exploring changes in computer self-efficacy during

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Theses and Dissertations

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Exploring Changes In Computer Self-EfficacyDuring Graphics Skills AcquisitionMichele DamesUniversity of South Carolina

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EXPLORING CHANGES IN COMPUTER SELF-EFFICACY DURING

GRAPHICS SKILLS ACQUISITION

By

Michele Dames

Bachelor of Arts

The College of New Jersey, 1995

Master of Arts

University of South Carolina, 2003

Submitted in Partial Fulfillment of the Requirements

For the Degree of Doctor of Education in

Curriculum and Instruction

College of Education

University of South Carolina

2016

Accepted by:

Susan Schramm-Pate, Major Professor

Suha Tamim, Committee Member

Michele Maher, Committee Member

Tena B. Crews, Committee Member

Lacy Ford, Senior Vice Provost and Dean of Graduate Studies

ii

© Copyright by Michele Dames, 2016

All Rights Reserved.

iii

ACKNOWLEDGEMENTS

Thank you to every teacher who helped me arrive at this place in my educational

journey. I am especially grateful to Dr. Schramm-Pate for her guidance, patience, and

encouragement. I am also thankful for the time and valuable feedback of my committee

members, Dr. Maher, Dr. Tamim, and Dr. Crews. Special thanks to Amber F. for your

knowledge and support.

My parents always valued education and I thank them for instilling that in me.

Thank you Mom and Dad for loving, guiding, supporting, and inspiring me. My mother is

the original Dr. Dames and I am proud to follow your lead. Thank you for showing me

what can be accomplished with hard work. I thank my father for always being my

biggest fan and encouraging me. May the buttons pop off of your shirt Dad!

Many thanks to my husband Kevin for all of your love and support. I love you

very much and am proud to be married to such a wonderful person.

A million thanks to my friends and support team: Melissa, Aunt Anne, Brandy,

Alicia, and Amber V. You always lend an ear and remind me I am stronger than

I think.

iv

ABSTRACT

The present action research study involved a participant-researcher and her

undergraduate students enrolled in the Course: Visual Arts Computing, at the University

of South Carolina from 2012 to 2013. This research examined six sections of the course

with an average of 20 students in each section, totaling 120 participants. The overarching

Research Question for the present study was: What factors from social cognitive theory

(cognitive, environmental, behavior) influenced students’ self-efficacy with computer

technology in an undergraduate graphic arts course? To answer this question the

participant-researcher administered a pretest and posttest of the computer self-efficacy

scale by Compeau and Higgins (1995b). The course focused on learning foundational art

and graphic design concepts through projects created with the graphics software, Adobe

Photoshop. “Graphic Skills Acquisition” (GSA) which is associated with improved

“computer self-efficacy,” was used in this action research study to increase students’

confidence levels with computers and enhance feelings of positivity when interacting

with technology in general. The research showed, based on the pretest and posttest scale,

GSA has the potential to influence academic student achievement, workplace

productivity, and personal computer self-efficiency outside of the course. The factors

identified from Bandura’s social cognitive theory were: independent learning

(environmental), new and unfamiliar tasks (cognitive), and behavior modeling (behavior).

v

TABLE OF CONTENTS

Acknowledgements ........................................................................................................ iii

Abstract .......................................................................................................................... iv

Chapter One: Introduction ............................................................................................... 1

Bandura’s Social Cognitive Theory ........................................................................ 2

Compeau and Higgins Scale ................................................................................... 4

Statement of the Problem ....................................................................................... 5

Justification for the Study ....................................................................................... 6

Action Research Methodology ............................................................................... 8

Research Question.................................................................................................. 9

Purposes of the Study ............................................................................................. 9

Additional Theory ................................................................................................ 10

Definitions of Terms ............................................................................................ 10

Researcher’s Positionality .................................................................................... 11

Limitations ........................................................................................................... 13

Scope ................................................................................................................... 14

Significance of the Study ..................................................................................... 14

Findings of the Present Study ............................................................................... 17

Summary.............................................................................................................. 17

vi

Chapter Two: Literature Review ................................................................................... 19

Problem Statement ................................................................................................ 19

Purpose Statement ................................................................................................ 20

Social Cognitive Theory ....................................................................................... 21

Self-Efficacy ........................................................................................................ 22

Computer Self-Efficacy ........................................................................................ 26

Computer Self-Efficacy Scale .............................................................................. 37

Graphics ............................................................................................................... 39

Conclusion ........................................................................................................... 45

Chapter Three: Methodology ........................................................................................ 46

Problem Statement ............................................................................................... 46


Magnitude ............................................................................................................ 47

Strength ............................................................................................................... 48

Action Research Design ....................................................................................... 48

Focus Stage: Developing an Area of Focus ......................................................... 48

Acting Stage: Data Collection ............................................................................. 49

Reflective Stage: Data Analysis .......................................................................... 50

Analysis Stage: Reflecting on the Data ................................................................ 51

Theoretical Framework ........................................................................................ 52

Participants .......................................................................................................... 53

Summary.............................................................................................................. 54

vii

Chapter Four: Research Findings .................................................................................. 55



Findings ............................................................................................................... 57

Magnitude ............................................................................................................ 61

Strength ............................................................................................................... 62

Summary.............................................................................................................. 71

Chapter Five: Implications and Conclusions ................................................................. 72



Overview of the Research Study and Methodology .............................................. 73

Findings ............................................................................................................... 75

Implications ......................................................................................................... 80

Independent Learning ........................................................................................... 80

Access to Support Materials ................................................................................. 81

Engaging in Unfamiliar Tasks .............................................................................. 82

Behavior Modeling .............................................................................................. 82

Summary.............................................................................................................. 83

Action Plan .......................................................................................................... 83

Recommendations for Graphic Arts Faculty ......................................................... 86

Recommendations for Future Research ................................................................ 87

Conclusions ......................................................................................................... 88

References ..................................................................................................................... 89

viii

Appendix A: Computer Self-Efficacy Scale ................................................................ 109

Appendix B: Institutional Review Board (IRB) Approval ............................................ 111

ix

LIST OF TABLES

Table 4.1 Computer Self-Efficacy Scale Questions ........................................................ 59

Table 4.2 Computer Self-Efficacy: Percent Change by Question .................................. 59

Table 4.3 Magnitude: Percent Change by Section ......................................................... 62

Table 4.4 Magnitude: Percent Change by Question ....................................................... 63

Table 4.5 Strength: Percent Change by Section ............................................................. 64

Table 4.6 Strength: Percent Change by Question .......................................................... 64

Table 4.7 Magnitude, Strength, Computer Self-Efficacy: Percent Change Comparison .. 66

Table 4.8 t-Test: Two Sample Assuming Unequal Variances, Section 1........................ 67

Table 4.9 t-Test: Two Sample Assuming Unequal Variances, Section 2........................ 67

Table 4.10 t-Test: Two Sample Assuming Unequal Variances, Section 3...................... 68




Table 5.1 Action Research Plan ..................................................................................... 84

x

LIST OF FIGURES

Figure 2.1 Model of Reciprocal Determinism ................................................................ 21

Figure 4.1 Sample of Computer Self-Efficacy Measure ................................................. 58

1

CHAPTER ONE

INTRODUCTION

The purpose of Chapter One: Introduction is to provide an overview of the present

action research study, which aimed to address the following research question:

What factors from social cognitive theory (cognitive, environmental, behavior)

influenced students’ self-efficacy with computer technology in an undergraduate

graphic arts course?

The present action research study involved the ways in which developed or

developing societies and nations require citizens to integrate technology into everyday

life and rise to the challenge of actively participating in the global digital World Wide

Web. This integration involves electronic transactions across fields of communication,

finance, research, transportation, education, and government (Petrina, 2000). The volume

of information is increasing at an accelerating pace, adding to databases, search engines,

libraries, and other resources as technological innovations continually arrive at homes,

schools, and places of employment. However, do these citizens feel efficacious in

actually using and integrating technology within everyday lives and professions? To

answer this overarching question, the present action research study focused on and the

researcher-participant investigated undergraduate students at the University of South

Carolina who are involved in a visual arts computing course from 2012 to 2013.

2

In 2008, the Cooperative Institutional Research Program (CIRP) reported 40% of

incoming first-year university students nationwide self-reported abilities of above

average or in the highest 10% in computer skills. Madigan, Goodfellow, and Stone

(2007) surveyed first year students at a large university and reveal “students are actually

less skilled than they perceive” (p. 413). First year college students are presumed to be

technologically advanced and possess high computer self-efficacy, however in reality

introductory skills are weak (2007).

Technology elevates education by increasing control and access to support

individual potential. Through educational tools and software, learners can be tracked

through metrics and assessments customized from independent needs (Collins, 2009).

These educational technologies may be visual, oral, or computer-based, with portability

options through an array of devices. These advanced instruments collect rich data which

can inform decisions involving students, educators, administrators, professionals, and

business people.

Bandura’s Social Cognitive Theory

How efficacious do visual art students feel when it comes to utilizing and

integrating technology? The present study was based on the work of psychologist Albert

Bandura (1986; 1991; 1997) who asked questions regarding the human power to steer

personal choices in education and life. Bandura argued efficacy acts as the gatekeeper

for every individual educational journey and progress is moderately dependent on unique

conscious self-assessments. Skills are the acquisition of knowledge, facts, and processes

independent from self-efficacy, which is what an individual believes can be accomplished

with those skills.

3

Self-efficacy is rooted in social cognitive theory, a concept involving three major

classes of interactive determinants: cognitive and personal factors, behavior, and

environment. Cognitive and personal factors include beliefs, values, outlook, and lessons

learned from triumphs and failures. Behavior is built from continuous interactions and

reactions, individual experience, and noticing the behavior of others. The environmental

components include tangible habitats as well as what people, culture, and atmosphere

exist inside.

Cognition, behavior, and environment function “interactively as determinants of

each other” in a cycle of reciprocal determinism (Bandura, 1986, p. 23). Therefore,

thoughts and feelings lead to behaviors and attract people to specific locations; a student

interested in drawing may enroll in an art course. An efficacious student may enroll in a

particular course expecting success. Environment has the potential to shape behavior

when the efficacious student realizes personal shortcomings, struggles in the class, and

changes the level of effort. Finally, surroundings can alter behavior when the course is in

the evening hours and the student is a morning person.

It is evident movement in one part of the triad influences the other components

with returned effort, but not necessarily at the same time or with equal strength (Bandura,

1986). A person trying to lose weight fails with cognition alone because behavioral

changes, such as exercise and monitoring the environment are crucial pieces. Experience

in the form of unhealthy habits, may impact effort when a person with an eating disorder

attempts to lose weight with distinct environment, cognitive, and behavioral challenges.

The present study was based within the work of social cognitive theory (Bandura

1986; 1991; 1993) and argues learners who aim to think individually, understand, and

guide their own educational experience identify as more efficacious. This base and

4

framework contends traditional one-sided classroom environments do not engage learners

or allow independent thinking and therefore fail to maximize the educational experience.

Freire (1970) described this as the “banking concept of education,” where students are

empty accounts and simply receive what the teacher deposits (p. 72). Dewey (1933)

indicated collecting facts and data alone does not develop learning habits, a deeper thirst

for continued knowledge, or the motive to be an active participant in society (Dewey,

1933). Bandura (1986; 1991; 1993) discussed social cognitive theory and self-regulation,

the process of empowerment through concentrating attention on efforts and directing

efforts towards set goals.

The participant-researcher of the present study advocated democratic classroom

methods fostering the growth of self-efficacy and empowering the student during GSA.

Bandura (1991) stated experience as the most powerful influence on self-efficacy and the

course structure provides substantial time for interaction with the graphic software,

Adobe Photoshop. The course met for 2 hrs 45 min twice a week and the computer lab

classroom was available for extra time. Independent work time was encouraged and

students worked at various paces and made decisions about what projects to concentrate

on. The projects allowed the students a lot of creative freedom and expression. The

student voice was essential to individual progress and was treated with value and worth,

observing and encouraging self-examination, promotion, and self-efficacy.

Compeau and Higgins Scale

The present action research study was not only based on Bandura’s social

cognitive theory and self-efficacy, but also on a scale designed to measure computer self-

efficacy. Computer self-efficacy is defined as where self-efficacy, technology, and

education intersect and consider “a judgment of one’s capability to use a computer”

5

(Compeau & Higgins, 1995b, p. 192). In the present action research study, the

participant-researcher identified low computer self-efficacy in undergraduate students

during graphics skills acquisition (GSA). Evidence of low computer self-efficacy was

identified through observation and reflective entries, then the participant-researcher

explored options to potentially assist the students.

The Compeau and Higgins’s (1995b) Scale was used by the participant-researcher

to determine if GSA impacted the participants’ computer self-efficacy. The participant-

researcher chose this particular scale since previous research suggests a positive

relationship between GSA and computer self-efficacy in preservice teachers (Chu, 2003),

military trainees (Downey & Zeltmann, 2009), and non-traditional students (Hasan,

2003). These students were similar to the group of participants in the present study. In

particular, the present study examined 120 undergraduates enrolled in six sections of the

Visual Arts Computing Course at the University of South Carolina over the years 2012 -

2013.

Statement of the Problem

Karsten and Schmidt (2008) report college students have low computer self-

efficacy; the participant-researcher in the present study identified low computer self-

efficacy in her college students. This problem was identified through observation and

reflective writings of some undergraduate art students enrolled in the Course: Visual Arts

Computing at USC. This Course focused on learning foundational art and graphic design

concepts through projects created with the graphics software, Adobe Photoshop.

The participant-researcher of the present action research study identified the

problem through observation and journal entries where her students in her Visual Arts

Computing course at USC reported on progress and if any material was unclear. For

6

example, one student expressed concern with the pace of the instruction and memory,

stating difficulty with working quickly enough or remembering the steps to complete a

task. Another student shared feelings of confusion and struggle with the graphic software.

This feedback prompted the participant-researcher to explore options to

potentially assist the students. A lack of computer self-efficacy negatively impacts the

will to pursue difficult tasks and persist, both important to academic achievement

(Bandura, 1977, 1997; Torkzadeh & Van Dyke, 2002). Computer self-efficacy has been

shown to have a significant, positive relationship with academic self-efficacy (Jan, 2015).

Venkatesh and Davis (1996) discussed the need to design effective training in an

effort to improve user acceptance and computer self-efficacy. The research utilized the

Compeau and Higgins (1995b) computer self-efficacy scale at two different points in

time to determine if changes occurred in undergraduate students enrolled in an

introductory systems course. Following Venkatesh and Davis (1996), the participant-

researcher of the present action research study began using this scale at the beginning and

end of each semester from 2012-2013 to investigate the relationship between GSA and

computer self-efficacy in her students. The participant-researcher of the present action

research study examined six sections of the course with an average of 20 students in each

section, totaling 120 participants.

Justification for the Study

Individuals with higher computer self-efficacy have less anxiety, more

confidence, and increased positivity when interacting with technology (Conrad & Munro,

2008; Morris & Thrasher, 2009). These benefits can influence everything connected to

technology including academic student achievement, workplace productivity, personal

efficiency, and life occurrences in a gratifying way. This supports a need to develop and

7

raise computer self-efficacy to foster engaged, curious, independent, and confident digital

citizens.

Computer self-efficacy has the potential to be strengthened through experience

and training, (Cassidy & Eachus, 2002; Chu, 2003; Brinkerhoff, 2006; Beas & Salanova,

2006) especially if the participant sees the relevance and connects the new knowledge to

future success (Albion, 2001). Early research in this field is rooted in business,

originating from researchers in the 1970s noticing resistance to new machines and then

attempting to identify what components drive a person to adopt or reject technology

(Compeau & Higgins, 1995b). This background prompted focused research on business

applications and often suites of programs including graphics or presentation software.

Studies on these suites indicated a significant positive relationship between graphics and

presentation software and computer self-efficacy (Chu, 2003; Downey & Zeltmann,

2009; Hasan, 2003).

Individuals often have less experience with graphics software and Bandura (1986)

noted challenging and unfamiliar tasks have the strongest influence self-efficacy.

Productivity software is often taught first, such as word processing software so

individuals understand the basics before moving to more advanced software. The nature

of art and design is interdisciplinary and activates thinking skills in visual drawing,

planning and drafting, math, and computer science (Ettinger, 1988). Graphics have the

potential to form and increase computer self-efficacy and trainers and educators should

provide more opportunities for interactions with this area of study (Busch 1995; Hasan

2003). This earlier research found connections between graphics and computer self-

efficacy using standard graphic software, such as Microsoft PowerPoint. The present

study was concentrated the advanced graphics program, Adobe Photoshop, a

8

comprehensive and powerful graphics program considered the industry standard for

image manipulation (Clawson, 2015; Cookman, 2003).

Action Research Methodology

The present action research study is a systematic inquiry designed to gain

knowledge about a singular situation (Mertler, 2013). Undergraduate students enrolled in

the course, Visual Arts Computing at the University of South Carolina were studied.

Data from the Compeau and Higgins (1995b) computer self-efficacy scale were analyzed.

The participant-researcher of the present action research study was also the

instructor of the visual graphics computing courses at USC and the 120 undergraduate

students who were examined. Mertler (2013) states an action research approach is

commonly used in educational research and provides the opportunity to preserve the

actions of real events. The participant-researcher administered the Compeau and Higgins

(1995b) scale in 2012 after identifying low computer self-efficacy through observing and

reflective writings for two years.

The participant-researcher of the present action research study used the ten item

scale designed by Compeau and Higgins (1995b) based on social cognitive theory and

computer self-efficacy. The first part of each question asked respondents in the present

action research study to anticipate abilities on a computer task through a fictitious

scenario, reading “I could complete the job using the software package… […if I had…].”

The second part involved participants completing the sentence with options for support

such as the software manual, additional time, and human assistance. Participants

indicated personal level of confidence based on the varying degrees of support and what

would be more helpful (see Appendix A). The Scale is also designed to push the

9

participants to consider future behavior as opposed to prior past proficiencies (Bandura,

1986; Compeau & Higgins, 1995b).

The data for the present study collected from the Computer Self-Efficacy Scale

was examined using Microsoft Excel and the Statistical Package for the Social Sciences

(SPSS). The present action research study used a single group pretest posttest design.

Data was analyzed using a paired sample t-test conducted to compare the change between

pre-post scores of the computer self-efficacy scale. Six sections of the course were

analyzed to verify results and recognize trends within the local and particular student

population studied.

Research Question




Purposes of the Study

The primary purpose of the present action research study was to investigate if the

use of graphics software impacts computer self-efficacy as previous research suggests

(Chu, 2003; Downey & Zeltmann, 2009; Hasan, 2003). Factors influencing computer

self-efficacy during graphic skills acquisition were identified by the participant-

researcher from Bandura’s Social Cognitive Theory (i.e., cognitive, environmental,

behavior factors).

The secondary purpose of the study was to recognize trends within the local and

particular student population studied since individuals with higher computer self-efficacy

have less anxiety, more confidence, and increased positivity when interacting with

technology (Conrad & Munro, 2008; Morris & Thrasher, 2009). The tertiary purpose of

10

the study was to report on the advanced graphics program, Adobe Photoshop. This

comprehensive program is considered the industry standard for image manipulation

(Clawson, 2015; Cookman, 2003). The final purpose is to report the results of the

Compeau and Higgins (1995b) computer self-efficacy scale used to determine the factors

which influenced computer self-efficacy with the 120 undergraduates.

Additional Theory

In previous research, Chu (2003) found frequent use of computer graphics and

presentation software to be a strong predictor of computer self-efficacy. Downey and

Zeltmann (2009) researched the relationship between six different software packages and

computer self-efficacy. Results showed a strong correlation with graphics presentation

software (Microsoft PowerPoint) and the high competency group, which also had higher

computer self-efficacy. Hasan (2003) investigated specific types of computer experience

and found a significant positive relationship between individuals with graphics

applications experience and computer self-efficacy beliefs.

These positive connections between graphics and computer self-efficacy support a

need to increase experience with graphics software to raise computer self-efficacy.

Research shows users report the least amount of experience with graphics software

compared to other applications (Hasan 2003; Wilfong 2006). Bandura (1986) stated new

and intriguing tasks are the most influential on self-efficacy. Focused inquiry on an

advanced graphics program, specifically Adobe Photoshop, has not been examined and

the present study intended to give attention to this under researched area.

Definitions of Terms

The following definitions are for the purposes of this study:

11

1. Art Studio: This is an area of study within the Art Department at the University

of South Carolina encompassing the making of art in a studio setting. Graphic

design, computer graphics, photography, ceramics, painting, and illustration are

included in art studio. Art studio classes typically meet for longer class period

because the intent is to have studio resources to work with in an extended time

frame. Additional areas of study in art may include art history, media arts, and art

education (Art Department, 2016).

2. Computer self-efficacy: “A judgment of one’s capability to use a computer”

(Compeau & Higgins, 1995b, p. 192).

3. Graphics: An image created or edited through using a computer. Graphics

software may refer to a generic program included with the computer, within an

application suite (Microsoft PowerPoint), or an independent application. The

present study researched an advance graphics program called Adobe Photoshop,

this may be installed as part of the Adobe suite.

4. Self-Efficacy: A psychological term referring to an individual’s belief in

personal abilities. “Expectations of personal efficacy determine whether coping

behavior will be initiated, how much effort will be expended, and how long it will

be sustained in the face of obstacles and aversive experiences” (Bandura, 1977, p.

191).

Researcher’s Positionality

The participant-researcher of the action research study was also the instructor in

the courses examined between 2012 -2013. As the participant-researcher, my underlying

assumption is individuals, educators, and trainers should enhance and improve computer

self-efficacy. This assumption implies people strive to be active and engaged in a

12

technological environment and as part of a global digital citizenry also known as the

World Wide Web. The participant-researcher assumes students are honest in answering

the questions on the Computer Self-Efficacy Scale developed by Compeau and Higgins

(1995b). The participant-researcher also assumes the majority of the 120 undergraduate

students who participated in the present action research study desire a democratic

classroom where students strive for ownership and yearn to be more than spectators in the

learning process while increasing self-efficacy in computer technology.

As the participant-researcher in the present action research study, I considered my

insider/outsider status in regards to position, power, and knowledge construction

(Merriam, et al., 2001). My insider access allowed me to reach my community of

graphic arts students and create a dialog about the subject matter; as a former graphic arts

student myself, this was very rewarding. I believe my own computer self-efficacy,

graphic arts, and Adobe Photoshop experience worked as positive modeling for the

students. I strived to create an open, safe, classroom environment where students were

encouraged to ask questions and experiment.

My outsider professor status clearly marked me as authoritative, non-peer, and

other. These undergraduate students often remarked about my vast knowledge of Adobe

Photoshop and often voiced frustration about never reaching my level of expertise. As an

outsider, I was considered the expert who everything comes easy to. The considerable

age difference between myself and these student participants also confirmed my outsider

status.

I was comfortable switching between insider and outsider positions because it

gave me great insight from within and perspective from afar. As the participant-

researcher, I often reflected on my position through the research process and remembered

13

the present action research represents only one local and particular situation in one local

and particular course. I aimed to report accurate data not altered or filtered through my

personal lens and to minimize participant-researcher bias.

The participant-researcher of the present action research study considered ethical

issues and the special needs of the population being studied. Individuals in the present

study were treated with respect and participation in class activities were encouraged. The

surveys were not required for the class and data collected was used for feedback, not for

any grading. This action research was reviewed by the USC Institutional Research Board

(IRB) and exempt from human subject research.

All research may be impacted by additional independent variables, potentially

altering the dependent variable (Johnson & Christensen, 2000). In the present action

research study, the 120 undergraduate student respondents engaged in other technology

interactions, which may have possibly altered computer self-efficacy. To maximize

validity all responses occur in a formal classroom instruction setting. The Compeau and

Higgins (1995b) computer self-efficacy scale was distributed, completed, and collected

immediately and then stored in a secure location.

Limitations

The present research study was limited to one course over six different sections of

the course in 2012 and 2013 in the Art Department at the University of South Carolina.

Undergraduate student participants were the research subjects and the participant-

researcher was the professor of record for the course in each section offered between

2012 and 2013. Therefore, a limitation of the present study involves this select group of

students who were aware of completing a task for an authority figure (the participant-

researcher) responsible for reporting the final grade in the course.

14

Participants enrolling in a graphics course may have a more positive attitude

about the subject matter and could have previous experience with photography,

journalism and school newspapers, altering individual perspective and computer self-

efficacy (Cookman, 2015). The course involved learning foundational art and graphic

design concepts through projects created with the graphics software, Adobe Photoshop.

Research on one software package is also a limitation of the present study. In addition,

all data is collected in evening courses and this may impact results if individual cognitive

resources are depleted late in the day.

Scope

The present action research study aimed to determine what factors originating

from social cognitive theory influence computer self-efficacy during graphics skills

acquisition by using the Compeau and Higgins (1995b) scale. Student-participants were

undergraduate students enrolled in a graphic arts course at the University of South

Carolina. Results found from the present action research study regarding the relationship

of the students’ skills development to computer self-efficacy can recognize trends within

the local and particular student population studied.

Significance of the Study

The present study describes a local and particular graphic arts course in higher

education and identifies what factors influenced the development of computer self-

efficacy in 120 student-participants. The present study examined computer self-efficacy

in undergraduate students enrolled in the course, Visual Arts Computing at the University

of South Carolina.

Previous studies showing connections between GSA and computer self-efficacy

researched standard graphic software, such as Microsoft PowerPoint (Downey &

15

Zeltmann, 2009; Hasan, 2003). The present study involved focused inquiry on an

advanced graphics program, Adobe Photoshop. This comprehensive program is

considered the industry standard for image manipulation (Clawson, 2015; Cookman,

2003). Identifying factors influencing the development of computer self-efficacy can help

recognize trends within the local and particular student population studied. Increased

computer self-efficacy equals less anxiety, more confidence, and higher positivity when

interacting with technology (Conrad & Munro, 2008; Morris & Thrasher, 2009).

Knowledge Generation. Improving GSA and computer self-efficacy fosters

technological literacy so students can gain the knowledge necessary to find credible

information and learn. This is especially essential with the growing amount of data on

the Internet students need to identify, navigate through, and evaluate for validity. Digital

skills are also crucial for daily employment tasks, registering for a class, financial

management, and communicating inside and outside of work.

Professional Application. Society and educational institutions often assume a direct

correlation to youth and technology, however sometimes students inflate internal

technological knowledge. First year college students are “assumed to possess a high

degree of technological sophistication” when in reality individual introductory skills are

weak (Madigan, Goodfellow, & Stone, 2007, p. 410). This contributes to teachers and

trainers making assumptions about starting knowledge, forming irrelevant curriculum,

and reporting inaccurate results about what has been gained and achieved in the

environment. In contrast, teachers and trainers who can discern what factors influence

computer self-efficacy can be more prepared to teach and develop positive attitudes and

confidence (Oliver & Shapiro, 1993).

16

Understanding changes in computer self-efficacy can help moderate and guide

curriculum, adjusting for students with low and high levels. Oliver and Shapiro (1993)

proposed advanced tasks for those who are prepared and need extra stimulation while

providing encouraging activities for students who need support and direction. This

customized curriculum aligns with Dewey (1900; 1902; 1910; 1922; 1934) and Friere

(1970) through forming a learner centered atmosphere where students are involved

participants.

Businesses who hire recent graduates and working adults with high computer

self-efficacy could potentially save time and money on training in addition to becoming

more efficient and profitable (Morris & Thrasher, 2009). Prior knowledge about

computer self-efficacy of employees can supply organizations with an estimate of current

skills and how much training may be needed, both vital for resource and project planning

(Kher, Downey, Monk, 2013). Organizations will be able to understand more about what

workers are technologically capable of and formulate personal growth plans for

professional development. Developing computer self-efficacy for current workers is

worthwhile because employees are valuable human resources, a productive and

experienced group in an ongoing use content (Deng, Doll, & Truong, 2004). Individuals

are more engaged when training is found to be personally relevant and Albion (2001)

stated the training content needs to emphasize the importance of computers to accomplish

future successes.

Social Change. When computer self-efficacy grows, it builds a foundation for

confidence with technology and future successes, leading to expanded adoption of

information technology throughout schools and businesses. A person is more inclined to

do something achievable; Potosky (2002) asked “who is likely to learn to use computers

17

and new software [?] People who think they can” (p. 242). Expanding computer self-

efficacy fuels education and economic success as technology infuses communication,

finance, health care, transportation, academics, and business.

Increased computer self-efficacy can also assist in closing the digital divide, the

space separating those who have technology to use and learn from, with those who have

limited to no access (Servon & Nelson, 2001). Those with less experience have lower

computer self-efficacy and higher levels of anxiety about technology. Servon and Nelson

(2001) promote education through community technology centers, a location where

novice users can learn basic skills and build computer self-efficacy.

Findings of the Present Study

Findings include factors identified from Bandura’s social cognitive theory

involving:

1. Environmental: Independent Learning

2. Cognition: New and Unfamiliar Tasks

3. Behavior: Behavior Modeling

4. Environmental: Access to Support Materials, No Time Constraints

Summary

This present study identified factors and provided information to assist in

developing methods to foster and grow computer self-efficacy in a graphics context. An

important factor in modern society, computer self-efficacy contributes to constructive use

of information systems in educational and professional environments (Stephens, 2006).

The Compeau and Higgins (1995b) computer self-efficacy scale from several semesters

of a graphic arts course provide data for analyzing. Action research methods were

18

administered to answer the research question and generate knowledge. The research

showed based on the pre-and posttest scale, GSA has the potential to influence academic

student achievement, workplace productivity, and personal computer self-efficiency

outside of the Course.

Chapter One presented theoretical background and terminology while introducing

the problem, purpose, and research question. Chapter Two contains a detailed literature

review on the history of computer self-efficacy, the sources, and findings in previous

studies. Chapter Three is the methodology and includes information about the Action

Research, theoretical framework, participants, and data analysis. Chapter Four reports

the detailed results of the research and Chapter Five provides a summary discussion and

suggests areas for future research.

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CHAPTER TWO

LITERATURE REVIEW

The purpose of Chapter Two: Literature Review is to describe the literature

associated with computer self-efficacy which influenced the present study. Information

presented here is used to address the following research question:




The present study explored graphic skills acquisition as related to computer self-

efficacy over the academic years 2012-2013 in a graphic arts course with undergraduate

students at the University of South Carolina. The students were exposed to the program

Adobe Photoshop in the course. The sources of efficacy, early computer studies,

physiological states and methods of measurement are discussed in this Chapter Two

which also provides the context of the methodology and establishes the need for further

exploration into graphics and computer self-efficacy.

Problem Statement

The participant-researcher of the present action research study identified low

computer self-efficacy as a problem in her classroom. This problem was identified

through observation and journal entries of undergraduate students enrolled in her

graphics arts course. This prompted the participant-researcher to explore options

20

through action research methods to better her teaching and increase students’ computer

self-efficacy.

Previous research showed computer self-efficacy can be strengthened through

experience as well as training (Cassidy & Eachus, 2002; Chu, 2003; Brinkerhoff, 2006;

Beas & Salanova, 2006). Additional studies indicated a significant positive relationship

between graphics and computer self-efficacy (Chu, 2003; Downey & Zeltmann, 2009;

Hasan, 2003). The participant-researcher began to administer the Compeau and Higgins

(1995b) computer self-efficacy scale to gain knowledge for developing an action plan to

incorporate change and improve her teaching practice.

Purpose Statement

The purpose of the present action research is to describe the undergraduate

students’ self-efficacy in a computer graphic arts course. The secondary purpose is to

administer a scale developed by Compeau and Higgins (1995b) to align with Bandura’s

(1977) social cognitive theory. This scale considers the various determinants of social

cognitive theory (cognitive, behavior, environment) and two of the dimensions of self-

efficacy, magnitude (level) and strength.

Chapter Two also focuses on “experience,” the most influential source of

computer self-efficacy and also the most frequently studied. The research presented in

Chapter Two certified the power of experience and specifically examined confidence,

environment, emotion, and the relationship with different types of experience, including

graphics. Evidence in this literature review demonstrated the need for dedicated research

in the area of graphics and computer self-efficacy. Studies including graphics, reporting

intriguing findings, or calling for further exploration in this area are also discussed.

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Social Cognitive Theory

Social cognitive theory examines human development including thought,

motivation, and action to steer life choices (Bandura, 1986). This theory proposes

individual behavior is not a pure result of static thoughts, instinctive responses, or

pressure from outside influences. Bandura (1986) explains social cognitive theory drives

people through three major classes of interactive determinants: cognitive or personal

factors, behavior, and environment.

Social cognitive theory arranges cognition, behavior, and environment into a

trinity known as the model of triadic reciprocality (see Figure 2.1, Bandura, 1986, 1997).

Cognitive and personal factors include beliefs, values, outlook, and lessons learned from

triumphs and failures. Behavior is built through individual continuous interactions, the

results of those actions, personal observations, and reflection. The environmental

components include tangible habitats as well as items and people who exist inside the

area.

22

The triad of cognition, behavior, and environment function “interactively as

determinants of each other” in a cycle of reciprocal determinism (Bandura, 1986, p. 23).

Thoughts and feelings lead to behaviors and draw people to specific locations, thus a

smoker may frequent a restaurant where smoking is permitted on the outside patio.

Environment has the potential to shape behavior when a child learns what is acceptable or

not satisfactory through family and region. Also, environment can alter behavior when a

talkative person enters a library or a quieter person attends a boisterous sporting event.

Bandura (1986) states movement in one part of the triad influences the other

components with returned effort, but not at the same time or with equal strength. A

person attempting weight loss will likely fail with cognition alone because behavioral

changes, such as exercise and monitoring the environment are crucial pieces. Experience

in the form of unhealthy habits, may impact effort when a person with an eating disorder

attempts to lose weight with distinct environment, cognitive, and behavioral challenges.

The triad of reciprocity grows with the principles of self-regulation, a

motivational process of observing, guiding, and shaping efforts to promote advancement

to desired goals. This self-regulation cycle creates constant review with an internal pulse

and the ability to strategize when necessary. Bandura describes this as an “ongoing

exercise of self-influence” (Bandura, 1991, p. 248).

Self-Efficacy

Self-efficacy is a component of social cognitive theory involving, “people’s

judgments of their capabilities to organize and execute courses of action required to attain

desired types of performances” (Bandura, 1986, p. 391). Bandura (1986) expresses self-

efficacy is not individual actions or abilities but rather what a person believes can be

23

accomplished with those attributes. Efforts will be met with resistance and failures and

those with strong self-efficacy will persevere.

Personal assessment of task ability determines self-efficacy while belief about the

task result is regarded as outcome expectation (Bandura, 1986, 1997). Decisions

concerning what to engage in and how committed an individual will be are made in

conjunction with self-efficacy (Compeau & Higgins, 1995b). Individuals guide thoughts

into believing “a given course of action will cause a given outcome (outcome

expectation), yet question whether or not they can carry out the action (efficacy)” (Oliver

& Shapiro, 1992, p. 82). For example, a person who considers herself a bad speaker

imagines delivering an incoherent presentation. People are more compelled to attempt an

activity if the outcomes have clear and positive benefits; the potential gains from the

outcomes could be material, sensory, token, or social (Bandura, 1986, 1997).

Efficacy dimensions. These individual evaluations of self-efficacy fluctuate by

level, strength, and generality (Bandura, 1986, 1997). Magnitude or level relates to how

hard a task is and if a person believes it can be accomplished. For instance, an individual

may feel intimidated by an arduous task but comfortable and confident with an easy task.

Each potential task is evaluated and judged therefore every assessment will vary in

strength. These evaluations are substantiated by positive and negative feedback from

experiences. Self-efficacy generality is rooted in situations and circumstances therefore

perceptions may not be transferrable.

Sources. Bandura (1977, 1997) identifies four primary sources for self-efficacy:

enactive experience, vicarious experience, verbal persuasion, and physiological state.

Enactive experiences (referred to as performance accomplishments in earlier work) are

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developed through practice, succeeding and failing, on the way to mastery. Vicarious

experience shows individuals difficult things are possible through modeling; observing

successes in someone similar helps the person believe in personal abilities and future

achievements. Verbal persuasion through positive oral feedback encourages and

motivates people. Physiological states (emotional arousal in earlier work) are the

influence of feelings such as anxiety on self-efficacy.

Various factors can contribute to each source of self-efficacy, such as behavior

modeling to vicarious experience, suggestion to verbal persuasion, and attribution to

physiological states. Bandura (1977) advises contributing factors are not exclusive. For

example, relaxation as a source most likely contributes to self-efficacy through

physiological state. However, a relaxed state may strongly influence an experience.

Enactive/Vicarious experience. Enactive experiences are any activities directly

involving individuals through participation and learning through doing. These activities

have the most powerful impact on self-efficacy, built during triumphs and reduced when

facing defeats. Multiple negative exchanges can deflate positive beliefs efficiently

however instant abundant achievement produces high expectations and a presumption

such trend will continue. When experiencing several successes, inevitable failures can

surprise and confuse individuals who will withdraw and surrender responsively. Self-

efficacy requires cultivation through positive and negative experiences and the

recognition these do not have to paralyze progress. Perseverance is imperative to improve

self-efficacy and enable individuals to discover what can be accomplished with

maintained drive (Bandura, 1977).

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Vicarious experience is acquired through indirect learning experiences such as

reading, observation, or modeling. These build self-efficacy for individuals because it

provides the opportunity to stay protected while witnessing model behavior free from

potential harm. Observation instead of participation is less reliable in generating self-

efficacy, yet spectators can view from a safe area and gain valuable knowledge about the

task and standard expectations (Bandura, 1997). The chief benefit of modeling is people

gain new perspectives on personal capabilities which previously had no reference for

comparison (Bandura, 1997; Bandura & Jourden, 1991; Wood, 1989).

Verbal persuasion. Verbal persuasion from others is a popular method of

increasing self-efficacy. Although simple and convenient to deliver, this approach may

not always be accepted by the recipients with reservations about personal abilities.

Bandura (1997) states the strongest contributor to self-efficacy is the authentic self, but

when additional people express confidence in someone it can help quell personal doubts.

A conflict arises when the individual dismisses the message shared because it challenges

personal negative and low beliefs about abilities. Great success can arrive from

reasonable and rational suggestions the person finds attainable and therefore accepts

(Bandura, 1997).

Physiological states. Physiological and affective states are the final and least

powerful source of self-efficacy; often occurring when difficult situations trigger and

breed anxiety, impacting abilities and self-confidence (Bandura, 1997). These

uncomfortable feelings often begin early in anticipation of the upcoming situation and the

powerful emotions commonly grow to a degree which surpasses the prompt. For

example, a person with social anxiety may build strong concerns and obsess for weeks

26

about an upcoming event. This may be similar to an athlete experiencing heightened pain

before an extremely competitive game. This preliminary behavior is likely to create more

anxiety than the occasion itself would warrant. Bandura (1997) states some physiological

states, although individualized and highly unique, can be moderated through stress

reduction, healthy lifestyle, and connections to others.

Effects. Self-efficacy gains more recognition as researchers realize the impact it

has on various situations including learning, careers, academics, and health. Bandura and

Schunk (1981) examine self-motivation and goal setting to report student “persistency

increased the likelihood of success” and self-efficacy worked like an omen for

achievement (p. 596). Several studies establish substantial positive relationships between

academic areas and self-efficacy (Multon & Brown, 1991; Schunk & Ertmer, 1990;

Wood & Locke, 1987).

Additional work examines behaviors such as weight management and states self-

efficacy is “an important mediating mechanism” (Clark, Abrams, & Niaura, 1991, p.

739). Research also discovers women with low self-efficacy neglect personal talents and

choose to not pursue potential career choices (Hackett & Betz, 1981). These studies are a

small representation of early self-efficacy studies. At this point it is evident self-efficacy

has tremendous power to impact personal choices.

Computer Self-Efficacy

The self-efficacy research of Bandura and others begins to be applied to

computers. Bandura (1978) defines self-efficacy as a “judgment of one’s ability to

execute a certain behavior pattern” (p. 240). Computer self-efficacy is defined as “a

judgment of one’s capability to use a computer” (Compeau & Higgins, 1995b, p. 192).

27

Self-efficacy involves making personal estimates about performance pertaining to future

events (Compeau, 1992). These judgments go beyond smaller “component acts” such as

how to start or shift a car and consider overall behavior or “generative capabilities” like

navigating difficult traffic (Bandura, 1986, p. 397). Similarly, computer self-efficacy is

not pertaining to an individual’s computer skills, but rather the power to collectively

utilize those skills to accomplish a task (Compeau, 1992).

Personal assessments of self-efficacy will vary by level, strength, and generality

(Bandura, 1986, 1997). A person appraises self-efficacy level by thinking about how

hard a task is and if it seems possible to accomplish it. Challenging tasks may intimidate

while easier tasks appear to be within reach and more achievable. Strength is formed

when possible tasks are evaluated and judged; these are substantiated by positive or

negative feedback and past experiences. Self-efficacy generality is rooted in situations

and circumstances, therefore perceptions may not be transferrable.

Self-efficacy is rooted in social cognitive theory, where cognitive, behavioral, and

environment determinants exist in an ongoing reciprocal relationship. This theory

proposes decisions and actions are not merely based on individual factors such as

knowledge, skills, and motivation but rather in conjunction with self-efficacy acting as

gatekeeper. Bandura (1977) claims “choice behavior and effort expenditure are governed

in part by percepts of self-efficacy rather than by a drive condition” (p. 203).

History. Computer self-efficacy has a strong base in business and management

information science (MIS) disciplines. “Understanding the factors that influence an

individual's use of information technology has been a goal of MIS research since the mid-

1970s, when organizations and researchers began to find the adoption of new technology

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was not living up to expectations” (Compeau & Higgins, 1995b, p. 189). Any

information learned about why individuals accept or reject technology had the potential

to streamline processes and conserve capital where substantial amounts of money can be

invested into systems the users refuse to use (Venkatesh & Davis, 1996). For example,

research is conducted on assembly line workers (Rozell & Gardner, 1999), business

executives, and employees (Burkardt & Brass, 1990). Early research from prominent

computer self-efficacy researchers Compeau and Higgins (1995b, 1999) gather data from

subscribers of a business periodical.

Computer self-efficacy research also occurs in higher education environments.

Torkzadeh and Koufteros (1994) found an increase in computer self-efficacy post

training in undergraduates enrolled in an introductory computer course. Employees at

several large state universities (Gist, Schwoerer, & Rosen, 1989; Harrison & Rainer,

1992, 1997) and undergraduates in computer courses have been investigated to learn how

to enhance computer self-efficacy (Ertmer, Evenbeck, Cennamo, & Lehman, 1994;

Karsten & Roth, 1998). Undergraduates in business, education, nursing (Kinzie,

Delcourt, & Powers, 1994), psychology (Mitchell, Hopper, Daniels, George-Falvey, &

James. 1994), and graduate business students (Davis, Bagozzi, & Warshaw, 1989) were

also used as subjects in computer self-efficacy research. Undergraduates in an art or

graphics context are underrepresented in the research and the present study aimed to

address this gap.

General and task specific. Computer self-efficacy research primarily studies

general computer self-efficacy however there are other perspectives; selected

contributions related to the present study will be identified here. Marakas, Yi, and

29

Johnson (1998) introduce a division of computer self-efficacy into two categories. The

category of general computer self-efficacy (GCSE) is defined as spanning many

applications and is gathered over time. Task specific computer self-efficacy (TSE)

broadly encompasses an application (specific) or a particular work activity (task).

Agarwal, Sambamurthy, and Stair (2000) propose software-specific self-efficacy

(SSE), defined as a person’s “feeling of self-efficacy relative to a specific software

package” (p. 422). This construct asks subjects to demonstrate proficient and successful

use of specific features of software packages. Downey and McMurtrey (2006) introduce

summative general computer self-efficacy (SGCSE), which is task based and combines

results from “specific self-efficacies of computer activities” (p. 385). Subjects are asked

to evaluate personal capabilities over six sub-domains by agreeing or disagreeing with

statements like, “I believe I have the ability to cut, copy, and paste in a word processing

document” (Downey and McMurtrey, 2006, p. 393.). The results from these questions

about specific computer self-efficacy and used to form summative general computer self-

efficacy (SGCSE).

These constructs all support expanded computer experiences, which naturally

supplements general computer self-efficacy. The specific method is debated to be

essential for measuring the degree of self-efficacy as it correlates to a detailed task or

application, whereas general measures are considered crucial for generality and static

results (Torkzadeh, Chang, & Demirhan, 2006). Specific (SCSE) produces fast,

immediate results and advancement, while general (GCSE) develops gradually as overall

computing knowledge increases (Agarwal, 2000; Marakas, Yi, & Johnson, 1998).

30

Guinea and Webster (2011) report task self-efficacy affects computer self-

efficacy more if one operation is familiar and one is new. Personal assessment on a

specific task may be higher if the general software context is familiar. Conversely, the

general computer self-efficacy could also be higher if a computer task is recognized. If a

task or software package are new to a person, the individual will spend more time

analyzing and preparing skills before moving forward (Gist & Mitchell, 1992). With this

discovery Guinea and Webster (2011) recognized computer self-efficacy modifications

“do not occur in a vacuum, but in the context of a task” (p. 978). The present study

involved the graphics program Adobe Photoshop and how it impacts general computer

self-efficacy as defined by Compeau (1992). Data from the Compeau and Higgins

(1995b) computer self-efficacy scale, a general construct, will be analyzed.

Experience. Bandura (1977, 1997) states experience is the strongest source of

self-efficacy and it is widely studied in relation to computer self-efficacy. One of the

earliest studies finds consenting, novice users with little computer experience as fearful

and intimidated because the unfamiliar brings discomfort (Hill, Smith, & Mann, 1987).

Despite good intentions and a motivation to learn, results found students with low self-

efficacy were less likely to enroll in computer courses. These individuals are discouraged

when sensing a lack of control and inability to regain it. The research concludes

experience alone will not impact future individual computer usage, but rather efficacy

must be altered (Hill, Smith, & Mann, 1987).

Vicarious Experience. The concept of computer self-efficacy is also explored

through vicarious experience as Gist, Rosen, and Schwoerer (1988) study various training

methods to assist users with learning to work with computers and the growing amount of

31

technology in the workplace. Modeling achieved the greatest results and allowed users to

expand trust in personal potential. Bandura (1977) reported subjects achieved higher

skill development through modeling than other techniques; modeling also benefitted

people who started with high self-efficacy. Training incorporating behavior modeling is

shown to influence computer self-efficacy more than traditional lecture methods (Moos

& Azevedo, 2009).

Types and time. The dynamic relationship between experience and self-efficacy is

affected by the type, time, and variety of exposure. An assortment of software

experiences produces a positive relationship with computer self-efficacy (Cassidy &

Eachus, 2002; Chu, 2003). Acquiring a high quality and wide variety of skills promotes

liking, and people who devote more time to computers identify as more knowledgeable

(Beckers & Schmidt, 2003). The nature of computer self-efficacy is alive and active,

requiring nourishment and reinforcement through technical participation.

One recommendation encourages a prescription of more than three computer

training courses, each covering a separate software application to increase computer self-

efficacy (Havelka, 2003). As expected, more computer courses also contributed to higher

computer self-efficacy, especially classes covering spreadsheets and databases (Albion,

2001). The work of Johnson (2005) agreed, it proved users with database software

experience have higher application specific computer self-efficacy. Students who

reported more hours per week of total computer use had higher computer self-efficacy

(Albion, 2001).

Salanova, Grau, and Llorens (2000) study workers at five different companies and

report a positive relationship between computer use times and computer self-efficacy.

32

The high efficacy group also exhibited low levels of burnout or disinterest in work; this

supports the connection between self-efficacy, confidence and perseverance. Havelka

(2003) researches the impact of years of usage, computer courses, and number of

software or packages or programming languages learned. Results suggest a positive

correlation between years and software self-efficacy with the largest positive correlation

in computer self-efficacy between participants reporting over five years of experience.

Kher, Downey, and Monk (2013) conduct a longitudinal investigation to determine

how long training needs to be in order to increase computer self-efficacy. This research

learned the ideal length of training is two and half months noting the nonlinear growth

process is slow to start with the biggest change occurring in the last half of the time

period. After about two months computer self-efficacy had improved and any additional

time beyond allowed for the benefits to be applied (Kher, Downey, & Monk, 2013).

Brinkerhoff (2006) attributes substantial growth to the longer format of a structured

technology academy where participation included instruction, projects, and exercises.

The surveys in the present study were given at the beginning and end of a semester

spanning approximately 15 weeks (3 months and a half months).

Karsten and Schmidt (2008) report over 80% of business students use a computer

daily, but increased usage did not translate into higher computer self-efficacy when

compared to previous years. This may be attributed to students using computers more

often and for greater purposes in the most recent years. Expanded use includes

communication through email, expanded online offerings in content, news, social media,

and services such as online banking and shopping. However this increased usage did not

produce higher levels of general computer self-efficacy, perhaps because those tasks are

33

not challenging and merely demand “repetitive use of a limited range of skills, primarily

entering text” (Karsten & Schmidt, 2008, p. 449). This supports a premise from Bandura

(1986) stating self-efficacy is significantly altered through challenging and unfamiliar

tasks, expanding user perspective.

Home possession. Hours of total computer use time is also influenced by home

possession, usage is likely to increase if computers are easily accessible and in close

proximity (Albion, 2001). Home access as part of a supportive family and educational

atmosphere has a strong connection to computer use (Hsu & Huange, 2006). Cassidy and

Eachus (2002) presented additional support for computer ownership and total software

programs acquainted with and both in positive alignment with computer self-efficacy.

Bauer (2003) substantiates the powerful associations between computer self-efficacy and

usage hours, reported ubiquitous experience, and the number of software programs used.

The present study involved significant computer use time, the class meets in the evenings

two times a week and each session is 2 hours and 45 minutes.

Environment. Users identify the most desirable environment as supportive,

casual, and subdued, with skilled assistance available (Beckers & Schmidt, 2003). A

positive, encouraging, “non-threatening” atmosphere fosters extended confidence in

computer self-efficacy and propels individuals to overcome obstacles (Bandura, 1986;

Ertmer, Evenbeck, & Cennamo, 1994, p. 58). Success breeds confidence, while defeat

creates perennial negativity, especially if the deficiency happens at the beginning of the

experience (Bandura, 1977). Additional research reveals self-efficacy derived from

computer experience is most influenced by pertinent encounters centered on “quality

rather than quantity” (Ertmer et. al, 1994, p. 58; Karsten, 1998).

34

Positive conditions and experiences connect and cycle to form a sustained

sequence of healthy, hopeful general computer instincts and beliefs. These broad

computer opinions anticipate understanding and comfort level of an application more

than the time spent working on the same software (Venkatesh, 2000). This occurs in part

because increased self-efficacy beliefs create relaxed, secure working conditions by

reducing anxiety and anger; a light, playful atmosphere fosters experimentation free from

harsh consequences (Potosky, 2002; Wilfong, 2006). Bandura (1977) concurs people are

prone to believe it is possible to achieve favorable outcomes when there are no feelings

of strain or anxiety. Therefore a steady, calm environment fosters a pleasant, assured

temperament, leads users to expand personal possibilities and potential.

Emotion. Agarwal and Karahanna (2000) call for a more holistic approach to

technology interactions to foster intrinsic motivation, richer participation, and encourage

an atmosphere of play. Potosky (2002) shows users who are playful during training

instruction demonstrated the greatest efficacy after the workshop was completed.

Compeau and Higgins (1995a) also mention the power of physiological states on

performance, for instance participants can see the tasks as enjoyable and associate those

tasks with play.

Additional research by Webster and Martocchio (1992) establishes a positive

relationship between computer self-efficacy and playfulness. Motivation can act as a

gatekeeper for self-efficacy, permitting only what the individual deems as enjoyable or

pertinent to the personal mission (Deng, Doll, & Truong, 2004). It is also possible

individuals enter into a certain state of mind or ‘flow,’ as a result of being fully immersed

in the work (Csikszentmihalyi, 1990). Agreeable interactions nurture acceptance and

35

can develop into a positive attitude towards computers, suggesting each individual point

of view about computers is actually a “self-fulfilling prophecy” (Rozell & Gardner,

1999). Computers can function as a Rorschach inkblot test, where deep rooted past ideas

and experiences surface and heavily impact the present (Turkle, 1980).

Psychological states can alter attitudes about technology, for example a positive

state of mind can evoke confidence, increase self-efficacy and decrease computer anxiety

(Beas & Salanova, 2006; Conrad & Munro, 2008). Individuals with high self-efficacy

were more frequent computer users, and experienced less anxiety Compeau & Higgins,

1995b). Personalities with negative tendencies may feel less in control and be quick to

blame others for failures but self-efficacy has the power to regulate anxiety and as

knowledge grows, anger and anxiety are reduced (Johnson, 2005; Kay, 2008; Saade &

Kira, 2009). Intrinsic motivation, curiosity, and positive attitude all positively impact

computer self-efficacy (Moos & Azevedo, 2009).

Rozell (2000) utilizes Seligman’s causal attribution studies to conclude optimists

envision success and are more apt to persevere when encountering technical issues.

Situations prompt positive or negative reactions based on self-efficacy and can generate

an inner conflict equal to the outer condition (Bessière, Newhagen, Robinson, &

Shneiderman, 2006). This can create a competition and fight for control between the

individual and the computer, similar to winning or losing a game.

State of mind and self-assessment of capabilities contain the power to either steer

a user toward a positive outcome or to sabotage it. Beckers and Schmidt (2001) express

“emotion influences beliefs” instead of the reverse, confirming actions are led by feelings

(p. 46). Humans are subjective, and maintaining a conscious awareness of attitude is

36

difficult to achieve. Deng, Doll, and Truong (2004) discover intrinsic motivation

indirectly connects computer self-efficacy and effective application use, working as a

catalyst for determination. Conviction collaborates with attention, prolonged attempts,

dedication, and action to create a platform to achievement. Efforts lead to achievements

and the outcome is celebrated, intensifying self-efficacy and intrinsic motivation.

Kim, H. Chan, and Y. Chan (2007) investigate how thoughts and feelings effect

decisions to continue, delay, or abandon technology usage. This research describes

technology users as active service customers and analyzes attitude, beliefs, and possible

and emotional gains of these users. The results report the largest influence on continued

use is pleasure, formed from responses including happy, pleased, and satisfied. Arousal

ranks second, linking to excitement and stimulation; usefulness places third, attached to

task completion and saving time.

Resource allocation. Experiences build familiarity and comfort while preparing

the brain to connect and construct advanced knowledge by shifting resource allocation.

The process of learning new information entails laborious, concentrated, beginning stages

where “few spare resources [exist during] skill acquisition,” however as experience and

ability grow, tasks become automated and use fewer cognitive resources (Hackbarth,

Grover, & Yi, 2003; Kanfer & Ackerman, 1989, p. 687). Technical, detailed steps

transform into instinctive, undemanding processes individuals promptly master creating

forward momentum and a confident environment for efficient learning at a faster, higher

level. Technology interactions allow individuals to develop abilities through usage and

practice until identifying technology structures is increasingly effortless (Hackbarth,

Grover, & Yi, 2003).

37

Changes over time. Bhattacherjee and Premkumar (2004) present user attitudes

and beliefs as dynamic results of current and past experiences, focusing on the under-

researched area of “temporal changes” occurring during the course of usage (p. 230).

Bhattacherjee (2001) suggests favorable outcomes are contingent upon “continued use

rather than first-time use” (p. 342). Technology resources have to extend beyond the

initial activities to provide value and established usage after adoption is essential is

essential to affirm mastery (Hackbarth, Grover, & Yi, 2003; Kim, H. Chan, & Y. Chan,

2007).

Survey data acquired one year apart shows the relationship between self-efficacy

and computers does not have an expiration date as the results continue to provide

valuable and accurate predictions (Compeau, Higgins, & Huff, 1999). Venkatesh and

Davis (1996) collect computer self-efficacy measures at different points in time and

confirm efficacy did not change. This is problematic as individuals are inclined to “form

and hold stronger computer self-efficacy beliefs, both positive and negative” (p. 473).

There is evidence to suggest adoption may stem from human or organizational pressure

but continued utilization is grounded exclusively in beliefs (Karahanna, Straub, &

Chervany, 1999).

Computer Self-Efficacy Scale

Methods to measure computer self-efficacy develop in the late 1980s and early

1990s. These first studies utilize questionnaires to inquire about individual levels of

comfort with software and tasks, working styles, the benefits of computer education, and

future plans to purchase or use computers (Gist, Rosen, and Schwoerer, 1988; Hill,

Smith, & Mann, 1987). Several researchers also work with computer instructors to

38

develop scales from the literature to target confidence (Davis, Bagozzi, & Warshaw,

1989; Murphy, Coover, & Owen, 1989).

Compeau (1992) examined how self-efficacy impacted computing, researched

these established scales, and started to formulate her own measure. Compeau critiqued

existing scales and noted her construct aims to stay authentic to Bandura’s social

cognitive theory. (1986, 1987). This is achieved through applying the three determinants

of social cognitive theory, cognition, behavior, and environment; as well two dimensions

of self-efficacy, magnitude (or level) and strength. The scale does not directly measure

generality, the third dimension of self-efficacy. The initial work on forming the scale is

part of Compeau’s (1992) dissertation with chief advisor, Dr. Chris Higgins.

The Compeau and Higgins (1995b) computer self-efficacy scale is published

several years later. The authors define self-efficacy as “the belief that one has the

capability to perform a particular behavior,” and determine computer self-efficacy

pertains to the “judgment of one’s capability to use a computer” (Compeau & Higgins,

1995b, p. 189, p. 192). The Compeau and Higgins (1995b) scale develops into a leading

measurement in computer self-efficacy research and is widely adopted in various forms

for numerous projects (Agarwal & Karahanna, 2000; Chu, 2003; Downey & Zeltmann,

2009; Hasan, 2003; Huffman, Whetten, & Huffman, 2013; Jan, 2015).

Social Cognitive Theory Determinants. The Compeau and Higgins (1995b) scale

applies social cognitive theory to computer self-efficacy. The questions asked relate to

the determinants, cognition, behavior, and environment. The instrument contains ten

items and the first part of each question asks respondents to anticipate abilities on a

computer task through a fictitious scenario. The first part is “I could complete the job

39

using the software package… […if I had…]” (Compeau & Higgins, 1995a, p. 140). The

second part completes the sentence with options for support such as the software manual,

additional time, and human assistance. Next the individual ranks the varying degrees of

support by what would be most helpful (see Appendix A). The scale is designed to push

the subjects to consider future behavior and efficacy as opposed to prior proficiencies

(Bandura, 1986; Compeau & Higgins, 1995b).

Self-Efficacy Dimensions. The Compeau and Higgins (1995b) scale incorporates

two dimensions of self-efficacy, magnitude (or level) and strength. Magnitude or level is

defined as “the level of task difficulty one believes is attainable” (p. 192). The scale

measured magnitude by counting the number of yes or no responses to the ten questions.

Bandura (1977) states magnitude becomes apparent when a person decides what is

personally achievable from a list of tasks. An individual with a higher magnitude of self-

efficacy can envision completing a challenging task while those with lower magnitude

may only picture success.

The second element of self-efficacy is strength, the “level of conviction about the

judgment” and is scored through the number rating for each question (Compeau &

Higgins, p. 192). The person with a low degree of strength lives on an unstable

foundation easy to diminish; higher strength creates a firm base and is more likely to

persist in challenging circumstances. Perceptions of strength are substantiated by

positive or negative feedback and experiences.

Graphics

Graphics include digital images created entirely within computer software or just

edited using computer software and tools. Images are widely used across many

40

disciplines and so are graphics, visuals are often produced to communicate messages in

business. Often these graphics are in the form of low to medium quality when a part of

presentation software. In news, marketing and advertising images are of higher quality

for mass production and high profile campaigns. Medical imaging is excellent quality

too as precision and measurement are paramount.

Early graphics included with computers were simple and now imaging is more

advanced with the Adobe Photoshop software. Image manipulation used to be limited to

shapes, objects, or color however Adobe Photoshop gives users the power to edit images

by individual pixels. This comprehensive program is considered the industry standard for

image manipulation (Clawson, 2015; Cookman, 2003). Additional software exist for

developing page layout, logo creation, and website design. There are some intersections

between those programs and Adobe Photoshop, but Photoshop is primarily for digital

imaging and creating graphics. The present study examined graphics and computer self-

efficacy through the discipline of art and management information science (MIS).

In the field of graphics arts, Nielsen, Fleming, and Kumarasuriyar (2010) attempt

to improve student self-efficacy in a digital communications course by restructuring it to

mandate additional technology and digital software tools. The instructor changed the

behavior of students by mandating these tools and the course outcomes showed

improvement in student satisfaction, course completion, and an increase in grades. These

positive outcomes signify the need to expand opportunities for graphics interactions,

especially in curriculum and training environments (Busch 1995; Hasan 2003).

One graphics instructor reports technology deficiencies in students “complicate

instruction” and hinder communication because many “concepts and terms are alien”

41

(Koning, 2012, p. 82). This instructor alters behavior through customized course

software, streamlining difficult technical processes for the students. This change releases

cognitive resources in the students and redirects it to the more creative aspects of the

project.

Additional studies related to art and graphics stress the positive environment and

peer support. Stokrocki (1986) reports prominent technical support issues but students

praised the playful moments and encouraging teacher. Freedman and Relan (1992)

research computer graphics and social processes to show how peer interaction is crucial

to aesthetic choices

In business environments, applications including word processing, spreadsheet,

and database software are frequently analyzed in connection to computer self-efficacy

(Busch 1995; Compeau & Higgins, 1995a; Kinzie, Delcourt, & Powers, 1994; Rozell &

Gardner, 1999). Computer self-efficacy and graphics are under researched. Torkzadeh

and Koufteros (1994) do mention graphics in a listing of other microcomputer

applications, plausibly referring to presentation software (Microsoft PowerPoint) part of

the suite.

Studies analyzing the impact of various computer applications on computer self-

efficacy have shown experience with graphics tasks and software to have powerful

effects. Hasan (2003) explores computer experience through eight types of specific

software. Results show standard applications such as word processing yielded negligible

change, but experience with graphics and programming generated the greatest

advancement in computer self-efficacy.

42

Graphics programs are frequently used less than other applications and are

considered advanced, demanding applications requiring users to stretch skills beyond

basic, typical usage. Participants report the least amount of experience with graphics yet

it is found to have significant impact on computer self-efficacy (Chu, 2003; Wilfong,

2006). This confirms while familiar operations are tedious and unchallenging,

unexplored, demanding actions inspire and heighten self-efficacy assessment (Bandura

1986).

Graphics programs in this business context are primarily defined as PowerPoint

presentation software and the participants rate personal computer self-efficacy,

competence, or frequency of use on a specific measure. This example from Downey and

McMurtrey (2007) asks the respondent to complete the sentence with a yes or no answer

and provides a scale to capture magnitude ranging from minimal to full confidence.

“Graphic Programs AS-CSE [Application-Specific Computer Self-Efficacy]

I believe I have the ability...

. . . to copy an individual slide from one graphic slide presentation to another.

. . . to import text from another application (e.g., word processor) to a slide.

. . . to use a graphic presentation program (e.g., Power-Point) to convey

information to others.

. . . to manipulate objects on a slide (align, tilt, rotate, etc.).

. . . to add color to words or objects on a slide.

. . . to copy or delete slides from a graphic slide presentation.

. . . to use a slide template to create a new graphic slide presentation”

(Downey & McMurtrey, 2007, p. 393).

43

Hasan (2003) explores different types of computer experiences to determine how

each impacts computer self-efficacy and concludes graphics have a powerful and

important effect. Students in a computer information system class use a survey to report

experiences with eight different application domains and computer self-efficacy is

collected using an adapted version of the Compeau and Higgins (1995b) scale. Outcomes

show individuals have the highest experience with word processing and computer games

and the lowest amount of experience with computer programming, database experience,

and graphics.

Multiple regression reveals computer programming and graphics applications

rank as the top two strong influencers on computer self-efficacy while more standard

applications like word processing had little effect. This demonstrates graphics are

beyond conventional computer knowledge and therefore strengthen self-efficacy in a

more substantial manner, precisely as Bandura (1986) stated new and demanding tasks

influence self-efficacy more than simple and effortless exercises (Hasan, 2003).

Wilfong (2006) concentrates on five of the eight application domains from Hasan

(2003) and produces similar support for graphics in the context of computer anxiety and

anger. The participants are students in an assortment of courses with the majority from

health and community service and the remaining from math, computer science, and

psychology. The highest experience applications are word processing and internet

browsing while the applications with the lowest experience are graphics and spreadsheet

programming. The low experience software is also the most challenging; it proves to

have strong connections to anxiousness and anger and graphics are found to have the

most significant impact on computer anxiety. This finding shows enhancing computer

44

self-efficacy through demanding applications such as graphics using social cognitive

theory and behavior modeling has the potential to provide comfort and confidence

(Bandura 1977, 1986; Wilfong 2006).

Downey and Zeltmann (2009) agree graphics programs may increase computer

self-efficacy; the research analyzed application domain competency levels to determine

how efficacy is shaped, asking as competence grows does computer self-efficacy grow in

equal proportion? The study collects computer competency through the scale designed

by Munro, Huff, Marcolin, and Compeau (1997) and computer self-efficacy utilizing the

Compeau and Higgins (1995b) measure. Results for six different programs are separated

into high or low competency groups and graphic programs place in the high zone, above

spreadsheets and databases.

The researched discovered the connection between competence and computer

self-efficacy is contingent upon the domain application used and graphics programs are

shown to have a strong relationship for the high competence group. Individuals in the

lower competence group less familiar with graphic programs have lower computer self-

efficacy, thus results suggest graphics may be a useful application domain for enhancing

computer self-efficacy.

Additional research on preservice teachers offers evidence affirming the value of

graphics and the possible contributions it can provide to computer self-efficacy. Chu

(2003) discusses results of a study to increase computer self-efficacy, “one unexpected

finding was the use frequency of computer graphic as the significant predictor of

computer self-efficacy” and calls for additional research (p. 139). The present action

45

research study explored changes in computer self-efficacy during a specific course

dedicated to one advanced graphics application, Adobe Photoshop.

Conclusion

Chapter Two: Literature Review presented the important aspects of previous work

in the field of computer self-efficacy, beginning with a history of self-efficacy and the

early connections to computing. Relevant research is synthesized and themes such as

sources, experience, emotion, and measurement are identified and explored. The

majority of the studies are within a business, information technology, MIS, or health

sciences context (Haffer & Raingruber, 1998; Nevalainen, Mantyranta, & Pitkala, 2010;

Saeed, Yang, & Sinnappan, 2009; Thorpe, 2004; Williams, 2004). However there are

indicators graphics have a significant influence on computer self-efficacy and there is a

need for further research in this area.

46

CHAPTER THREE

METHODOLOGY

The purpose of Chapter Three: Methodology is to describe the action research

methods used to address the following research question:




Problem Statement



through observation and journal entries of undergraduate students enrolled in her graphic

arts course. This prompted the participant-researcher to explore options through action

research methods to better her teaching and increase students’ computer self-efficacy.








47

Purpose Statement







The growth of technology calls for digitally developed users and creates a need to

raise computer self-efficacy, leading to less anxiety, more confidence, and increased

positivity when interacting with technology (Conrad & Munro, 2008; Morris & Thrasher,

2009). The present action research study took place over the 2012-2013 academic year at

the University of South Carolina with undergraduate students who enrolled in the course,

Visual Arts Computing. The scale developed by Compeau and Higgins (1995b) to align

with Bandura’s (1977) social cognitive theory was administered to the participants. This

instrument considers the determinants of social cognitive theory, cognition, behavior, and

environment. It also looks at the two dimensions of self-efficacy, magnitude (level) and

strength.

Magnitude

Compeau and Higgins (1995b) define magnitude as “the level of task difficulty

one believes is attainable” (p. 192). Bandura (1977) states magnitude becomes apparent

when individuals decide what is personally achievable from a list of tasks ranked in order

of difficulty. A person with a higher magnitude of self-efficacy can envision completing

a challenging task while those with lower magnitude may only picture success with the

48

easier tasks. In the present study, magnitude was scored from the scale through counting

the number of yes or no responses to the ten questions.

Strength

Strength refers to the “level of conviction about the judgment” (Compeau &

Higgins, p. 192). A low degree of strength lives on an unstable foundation and can be

easily diminished while higher strength creates a firm base and is more likely to persist in

challenging circumstances. In the present study the computer self-efficacy scale was

scored using the number rating to measure strength.

Action Research Design

The present study uses action research with a single group pretest posttest design

within six different sections of a single undergraduate graphic arts course at the

University of South Carolina. The participant-researcher conducted systematic inquiry in

the teaching and learning process and aimed to improve quality or effectiveness.

According to Mertler (2013), there are four steps to the action research process:

1. Focus Stage: Developing and Area of Focus

2. Acting Stage: Data Collection

3. Analysis Stage: Data Analysis

4. Action Plan Stage: Developing a Plan

Focus Stage: Developing an Area of Focus

The first step in the present action research study is a planning stage and the

development of an area of focus. In the present study an area of focus was identified

when the participant-researcher observed low computer self-efficacy in students during

graphics skills acquisition (GSA). The participant-researcher was also the instructor in

49

the courses examined (Cohen, Manion, & Morrison, 2011). This educational research

provided the participant-researcher the opportunity to preserve the actions of real events

in the graphics arts classroom while improving the utility and effectiveness of the

instructional process and students’ scholarly activity (Mertler, 2013).

Acting Stage: Data Collection

The second step in action research methods is the acting stage, including data

collection. Fraenkel and Warren (1993) discuss three types of data collection:

observation, interviews, and documents. In the present action research study observation

occurred in the classroom the participant-researcher noted and recorded student actions.

The participant-researcher in the present study observed low computer self-efficacy in

college students during graphic skills acquisition (GSA).

A second type of data collection used in action research methods are existing

documents. The students in the present study kept class journals to report progress and

any unclear information. One participant shared concerns with not being fast enough in

completing all steps to successfully achieve a workflow. Another student reported

feeling overwhelmed and confused.

The third type of data collection can happen through interviews or written

questions or forms through a survey, in the present study action research study this is

represented as a scale (Mertler, 2013). The Compeau and Higgins (1995b) computer

self-efficacy scale was distributed in a paper format once within the first two weeks of

the course and again during the last two weeks of end of the semester. The concept of

computer self-efficacy was discussed when the scale was distributed; students were told

the goal of the scale was to explore changes in computer self-efficacy after graphic skills

acquisition.

50

The Compeau and Higgins (1995b) computer self-efficacy scale consist of ten

questions which each have two parts (see Appendix A). The first part of each question

asks respondents to predict abilities on a computer task through a fictitious scenario, “I

could complete the job using the software package… […if I had…]” (Compeau &

Higgins, 1995a, p. 140). The user completes the sentence in the second part with options

for support such as the software manual, additional time, and human assistance. Last, the

individual ranks the varying degrees of support by what would be most helpful. The

scale is designed to push the subjects to consider future behavior and efficacy as opposed

to prior proficiencies (Bandura, 1986; Compeau & Higgins, 1995b).

The scales were collected from six different sections of the course, two sections

occurred in the spring semester of 2012, two in the fall semester of 2012, and two in the

spring semester of 2013. Each section was comprised of pre and post scales, though not

always in equal numbers, as class size changed from start to finish. Each set was coded

with a letter and number to indicate the semester, section, and whether it was from the

beginning or end of the semester.

Scales were identified as invalid if one or more parts were incomplete, this

situation occasionally happened with the first part when subjects missed the yes or no

question before the scale (see Appendix A). After eliminating invalid scales, the

remaining scale results were reported outload by the independent reader and then

recorded into a Microsoft Excel spreadsheet.

Reflective Stage: Data Analysis

The participant-researcher reflected on the data with her student-participants.

Through reflective writings for four years the participant-researcher worked with students

to improve the effectiveness of the graphic arts course. The scales examined by the

51

participant-researcher were collected from 2012-2013. Scales were identified as invalid

if one or more parts were incomplete, this occasionally happened with the first part when

subjects missed the yes or no question before the scale (see Appendix A). After

eliminating invalid scales, the remaining scale results were reported outload by the

independent reader and then recorded into a Microsoft Excel spreadsheet.

The total number of valid pretest scales collected at the beginning of the semester

was 120. The pretest was distributed and collected by the participant-researcher within

the classroom environment. The posttest was also distributed and collected by the

participant-researcher in the classroom at the end of the semester when class size is

smaller, due to students dropping the class. The total number of valid posttest scales

collected at the end of the semester was 88, this equals a response rate of 73%. The study

was reviewed by the Institutional Review Board (IRB) and approved, it was found to be

exempt from human research subject regulations.

Analysis Stage: Reflecting on the Data

The participant-researcher reviewed the Compeau and Higgins (1995b) scale

administered to the students, the ten-item scale applies social cognitive theory to

computer self-efficacy. The first part of each question asks respondents to anticipate

abilities on a computer task through a fictitious scenario, reading, “I could complete the

job using the software package… […if I had…]” (Compeau & Higgins, 1995a, p. 140).

The second part completes the sentence with options for support such as the software

manual, additional time, and human assistance.

The individual then ranked the varying degrees of support by what would be most

helpful (see Appendix A). The scale was designed to push the subjects to consider future

behavior as opposed to prior proficiencies (Bandura, 1986; Compeau & Higgins, 1995b).

52

The participant-researcher reviewed each item on the scale with the students in order to

develop an effective action plan to improve the students’ self-efficacy and thus the utility

and effectiveness of the course.

The third step in the action research methods process involves the developing

stage and analyzing the data. The data collected from the computer self-efficacy scale

was examined using Microsoft Excel and the Statistical Package for the Social Sciences

(SPSS). The answers from the participants were inspected for accuracy and then entered

into the software Microsoft Excel and later exported into Statistical Package for the

Social Sciences (SPSS) for additional analysis. Microsoft Excel was used to calculate

totals, averages, and percentages of scale responses and detailed results. Statistical

Package for the Social Sciences (SPSS) was used for the paired sample t-tests.

The present study used action research with a single group pretest posttest design.

Data was analyzed using a paired sample t-test conducted to compare the change between

pre-post scores of the computer self-efficacy scale. A t-test was selected for analysis

because it compares two means from the same groups. Six different sections of the

course were analyzed to verify results and recognize trends. The detailed findings will be

discussed in Chapter Four.

Theoretical Framework

The theoretical framework for this research was grounded in the work of Bandura

(1986, 1991, 1993), Dewey (1900; 1902; 1910; 1922; 1934), and Friere (1970). Bandura

and social cognitive theory show human conduct as a collection of individual cognitive

and personal factors, behavior, and environment, all constantly interacting. Dewey and

progressive educational theory support critical thinking, active change, and student

development in education. Friere discusses critical education theory and the importance

53

of student voice and a democratic educational experience. These theories encourage

students to be personal pedagogical leaders and engage in independent thought, rather

than simply receiving information from authority figures.

Participants

The participants included in this study were 120 undergraduate students enrolled

in an Art Studio course, Visual Arts Computing at the University of South Carolina in

Columbia, South Carolina during 2012 and 2013. This class was mandatory for most art

majors. However, it was also open to all students and served as a general arts

requirement depending on major. The class met twice a week in the evenings and each

session was 2 hrs 45 min, standard length for an Art Studio course. Classes designated as

Art Studio need specific equipment in the room, such as a pottery wheels for ceramics or

a darkroom for processing photographs. In the present study the course objectives

required expensive computer equipment and software, specifically the graphics software

Adobe Photoshop.

The students in the course were taught foundational art concepts through the

digital imaging graphics program, Adobe Photoshop. Each student in the course would

learn about tools and methods and be given the opportunity to apply the new knowledge

to various projects. For example, one project concentrates on creating depth through

different values, meaning the lightness or darkness of color. The participant-researcher

demonstrates various ways to achieve this through examples but each student has

different personal images to work. The students would customize methods in individual

projects.

54

Summary

Utilizing the Compeau and Higgins (1995b) computer self-efficacy scale, the

present study identified factors originating from social cognitive theory (cognitive,

environmental, behavior) influencing computer self-efficacy during graphic skills

acquisition. This study used a convenience non random sample of students enrolled in

Visual Arts Computing, a course open to all students and may serve as a general arts

requirement.

This present study utilized an action research method approach and there are four

steps involved in the process. First the area of focus was identified as low computer self-

efficacy in the participant-researcher’s class. Second data collection occurred in the form

of observation, surveys, and document data. For the third step, results were analyzed

using a paired sample t-test to compare the change between pre-post scores of the

computer self-efficacy scale. Six different sections of the course were analyzed to verify

results and recognize trends. The fourth step in action research design is developing an

action plan and this will be presented in Chapter Five.

This Chapter Three described the methodology used for the present study and

Chapter Four presents the results of the collected data.

55

CHAPTER FOUR

RESEARCH FINDINGS

The purpose of Chapter Four: Research Findings is to present the results of the

research findings used to address the following research question:




Problem Statement













56

Purpose Statement







Chapter One contained an overview of the research study, including theoretical

background, problem, purpose, and research question. Increased computer self-efficacy

has the potential to positively influence academic student achievement, workplace

productivity, and personal efficiency. Research has found college students have low

computer self-efficacy (Karsten & Schmidt, 2008). The participant-researcher in the

present study observed low computer self-efficacy in undergraduate students enrolled in

the course, Visual Arts Computing, at the University of South Carolina.

The present study investigated a course focused on teaching foundational art and

graphic design concepts through projects created with the graphics software, Adobe

Photoshop. There is evidence graphics can have a significant positive relationship with

computer self-efficacy (Chu, 2003; Downey & Zeltmann, 2009; Hasan, 2003). The

Compeau and Higgins (1995b) computer self-efficacy scale was administered to the

students enrolled in this graphics course, Visual Arts Computing. The present study

analyzed the results of the scale to identify factors influencing computer self-efficacy

during graphic skills acquisition.

57

The research question for this study is as follows:




Chapter Two of this dissertation provides a detailed literature review

concentrating on the history of computer self-efficacy, the sources, and findings in

previous related studies. Computer self-efficacy is a specific type of self-efficacy and a

component of social cognitive theory. This theory examines human development,

including thoughts, motivations, and actions to steer life choices (Bandura, 1986). Self-

efficacy is a person’s judgment of personal capabilities to achieve something, it is not

their skills but rather what an individual believes can be accomplished with those skills

(Bandura, 1986).

Self-efficacy is researched in relation to numerous topics, including academic

achievement, weight loss, and motivation. Computer self-efficacy studies started within

business environments to gain more knowledge about why individuals accept or reject

technology. The impact of environment, emotion, computer experience, and graphics

experience on computer self-efficacy were addressed.

Findings

The Compeau and Higgins (1995b) computer self-efficacy scale begins with a

sample answer at the top to assist respondents (Figure 4.1). In the sample response the

individual reported yes to completing the job when provided with step by step

instructions and felt moderately confident (level of 5).

58

Table 4.1 displays the questions on the scale and Table 4.2 shows the percent

change for each question between pretest and posttest. The computer self-efficacy

composite score was derived from the levels of the questions where the respondent

answered yes. This procedure of totaling the positive responses was utilized by Lee and

Bobko (1994) in work on self-efficacy beliefs. This same process was employed

successfully by Downey and McMurtrey (2007) and Downey and Zeltmann (2009).

Table 4.2 displays the average of the total responses for each question and then

calculates the percent change across all six sections of the course. The most significant

increase (42%) is if the subject had never used a similar package before (Q-2). The

second largest increase (33%) reported subjects rated higher computer self-efficacy if no

one is around to assist (Q-1).

Social cognitive theory has three determinants: cognition, behavior, and

environment. Bandura (1986) connects the determinant of environment to tangible

habitats and human assistance. There are six questions on the Compeau and Higgins

(1995b) computer self-efficacy scale related to the determinant of environment. Three of

these questions link to human support, two questions involve software support resources,

and one relates to having no time constraints.

59

Table 4.1

Computer Self-Efficacy Scale Questions

Item

Question Text

I could complete the job using the software package…

Q-1 if there was no one around to tell me what to do as I go.

Q-2 if I had never used a package like it before.

Q-3 if I had only the software manuals for reference.

Q-4 if I had seen someone else using it before trying it myself.

Q-5 if I could call someone for help if I got stuck.

Q-6 if someone else helped me get started.

Q-7 if I had a lot of time to complete the job for which the software was

provided.

Q-8 if I had just the built in help facility for assistance.

Q-9 if someone showed me how to do it first.

Q-10 if I had used similar packages before this one to do the same job.

Table 4.2

Computer Self-Efficacy: Percent Change by Question

Item

Section

___________________________________________

Percent

Change 1 2 3 4 5 6

Q-1 19 31 9 34 44 59 32.67

Q-2 5 50 29 38 40 89 41.83

Q-3 4 18 -13 8 47 16 13.33

Q-4 -1 23 17 16 32 31 19.67

Q-5 -8 10 1 6 10 13 5.33

Q-6 -6 4 -1 11 22 13 7.17

Q-7 3 9 -1 12 16 22 10.17

Q-8 3 9 22 15 34 24 17.83

Q-9 -5 -2 0 7 11 1 2

Q-10 -3 -3 2 10 8 2 2.67

Average 1.1 14.9 6.5 15.7 26.4 27 15.27

60

The three questions aligned to environment through the availability of human

assistance ask if is there a person present to guide me (Q-1), to help me get started (Q-6),

or accessible through phone for assistance (Q-5). The participants in the present study

report higher computer self-efficacy when there is no person available to assist (32%), get

started (7%), or accessible by phone (5%).

Two questions are associated to environment in a support capacity, access to

software manuals (Q-3) and built-in help (Q-8). These questions show about a 15%

increase from pretest to posttest. The last item relating to environment is time and

proposes the subject has plenty of time to work on the software package (Q-7). This

question also showed an increase after graphic skills acquisition (10%).

Bandura (1986) states cognition calls for thought, recall, and comparison, this

relates to two questions in the Compeau and Higgins (1995b) scale. The questions

requiring thought, recall, and comparison asked the subject about previous experience

with similar packages to accomplish a comparable task. One question asked if there was

no previous similar experience (Q-2) and the other questions asked if there previous

experience with similar packages to accomplish a comparable task (Q-10). The

respondents reported a strong increase in computer self-efficacy (42%) if there was no

prior use with a package like it before. This supports research stating higher computer

self-efficacy equals more confidence and increased positivity when interacting with

technology, especially new and challenging tasks (Bandura, 1986; Conrad & Munro,

2008; Morris & Thrasher, 2009).

Questions connected to the social cognitive theory determinant of behavior align to

modeling from a guide rather than human assistance; vicarious experience is the second

strongest source of self-efficacy (Bandura, 1997). Observation instead of participation

61

allows participants to view from a safe area and gain valuable knowledge about the task

and standard expectations (Bandura, 1997). Individuals can gain new perspectives on

personal capabilities that previously had no reference for comparison (Bandura, 1997;

Bandura & Jourden, 1991; Wood, 1989).

Two questions related to behavior describe a successful person accomplishing the

task. The posttest reports a 20% increase in computer self-efficacy for the question about

the participant being able to complete the job if witnessing someone else doing it before

personally trying it (Q-4). The question about participants being shown how to do

something before attempting it (Q-9) increased 2%. These findings suggest participants

want to observe, experiment, and be independent thinkers who are provided support

through behavior modeling instead of being told how to do something systematically.

Magnitude

The present action research methods study identified factors from Bandura’s

social cognitive theory influencing computer self-efficacy. The instrument used

incorporated two dimensions of self-efficacy: magnitude (level) and strength (Bandura,

1977). Magnitude or level is defined as “the level of task difficulty one believes is

attainable” (Compeau & Higgins, 1995b, p. 192). In the present study, magnitude was

scored from the scale through counting the number of yes answers (Compeau & Higgins,

1995b).

Table 4.3 shows these results of magnitude across six different sections of the

course, it was strong in both pretest and posttest with a small increase in the majority of

sections. Sections 6 had the highest change and sections 2 and 4 showed no change, both

of these sections met for class on Tuesdays and Thursdays.

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Table 4.3

Magnitude: Percent Change by Section

Section N Pretest Posttest Percent Change

1 26 .85 .92 .01

2 17 .95 .95 0

3 24 .95 .97 .02

4 17 .94 .94 0

5 14 .89 .92 .03

6 24 .91 .96 .06

Average 20 .91 .94 .02

Table 4.4 shows changes in magnitude by percent change per question, pretest to

posttest by question on the Compeau and Higgins (1995b) computer self-efficacy scale.

The average change over all the questions is very small (-.12%) and the question with the

highest change (-.18%) asked if the respondent had used a similar package (Q-10). The

question with the second highest change (-.17%) asked about if someone showed the

respondent how to do it first (Q-9). The question regarding previous use of similar

packages (Q-2) barely changed at all (-.03%). The question relating to the absence of

human assistance (Q-1) showed almost no change (-.08%). The question about access to

software manuals (Q-3) also displayed minimal change (-.06%).

Strength

The second dimension of self-efficacy is strength, referring to the “level of

conviction about the judgment” (Compeau & Higgins, 1995b, p. 192). Individual

thoughts mold and regulate strength, partially built through successes and reduced by

failures (Bandura, 1986). Strength was scored following the Compeau and Higgins

(1995b) method of summarizing the respondent’s answer on the confidence level

63

Table 4.4

Magnitude: Percent Change by Question

Item

Section

___________________________________________

Percent Change 1 2 3 4 5 6

Q-1 -.04 .33 -.81 -.13 .1 .06 -0.08

Q-2 -.15 .33 -.76 -.07 .33 .15 -0.03

Q-3 .15 .4 -.86 -.2 .09 .07 -0.06

Q-4 -.04 .23 -.81 -.06 -.21 -.1 -0.16

Q-5 -.08 .23 -.8 -.12 -.07 -.1 -0.16

Q-6 .08 .23 -.81 -.12 -.07 -.1 -0.13

Q-7 -.11 .23 -.81 -.12 -.07 -.1 -0.16

Q-8 -.04 .15 -.79 -.19 .2 0 -0.11

Q-9 -.08 .15 -.81 -.12 -.07 -.1 -0.17

Q-10 -.08 .15 -.81 -.18 -.07 -.1 -0.18

Average -0.04 0.24 -0.81 -0.13 0.02 -0.03 -0.12

scale, ranging from one to ten. This scoring method recommends counting a response of

no as a zero and the present study also follows this process.

Table 4.5 displays the percent change for strength across the six different sections

of the course by section. The average change in strength from pretest to posttest was

minimal (.14%) and the strongest increases were in section 5 (.24%) and 6 (.25%). These

two sections with the highest increase are both from the spring semester of 2013. The

lowest change was in section 1 (.01%) and section 3 (.07%), both from 2012.

Table 4.6 shows the percent change strength by question, an overall an increase of

19.77%. The question with the highest increase (73%) is when the respondent never used

a package like it before (Q-2). The question with the second most

64

Table 4.5

Strength: Percent Change by Section

Section N Pretest Posttest Percent Change

1 26 6.50 6.58 .01

2 17 6.92 7.73 .12

3 24 6.99 7.45 .07

4 17 6.81 7.68 .13

5 14 5.75 7.16 .24

6 24 5.97 7.44 .25

Average 20 6.49 7.34 .14

Table 4.6

Strength: Percent Change by Question

Item

Section

___________________________________________

Percent

Change 1 2 3 4 5 6

Q-1 23 31 25 20 71 87 43.83

Q-2 -4 63 67 68 102 142 73

Q-3 25 37 -34 -2 44 36 17.67

Q-4 3 23 17 23 12 31 18.17

Q-5 -8 10 6 6 10 13 6.17

Q-6 -6 4 -1 11 22 13 7.17

Q-7 -2 9 -1 12 16 21 9.17

Q-8 8 2 35 5 34 29 18.83

Q-9 -5 -2 0 7 11 3 2.3

Q-10 -3 -5 2 10 8 2 2.3

Average 3.1 17.2 11.6 16 33 37.7 19.77

65

significant increase (44%) was (Q-1) if no one was around to tell respondent what to do

along the way. The third and fourth most significant increases involve if the participant

had seen someone else using it before trying it (Q-4) and if just the built-in help facility

was available for assistance (Q-8).

Computer self-efficacy magnitude displayed the least percent change (2%) in the

questions regarding if someone showed the subject how to do it first (Q-9), if there was

prior use with similar packages before (Q-10), and if someone could be called for

assistance when needed (Q-5).

Table 4.7 displays a percent change comparison of magnitude, strength, and

computer self-efficacy by question. The highest positive increase was in using new and

unfamiliar software (Q-2). Strength was raised 20%, higher than the computer self-

efficacy composite of 15%. The second highest positive increase was shown in the

question regarding no one around to tell the use what to do along the way (Q-2). The

question with the third highest positive increase addresses the use of built in help for

assistance (Q-8). Conversely, the questions with the lowest changes involve if someone

showed the individual how to do it first (Q-9) and if the user had used a similar software

before this one to do the same job (Q-10).

Table 4.8 shows the results of a paired samples t test, conducted to evaluate

whether students in Section 1 reported different confidence outcomes across pretest and

posttest outcomes. There was a significant difference in the scores for the posttest (M=

7.44, SD= 2.71) as compared to the pretest (M=5.88, SD=2.98) conditions; t(351)= -5.19,

p<0.05.

66

Table 4.7

Magnitude, Strength, Computer Self-Efficacy: Percent Change Comparison

Item Question Magnitude Strength Computer

Self-Efficacy

Q-1 …if there was no one around to

tell me what to do as I go

-0.08 43.83 32.67

Q-2 …if I had never used a software

like it before

-0.03 73 41.84

Q-3 …if I had only the book or

software manuals for reference

-0.06 17.67 13.167

Q-4 …if I had seen someone else

using it before trying it myself

-0.16 18.17 19.67

Q-5 …if I could call someone for

help if I got stuck

-0.16 6.17 5.34

Q-6 …if someone else had helped

me get started

-0.13 7.17 7.17

Q-7 …if I had a lot of time to

complete the job for which the

software was provided

-0.16 9.17 10.17

Q-8 …if I had just the built-in help

for assistance

-0.11 18.83 17.83

Q-8 …if someone showed me how

to do it first -0.17 2.3 2

Q-10 …if I had used a similar

software before this one to do

the same job

-0.18 2.3 2.67

Average -0.12 19.77 15.25

As required for the pair samples t-test, an equal number of responses were

compared (n this case, 178 cases) in the pretest and posttests. Section 1 represents a

course scheduled to meet twice a week, Mondays and Wednesdays during the spring

semester of 2012.

Table 4.9 displays a paired samples t test conducted to evaluate whether students

in Section 2 reported different confidence outcomes across pretest and posttest outcomes.

There was a significant difference in the scores for the posttest (M= 7.14, SD= 2.98) as

compared to the pretest (M=5.69, SD=2.98) conditions; t(253)= -4.01, p<0.05.

67

Table 4.8

t-Test: Two Sample Assuming Unequal Variances, Section 1

Pre Post

Mean 5.882022472 7.443820225

Variance 8.816511141 7.332984193

Observations 178 178

Hypothesized Mean Difference 0

df 351

t Stat -5.185078276

P (T<=t) one tail 1.82744E-07

t Critical one-tail 1.6492064

P (T<=t) two tail 3.65488E-07

t Critical two-tail 1.966745561

Table 4.9


Pre Post

Mean 5.689922 7.139535

Variance 9.293726 7.558503



df 253

t Stat -4.01068

P (T<=t) one tail 3.99E-05


P (T<=t) two tail 7.97E-05


68


compared (in this case, 129 cases) in the pretest and posttests. Section 2 represents a

course scheduled to meet twice a week, on Tuesdays and Thursdays during the spring

semester of 2012.

A paired samples t test was conducted to evaluate whether students in Section 3

reported different confidence outcomes across pretest and posttest outcomes. Table 4.10

shows there was a significant difference in the scores for the posttest (M= 7.68, SD=

2.82) as compared to the pretest (M=6.70, SD=2.90) conditions; t(294)= -2.95, , p<0.05.


compared (in this case, 148 cases) in the pre and post-tests. Section 3 represents a course

scheduled to meet twice a week, Monday and Wednesday during the fall semester of

2012.

Table 4.10


Pre Post

Mean 6.695945946 7.675675676

Variance 8.403520868 7.934914506



df 294

t Stat -2.948708979

P (T<=t) one tail 0.001723567


P (T<=t) two tail 0.003447134


69

Table 4.11 displays results of a paired samples t test was conducted to evaluate


posttest outcomes. There was no significant difference in the scores for the posttest (M=

7.38, SD= 2.73) as compared to the pretest (M=7.46, SD=2.92) conditions; t(76)= 0.12,

p>0.05. It should be noted the pretest scores were higher than the posttest scores.



scheduled to meet twice a week, Tuesdays and Thursdays during the spring semester of

2012.

Table 4.11


Pre Post

Mean 7.461538462 7.384615

Variance 8.728744939 7.453441

Observations 39 39


df 76

t Stat 0.119418154

P (T<=t) one tail 0.452629593


P (T<=t) two tail 0.905259185


Table 4.12 contains data from a paired samples t test, conducted to evaluate


posttest outcomes. There was a significant difference in the scores for the posttest (M=

70

7.68, SD= 2.70) as compared to the pretest (M=6.94, SD=2.76) conditions; t(250)= -2.17,

p<0.05.



scheduled to meet twice a week, on Mondays and Wednesdays during the spring semester

of 2013.

Table 4.12


Pre Post

Mean

6.936508 7.68254

Variance 7.627937 7.322413



df 250

t Stat -2.16579

P (T<=t) one tail 0.015636


P (T<=t) two tail 0.031273


Table 4.13 shows a paired samples t test, conducted to evaluate whether students

in Section 6 reported different confidence outcomes across pretest and posttest outcomes.

There was no significant difference in the scores for the posttest (M= 6.56, SD= 3.01) as

compared to the pretest (M=6.48, SD=2.72) conditions; t(467)= -0.30, p>0.05.



71

scheduled to meet twice a week, on Tuesdays and Thursdays during the spring semester

of 2013.

Table 4.13


Pre Post

Mean

6.481013 6.561181

Variance 9.072731 7.382894



df 467

t Stat -0.30424

P (T<=t) one tail 0.380539


P (T<=t) two tail 0.761077


Summary

Chapter Four presented the results and statistical analysis of the data collected in

the present study. Four of the six sections (66%) showed significant difference in the

scores for the posttest as compared to the pretest. This finding supports graphics skills

acquisition had a positive influence on computer self-efficacy.

The highest positive increase was in computer self-efficacy with using new and

unfamiliar software (Q-2). The second highest positive increase in computer self-

efficacy was if no one was around to assist (Q-1). Strength was raised 20%, higher than

the computer self-efficacy composite of 15%.

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CHAPTER FIVE

IMPLICATIONS AND CONCLUSIONS

The purpose of Chapter Five: Implications and Conclusions is to summarize the

research study and the findings from the data collection, analysis, and reflection. An

Action Plan for graphics arts course improvement is also included in this Chapter.

Chapter Five begins with an overview of the action research study and methodology and

then transitions to discussion and implications about the Action Plan. Lastly, the Chapter

offer suggestions for future research.

The implications and conclusions presented here are used to address the following

research question:




Problem Statement






73








Purpose Statement

This present action research has a purpose, to describe the undergraduate

students’ self-efficacy in a computer graphic arts course. A second purpose is to

administer the Compeau and Higgins (1995b) scale, developed by to align with

Bandura’s (1977) social cognitive theory. This scale considers the various determinants

of social cognitive theory (cognitive, behavior, environment) and two of the dimensions

of self-efficacy, magnitude (level) and strength.

Overview of Research Study and Methodology

Technology is expanding and calling for more digitally developed users to

interact, work, develop, and build projects with these new tools and systems. This

growth creates a need to raise computer self-efficacy, leading to less anxiety, more

confidence, and increased positivity when interacting with technology (Conrad & Munro,

2008; Morris & Thrasher, 2009). Research has discovered experience and training to

both have the potential to grow computer self-efficacy (Cassidy & Eachus, 2002; Chu,

2003; Brinkerhoff, 2006; Beas & Salanova, 2006). It is important to note the type and

variety of exposure is crucial; an assortment of different interactions can build computer

self-efficacy through active technical participation.

74

Various computer programs have been studied to test the individual program’s

impact on computer self-efficacy, including graphics. The Microsoft Office Suite, word

processing, spreadsheet, and database software were frequently analyzed in these studies

within the academic areas of business and management information science (MIS)

disciplines (Agarwal & Karahanna, 2000; Burkardt & Brass, 1990; Compeau & Higgins,

1995a; Rozell & Gardner, 1999). Graphics software was often included in these studies

as part of the Microsoft Office Suite, mostly in the form of Microsoft PowerPoint

presentation software. Several studies suggested a positive relationship between graphics

skills acquisition and computer self-efficacy in preservice teachers (Chu, 2003), military

trainees (Downey & Zeltmann, 2009), and non-traditional students (Hasan, 2003).

The present study examined computer self-efficacy in 120 undergraduate students

enrolled in graphics course, Visual Arts Computing at the University of South Carolina.

The course taught foundational art and graphic design concepts through projects created

with the graphics software, Adobe Photoshop. The participant-researcher administered

the Compeau and Higgins (1995b) scale in 2012 and 2013 to determine if graphic skills

acquisition impacted computer self-efficacy.

The Compeau and Higgins (1995b) computer self-efficacy scale applies the work

of Bandura’s (1986) social cognitive theory to computer self-efficacy. The instrument

consists of ten items and each question asks respondents to anticipate abilities on a

computer task through a fictitious scenario. The first part reads, “I could complete the

job using the software package… […if I had…]” (Compeau & Higgins, 1995a, p. 140).

Next, the second part asks the respondent to complete the sentence with options for

support including the software manual, additional time, and human assistance. Subjects

then rank personal level of confidence on the scale with each resource (see Appendix A).

75

This design is intended to push the subjects to consider future behavior as opposed to

prior proficiencies (Bandura, 1986; Compeau & Higgins, 1995b).

Personal evaluations of self-efficacy change based on magnitude (level) and

strength (Bandura, 1986, 1997). Magnitude relates to how hard a task is and if a person

believes it can be accomplished. In the present study, magnitude was scored from the

scale through counting the number of yes or no responses to the ten questions. Strength

refers to the “level of conviction about the judgment” (Compeau & Higgins, p. 192). The

present study scored strength through the number rating for level.

Action research methods with a single group pretest and posttest design were used

to gain knowledge about the students in the learning environment (Mertler, 2013). Data

from the Compeau and Higgins (1995b) computer self-efficacy scale was analyzed using

Microsoft Excel and the Statistical Package for the Social Sciences (SPSS). Data was

analyzed using a paired samples t-test conducted to compare the change between pre-post

scores of the computer self-efficacy scale.

Findings

To answer the research question for the present study factors from Bandura’s

social cognitive theory are identified. These factors influenced 120 art students’ self-

efficacy with computer technology during an undergraduate visual art course. Computer

self-efficacy increased in the majority of the sections were examined. Data analysis

revealed a significant difference in the scores from the scale for the posttest as compared

to the pretest in four out of six sections. The paired samples t test showed no significant

difference in section 4 or 6, both are classes scheduled to meet on Tuesday and Thursday

nights. However, since section 2 also met on those same days and did show a significant

change in computer self-efficacy, no conclusions can be drawn from this observation.

76

The factors identified from social cognitive theory influencing computer self-

efficacy have two dimensions: magnitude (level) and strength (Bandura, 1977).

Magnitude refers to task difficulty and whether an individual believes it can be personally

accomplished; higher magnitude is usually assigned to easier tasks and lower magnitude

to challenging ones. Magnitude was scored through counting the number of yes answers

indicated by the respondents (Compeau & Higgins, 1995b). The magnitude did increase

in four out of six of the different course sections. The two most recent sections in spring

of 2013 had the highest increases, section 5 (.063%) and section 6 (.06%). Section 2 and

4 had no change (0%), both of these sections were met on Tuesdays and Thursdays.

Strength is formed when a person evaluates individual confidence level with a

task and this was calculated using the number rating on the computer self-efficacy scale.

The present study shows an average increase in strength of 20% over all six sections.

Growth in sections 5 (33%) and 6 (38%) were significantly more than the other sections

with increases of 3% (section 1), 17% (section 2), 12% (section 3), and 16% (section 4).

This is an interesting finding since both sections 5 and 6 were in the same semester yet

there were no major changes to the course or content.

Computer self-efficacy as a composite score was calculated from the levels

indicated on all questions answered yes. The average growth over all six sections was

15%, this is less than the strength increase of 20%. Again, the highest increases are in

sections 5 (26%) and 6 (27%) and smaller changes are in section 1 (1.1%), section 2

(14.9%), section 3 (6.5%), and section 4 (15.7%).

Results of the present study reported a 15% increase in computer self-efficacy

composite and a 20% increase in strength. The first part of each scale question, where

students indicated yes or no for the self-efficacy dimension of magnitude barely changed

77

(-.12%). The second part of the question where the respondent selected a specific

strength rating to represent level of confidence increased more (20%) than the overall

composite increase (15%). This demonstrates the students still indicated yes (magnitude)

at the end of the semester posttest, but felt more strongly about the strength or “level of

conviction about the judgment” (Compeau & Higgins, 1995b, p. 192). This finding

provides evidence individual confidence developed more firmly throughout the semester

and increased during graphic skills acquisition.

Conversely, the research of Lee and Bobko (1994) found the opposite of the

present study, the self-efficacy composite increased more than the strength. The Lee and

Bobko (1994) study used a different computer self-efficacy measurement from the

present study. A higher increase in self-efficacy composite may be attributed to

respondents who are quick to say yes to the first part (magnitude) when personal

capabilities are in question regarding technology. This supports the high percentage of

students who self-report above average in computer skills, however are “less skilled than

they perceive” (Madigan, Goodfellow, & Stone, 2007, p. 413).

Magnitude, strength, and composite computer self-efficacy results also varied by

question on the scale. There are minimal changes to magnitude and two questions show

the highest amount of growth pretest to posttest. The questions with the largest increase

asked if the respondent has used a similar package before (Q-10) or if someone showed

the respondent how to do it first (Q-9). The question discussing previous use of similar

packages (Q-2) minimally changed (-.03).

These results are consistent with experience as the strongest source of self-

efficacy Bandura (1977; 1997). This also positively aligns with vicarious experience as

essentially the second strongest source of self-efficacy and the power of behavior

78

modeling. Bandura (1977) reported subjects achieved higher skill development through

modeling than other techniques. Gist, Rosen, and Schwoerer (1988) studied various

training methods to assist users with learning to work with computers in the workplace.

Modeling achieved the greatest results and allowed users to expand trust in personal

potential.

Computer self-efficacy strength grows 20% from pretest to posttest with

extremely high increases in two questions. The question with a 73% gain asks for a

confidence rating if the respondent never used a package like it before (Q-2). After

acquiring graphics instruction, the respondents are more confident with successfully

using new and unfamiliar software in the future. This suggests the recent work in

graphics raised computer self-efficacy and supports the work of Bandura (1986) stating

new and intriguing tasks are the most influential on self-efficacy.

Respondents have higher computer self-efficacy for future tasks with new and

unfamiliar software in part because of recent successes. Most of the students successfully

completed a course in graphic software, after not knowing graphics software well. Users

have low levels experience with graphics and the most knowledge about word

processing, Internet, and computer games (Hasan 2003; Wilfong 2006).

The computer self-efficacy strength score with the second most significant

increase 44% was (Q-1) if no one was around to tell respondent what to do along the

way. This finding directly connects to social cognitive theory, supporting learners who

aim to think individually, understand, and guide their educational experience (Bandura,

1986; 1991; 1993). This also relates to self-regulation, the process of empowerment

through concentrating attention on efforts and directing efforts towards set goals.

79

Developing independent learners challenges traditional one-sided classroom

environments and do not engage students or allow for unconventional thinking and

therefore fail to maximize the educational experience. Freire (1970) described this as the

“banking concept of education,” where students are empty accounts and simply receive

what the teacher deposits (p. 72). Dewey (1933) indicated collecting facts and data alone

does not develop learning habits, a deeper thirst for continued knowledge, or the motive

to be an active participant in society. This finding is due in part to the participant-

researcher advocating democratic classroom methods, fostering the growth of self-

efficacy and empowering the student during graphic skills acquisition.

One computer self-efficacy strength question with a minimal percent of change

(2%) asked if the subject had any prior experience with a similar package (Q-10). The

average answer for this scale question was high at 8 or 9 out of 10, on both the pretest

and posttest. Enactive experience is the primary source of self-efficacy and involves

people learning through participation and doing. The minimal change on the question

may be attributed to individuals already knowing experience strengthens belief in

yourself. People, especially students understand that experience and doing something

multiple times increases self-confidence in that activity. This applies to everything from

piano practice and math homework, to riding a bicycle.

The second computer self-efficacy strength question with a very small percent of

change (2%) inquired about if someone showed the subject how to do it first (Q-9). This

connects to behavior modeling or vicarious experience, the second strongest source of

self-efficacy. This scale question also had a high average answer at 8 or 9 out of 10, on

both the pretest and posttest. This finding regarding the strong power of vicarious

experience or modeling both before and after may be due to an individual’s long history

80

with the concept. “After infants discover that they can exercise some control over

aspects of their immediate environment, they draw on vicarious experiences to expand

and verify their sense of personal efficacy” (Bandura, 1997, p. 167).

Throughout life, many life tasks and actions have no firm guidelines to measure

success and people rely on comparisons to self-assess (Bandura, 1997). These personal

evaluations will vary depending on the comparison group chosen, people who observe

those who are more similar to themselves, see the ideal as more attractive and attainable

(Bandura 1997; Wood, 1989). Same-age peers are crucially important in behavior

modeling (Wood, 1989). The chief benefit of modeling is people gain new perspectives

on personal capabilities which previously had no reference for comparison (Bandura,

1997; Bandura & Jourden, 1991; Wood, 1989).

The composite computer self-efficacy increased an average of 15% and the most

significant increase (42%) is if the subject had never used a similar package before (Q-2).

The second largest increase (33%) noted subjects rated higher computer self-efficacy if

no one is around to assist (Q-1). These same two questions were also the most increased

in computer self-efficacy strength however reversed, the question about no one around to

assist (Q-1) had a stronger change than if the subject had never used a similar package

before (Q-2).

Implications

The results of this study suggest graphic skills acquisition had a significant

positive impact on the computer self-efficacy of the 120 student-participants.

Independent Learning

The data shows the most substantial factor from the environment was independent

learning (Q-1). Working independently empowers the user to feel in control and provides

81

a sense of ownership about future direction. Collins (2009) says it is essential to support

individual choice in how and what students learn. By enabling student-participants we

encourage creating an individual approach and allow students to decide where to begin

and what to give attention to, all based on individual and particular needs.

Individuals in my class sought activities to engage and empower; the students had

an inherent need to feel in control and claim educational ownership (Baggetun &

Wasson, 2006; Bandura 1986, 1991). Students shared the favorite projects were ones

with more flexibility and freedom. One example is the self-portrait where students were

encouraged to create personal images and connections. Students were excited to create

unique pieces of art, original and personal. One student asked if the class could be

allowed to do one project on anything selected with minimal direction. This is truly the

foundation of self-efficacy, the human desire to control life situations through minimizing

failure and constantly reaching for success, goals, and rewards (Bandura 1986; 1991;

1997).

Access to Support Materials

An important factor from the social cognitive theory determinant of environment

is access to support materials (Q-8). Learners in my class benefited from comprehensive

software assistance easily accessible from within the active and current program. Help

areas were created by the software manufacture and provided accurate and updated

information. The help was usually visible through a link or icon directly in the software,

and were searchable, and extremely convenient for references and inquiries.

It took the student-participants some time to find the correct answer to my

questions but patience and “persistency increase[s] the likelihood of success” (Bandura &

Schunk, 1981, p.596). When a student-participant did not feel pressured by time s/he

82

felt free to experiment, make multiple attempts, and pause for reflection and

modification (Dewey, 1933).

Engaging in Unfamiliar Tasks

Another strong factor from social cognitive theory contributing to increased

computer self-efficacy is cognition when engaging in new and unfamiliar tasks (Q-2).

These unprecedented actions called for additional thought, recall, and comparison to

previous and comparable computer activity from the student-participants. Then the

thought process transitioned to compositing knowledge to move the task forward. After

acquiring graphics instruction, the respondents were confident with successfully using

new and unfamiliar software in the future. This supports the work of Bandura (1986)

stating new and intriguing tasks are the most influential on self-efficacy.

Behavior Modeling

Factors from behavior include vicarious experience or behavior modeling (Q-4).

In this situation, student-participants had the opportunity to observe the participant-

researcher demonstrate various techniques with the tools in the software. The students

stayed protected through observation and did not have to prove their knowledge,

demonstrate proficiency, and risk failing while trying the technique in front of the

participant-researcher and other classmates. The students witnessed an ideal future

situation in a tangible way and visualized personal success through the model. Behavior

modeling greatly assisted people with phobias and anxieties in my class and some made

remarkable progress with the aid of confident and relatable guides (Bandura, Blanchard,

& Ritter, 1969). After overcoming a phobia, the student-participant displayed a reduction

in general anxiety about other aspects of their life as well.

83

Although my students felt more efficacious through behavior modeling and the

ability to see someone else using the software before trying it, my learners did not want

me walking through the process step by step or assisting to complete the job. My

learners shared a preference for support documents and time to approach the task in an

individual way at a preferred personal pace. The students want examples and resources

first followed by autonomy and time for independent learning. My data shows my

student-participants felt most confident in completing future computer tasks if there was

no prior use of a similar software and when I was not around to assist.

Summary

My findings of the present study show an interesting relationship between

independent learning and behavior modeling. Student-participants wanted to have a

visual confirmation or proof through behavior modeling to show the task was possible

and could be accomplished. The student-participants felt confident after witnessing me

or someone else showing the process and achieving the task.

Action Plan

The final step in the action research process is the reflection stage and the

development of an action plan where knowledge gained is used to resolve the area of

focus identified in step one. (Mertler, 2013). Once the plan is applied, it should be

monitored and adjusted as needed to adapt individual environments, students, and

teachers. Mertler (2013) states the plan may be a formal document or a set of guidelines

for carrying out the recommendations. Table 5.1 is an action planning table outlining the

results and future steps to action.

84

Table 5.1

Action Research Plan

Summary of

Research

Question and

Findings

Recommended

Actions

Who Will

Collect

Data?

Timeline Resources

Necessary

1. What factors

from social

cognitive theory

(cognitive,

environmental,

behavior)

influenced

students’

self-efficacy with

computer

technology in an

undergraduate

graphic arts

course?

The factors are

independent

learning

(environmental),

new and

unfamiliar tasks

(cognitive),

behavior

modeling

(behavior),

access to support

materials and no

time constraints

(environmental)

1. Students

are given

more time to

work alone

2. Increased

resources are

available to

students

3. Students

observe

processes

through

behavior

modeling and

witness

someone

successfully

completing a

task or

project

1. The

participant-

researcher

1. Over a

semester

long class

1. Additional

time in the

computer lab

for the class.

2. Quick

access to

support

documentation

already

professionally

created

(software

company

resource).

3. Resources to

create

additional

documentation

specific to the

class projects.

4. Resources to

conduct

demonstrations

, computer and

projector

The results of this study support graphic skills acquisition has the potential to

influence computer self-efficacy in a positive away. The factors identified from

Bandura’s social cognitive theory were:

85

1. Cognition: New and Unfamiliar Tasks

2. Environmental:

a. Independent Learning

b. Access to Support Materials

c. No Time Constraints

3. Behavior: Behavior Modeling

Based on the present study, I will increase independent learning in the classroom

and students will have more autonomous time to work with and to practice concepts.

Students are often driven by deadlines and the creative process has a different timeline

requiring space for some reflection and self-critique. I have often noticed when the class

is over more than half of the students stay to continue to work on projects independent.

While working, individuals get into a zone and can use this time to experiment.

Incorporating work time during the class can help achieve this.

In supporting independent learning I must consider the resources students will

need to be successful. Gaining access to support materials created by the publisher

would be very helpful so the students could find answers quickly and directly from the

source. This would minimize the time spent searching for an answer online through

numerous search results claiming to have the answer. Contextual help could also

streamline the process so students can get the help needed exactly where and when the

question is directly in the program. In additional to software resources, I need to grow

my resources specific to the course to provide more guidance for these independent

learners.

86

Cognitively, computer self-efficacy increased when the task was new and

unfamiliar, therefore I can incorporate many new techniques in the course. The student

may be aware of one use for a specific tool but I can share multiple uses. I can also build

lessons around the use of several tools coming together in creative ways to create a new

effect.

Another factor I can increase is behavior modeling, the students reported high

levels of feeling more efficacious when given the opportunity to see someone else using

it before trying it themselves. I use behavior modeling when I show various techniques

or processes in a live demonstration. This could also be created on a video so it could be

watched when needed at a certain part of the project. A video would also assist those

students who need different timing or perhaps have a language barrier. This active

research study was made possible through the support of the education and art

departments and the numerous participating students. This study increased my students’

computer self-efficacy and motivated me to improve my teaching in specific ways.

Recommendations for Graphic Arts Faculty

Based on the findings presented in this dissertation, the participant-researcher has

the following recommendations for graphic arts faculty

1. Allow students to work independently, make independent aesthetic decisions,

and develop a personalized path to completing the project objectives.

2. Challenge students with new and unfamiliar tasks by introducing advanced

techniques and methods for experimentation.

3. Provide support and resources through materials students can explore alone,

accessible both in and out of class.

87

4. Provide a lot of diverse examples, approach the material in various individual

ways, and allow the students to find the approach best suited

5. Use behavior modeling to share your expectations and what can potentially be

achieved in the project.

Recommendations for Future Research

The present study has several limitations and provides opportunities for future

research. First, participants were derived from a convenience sample of students enrolled

in Visual Arts Computing at the University of South Carolina. Secondly, the Compeau

and Higgins (1995b) computer self-efficacy scale was the only instrument used to collect

data. Third, due to the historical document data used, demographic information was not

included in the research design.

The participant-researcher suggests future research on graphic arts and Adobe

Photoshop classes at the same university with various instructors. This research could

also be conducted at different universities and community colleges, where students often

have less access to technology. An instrument to gain knowledge about technology

access and ownership could also inform educators and administrators in higher education.

Cassidy and Eachus (2002) present support for positive alignment between both

computer ownership and total software programs with computer self-efficacy.

Future research could include other items of measurement in addition to computer

self-efficacy and technology access. Measurements for computer attitudes and growth

mindset could be used to understand connections between several concepts and can work

together to improve computer self-efficacy. Demographic information on gender, race,

88

major, and age could provide additional information to help identify learners who need

more attention.

Conclusions

The data presented supports graphic skills acquisition as a method for increasing

computer self-efficacy in a graphics arts course. This increased self-efficacy helped

develop more independent and confident computer users; scale results showed students

were more efficacious when working on new programs with no assistance. Graphics

experience has the ability to substantially shape computer self-efficacy and this signifies

a need to expand opportunities for graphics interactions, especially in curriculum and

training environments (Busch 1995; Hasan 2003). Educators and trainers should consider

including graphics as a component of technology projects to help enhance computer self-

efficacy.

The evidence shows learners who received graphics instruction are more

efficacious about using new and unfamiliar software in the future. Original,

unprecedented actions call for individuals to engage in additional thought, recall, and

comparison to previous and comparable computer activity. Thoughts then transition to

composite knowledge and work to move the task forward. Cognition, behavior, and

environment function “interactively as determinants of each other” in a pattern of

reciprocal determinism (Bandura, 1986, p. 23). Positive conditions and experiences

connect and cycle to form a sustained sequence of healthy, hopeful general computer

instincts and beliefs.

89

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APPENDIX A

COMPUTER SELF-EFFICACY SCALE

DEVELOPED BY D. COMPEAU AND C. HIGGINS (1995B)

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APPENDIX B

INSTITUTIONAL REVIEW BOARD (IRB) APPROVAL

exploring changes in computer self-efficacy during

Documents