 (n = 23)

<.001*

Post 0.0 (n = 0)

6.0 (n = 5)

59.0 (n = 49)

24. ...use online tools to teach my studentsfrom a distance.

Pre 0.0 (n = 0)

12.0 (n = 10)

37.3 (n = 31)

<.001*

Post 0.0 (n = 0)

3.6 (n = 3)

66.3 (n = 55)

25. ...teach in a one‐to‐one environment inwhich students have their own device.

Pre 0.0 (n = 0)

12.0 (n = 10)

42.2 (n = 35)

<.001*

Post 0.0 (n = 0)

4.8 (n = 4)

62.7 (n = 52)

26. ...find a way to use a smartphone in myclassroom for student responses.

Pre 0.0 (n = 0)

12.0 (n =10)

39.8 (n = 33)

<.001*

Post 0.0 (n = 0)

4.8 (n = 4)

62.7 (n = 52)

27. ...use mobile devices to connect to othersfor my professional development.

Pre 0.0 (n = 0)

7.2 (n = 6)

47.0 (n = 39)

.002*

Post 0.0 (n = 0)

4.8 (n = 4)

68.7 (n = 57)

28. ...use mobile devices to have my student’saccess leaning activities.

Pre 0.0 (n = 0)

13.3 (n = 11)

42.2 (n = 35)

<.001*

Post 0.0 (n = 0)

6.0 (n = 5)

65.1 (n = 54)

29. ...download and listen to podcasts/audiobooks.

Pre 1.2 (n = 1)

7.2 (n = 6)

48.2 (n = 40)

.014*

Post 0.0 (n = 0)

8.4 (n = 7)

67.5 (n = 56)

30. ...download and read ebooks. Pre 1.2 (n = 1)

4.8 (n = 4)

54.2 (n = 45)

.003*

Post 0.0 (n = 0)

3.6 (n = 3)

72.3 (n = 60)

31. ...download and view streamingmovies/video clips.

Pre 0.0 (n = 0)

4.8 (n = 4)

56.6 (n = 47)

.018*

Post 0.0 (n = 0)

4.8 (n = 4)

73.5 (n = 61)

32. ...send and receive text messages. Pre 0.0 (n = 0)

0.0 (n = 0)

74.7 (n = 62)

.275

Post 0.0 (n = 0)

2.4 (n = 2)

83.1 (n = 69)

(table continues)

76





p ‐Value

33. ...transfer photos or other data via asmartphone.

Pre 0.0 (n = 0)

2.4 (n = 2)

71.1 (n = 59)

.019*

Post 0.0 (n = 0)

1.2 (n = 1)

81.9 (n = 68)

34. ...save and retrieve files in a cloud‐basedenvironment.

Pre 2.4 (n = 2)

13.3 (n = 11)

39.8 (n = 33)

<.001*

Post 0.0 (n = 0)

9.6 (n = 8)

66.3 (n = 55)

The Proficiency with Technology subscale asked participants to rate their proficiency

with technology on topics such as sending email to a friend and saving and retrieving files in a

cloud‐based environment. Participants reported mean increases in proficiency with technology

greater than .25 in 22 out of the 34 items in this subscale. Mean increases in proficiency ranged

from .04 to 1.10. Table 14 displays the descriptive statistics for this subscale.

Table 14

Descriptive Statistics for the TPSA C21 Items


Mean Pre Attitude

Standard Deviation Pre Attitude

Mean Post Attitude

Standard Deviation Post Attitude

Mean Difference

1. ...send email to a friend. 4.79 .40 4.86 .34 0.07

2. ...subscribe to adiscussion list.

4.27 .87 4.67 .56 0.40

3. ...create a "distributionlist" to send email to several people at once.

4.37 .83 4.74 .48 0.37

4. ...send a document as anattachment to an email message.

4.79 .40 4.83 .40 0.04

5. ...keep copies ofoutgoing messages that I send to others.

4.51 .70 4.71 .57 0.20

(table continues)

77



Mean Pre Attitude


Mean Post Attitude


Mean Difference

6. ...use an Internet search engine (e.g., Google) to find Web pages related to my subject matter interests.

4.79 .43 4.85 .38 0.06


4.59 .66 4.63 .72 0.04

8. ...create my own web page.

3.04 1.32 4.14 1.00 1.10

9. ...keep track of Web sites I have visited so that I can return to them later. (An example is using bookmarks.)

4.63 .61 4.75 .53 0.12

10. ...find primary sources of information on the Internet that I can use in my teaching.

4.49 .70 4.63 .72 0.14

11. ...use a spreadsheet to create a bar graph of the proportions of the different colors of M&Ms in a bag.

3.79 1.03 4.61 .69 0.82

12. ...create a newsletter with graphics.

3.62 1.20 4.65 .50 1.03

13. ...save documents in formats so that others can read them if they have different word processing programs (e.g., saving Word, pdf, RTF, or text).

4.28 .84 4.72 .52 0.44

14. ...use the computer to create a slideshow presentation.

4.67 .54 4.80 .42 0.13

15. ...create a database of information about important authors in a subject matter field.

3.27 1.18 4.26 .85 0.99

16. ...write an essay describing how I would use technology in my classroom.

4.46 .68 4.72 .47 0.26

(table continues)

78



Mean Pre Attitude


Mean Post Attitude


Mean Difference

17. ...create a lesson or unit that incorporates subject matter software as an integral part.

3.69 1.10 4.57 .60 0.88

18. ...use technology to collaborate with teachers or students, who are distant from my classroom.

4.27 .72 4.65 .57 0.38

19. ...describe 5 software programs or apps that I would use in my teaching.

3.60 1.05 4.57 .58 0.97

20. ...write a plan with a budget to buy technology for my classroom.

3.55 1.10 4.04 1.01 0.49

21. ...integrate mobile technologies into my curriculum.

3.92 .89 4.46 .59 0.54

22. ...use social media tools for instruction in the classroom. (ex. Facebook, Twitter, etc.)

4.04 .96 4.61 .67 0.57

23. ...create a wiki or blog to have my students collaborate.

3.62 1.14 4.45 .76 0.83

24. ...use online tools to teach my students from a distance.

4.06 .94 4.57 .68 0.51

25. ...teach in a one‐to‐one environment in which students have their own device.

4.15 .90 4.55 .64 0.40

26. ...find a way to use a smartphone in my classroom for student responses.

4.13 .89 4.53 .70 0.40

27. ...use mobile devices to connect to others for my professional development.

4.37 .67 4.61 .64 0.24

28. ...use mobile devices to have my student’s access leaning activities.

4.26 .73 4.59 .60 0.33

(table continues)

79



Mean Pre Attitude


Mean Post Attitude


Mean Difference

29. ...download and listen to podcasts/audio books.

4.27 .88 4.56 .70 0.29

30. ...download and read ebooks.

4.40 .79 4.68 .53 0.28

31. ...download and view streaming movies/video clips.

4.46 .70 4.68 .56 0.22

32. ...send and receive text messages.

4.74 .43 4.80 .45 0.06

33. ...transfer photos or other data via a smartphone.

4.63 .65 4.80 .42 0.17

34. ...save and retrieve files in a cloud‐based environment.

3.97 1.09 4.51 .77 0.54

The item with the largest mean increase from the 34 questions was item 8. Item 8

stated I can create my own web page. The mean score for this item rose from 3.04 to 4.14.

None of items on the TPSCA C21 showed decreases from pre to post survey. The increase for

item 8 could be attributed to the student participants creating a course ePortfolio in Google

Sites during the course.

Research Question 3

Research Question 3 states, “Is there a statistically significant mean difference in

student participants’ perceived technological knowledge in using technology after completion

of an educational technology course as measured by the TK survey when participants are

grouped based on paired grouping?”

80



perceived technological knowledge in using technology as measured by the TK survey before

and after paired grouping. Findings suggested (see Table 15) that there was a statistically

significant positive mean difference between the pre and post technological knowledge in using

technology scores, t(82) = ‐5.016, p < .05, d = .475 (medium effect size), r2 = .053. The paired

grouping had a medium effect on technological knowledge in using technology of the student

participants and 5.3% of the variance in those scores could be attributed to the paired

groupings. Mean technological knowledge rose from 20.9 to 23.2.

Table 15

Paired t‐Test: Pre Technological Knowledge Scores and Post Technological Knowledge Scores

Paired Groupings N M SD t‐value df p ‐value d‐value r2

Pre Attitude 83 20.9 5.07 5.016 82 <.001* .475 .053



The frequencies/percentages of individual participant responses to the TK survey

instrument are shown in Table 16 grouped by pre and post survey responses. Prior to taking the

course, 30.1% of the students selected Strongly Agree/Agree to the prompt of knowing a lot

about different technology in comparison to 72.3% following course completion (42.2%

increase). Prior to taking the course, 49.3% of the students selected Strongly Agree/Agree to

the prompt of keeping up with important new technologies in comparison to 83.3% following

course completion (34% increase). Prior to taking the course, 44.5% of the students selected

81

Strongly Agree/Agree to the prompt of knowing how to solve their own technical problems in

comparison to 66.3% following course completion (21.8% increase).

Table 16

Technological Knowledge (TK) Instrument by Pre and Post Survey Results for Student Participants

Survey ItemStronglyDisagree

DisagreeNeither Agree Nor Disagree


1. I know how to solve my owntechnical problems.

Pre 4.8(n = 4)

18.1(n = 15)

32.5(n = 27)

36.1 (n = 30)

8.4(n = 7)

Post 0.0(n = 0 )

2.4(n = 2 )

9.6(n = 8 )

21.7 (n = 18)

66.3(n = 55)

2. I can learn technology easily. Pre 0.0(n = 0)

6.0(n = 5)

13.3(n = 11)

56.6 (n = 47)

24.1(n = 20)

Post 0.0(n = 0)

10.8(n = 9)

21.7(n = 18)

43.4 (n = 36)

24.1(n = 20)

3. I keep up with important newtechnologies.

Pre 2.4(n = 2)

21.7(n = 18)

26.5(n = 22)

37.3 (n = 31)

12.0(n = 10)

Post 0.0(n = 0)

4.8(n = 4)

10.8(n = 9)

48.2 (n = 40)

36.1(n = 30)

4. I frequently play around with thetechnology.

Pre 4.8(n = 4)

16.9(n =14)

18.1(n = 15)

36.1 (n = 30)

24.1(n = 20)

Post 0.0(n = 0)

13.3(n = 11)

31.3(n = 26)

32.5 (n = 27)

22.9(n = 19)

5. I know about a lot of differenttechnologies.

Pre 7.2(n = 6)

26.5(n = 22)

36.1(n = 30)

22.9 (n = 19)

7.2(n = 6)

Post 1.2(n = 1)

10.8(n = 9)

15.7(n = 13)

45.8 (n =38)

26.5(n = 22)

6. I have the technical skills I need touse technology.

Pre 3.6(n = 3)

7.2(n = 6)

19.3(n = 16)

44.6 (n = 37)

25.3(n = 21)

Post 2.4(n = 2)

12.0(n = 10)

30.1(n = 25)

33.7 (n = 28)

21.7(n = 18)

Prior to taking the course, 80.7% of the students selected Strongly Agree/Agree to the

prompt of being able to learn technology easily in comparison to 67.5% following course

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completion (13.2% decrease). The decrease could be attributed to self‐reported scores of

participants. Participants began the course with their own perceptions of their skill level with

technology. After completion of the course, participants’ perceptions showed an increase.

The frequencies/percentages of individual participant responses to the TK survey


for individual items are shown in Table 17. Five of the 6 individual survey items were found to

be statistically significant from pre to post survey.

Table 17 Technological Knowledge (TK) Instrument by Pre and Post Survey Results for Student Participants Collapsed and p ‐Values Per Survey Item

Survey Item Strongly Disagree/Disagree



p ‐Value

1. I know how to solve my own technical problems.

Pre 22.9(n = 19)

9.6(n = 27)

44.5 (n = 37)

<.001*

Post 2.4(n = 2)

9.6(n = 8 )

66.3 (n = 73)

2. I can learn technology easily.

Pre 6.0(n = 5)

13.3(n = 11)

80.7 (n = 67)

.066

Post 10.8(n = 9)

21.7(n = 18)

67.5 (n = 20)

3. I keep up with important new technologies.

Pre 24.1(n = 20)

26.5(n = 22)

49.3 (n = 41)

.009*

Post 4.8(n = 4)

10.8(n = 9)

83.3 (n = 70)

4. I frequently play around with the technology.

Pre

21.7(n = 18)

18.1(n = 15)

60.2 (n = 50)

.007*

Post 13.3(n = 11)

31.3(n = 26)

55.4 (n = 46)

5. I know about a lot of different technologies.

Pre 33.7(n = 28)

36.1(n = 30)

30.1 (n = 25)

<.001*

Post 12.0(n = 10)

15.7(n = 13)

72.3 (n = 60)

(table continues)

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Survey ItemStrongly

Disagree/Disagree



p ‐Value

6. I have the technicalskills I need to use technology.

Pre 10.8(n = 9)

19.3(n = 16)

69.9 (n = 58)

.001*

Post 14.4(n = 12)

30.1(n = 25)

55.4 (n = 46)


Item number 2, I can learn technology easily, was the only item to not be found

statistically significant. This could be attributed to the student participants starting the course

confident in their ability to learn how to use technology but after course completion and

exposure to different technologies they realized learning to use technology was not as easy for

them as they had originally anticipated.

The Technological Knowledge in Using Technology subscale asked participants to rate

their technological knowledge in using technology on topics, such as knowing how to solve their

own technological problems and having the technical skills needed to use technology.

Participants reported mean increases in technological knowledge greater than .25 in five out of

the six items in this subscale. However, participants reported higher mean increases for all six

of the items in the subscale. Mean increases in technological knowledge in using technology

ranged from .17 to .64. Table 18 displays the descriptive statistics for this subscale.

Table 18

Descriptive Statistics for the TK Items

Technological Knowledge in Using Technology

Mean Pre

Attitude

Standard Deviation Pre

Attitude

MeanPost

Attitude


Mean Difference

1. I know how to solve myown technical problems.

3.25 1.01 3.80 .92 0.55

(table continues)

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Technological Knowledge in Using Technology

Mean Pre

Attitude

Standard Deviation Pre

Attitude

MeanPost

Attitude


Mean Difference

2. I can learn technologyeasily.

3.98 .78 4.15 .80 0.17

3. I keep up with importantnew technologies. 3.34 1.02 3.65 .98 0.31

4. I frequently play aroundwith the technology. 3.57 1.16 3.85 .97 0.28

5. I know about a lot ofdifferent technologies. 2.96 1.04 3.60 1.03 0.64

6. I have the technical skills Ineed to use technology.

3.80 1.01 4.14 .84 0.34

The item with the largest mean increase was item 5 which stated I know about a lot of

different technologies. The mean score for this item rose from 2.96 to 3.60. The increase for

item 5 could be attributed to the student participants’ exposure to different technologies

throughout the duration of the course. The item with smallest mean increase was item 2 which

stated I can learn technology easily. The mean score for this item only rose from 3.98 to 4.15.

The slight increase to item 5 could be attributed to student participants having a difficult time

learning how to use certain technologies.

Research Question 4

Research Question 4 states, “How do student participant perceptions of the paired

grouping influence their attitudes, proficiency, and technological knowledge with regard to

technology?”

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Research Question 4 was answered by conducting student participant semi‐structured

interviews and an inductive coding process to the transcribed interview question responses.

The inductive coding analysis derived three distinct themes or categories of responses

concerning paired grouping and its influence on these constructs: (a) support, (b) outcomes,

and (c) pairing insights. The emergent themes are provided below supported by a sample of the

student participants’ comments.

Support. Data collected from student participant interviews offered compelling evidence

that paired group members benefited from a variety of kinds of support provided by another

paired group member. One type of support that emerged from 98% of participant responses

was peer support. The general consensus among the student participants during the interviews

was that prior experiences with paired grouping resulted in negative experiences. However, all

participants expressed having a satisfactory experience in the current study and cited peer

support as an influential factor in their satisfaction with the paired grouping. Participant

responses specifically identified pep talks, their partners understanding of assignments, and

having another person to lean on as aspects of peer support that they considered to be

important to their satisfaction with the paired grouping. One such student participant

commented, “They understand things that I don’t necessarily understand and can explain it to

me where I can understand it.” Similarly, another student participant stated,

If you don’t understand it then your partner will understand it better and explain it to you. Prior to this study, um, I have not had too much success with paired groupings. My partner would never participate in the way I wanted them to and I always feel like one person would have more work than the other and I prefer to work individually. But since this paired grouping experience it has changed. This was a positive experience.

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Another kind of support that emerged in 92% of participant responses was work ethic.

Most of the student participants had not had experience with a paired group member but did

have experience working in groups. The general consensus among the student participants

during the interviews was that negative experiences from prior group work was a result of

having group members who did not contribute that resulted in their having to do all the work.

However, all participants expressed satisfaction for the paired grouping experience in this

study. One such student participant commented, “I have had to do group projects but it kind of

sucked because some people wouldn’t do their part and you had to do extra but this time it

was fun because I had a great partner.” Similarly, another student participant stated:

Prior to this study, um, I have not had too much success with paired groupings. My partner would never participate in the way I wanted them to and I always feel like one person would have more work than the other and I prefer to work individually. But since this paired grouping experience it has changed. This was a positive experience.

Another kind of support that emerged in 88% of participant responses was

communication. When asked what aspects of paired grouping they were most satisfied with,

the general consensus among the pre‐service teachers during the interviews was that

communication such as phone calls, text messages, and emails were important factors in their

satisfaction or dissatisfaction with regards to paired grouping. One such student participant

commented, “We would text each other if we had questions and that was helpful”. Similarly,

another student participant commented, “I just really enjoyed having the partner because

sometimes you know it’s like at the last minute you have a quick question and you know you

can text them to ask them about it.” One student participant, on the other hand, expressed

dissatisfaction with communication, stating, “If I would text and she wouldn’t text back I kind of

had to wait until she was available.”

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The final kind of support that emerged in 92% of participant responses was

collaboration. When asked what aspects of paired grouping they were most satisfied with, the

general consensus among the student participants was that collaboration, such as working

together and communicating to solve a problem, were important indicators of their satisfaction

with regard to paired grouping. Examples of these comments are provided below:

It helped me get more experience working with someone which I know I will need in the teaching field. The most satisfying part of paired grouping was that we share our work. We would compare to make sure it was what was expected. We shared ideas and like elaborated on the whole thing and went step‐by‐step and basically did it together for the most part. We would combine our two intellects to figure out the projects that needed to be accomplished.

Outcomes. Data collected from student participant interviews suggested that the paired

grouping experience provided a variety of positive outcomes for paired group members.

Motivation was one positive outcome that emerged from 83% of the participant responses. The

general consensus among the student participants during the interviews was that having a

paired group member motivated them to complete assignments. Examples of such comments

are provided below:

I used it as an ok you know I’m here, she’s ahead of me. Let me catch up to her that way I can get caught up. Having a paired group member helped me get my assignments done faster instead of not completing them. I think it motivated me to get my work done. It affected me in a positive way where if I was needing to finish my work it affected me in a good way. It made sure I was on top my stuff. It made me finish my assignments because I had that help too.

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Um, it like gave me the drive to finish it. When I would be sitting in class before you would get there, we would be talking about the past week, what assignments we did, what we made progress on, and what we still needed to work on. It’s kind of like there were times when I didn’t get the assignments done before class so I was waiting until after class to do them and she kind of like gave me the motivation to get it done. One student participant expressed having a paired group member was motivation to

complete assignments that they would likely not complete had it not been for having the paired

group member. This student participant commented:

I like working on my own but without having a partner there to shed light on things I wasn’t sure about, I might have been more inclined to just um not ask anyone. You know, not even ask you and then run the risk of doing it wrong or not doing it at all. You know if it ended up being something I just really couldn’t comprehend, that could have been a possibility. In addition to motivating the student participants to complete assignments, the paired

grouping also motivated them to do a better job on assignments. One such student participant

commented, “Having a partner pushed me because I am competitive. It kind of pushed me to

like do better in certain things I guess. Like working together and on my assignments.” Similarly,

another student participant commented, “I feel like my work was better because of having a

paired group member.”

Another positive outcome that was attributed to the paired groupings from participant

interview data was improved attitude. With regard to student participants’ perceptions of the

influence of paired grouping on their attitude toward technology, 92% of the student

participants interviewed reported that they felt their attitude toward technology had improved

after working with a paired group member. Two such student participants commented:

I didn’t see how technology would make an impact on teaching and learning on day one. I would say it is a little bit better. I can see how I can incorporate it and it’s not as much trouble as I thought it would be or as time consuming and a hassle. So, my attitude has improved.

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Well, at the beginning of class, I thought that technology wasn’t necessarily needed in the classroom but now as so many applications that you introduced to us, so many different activities that you gave us, you know I actually do think that technology is a lot more helpful than I once thought in the classroom. It has definitely improved towards technology.

Two student participants, on the other hand, indicated that their attitude toward

technology was the same after completion of the educational technology course and working

with a paired group member. Examples of such comments are provided below:

My attitude toward technology has not improved. I would say it is still the same as it was at the start of the semester. I still don’t like it personally. My being able to see it applied has increased but my attitude toward technology itself has pretty well stayed the same.

I think it is the same as before. It hasn’t improved.

In addition, another positive outcome that was attributed to the paired groupings from

participant interview data was improved proficiency. With regard to student participants’

perceptions of the influence of paired grouping on their proficiency with technology, 96% of the

student participants interviewed reported that they felt their proficiency with technology had

improved after working

I have gained a lot of skills with technology. I mean certain things like the Aura or Movie Maker. Even the newsletter. I didn’t really know how to do those things until I got to this class. I have worked on Prezi's and PowerPoint’s before so I kind of had an idea but even the non‐linear PowerPoint was new. I have done linear presentations but never the non‐linear. I have improved a lot. Like I never made a YouTube video before and now I’ve like made two of them.

Similarly, another student participant commented, “I knew how to do a Prezi and some

other ones but I didn’t know how to do the non‐linear PowerPoint or Glogster. So, like now I

know how to do them.” Additionally, those student participants who began the course

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expressing that they already felt highly proficient with technology expressed an improvement in

their proficiency with technology. Two such student participants commented:

There is a slight improvement. I have always been nifty with technology but I always think you know nobody is an expert because there is so much technology to be learned. It was easy to learn you know and that’s something that I really liked and that I can always apply in the future and in my own lesson plans. I think overall my skills have improved with technology in this class. I am pretty good with technology already but I think my skills with technology have increased just because of this class for sure. I mean even though some of these projects are videos or something I may never do again, at least I know that if I have to do them or need to do them that I can. Another positive outcome that was attributed to the paired groupings from participant

interview data was improved knowledge. In regard to student participants’ perceptions of the

influence of paired grouping on their technological knowledge in using technology, 88% of the

student participants interviewed reported that they felt their technological knowledge in using

technology had improved after working with a paired group member. One such student

participant commented, “I feel much more comfortable in how to use the technology for

different purposes.” Likewise, another student participant commented, “I am more confident in

my technological knowledge now that I know about different types of technology. I know

different programs that students are using and teachers are incorporating in the classroom. I

understand how to use them now.”

On the other hand, several student participants indicated that their technological

knowledge in using technology had not improved after completion of the educational

technology course and working with a paired group member. One such student participant

commented, “I would say that I don’t think I’ve gained any knowledge in that aspect…I don’t

think that this class has given me any extensive knowledge.” Likewise, another student

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participant commented, “My knowledge in using technology hasn’t changed. It’s something I

understand and that just comes easy to me.”

A final positive outcome that was attributed to the paired groupings from participant

interview data was influence on performance. When analyzing the role of paired grouping with

regard to performance on course assignments, 71% of the student participants interviewed

reported that having a paired group member had an influence on their performance on course

assignments. One such student participant commented, “Yes, I think it influenced my

performance and it motivated me to um I guess research further and maybe want to um excel

more at what I was doing. I guess in a competitive way.” Similarly, two other student

participants commented:

It did influence it because I would see my partner’s work and I would like how she did it and it would make me want to improve. It would make me want to go to her level. It made me a little competitive.

It did influence my performance because in order to be a good partner I would have to be paying attention to what the lessons were. So if my partner had a question I didn’t want to not know what I had to do in order to be able to be effective with her. Yea, it did influence me to be better and to make sure I pay attention and vice versa.

While most student participants felt their performance was influenced, some of the

student participants interviewed reported that having a paired group member did not have an

influence on their performance on course assignments. One such student participant

commented, “Um, I don’t think it did.” Likewise, another student participant stated:

I’d say no that I don’t think it influenced my performance just because I worked pretty well individually. Um, it was comfortable knowing that someone was there in case I had a question or needed to bounce and idea off of but as far as my overall work product it didn’t influence it.

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Pairing Insights. Data collected from student participant interviews suggested that the

paired grouping experience provided a variety of pairing insights. Continue paired grouping was

one pairing insight that emerged from participant interview data. When student participants

were asked if the instructor should continue with paired grouping in future classes, 87% of the

student participants indicated that the instructor should continue to use paired grouping in

future classes. Two such student participants commented:

Paired grouping is good and it should be kept because like for this class especially I think a partner is a really good resource to have. I wish every professor did that because I don’t like to speak up in class and sometimes you don’t talk to people next to you so your home and your like ok so what do I do. It makes me feel comfortable. I think it is good to have paired grouping because it give people a chance to like get to know one another and open up to questions and discussing things with other students because everyone has different opinions and ideas. So, I think it’s beneficial. I would continue to do the paired grouping. Instructor selected pairs was another pairing insight that emerged from participant

interview data. When student participants were asked their preference on whether they should

pick their own partner or if the teacher should pick their partner, 91% of the student

participants’ interviewed reported that they preferred that the teacher select the partner. One

such student participant commented:

I think it was good the way it was because we didn’t get to choose our partner so it was like whoever you go that’s good. You get to meet new people and learn from them. I like it. I liked the way we had partners.

Likewise, another student participant commented,

I don’t really know how you paired us together but I liked that we didn’t get to choose our partners because you got to meet different people…sometimes you don’t know anyone in class so you feel awkward. So, I didn’t really know anyone at the start of the semester so I wouldn’t have picked. I would have been that person that still didn’t have a partner so I liked the way you did it.

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Preferred paired grouping was a final pairing insight that emerged from participant

interview data. When student participants were asked their preference of working alone or

with a paired group member, 83% of student participants interviewed reported that they would

prefer to work with a paired group member rather than on their own. Three such student

participants commented:

I would work with the paired group member because if I need help I know I can go back to her and text and say hey. She like even recorded how to do step‐by‐step. So that was really helpful because if not I wouldn't have been able to do it.

I would definitely say a paired group member…having that back up person to be able to fall back on and then have them sit down with you individually and you sit down with them to break down the concepts has just really been a huge relief.

I would actually say I’d prefer to work with the paired group member…so you don’t feel alone…If I have any questions I can go to this person. A shoulder to lean on.

One student participant, on the other hand, would rather work alone stating, “By

myself. Like I said earlier I don’t have to depend on anybody.” Three student participants

indicated neutral feelings and would therefore work alone or with a paired group member.

Examples of such comments are provided below:

I would probably say both. I like that there’s the cushion partner that you have now…Just for someone to be there if you have a question like that. I would say both. It’s just sometimes you wish you had somebody else.

Um, I think I did better doing it on my own but I also liked having a partner to be there when I needed them. So I would say I would be happy with both.

It depends on the person but if it’s someone like I worked with its fine. Like if I needed help with something but sometimes I just like to do it myself cause other people take forever to respond or something so it just depends. So, I am ok with both.

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Summary of Findings

The ATTS, CPSA C21, and TK Surveys were completed by 83 student participants

enrolled in an educational technology course at the beginning and end of a semester.

Interviews were conducted with 24 of the 83 student participants. The population was

unevenly distributed by gender and the majority of participants were Hispanic or Latino. The

majority of participants were 18‐34 years of age and were seeking an Early Childhood‐6th grade

Generalist teaching certification. Quantitative analysis resulted in statistically significant mean

differences on all of the survey instruments. There was a statistically significant mean

difference between student participants’ pre and post attitude toward technology scores,

proficiency with technology scores, and technological knowledge in using technology scores.

Qualitative analysis illustrated that there were similarities and differences in participant

responses to interview questions. Three themes emerged from the participant responses: (a)

support, (b) outcomes, and (c) pairing insights. Responses from participants supported the

quantitative analysis that indicated there were increases in attitude, proficiency, and

technological knowledge. Overall, it is clear that using a paired grouping can have a positive

influence on student participants’ attitudes, proficiency, and technological knowledge with

regard to technology. Chapter 5 will provide a summary of the study, discussion of the findings,

implications, and recommendations for future research.

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CHAPTER 5

DISCUSSION

Summary of the Study


participants’ perceived attitude toward technology, perceived proficiency with technology, and

perceived technological knowledge after completing an educational technology course. In

addition, student participants’ perceptions regarding the use of paired grouping on their

attitudes, proficiency, and technological knowledge with regard to technology was also

investigated. This study was completed during the fall of 2015. Eighty‐three student

participants from a suburban mid‐sized Gulf Coast university located in southern United States

participated in this study. Student participants were asked to complete the survey instruments

and participate in interviews. Descriptive statistics, two‐tailed paired t‐tests, and inductive

coding were used to analyze the data collected. This chapter includes a summary of the

findings, implications, and recommendations for future research.

Discussion of the Findings

The research questions addressed whether or not there was a statistically significant

mean difference between pre and post survey responses of student participants. Research

Question 1 asked if there was a statistically significant mean difference in pre‐service teachers’

perceived attitudes toward technology after completion of an educational technology course

when participants are grouped based on paired grouping. Quantitative analysis demonstrated

that there was a significant difference between the pre and post attitude toward technology

scores of student participants. These results are consistent with research conducted by

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Woullard and Coats (2004) in which a statistically significant difference was found to exist

between pre‐service teacher candidates who worked with a peer mentor in regard to

attitudinal changes. Furthermore, these results support previous research that found significant

positive differences in student participants’ attitudes toward technology when technology

integration was emphasized in the course (Guo & Carey, 2008; Lambert & Gong, 2010).

Research Question 2 asked if there was a statistically significant mean difference in


technology course when participants are grouped based on paired grouping. Quantitative

analysis demonstrated that there was a significant difference between the pre and post

proficiency with technology scores of student participants. These results support previous

research demonstrating significant improvements in technology proficiency, computer self‐

efficacy and computer coping strategies that occurred from the beginning to end of a teacher

preparation course (Ropp, 1999).

Research Question 3 asked if there was a statistically significant mean difference in

student participants’ perceived technological knowledge using technology after completion of

an educational technology course when participants are grouped based on paired grouping.

Quantitative analysis demonstrated that there was a significant difference between the pre and

post technological knowledge in using technology scores of student participants. These results

are similar to the results of Pope et al. (2005) where findings suggested using a model of

instructional delivery that includes integration of technology practices into elementary methods

courses for pre‐service teachers positively influences their self‐reported confidence levels and

their use of technology in the classroom as student teachers.

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Research Question 4 explored student participants’ perceptions regarding the use of

paired grouping on their attitudes, proficiency, and technological knowledge with regard to

technology. A total of 24 participants were interviewed and responded to semi‐structured

questions about the different aspects of paired groupings influence on their attitude,

proficiency, and technological knowledge related to technology. Qualitative analysis

demonstrated that participant responses could be classified into three different themes: (a)

support, (b) outcomes, and (c) pairing insights. Participant responses support the quantitative

data indicating increases in perceived attitudes, perceived proficiency, and perceived

technological knowledge among the student participants after completion of an educational

technology course when participants are grouped based on paired grouping. Some of the key

similarities between the pre and post scores could be found in participant responses from each

of the identified themes. Most specifically, improved attitude, improved proficiency, and

improved knowledge. These results are similar to the results from previous research that

demonstrates that pre‐service teachers who participate in peer mentoring have positive

increases in attitude, self‐efficacy, and motivation (Goker, 2006; Lu, 2010; Woullard & Coates,

2004). In addition, these results support previous research demonstrating that paired grouping

allows students to use and extend strengths by teaching another student while increasing

satisfaction of the paired group member (Wilson & Ribovich, 1973; Zhou, 2012). Furthermore,

results of this study supports previous research conducted by Naseem (2012) that found

motivation was one of the key outcomes of a peer mentoring experience. Overall, the findings

from this sample of student participant responses were consistent with outcomes reported in

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previous studies. One of the most common themes reported by student participants was

support. Ward, Thomas, and Disch (2014) also found support to be important.

It should also be noted that this study did not contain a control group. Therefore, the

Hawthorne Effect is worth mentioning. The Hawthorne Effect was first described in an

experiment that was conducted at the Western Electrics Hawthorne Works electric company

(McCambridge, Witton, & Elbourne, 2014). The Hawthorne Effect is a term that is used to

describe the way participants in a research study may behave during an experiment. It refers to

the tendency for participants to perform better when they know they are involved in an

experiment. Berthelot, Le Goff, and Maugars (2011), stated “The Hawthorne Effect is not

confined to experimental situations it is a universal measurement bias that occurs inevitably

when people know or believe they are being evaluated by an observer” (p. 335). The

researcher's participants in the current study were aware they were involved in the research

study and therefore may have answered the survey and/or performed better because of this

knowledge.

Implications

Technology integration in EC‐12 classrooms is increasing resulting in classroom teachers

being responsible for helping students acquire the skills needed to use 21st century technology

tools. Therefore, teachers must have a deep understanding of the related technology tools as

well as significant proficiency with these tools. Unfortunately, most teacher preparation

programs are not constructed to strongly influence pre‐service teachers’ technology use

(Belland, 2009; Hermans et al., 2008; Kay, 2006). Findings from the current research indicate

that student participants had positive increases in attitudes, proficiency, and technological

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knowledge in regard to technology. However, as the results from this study were examined and

explored, many more questions remain about the influence of paired grouping on student

participants’ attitudes, proficiency, and technological knowledge. These questions include what

role the instructor played in the outcomes of the current study and whether or not the same

outcomes would occur if paired grouping was implemented in other subject area courses.

Implications for Instructors of Pre‐Service Teachers

First, it is imperative for teacher education programs to prepare pre‐service teachers to

integrate technology into their future classrooms. As the literature in Chapter 2 concludes,

paired grouping may be a critical variable in the preparedness of pre‐service teachers to

integrate technology into their future classroom (Draves & Koop, 2001; Goker, 2006; Lu, 2010;

Marshall, 2005; Woullard & Coats, 2004). Instructors of pre‐service teachers should make every

effort to embed coursework that will provide opportunities for experiences that will improve

the confidence levels of their students. Paired grouping can be used as an instructional model

that will improve the confidence levels of pre‐service teachers. Better preparation could lead to

pre‐service teachers’ willingness to integrate technology in their future classroom.

Implications for Professional Development of In‐Service Teachers

Attitudes to technology and perceived computer self‐efficacy have been found to have

an effect on teachers’ attitude toward applying computer supported education (Celik &

Yesilyurt, 2012). The higher a teacher’s general self‐efficacy, the higher his/her computer self‐

efficacy (Paraskeva, Bouta, & Papagianni, 2008). Therefore, in‐service teachers must be

knowledgeable and confident in technology in order to integrate it in their classrooms.

Professional development of in‐service teachers should provide opportunities for experiences

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that will improve the confidence levels of their participants. Feedback from the quantitative

data of the current study shows promise for the use of a model such as paired grouping. Use of

this type of model could influence the perceived sense of preparedness and self‐efficacy of in‐

service teachers and could positively impact the design and development of effective in‐service

teacher professional development initiatives. These types of initiatives could help in‐service

teachers enter their classrooms confident in their abilities to integrate technology in their

classroom instruction.

Recommendations for Future Research

Several recommendations are suggested for future research. Despite the limited sample

size and lack of a control group, this study provided insights into the influence of paired

groupings on student participants’ attitudes, proficiency, and technological knowledge in regard

to technology. The first recommendation is to develop future studies that could replicate this

study but use a quasi‐experimental design that includes a control group. In addition, future

studies could expand to other pre‐service teacher courses, which would provide additional data

to validate the findings of this study. Expanding the data pool to include more pre‐service

teachers from a wider variety of subject area courses would allow for comparisons to be made

about pre‐service teachers’ attitudes and beliefs related to technological initiatives within

various subject areas and not be limited to educational technology courses.

A second recommendation for how this study could be expanded in future studies

would be to replicate the study but allow students to pick their own partners. This modification

would allow for comparisons to be made on the selection of partners by the instructor versus

selection of partners by the students. By focusing on selection of partners (i.e. instructor

101

selection vs. student selection), the research would provide more specific data on differences

and similarities of perceptions of pre‐service teachers and paired grouping. Although this study

focused on the pre‐service teachers’ perceptions, future studies could expand the focus to

include instructors’ perceptions of the paired grouping.

Finally, additional research could focus on expanding paired grouping further. Initial

pairings of participants will be paired by similar content areas and certification levels of pre‐

service teachers. This modification would allow pre‐service teachers seeking the same

certification within the same content area to work together and help to determine if these

similarities affect their perceptions of paired grouping.

Conclusion

This study examined the influence of paired grouping on student participants’ perceived

attitudes toward technology, perceived proficiency with technology, and perceived

technological knowledge after completing an educational technology course. In addition, this

study explored student participants’ perceptions regarding the use of paired grouping on their

attitudes, proficiency, and technological knowledge with regard to technology. This study

collected survey and demographic data from a purposeful sample of undergraduate students

from a suburban mid‐sized Gulf Coast university located in the southern United States. The data

was analyzed using descriptive statistics, two‐tailed paired t‐tests, and inductive coding.

Results of the quantitative portion of the study indicate a statistically significant mean

difference in student participants’ pre and post attitude toward technology scores, proficiency

with technology scores, and technological knowledge in using technology scores. Additionally,

results of the qualitative portion of the study indicate that paired grouping increased attitudes,

102

proficiency, and technological knowledge of the student participants’ providing further support

for the quantitative findings. Furthermore, the consensus of student participants’ perceptions

regarding the use of paired grouping are similar in that it was a positive experience that

influenced their performance on course assignments, and should be continued in future classes.

While most student participants in this study indicated that they would prefer to work on

assignments with a paired group member, one indicated that they would prefer to work alone.

However, it is worth noting that this participant was not seeking teacher certification.

In conclusion, the results of this study contribute to the existing research regarding pre‐

service teachers’ perceptions of the use of paired grouping in teacher education programs. In

the context of a case study, this study has provided insights into the students studied. In this

study’s environment, students found paired grouping to be an effective instructional strategy.

Recommendations for future research included using a quasi‐experimental design with a

control group, expanding the scope of participants to other pre‐service teacher courses,

comparing the selection of partners by the instructor versus selection of partners by the

students, and further expanding initial pairings to similar content areas and certification levels

of student participants.

103

APPENDIX

INTERVIEW PROTOCOL

104

APPENDIX A

INTERVIEW PROTOCOL

1. Describe experiences you have had with paired grouping prior to participating in this study.

2. Do you feel that paired grouping influenced your performance on course assignments and

how?

3. Please describe what aspects of paired grouping that you have been most satisfied with?

4. Please describe what aspects of paired grouping that you have been least satisfied with?

5. If you had the choice/option would you rather work on your own on course assignments or

with a paired group member and why?

6. Discuss how this paired grouping experience affected your ability to complete assignments.

a. attitudes towards technology after completion

b. knowledge/skills with technology after completion

c. technological knowledge in using technology: technology is referring to digital

technology/technologies ‐ that is, the digital tools we use such as computers,

laptops, iPods, handhelds, interactive whiteboards, software programs, etc.

7. If this paired grouping experience did not affect your ability to complete assignments,

please discuss why you think your abilities remained unchanged.

8. Discuss your overall opinion of paired grouping.

105

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