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THE RELATIONSHIPS BETWEEN LOCUS OF CONTROL, TECHNOLOGY
USAGE, AND GRADES AMONG GRADUATE STUDENTS
by
Nazila Safavi
DR. CYD STRICKLAND, PhD, Faculty Mentor and Chair
DR. CHARLOTTE NEUHAUSER, PhD, Committee Member
DR. JASON WARD, PhD, Committee Member
Rhonda Capron, EdD, Dean of Technology, School of Business and Technology
A Dissertation Presented in Partial Fulfillment
Of the Requirements for the Degree
Doctor of Philosophy
Capella University
October 2016
© Nazila Safavi, 2016
Abstract
Using Rotter‘s (1966) survey, this study determined the level of student personality
factor Locus of Control (LOC) and examined whether or not it played a role in student
adaptation and usage of technology. In this study, a course website technology was
utilized for graduate students in face-to-face classrooms in a university. The student
participants had successfully completed the course Economics for Decision Making
during the period fall semester of 2013 to fall semester of 2015. The researcher examined
the number of times a student used the website by looking at the Learning Management
System (LMS) data that showed the frequency of students logging into the course
website and then correlated that data to student LOC. The study confirmed that students
with internal LOC level used the course website more often than the students with
external LOC level; however, the study rejected the assumption that student personality
factor Locus of Control had a significant impact on the frequency of course website
usage.
iii
Dedication
This dissertation is dedicated to my son, Alex. Although the challenges seemed
impossible at the time, having Alex in my life gave me the strength and determination to
go forward.
iv
Acknowledgments
My doctoral journey would not be complete without letting those who influenced
its successful completion know what a profound difference they have made in my life. I
am very grateful to have Dr. Cyd Strickland as my dissertation mentor and all the
valuable feedback, engagement, and encouragement, this work would not have been
possible. I am especially thankful to my father, Reyhan Safavi, for being a role model. I
feel accomplished to have followed his path. I am also thankful to my compassionate
mother Shahin for helping me when I was in need.
I would like to thank my committee members, Dr. Charlotte Neuhauser and Dr.
Jason Ward. I am especially thankful to Dr. Rowena for her feedback and guidelines.
v
Lastly, I want to thank my life partner, Diane, for possessing unending patience.
Without her support, none of this would have been possible. She held my hand and
believed in me even when I doubted myself.
vi
Table of Contents
Acknowledgments v
List of Tables x
List of Figures xi
CHAPTER 1. INTRODUCTION 1
Introduction to the Problem 1
Background of the Study 2
Statement of the Problem 4
Purpose of the Study 4
Rationale 6
Research Questions and Hypotheses 9
Definition of Terms 12
Assumptions and Limitations 14
Theoretical/Conceptual Framework 16
Organization of the Remainder of the Study 17
CHAPTER 2. LITERATURE REVIEW 19
Introduction: Technology in the Classroom 19
Technological Environments and Collaboration 19
Internal Locus of Control, Social Theory, and Learning Outcomes 36
Graduate Students and Locus of Control 38
Impact of Technology on University Students 39
Conclusion 42
CHAPTER 3. METHODOLOGY 44
vii
Research Design 45
Research Questions and Hypotheses 45
Variables 46
Sample 47
Instrumentation/Measures 49
Field Testing 50
Data Collection 51
Data Analysis 51
Data Cleaning 53
Validity and Reliability 54
Ethical Considerations 54
CHAPTER 4. RESULTS 56
Overview of the Study 56
Research Questions and Hypotheses 57
Data Collection 58
Population and Sample 61
Details of Analysis and Results 61
Summary of Results 73
Conclusion 73
CHAPTER 5. DISCUSSION, IMPLICATIONS, RECOMMENDATIONS 75
Summary of the Results 76
Statement of the Problem 76
Significance of the Study 77
viii
List of Tables
Table 1. Participant Demographics 60
Table 2. Frequency of Types of Locus of Control 62
Table 3. Learning Management System Data Example 62
Table 4. Grade Scale Sample 63
Table 5. Frequency of Different Levels of Website Usage 65
Table 6. Frequency of Grades 66
Table 7. Omnibus Tests of Model Coefficients 67
Table 8. Summary of Results 69
Table 9. Frequency of Website Usage and Internal or External Locus of Control 71
Table 10. Results of Chi-Square Tests I 72
Table 11. Results of Chi-Square Tests II 72
Table 12. Study Result Comparison to Other Studies 78
x
List of Figures
Figure 1. Theoretical framework depicting relationships between the variables I 17
Figure 2. Theoretical framework depicting relationships between the variables II 68
xi
CHAPTER 1. INTRODUCTION
Introduction to the Problem
Researchers have indicated that introducing technology into a college or graduate
level classroom improved student motivation, engaged students more effectively, and
helped students enjoy their learning (Bozorgi, 2009; Mouza, 2008). Mouza also stated that
the increase in student motivation and engagement translated into improved student
outcomes. Though the overall literature on technology integration has been positive, a gap
still exists between theory and practice. For example, a study conducted by Hikmet, Taylor,
and Davis (2008) found that the use of technology integration did not produce the expected
outcomes among graduate students. Hikmet et al. (2008) also identified that one problem
that contributed to the inefficacy of technology integration was the mechanistic view
educators held of technology. Consequently, technology does not always have a positive
impact on students. Therefore, graduate students may need to depend on factors related to
self-efficacy, such as locus of control (LOC) in order to benefit from technology
implemented in a class.
Rotter (1996) first introduced the concept of LOC, and he defined it as the degree to
which a person believes that his or her own agency or outside circumstances contribute to
achievement. Within this construct, Rotter distinguished between internal LOC and
external LOC by determining where within a set scale a person located the source of
achievement (Rotter, 1966). The current study focused on the correlation between student
achievement and frequency of technology use, considering the mitigating factor of locus of
control in a brick and mortar classroom with a course website.
1
Background of the Study
Locus of control (LOC) is one of the many psychological or personality variables
worthy of consideration in an examination of the impact of frequent use of technology on
student academic achievement (Alias, Akasah, & Kesot, 2012; Bozorgi, 2009; Hadsell,
2010). Rotter (1966) formulated the concept of locus of control and defined it as a construct
that can assess people’s perceived control over their actions. This dimension of personality
helps clarify people’s behavioral traits (Hadsell, 2010).
A good deal of literature has demonstrated the impact of LOC on learning (Alias et
al., 2012; Barzegar, 2011; Bozorgi, 2009; Hadsell, 2010, Tatar et al., 2008). The impact of
academic achievement may be influenced by the degree to which students attribute success
or failure to factors that may or may not be under their control (Alias et al., 2012; Bozorgi,
2009). Many investigators have researched the implications of LOC using Rotter’s (1966)
scale. For instance, researchers have shown that students who have an internal LOC
consider success on a test to be the result of their own effort. In contrast, individuals with
an external LOC believe that events are primarily related to chance and that they have no or
only slight control over them (Alias et al., 2012; Bozorgi, 2009; Hadsell, 2010). Studies
have indicated that college students with internal LOC earned noticeably higher grade point
averages compared to college students with external LOC (Alias et al., 2012; Bozorgi,
2009; Hadsell, 2010). The findings from these studies not only confirmed that students with
internal LOC benefit from the technology offered in the classroom (such as a course
website) more than students with external LOC but also showed that students with internal
LOC demonstrated greater adoption of the technology and utilized the technology more
often than the students with external LOC. Thus, LOC has also been shown to influence
2
adoption and frequency of usage of technology (Bozorgi, 2009; Chitty, Ward, Noble, &
Tiangsoongnern, 2009).
Chitty et al. (2009) examined factors that influenced student readiness to adopt self-
service technology (SST) as means of accessing higher educational content. Their results
also suggested that internal LOC had a positive impact on student perceived value of SST,
whereas external LOC negatively impacted student adoption of SST. Meuter, Ostrom,
Roundtree, and Bitner (2000) were among the first researchers to use the term self-service
technology and defined it to be “technological interfaces that enable customers to produce a
service independent of direct service employee involvement” (p. 50), and many other
researchers accepted this definition (Kelly, Lawlor, & Mulvery, 2013; Makarem, Mudambi,
& Podoshen, 2009). Researchers described some examples of SST, from the customers’
point of view, to be services such as ATM machines and online banking, distance
education, travel industry reservation services, online tax filing software and applications,
and mobile printing (Cunningham, Young, & Gerlach, 2008; Outfit, 2013).
According to Chitty et al. (2009), locus of control has a direct impact on student
frequency of use of technology and technology adoption. The current study sought to
explore the relationship between the frequency of usage of technology in the form of a
course website in a face-to-face classroom and student achievement in the form of grades,
as mitigated by locus of control.
Measuring frequency of technology use is a complex issue. Some research has
shown that the duration of time students spent working on certain class activities did not
make any difference in the final academic outcomes (Whitmer, Fernandes, & Allen, 2012).
Studies also found a lack of congruence between the amount of time spent on activities and
3
the number of course website hits. These studies showed that performing visual analysis on
the Learning Management System (LMS) logs using plots of time and number of hits
demonstrated how students spend most of their time on a course website. For example, the
results showed that students spent less time in discussion forums and more time reviewing
the course content (Whitmer et al., 2012). Also, Whitmer et al. (2012) explained that length
of time is not an ideal measure:
However, questions linger about the accuracy of time recorded by the LMS and the actual
time students spent on these activities. Statistical analysis has found that the distribution of
time spent (e.g. "dwell time") is heavily skewed toward 0, suggesting that this variable is
not a reliable measure. (“Early Findings” section, para. 3).
Therefore, this study considered the number of student visits to the course website rather
than how much time students spent on different activities.
Statement of the Problem
The problem that prompted this quantitative study was the limited number of
studies that researched the impact of student frequency of technology usage in the form of a
course website, in a face-to-face classroom, on student academic outcomes. Most studies
did not take into consideration the important student self-efficacy factor, locus of control
(LOC); therefore, the results of these studies were incomplete (Tatar et al., 2008; Whipp &
Lorentz, 2009).
Purpose of the Study
The purpose of this research was to contribute to the scientific body of knowledge
in the area of educational technology, where technology could be conducive to education
and benefit both students (in terms of their academic achievements) and instructors by
4
helping them identify and understand the student personality and psychological variable
locus of control (Rotter, 1966). Understanding this variable could help instructors deploy
and encourage technological solutions to improve student academic achievement. In other
words, understanding these variables could change the question from whether technology
should be incorporated in face-to-face classrooms to how technology should be assembled
in order to help improve student achievement. This study can help answer vital questions
such as why some students make use of the technology available to them more often than
others and how the students who do not practice the use of technology as often can be
encouraged to do so.
A non-experimental quantitative method was appropriate for this inquiry because
this study was not experimental, and its results would be used to determine causality. The
study site had access to a large population, which made a quantitative study suitable. In
addition, quantitative methods have been used in similar studies (Alias et al., 2012;
Barzegar, 2011; Bozorgi, 2009; Chitty et al., 2009).
Previous researchers have shown that students with internal LOC used technology
more frequently than students with external LOC. This researcher did not attempt to
replicate those studies. Instead, this study examined the variables frequency of logins to
course website, LOC, and grades to determine if LOC was a mitigating factor for students
to use the technology more frequently and thereby improve their learning outcomes (Alias
et al., 2012; Bozorgi, 2009; Barzegar, 2011).
The current study explored the relationship between the usage of a course website in
a face-to-face classroom and student achievement in form of grades as mitigated by locus
of control. The research aimed to correlate the criterion variable grades to predictor
5
variable course website usage (frequency), to examine if technology would improve student
grades if deployed and used. The mitigated predictor variables LOC (internal/external)
were student psychological and personality variables that played a role in the adaptation
and use of technology. If a student had an internal LOC, the student was expected to log
onto the course website more frequently (than students with external LOC) and use the
course content that could subsequently improve overall course grade. The students with
external LOC were expected to log into the course site less frequently and access the course
material less often than the students with internal LOC. Therefore, the students with
external LOC were expected to earn lower grades.
Rationale
The non-experimental approach aligned with research in the area of information
technology regarding technology in educational fields. This non-experimental design had
the advantage of precision measurements from a validated and reliable survey (Rotter,
1966) and was highly replicable. Although variability among participants’ responses was a
disadvantage, using a non-experimental design created a suitable framework for this
study’s focus on the hypotheses, research questions, and variables of the current study.
Because this study focused on the relationships between variables, a quantitative approach
was more suitable than a qualitative approach. This methodology was consistent with the
research questions, as well, in that it measured the quantitative relationships between
variables (Alias et al., 2012; Barzegar, 2011; Bozorgi, 2009; Hadsell, 2010). The research
methodology supported the purpose and aligned with the research questions of the current
study and therefore confirmed the appropriateness of the research design.
6
The researcher employed logistic regression models to verify whether a statistically
significant relationship existed between the predictor variables (type of LOC and frequency
of website use) and student grades. Seminal authors in this area of study employed similar
designs to determine the relationship between variables (Barzegar, 2011; Bozorgi, 2009).
The relationship between locus of control and student achievement in a brick and mortar
classroom with a course website has been studied similarly using Rotter’s (1966) survey
(Adeagbo, 2011; Bozorgi, 2009; Ogunyemi, 2013; Savage Grainge, Bulmer, Fleming, &
Allen, 2013; Tatar et al., 2008).
LOC has also been known to influence adoption and frequency of usage of
technology (Bozorgi, 2009; Chitty et al., 2009). This study measured frequency of use of
website by students via the LMS logs, a method employed by Bozorgi (2009). Since
looking into LMS data is a fairly new option, few studies have used this method. Most
scholars examined the relationship between the two variables of LOC and frequency of
logging into the website in less precise forms (Chitty et al., 2009). The existing research,
although it confirmed the relationship between LOC and student outcomes, suggested a
larger sample would result in clearer conclusions (Bozorgi, 2009). This researcher also
employed crosstabs to verify whether a statistically significant relationship existed between
different levels of LOC and frequency of website usage.
In addition, previous studies did not take frequency of website usage into
consideration (Alias et al., 2012; Barzegar, 2011), nor did they look at student outcomes;
instead, they examined only the variables LOC and frequency of login (Chitty et al., 2009).
This study’s topic built on previous work, such as the research done by Alias et al. (2012),
Barzegar (2011), Bozorgi (2009), Chanchary et al. (2008), and Chitty et al. (2009). These
7
researchers investigated the direct relationship between technology integration and its
usage in a face-to-face classroom and student performance. Some studies did not find a
relationship between technology integration in classrooms and student grades (Alsafran &
Brown, 2012; Barzegar, 2011).
Additional studies researched the relationship between student LOC and student
technology use (Adeagbo, 2011; Ogunyemi, 2013; Savage Grainge et al., 2013). In her
study, Bozorgi (2009) studied 198 freshman, sophomore, junior, and senior college students
all majoring in English. Using a course website, Bozorgi (2009) found that students with
internal LOC engaged more with the course and asked more questions than those with
external LOC. Her findings confirmed that students with internal LOC benefited more from
the technology of a website in a course than students with external LOC. The MLS logs of
these students also showed that students with internal LOC accessed the website more
frequently and demonstrated greater adoption of the technology than the students with
external LOC (Bozorgi, 2009; Chitty et al., 2009).
The current study mitigated the limitations evident in previous studies that did not
control for important variables. For example, Barzegara (2011) researched impacts of LOC
on student learning on 700 students in Islamic Azad University in Iran. His findings
showed that LOC did not have any impact on student learning outcomes. The study,
however, did not investigate how frequently students made use of a course website. The
current study included a variable of the frequency of usage of a website variable as well as
the variable of LOC. Since Barzegar (2011), among other researchers (Alsafran & Brown,
2012), did not take into consideration the frequency of use of technology, it is possible that
students did not make use of the course website or their usage was minimal. The current
8
study also looked into the works of the researchers who studied the relationship between
LOC and technology usage (Adeagbo, 2011; Alias et al., 2012; Bozorgi, 2009; Chitty et al.,
2009; Hadsell, 2010; Ogunyemi, 2013; Savage Grainge et al., 2013) as well.
Research Questions and Hypotheses
The overarching questions for this study were
RQ1. Is there a significant relationship between frequency of course website usage, type of
LOC, and student grades?
RQ2. Is there a difference in the frequency of course website use between students with
internal LOC and those with external LOC?
Aligned with the above research questions, this study’s null and alternate hypotheses were
Ho1 [Null hypothesis for RQ1]. There is no significant relationship between frequency of
course website usage, student grades, and type of LOC (as the predictor variable).
Ha1 [Alternative hypothesis for RQ1]. There is a statistically significant relationship between
frequency of course website usage, student grades, and type of LOC (as the predictor
variable).
Ho2 [Null hypothesis for RQ2]. There is no significant difference in the frequency of course
website usage between students with internal LOC and external LOC.
Ha2 [Alternative hypothesis for RQ2]. There is statistically significant difference in the
frequency of course website usage between students with internal LOC and external LOC.
Significance of the Study
This research contributed to the area of education technology by addressing the gap
in the literature regarding the relationship between technology usage and student
achievement by looking into the student personality and psychological variables with the
9
Locus of Control Scale (Bozorgi, 2009; Chitty et al., 2009). Researchers suggested that
larger samples must be used to further confirm findings (Alias et al., 2012). Bozorgi (2009)
selected a study sample from students in different disciplines, and Chitty et al. (2009)
examined LOC and the adaptation of technology and included frequency of course website
usage; however, the study did not look at grade improvements.
This research may help future students improve their learning outcomes. The
students may understand a variable of their personality that determines how they deal with
adaptation and usage of technology. Rotter (1966) classified students as having either an
internal or external LOC. Students with internal LOC perceive that they have greater
control over their environment. At the collegiate level, students with internal LOC are
much more likely to utilize incidental information to fine tune a strategy for course success
due to their belief that they play a role in their achievement (Alias et al., 2012; Hadsell,
2010). Bozorgi (2009) supported this conclusion and asserted that student’s internal sense,
of who they are, contributes to their perceived outcomes. Further, students with internal
LOC more frequently used the available technology, adapted more easily to use of new
technology, and effectively navigated all of the schedules and class locations that make up
the daily mechanics of college life (Alias et al., 2012; Bozorgi, 2009; Hadsell, 2010). Rotter
(1966) found that students with internal LOC made effective use of incidental information
to help them achieve academic success.
These tendencies contrasted with the behaviors of students with external LOC
(Bozorgi, 2009). In one study, Nordstrom and Segrist (2009) found that by the time
students reached the graduate level, LOC became a clear predictor of student achievement.
At this level, students with internal LOC not only participated more in class but also
10
expressed stronger desire to pursue graduate education. Therefore, LOC might be an
optimal construct by which to measure how student personality factors mediate student
achievement in the context of technology and its usage. Thus, it is important to deepen and
refine the understanding of the role of personality variables in mediating the impact of
technology on student academic success. Further research will be required to identify
solutions that could aid the students with external LOC in their adaptation and use of
technology.
A better understanding of the relationship between the personality variable LOC
and technology usage will help refine and actualize the general conclusion in the literature
that technology usage improved college and graduate-level student outcome in practice
(Alias et al., 2012; Bailenson et al., 2008; Bozorgi, 2009; Hadsell, 2010). A study of LOC
may also serve to refine noted instruments. In order to better understand why and how
technology helps or hinders learning, it is important to determine if LOC contributes by
mediating the impact of technology (Alias et al., 2012; Barzegar, 2011; Bozorgi, 2009;
Hadsell, 2010). This study contributed to research into the micro- and meso-dimensions of
technology usage and how it impacts student academic achievement by taking into
consideration the LOC personality factors of students.
Research focusing on the issue of the linkage between LOC and learning outcomes
in a technology-enriched classroom was insufficient, especially at the graduate school level
(Alias et al., 2012; Barzegar, 2011; Bozorgi, 2009). The limited existing studies confirmed
the relationship between the variables; however, all researchers recommended further study
using a larger sample. This study had access to a larger sample the required data, and
11
therefore analysis of the results of the current study will further help identify the
relationship between technology usage and grades as mitigated by LOC.
Definition of Terms
External locus of control. The belief by student, associated with a superficial
conception of learning and poor learning outcomes, that one’s achievement or failure in
learning is attributable to chance or circumstances or sources external to oneself (Bozorgi,
2009).
Internal locus of control. The belief by student, associated with high achievement
levels, that one’s achievement or failure in learning is attributable to personal
characteristics, initiative, drive, hard work, and strategic and managerial approaches to
learning, or sources internal to oneself (Bozorgi, 2009).
Locus of control (LOC). Developed by Rotter (1996), LOC measures the extent to
which a person believes that what happens to them in life is due to their own efforts or to
chance or circumstances with the former defined as internal LOC and the latter as external
LOC (Bozorgi, 2009).
Self-directed student. Considered the ideal college student, the self-directed student
is autonomous and can manage and strategically implement a course of study in terms not
only of course content, but also assignments, attendance, schedules, and other relevant data.
Self-directed students are believed to be characterized by an internal LOC (Cassidy, 2007).
Social cognitive theory. SCT holds that student beliefs about learning have an
impact on learning outcomes, including their sense of self-efficacy, expectancy of
achievement, and beliefs about the importance of the work, including LOC (Wang & Lin,
2007).
12
Technology integration. Moving beyond the idea of merely introducing technology
into the classroom, technology integration encompasses the idea that in order for
technology to have an optimal effect on learning, it must be integrated into the curriculum,
transform pedagogy (preferably in the direction of social constructivist practice), and be
utilized in such a way as to exploit the full potential of technology in terms of enhancing
student engagement, participation, collaboration, and sense of belonging to a community of
learning (Kelleher & O’Malley, 2006).
13
Assumptions and Limitations
Theoretical Assumptions
The concept of LOC, first introduced by Rotter (1996), comprises the degree to
which a person believes that his or her own agency, or outside circumstances, contributes to
achievement. Within this construct, Rotter detected internal LOC and external LOC, which
are determined by where within a set scale a person located the source of achievement. The
impact of academic achievement may be influenced by the degree to which students
attribute success or failure to factors that may or may not be under their control (Hadsell,
2010). According to Chitty et al. (2009), locus of control had a direct impact on student
frequency of use of technology and technology adoption. This study assumed the
theoretical validity of the impact of LOC on technology usage.
Topical Assumptions
Previous research in the area of the correlation between technology use and student
achievement resulted in contradictory outcomes (Barzegar, 2011; Bozorgi, 2009).
Barzegar’s (2011) study did not find a relationship between use of classroom technology
and student achievement. In contrast, Bozorgi (2009) did find a relationship between
student technology use and student achievement. Only a few studies looked into LOC as a
mitigating factor that could influence student technology use (Alias et al., 2012; Barzegar,
2011; Bozorgi, 2009; Chitty et al., 2009). In keeping with the topical assumption, the
sampling frame was composed entirely of graduate students in a field that was not related
to technology.
14
Methodological Assumptions
Quantitative methodology relies on objective data void of subjective considerations.
The choice of a quantitative methodology provided the opportunity to determine variable-
based correlations between frequency of use of technology and student achievement as
mitigated by LOC.
Other Assumptions
This study assumed that the students surveyed would give an honest appraisal of
their expectations and outcomes derived from the website in the course. It was also
assumed that students with internal LOC would be more responsive to the questionnaires
than those with external LOC.
Limitations
The use of SurveyMonkey was essential to reach the study’s sample population, and
the site is a well-known survey system with documented support features. However, some
disadvantages are common to online survey applications. The use of online survey systems
has been subject to limitations related to accuracy of the self-reported data, as some
participants are likely to respond inaccurately to the questionnaire to save time. Other
possible influences on the participants, such as speed of the Internet, can also impact the
respondent’s attitude (Maronick, 2009).
Other limitations included (a) Participants included only graduate students taking a
specific course; (b) The study did not account for gender, culture, age, or race of
participants; (c) The number of students with internal and external LOC were most likely
unequal; (d) Differences in the information taught in the classroom may have existed; (e)
Inconsistency may have resulted from the methods of the different instructors who teach
15
the class; (f) Differences in website content may have existed, although this was less likely
because content was usually instructor material downloaded from the publisher’s site. The
question of whether the integration of the website can be fine-tuned to improve outcomes
for external LOC students will be left to future studies.
Theoretical/Conceptual Framework
The research was based on the theory from Rotter (1966) that established principles
of LOC, together with a study conducted by Bozorgi (2009), which suggested that students
with internal LOC achieved improved outcomes using a course website.
Figure 1 demonstrates how the predictor variable, LOC (internal/external), played a
role in the adaptation and frequency of the use of technology (Bozorgi, 2009) and therefore
improved student grades. The criterion variable, grades, was analyzed as it was affected or
impacted by the predictor variable, frequency of course website usage (frequency of use),
to examine whether technology improved student grades if deployed and used. The
mitigated predictor variable, LOC (internal/external), was the student psychological and
personality variable, which played a role in the adaptation and use of technology.
16
Figure 1. Theoretical framework depicting relationships between the variables I.
This study explored the issue of the personal and psychological non-cognitive
variables internal and external LOC. These variables mediated the impact of technology on
student learning in face-to-face graduate school classrooms. Previous studies in the area of
technology in education identified that the presumed positive impact of technology was
more often differentiated according to student type (Adeagbo, 2011; Bozorgi, 2009; Chitty
et al., 2009; Ogunyemi, 2013; Savage Grainge et al., 2013; Tatar et al., 2008). Existing
studies on the positive relationship between internal LOC and student learning may be
differentiated based on type of course and whether or not technology was present
(Adeagbo, 2011; Bozorgi, 2009; Ogunyemi, 2013; Savage Grainge et al., 2013; Tatar et al.,
2008).
Organization of the Remainder of the Study
This chapter offered an overview of the purpose, rationale, and methodology, as
well as the theoretical and conceptual framework of this study. The remainder of this
17
dissertation is arranged into four chapters. Chapter 2 includes a comprehensive literature
review of the study’s key variables, LOC, frequency of website use, and student grade.
Chapter 3 covers the study’s ethical considerations and describes the details of
various aspects of the study’s methodology including design, sample, measurement
instrument, and data collection. Chapter 3 also discusses the study’s data analysis
methodology, including validity and reliability of the study, and Chapter 4 includes the
results. Chapter 5 sums up the results and the study’s implications and offers
recommendations for future research.
18
CHAPTER 2. LITERATURE REVIEW
Introduction: Technology in the Classroom
According to Singer (2008), Wang, Wu, and Wang (2009), and Mouza (2008),
research increasingly suggested that technology improves student motivation, student
engagement, and enjoyment of lessons. This literature review will include a discussion and
perspective in areas of technology, technology integration, technology and
computermediated environments, and the mismatch of technology and its uses in education
as they relate to LOC. Also included in the literature review is an explanation of LOC for
university students. In addition, this chapter will discuss the theory and learning outcome
that serves as an explanation for motivating students using technology and affective
components of technology used in education for expanding student knowledge.
This chapter examines studies wherein LOC has correlated with student perceived
benefits in technology integrated classrooms. Studies that examined how technology
integration supports improved learning outcomes will be reviewed, illustrating a trend away
from instrumental views of technology towards an approach, based on social cognitive
theory, that social and attitudinal issues mediate technology use.
Technological Environments and Collaboration
Literature on information and communication technology (ICT) or technology-
mediated learning (TML) found that it takes more to improve student learning than simply
placing technology in a classroom. As a result, researchers have begun to explore “the
explicit relationships among technology capabilities, instructional strategy, psychological
processes, and the contextual factors involved in learning” (Hikmet et al., 2008, p. 25).
Moreover, “the idiosyncrasy of teaching and learning make evaluation of the impact of ICT
19
in education even more challenging” (Hikmet et al., 2008, pp. 128-129). Hikmet et al. also
observed that “blind faith in the value of ICT, epitomized in the ‘build it and they will
come’ approach to implementation in corporate settings, is inappropriate in education” (p.
132). This distinction occurs because the education process is more complex than business,
and therefore one technological approach cannot not be utilized enterprise-wide (Hikmet et
al., 2008, p. 132).
Since more groups evaluated the provision of ICT and its possible integration to
college learning, models with greater sensitivity were developed to explain social and
pedagogical variables (Wang, 2009). Wang proposed a generic model of integration
consisting of pedagogy, social interaction, and technology. He argued that the didactic
methods used by the teachers, in conjunction with the learning environment, influence and
promote learning rather than the technology by itself. As such, Wang’s study joins others in
arguing that technology must be integrated according to constructivist learning theory as
well as interactivity design. Upon application of the web-based learning design at the
National Institute of Education in Singapore, Wang concluded that the didactic design and
the learning environment were suitable because the learners liked its flexibility and also
responded favorably to the collaborative learning that resulted. In order to fine-tune
technological integration beyond the one-size-fits-all approach, researchers have attempted
to pinpoint how digital technology affects aspects of personality and self. The research of
Benson and Mekolichick in 2001 tried to flesh out the “camouflaged micro and meso
patterns [that] exist that are significant for the way in which humans interact with and use
these devices” (p. 498). Thus, researchers have begun to explore the psychological
correlates of technology use, located in what is theorized as the person-technology nexus,
20
to better understand why some teachers and students use or do not use technology (Benson
& Mekolichick, 2001). In fact, a significant number of studies have found that technology
integration does improve college and graduate-level student outcomes (Bailenson et al.,
2008; Dori & Belcher, 2005; Hetzroni & Shrieber, 2004; Hodgkinson-Williams, Slay, &
Sieborger, 2008; Johnson & Levine, 2008; Kear et al., 2004; Kelleher & O’Malley, 2006;
MacGeorge et al., 2008; Milheim, 2007; Regan & Youn, 2008; Rummel & Spada, 2005;
Schmid, 2006; Selwyn, 2007; Singer, 2008; Tan & Morris, 2006; Vallance & Towndrow,
2007; Wang, et al., 2009). In addition to the individual studies that discuss how technology
integration improves student outcomes, the literature presented various case studies.
Case studies examined how technology integration at the college and graduate
levels improved student outcomes in a number of learning contexts, though some
differences between the undergraduate and graduate levels appear to be emerging
(Benson & Mekolichick, 2001; Cassidy, 2007; Whipp & Lorentz, 2009; Wigen et al.,
2003). Several models exist to guide incorporation of technology into education. Case
study research acknowledged the support of the use of technology in classrooms that
received support from the adoption of one model, Technology Acceptance Model
(TAM), usually used to describe business contexts and integration (Kelleher & O’Malley,
2006). The model posits that ease of use and an appraisal of technology’s usefulness leads
to an intention to use it. Davis, Bagozzi, and Warshaw (1989) developed
TAM to decipher computer-usage behavior. The theory is based on an information system
that models the way users accept and put a technology to use.
The model discusses how certain factors influence the decision of the users, when
they are first introduced to a new technology or software package. If a technology users
21
perceive an application as useful, then they are more likely to accept it and therefore are
more willing to use the application.
Kelleher and O’Malley (2006) applied the TAM to determine if message boards in a
university-level distance learning public relations class could provide new learning
opportunities. The researchers found that message board use also improved learning and
established an intention in students to use other technology. A study by Hetzroni and
Schrieber (2004) described how basic technology, such as computer-based word
processing, can be used to improve outcomes for post-secondary learning-disabled students
held back by dysgraphia or poor writing skills. Word processing was introduced to the
students in the context of assistive technology (AT). The study found that AT helped
students compensate for deficiencies in reading and writing and that these students
provided output that was more compliant with the established class standards. This
procedure helped students with learning disabilities overcome any digital disconnect they
may have experienced.
The Digital Disconnect? Technology and College
While a number of education technologists believe schools are headed toward a
cyber-campus environment, Selwyn (2007) noted that the introduction of new technologies
for undergraduate and graduate courses can be inconsistent from institution to institution.
Thus, what Selwyn called a digital disconnect persists between “the enthusiastic rhetoric
and rather more mundane reality of university ICT use” (p. 84).
Focusing on the social construction of technology, as well as a critical theory
perspective, Selwyn (2007) observed that the introduction of computers in higher education
is restricted by factors such as the interest of stakeholders. For example, the commercial
22
interests involved in providing software to universities have resulted in computer use being
permeated by the “material-semiotic infrastructure of business” and most computer use
merely “mirroring information-giving functions” that “rarely progresses beyond the
PowerPointlessness of Office applications which reduce scholarship to being taught how to
formulate client pitches and infomercials” (Selwyn, 2007,p. 88). As a result, students who
want to be strategic in their scholastic efforts decide not to extensively use computers, as
they do not necessarily guarantee better grades. Therefore, Selwyn (2007) recommended
that higher education should reengineer computer use and encourage students to “break
open the ‘black box’ of software code and modify software according to their own need and
demands, much as ‘game modders’ do with video games” (p. 92).
Other researchers have helped define the limits of current technological applications
in classrooms. For example, Vallance and Towndrow (2007) addressed concerns that ICT
is manifest in most college classrooms as teacher-centered PowerPoint (PPT) presentations,
which can repress analytical thinking in students. Acknowledging this problem, Vallance
and Towndrow presented a case study wherein PPT was used to increase student critical
thinking through effectively implementing PPT lessons that required student feedback.
Researchers have also documented positive responses to the use of technology in
classrooms. Audience Response Technology (ART) is popular among teachers using large
college-level lecture hall formats, even though research has been insufficient to validate the
benefits of the method (MacGeorge et al., 2008). ART allows teachers to ask questions and
receive immediate responses based on clickers in handheld devices. Proponents argue that
ART improves student engagement, helps teachers respond to immediate student feedback,
and allows the refocus or review of material if needed. Other students have found that ART
23
improves attention and helps them remember and learn better. To explore this further,
MacGeorge et al. (2008) questioned student participants in a college course using ART and
discovered that students described ART as easy to use and enjoyable, and stated that ART
aids increased awareness in regards to teacher expectations and course content, as well as
student accomplishment. In addition, the study found that student attitudes toward ART did
not fluctuate as the semester progressed. This outcome indicates that the novelty of the
technology did not just cause a momentary favorable response blip (MacGeorge et al.,
2008).
In 2007, Milheim studied how technology can also assist students outside the
classroom, where informal learning is beyond the newly created learning environments
offered by some educational organizations. Such invisible learning may occur in computer
searches, in episodes of comparative shopping, or in discussion groups, bulletin boards, and
help lines. In fact, several components of technology and lifelong learning assist college
students with available resources for academic and personal achievement. These include
online and web-based technologies, computer-mediated environments, e- and m-learning,
and blogs. Additional technology and recognition of LOC as relational to graduate student
achievement are areas discussed in the literature.
The concept of collaborative e-learning in higher education contributes to lifelong
learning. Lifelong learners are not people who would consume only the prepared course
content; they must also contribute and be a part of the learning processes themselves
(Fischer & Ostwald, 2002). Therefore, lifelong learning also involves sharing knowledge
and helping others learn. As a result, advances in information and communication
24
technology (ICT) and web-based learning have dramatically altered the definition of
lifelong learning.
The lifelong learning concept has emerged dramatically since ICT and web-based
technologies become a large part of the institutional practices of learning (Freitas &
Jameson, 2006). Freitas and Jameson (2006), researchers from University of Greenwich
and JISC infoNet, examined two collaborative team leadership models for web-based
learning. These models were Moodle and Learning Activity Management System (LAMS)
and were selected from two 2006 British e-learning pilots. The researchers examined the
models in four institutions of higher education in England, using e-learning techniques in a
collaborative environment. The authors found that collaborative e-learning promotes
lifelong learning in communities of web-based learning practices in institutes of higher
education (Fischer & Ostwald, 2002).
Further researchers found that laptop technology can promote lifelong learning
through inquiry-based activities. Tan and Morris (2006) examined how business students at
Idaho University used laptops to facilitate group work and collaborative problem solving.
The researchers found that as students use laptops for fulfilling classroom assignments,
they also learned that to manage their time and improve their efficiency, and the
technological component of their lives helped them stabilized their academic and personal
objectives. Team-based laptop use also improved student abilities to work well with others.
This study provided support to justify the provision of a computing environment for
university students.
Schmid (2006) evaluated the utility of interactive whiteboard technology in
language learning. Whiteboard technology involves a projector and screen that allows the
25
teacher to manage a flow of imagery and calculations with an electronic pen. Schmid
(2006) employed the critical theory of technology, refuting both instrumental and
deterministic views of technology in favor of a view that technology is enmeshed in human
agency and that humans constantly challenge the aspects of technology to influence or
change them. The most important element of critical theory is to evaluate the fundamentals
that configure technology and its usage.
While deterministic studies on computers and classrooms focused on how the
presence of technology alone can improve learning, others took an instrumental approach
and simply measured the impact of technology in improving student performance. Thus,
some of the theoretical themes that helped to reform the group of students also improved
their behavior and the practice of technology (Schmid, 2006). The study showed that as the
faculty of a doctoral program research course began to redesign the whiteboard towards a
more learner-centered and discursive pedagogical approach, students became more
motivated to learn and found the whiteboard exciting as it allowed them to share new
knowledge with the class. Schmid’s (2006) report found that whiteboard technology could
be used to enhance at-desk learner-centered classes, and it opened up channels of
communication between teacher and student in ways that improved learning for all.
Online and Web-based Technologies
Hodgkinson-Williams et al. (2008) proposed that information and communication
technologies (ICT) at the college level will only be fully realized if they are also involved
in the shift of education from content to performative knowledge or “valid ways of coming
into a relationship with the worlds are forms of action and engagement with and in the
world” (p. 435). Factors that limit ICT effectiveness include emphasizing content over
26
expertise and focusing on the individual learner rather than a community of practice as
outlined in social constructivist theory. In a community of practice, students interact and
mutually develop a new expertise. Hodgkinson-Williams et al. (2008) explained that a
strong case for networked or virtual learning must be based on the concept of communities
of practice, which encourages extending the boundaries of university learning.
Researchers have developed the C4P framework (content, conversation,
connections, context, and purpose), which simply states that knowledge comes from
connections and “conversation around content in context” (Hodgkinson-Williams et al.,
2008, p. 437). Hodgkinson-Williams et al. demonstrated the enactment of C4P in a virtual
learning community at Rhodes University in South Africa. However, the researchers noted
that the purpose was addressed in ad hoc and disjointed ways. As a result, the content was
often lost, though the use of a wiki as a hold-all was found to be helpful. Despite these
limitations, conversation and connections were strong, leading to the robust creation of new
knowledge. Despite some difficulties in project implementation, the C4P framework
provided a stable practice community.
Computer-Mediated Environments and Collaboration
Rummel and Spada (2005) argued that college or graduate-level computer-mediated
environments enable learning and work most effectively when distant users collaborate.
The study also found that collaboration requires a great deal of systematic and personal
support. Sources say collaboration exists when partners have common goals and a division
of labor so that they end up doing the work together. For example, desktop
videoconferencing, wherein people at remote computers discuss issues through audio-video
connections, is often impeded because it is different from face-to-face contact in terms of
27
grounding, turn-taking, and feedback. Grounding involves “establishing and sustaining
mutual understanding” and can be difficult to create in online environments (Rummel &
Spada, 2005, p. 207). Collaborators must also learn how to pool their unshared knowledge,
as failure to do so could hinder the situation, where the team members rely on one another
to finish the assignment (Rummel & Spada, 2005).
In reviewing these aspects, Rummel and Spada found that worked-out collaboration
is better online than problem solving. Collaboration encourages users to provide
explanations of solutions, while problem solving is often unguided and imposes a greater
cognitive load on learners. In addition, “superficial processing and an illusion of
understanding may be counteracted by promoting the elaboration and reflection of the
example, especially by eliciting self-explanations” (Rummel & Spada, 2005, p. 211). The
study by Rummel and Spada also explored how cooperation scripts improve online
communication and collaboration. Using a psychotherapeutic videoconference and dialog
analysis, researchers found that collaboration was acceptable, though the levels of
participant involvement varied. Overall, Rummel and Spada concluded that in this case,
technology improved the level of learning.
E- and M-Learning
While e-learning involves learning online, m-learning involves bringing learning to
students through mobile devices such as cell phones and personal digital assistants (Wang
et al., 2009). Wang et al. incorporated the unified theory of acceptance and use of
technology (UTAUT) to determine if age or gender, among other determinants, supported
or interfered with mobile learning at five universities in China. UTAUT, according to
Wang et al. (2009), “posits that performance expectancy, effort expectancy, social
28
influence and facilitating conditions are determinants of behavioral intention or use
behavior, and that gender, age, experience and voluntariness of use have moderating effects
in the acceptance of IT” (p. 95).
While it did not directly address LOC, the model nonetheless included self-
management of learning as one of the factors that determines behavioral intention with
regard to IT use. The concepts of UTAUT also specify that performance expectancy will be
greater for young men with regard to computers, possibly due to their greater motivation to
use IT. Perceived playfulness also refers to a state of mind involving “comparatively stable
characteristics of individuals that tend to be relatively invariant to situational stimuli” and
indicates a person’s “degree of cognitive spontaneity in microcomputer interactions”
(Wang et al., 2009, 99). Linked to flow theory, Wang et al. argued that playfulness will
influence male users more than female users. Finally, self-management involves both
readiness and comfort for online learning and the self-discipline to engage in it. The overall
results of the study indicated, “The effects of performance expectancy and perceived
playfulness on behavioral intention were significant, but no gender or age differences were
found to exist” (p. 214). Other areas that relate to technology advancement include blogs,
virtual environments, and web-based learning outcomes.
Blogs
Singer (2008) examined how blogs in university-level journalism and mass
communications classes improved learning. The blogs were used in the context of a
blended learning model. Based on Vygotsky’s ideas, blogs allow students to express their
learning in writing and thus support the definition of learning as “a knowledge construction
process that is discursive, relational, and conversational” (Singer, 2008, p. 12). Blended
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learning carried this notion into the online environment, where blogs allow students to
develop their critical and reflective thinking skills. Blog use also lets students feel more
agency, a stronger sense of community, and less isolation in their learning. In his review of
the use of an edublog in a journalism course, Singer found a high degree of engagement,
participation, and interaction as students responded to their peers’ posts. Such findings
support how blogs can create an optimal blended environment. Though most students
participated in the blog only because it was a course assignment, Singer nevertheless
noticed that once the learners submitted a posting, they remembered the course material
and therefore benefitted from this medium.
Virtual Environments
Bailenson et al. (2008) examined whether or not applying virtual environments in
undergraduate learning can improve student-teacher interaction. This research was based on
the transformed social interaction (TSI) theory in online worlds, in which teachers and
students can improve outcomes through transformed “social physics,” which puts all
students at the front and center of teacher attention (p. 103). Theoretically, immersive
virtual environments (IVE) like video games have potential advantages, such as more
psychologically prominent sensory information (Bailenson et al., 2008). Some IVEs
support the “neomillennial learning style” (Bailenson et al., p. 106), wherein users actively
shape environments, an activity that encourages critical thinking. Overall, IVEs can
improve learning because students can contemplate course material and connections that
may not be achieved in a face-to-face classroom.
Virtual agents encourage the construction rather than the consumption of
knowledge and provide convenient “personalized one-on-one learning experiences tailored
30
to the individual that would be prohibitively expensive otherwise” (p. 108). Viewing
information in complex ways has been found to “make it easier to understand abstract
concepts” (p. 108), which improves student visualization in topics such as architecture and
engineering. One of the most important advantages of IVEs is that they can record all
student microbehaviors and gradually build a database to be used to tailor learning
effectively. Bailenson et al. (2008) asserted that the most promising role of IVEs is in the
transformation of social interactions. For example, studies have shown that manipulating
avatars in IVEs can circumvent learning difficulties caused by mismatches between teacher
and student learning styles.
Conversely, Johnson and Levine (2008) argued that while immersive learning
environments are not new, virtual non-text based environments have been effectively used
because they add an “element of discovery and responsiveness” (p. 161). For instance,
avatar role-playing in virtual worlds allows users to practice techniques such as set design
in virtual theatres. Johnson and Levine also found that learning is similar in virtual
environments, where students move from accretion to structuring to tuning, or, using
Kolb’s model, they move from practice to reflection to understanding.
Additionally, Bailenson et al. (2008) studied interactions in virtual classrooms and found
that “using digital transformations of teachers and learners in CVEs can increase learning
compared to no transformations in the same environment” (p. 130). To be more specific,
the virtual environment allowed every student to sit front and center with respect to the
teacher. This equal proximity avoids the common classroom dynamic and instead optimally
engages students, resulting in improve learning (Bailenson et al., 2008). In a virtual
environment, the social emotional development of the learner can be influenced by
31
Maslow’s work. The 2008 study by Johnson and Levine used Maslow’s hierarchy of needs
model to describe how users in virtual worlds move from satisfying basic needs to seeking
out others and a sense of sharing and belonging. The top of Maslow’s pyramid, esteem,
only comes when one is able to exploit the affordances and differences in a virtual world.
This progression is concretely evident in college level teachers who often retrofit their
classroom methods into virtual classrooms only to discover later on what is appropriate
virtually (Johnson & Levine, 2008).
Dori and Belcher (2005) examined the use of technology-enabled active learning
(TEAL) at MIT using media-rich software in an interactive physics classroom, to determine
its impact on student understanding. This environment was created to counteract the
traditional brick and mortar lecture format that does “not foster active learning” (Dori &
Belcher, 2005). Constructivist learning theory motivated this change and holds that learning
is socially-based and not locked up in the individual mind. Thus, learning should take place
in communities of learners. In addition, based on Dewey’s ideas, Dori and Belcher defined
active learning as “solving problems, sharing ideas, giving feedback and teaching each
other” (p. 245).
Other researchers have found that technology-rich environments in science teaching
help students visualize principles better and thus improve their perceived learning outcomes
(Dori & Belcher, 2005). The TEAL project’s goal was to reduce the failure rate in
introductory electromagnetism courses by moving away from passive lecturing and towards
active learning. The results of the TEAL project were successful, reducing the failure rate
in the course to 5% as opposed to 13% in the control group. More importantly, assessment
scores indicated that TEAL students improved their understanding of key concepts in the
32
course, and researchers concluded that social constructivism improved the achievement of
all students (Dori & Belcher, 2005).
Web-Based Learning
Web-based learning environments have become a staple of distance learning
courses, which use both synchronous and asynchronous teaching and learning modules
based on the multimedia theory. The theory posits that combining words and images is
more effective than text alone, as seen in modules such as Web CT, Blackboard, message
boards, discussion threads, chat rooms, and whiteboards (Regan & Youn, 2008).
Web-based learning in graduate school. Much research has noted growth in post-
secondary online or web-based learning and its contribution to the advancement of
teaching. However, Correia and Davis (2008) observed that at the graduate level there is a
“decoupling of pedagogical responsibilities” (p. 2) in terms of faculty, indicating that
students should form learning communities or communities of practice in order to succeed.
Each type of community necessitates a different level of involvement from students. In
order to determine how students responded to such programs, Correia and Davis studied an
ICT-supported teacher education program at a large research university in the Midwest.
Reflections from student participants in the course described that the students had learned a
great deal about instrumental design and how to collaborate online and be successful
learners. However, Correia and Davis (2008) observed that factors such as student
contradictory feelings, their intentionality to gather and participate, and their ability to
manage conflicts in social relationships online influenced their views and prevented them
from forming a unified community of practice.
33
The Columbia Video Network of the Columbia University School of Engineering
and Applied Science was created to extend student reach beyond the campus into the world
(Jacobs & Shahjahan, 2007). The program makes lectures available to students via
downloadable and shareable files, with the goal of enhancing the outcomes of the average
engineering graduate student at Columbia, who are described as “typically self-motivated
individuals who are looking to challenge themselves by completing an ivy-league graduate
degree program while still working full time” (Jacobs & Shahjahan, 2007, p. 41). The
program was able to help students in terms of their flexibility to adjust to their situations.
Maushak and Ou (2007) also studied the impact of web-based environments and
computer-mediated communication on graduate student education, and investigated
whether the students appreciated the technology. The study focused in particular on how
instant messaging (IM) posting or synchronous communication, which is linked to
collaboration, improved graduate student experience of distance learning. To be more
specific, Maushak and Ou (2007) targeted promotive interaction, wherein individuals
encouraged each other to achieve group goals. Results showed that synchronous
communication was found to improve student outcomes through online interaction in web-
based graduate learning.
Chang and Chang (2008) examined how online concept mapping activity (CMA)
involving peer learning improved graduate student outcomes. The CMA was introduced
because graduate students reported difficulties in applying abstract concepts of learning
theory to practical learning situations. The students attributed the difficulty to either
cognitive information processing problems or low learning motivation. Studies similar to
Chang and Chang’s research found that CMAs appeared to improve motivation and
34
understanding, as students felt that the program helped them construct concepts. In their
study of 97 graduate students at a Southwestern public university, Chang and Chang (2008)
also concluded that the CMA improved understanding levels and the student motivation to
learn.
Forming communities at the graduate level is important in the postgraduate
experience, as communities counteract solitude (Devenish et al., 2009). Devenish et al.
stressed the role of study groups in strengthening the postgraduate experience, crediting the
groups with keeping students on course toward earning a degree. Once students arrive at
the graduate level, they more strongly desire to develop a sense of belonging in their
community. In this context, Devenish et al. described study groups as forming classic
communities of practice, an area of concern to graduate schools. By more effectively
creating communities of practice, web-based learning and study groups exemplified how
technology helps graduate students improve outcomes. These outcomes become the
impetus for researching LOC and effective learning strategies for graduate student as they
interact with technology.
Technology and independence. Cassidy (2007) argued that virtual learning
environments can succeed in improving student outcomes at the college level only if the
students have become independent learners, which involves self-assessment skills, self-
reflective thinking, goal-directed thinking, evaluation of current performance, and planning.
These skills constitute a strategic approach to learning, whereby students use time
management and other methods to improve their performance (Cassidy, 2007). Studies
have found a strong association between their own judgments of their academic skills and
their personal control of academics. Cassidy focused on the relationship between the self-
35
assessment skills of 167 incoming freshmen and their academic personal control, and
compared their self-assessment results with those of their tutors. Minimal differences
existed, indicating a high level of self-assessment skills among the freshmen. A college
student’s ability to seek help when needed in an online environment is believed to be
critical to learning success. However, studies such as Whipp and Lorentz’s (2009) found
that many students, including those in distance learning, failed to seek academic help for
various reasons, ranging from low self-efficacy to unresponsive teachers. To make up for
the lack of face-to-face communication online, teachers should use immediacy strategies,
such as using first names, humor, and digression. Social presence is crucial to a student’s
satisfaction with an online course, and teachers and students can establish more immediacy
online in many ways (Whipp & Lorentz, 2009).
Internal Locus of Control, Social Theory, and Learning Outcomes
Rotter, who devised the term locus of control (LOC) in 1954, stated that people
have either internal or external LOC (Rotter, 1966). Those with internal LOC believe that
they have something to do with their success, while those with external LOC attribute their
success to luck or external factors. People with internal LOC tend to exercise greater
control over their environments and are more open to seeking information that is relevant—
or may be appear to be relevant—to achieving their goals (Dollinger, 2000). Dollinger
specified that LOC can improve college student learning outcomes because those with
internal LOC are more likely to use incidental information, such as schedules and class
locations, to fine tune a strategy for course success.
LOC has been examined with regard to a number of behavioral issues, such as the
role of LOC in procrastination (Dollinger, 2000; Estrada et al., 2006; Fazey & Fazey,
36
2001; Galbraith & Alexander, 2005; Hall et al., 2002; Janssen & Carton, 1999; Karayurt &
Dicle, 2008; Kukulu et al., 2006; Libert et al., 2006; Mamlin et al., 2001). This research
also focused on college students, 95% of whom are known to procrastinate with the habit
getting worse the longer they are in college (Janssen & Carton, 1999). Procrastination has
also been linked to test anxiety, low self-esteem, and social anxiety; this information
supported these studies as an early attempt to link student behavior to student outcomes.
Studies have found that students with internal LOC perceive the relationship between
behavior and consequence and therefore are less likely to procrastinate.
However, other studies had mixed results, some of which found no link between
LOC and procrastination (Janssen & Carton, 1999). Janssen and Carton suggested that the
mixed results were due to researchers’ not adhering to Rotter’s warning that “domain-
specific expectancy scales should provide better predictions of specific behaviors than
generalized scales do,” and most studies made use of generalized scales Janssen and Carton
examined 32 undergraduate students at a Midwestern university to reevaluate the linkage
and found that students with internal LOC got to work on their assignments sooner and
took less time to complete them than the students with external LOC. These results linked
less procrastination with internal LOC and proposed that mixed findings of previous studies
were the result of misuse of expectancy scales.
Internal LOC has also been linked to improved self-efficacy as well as enhanced
emotional and psychological wellness (Karayurt & Dicle, 2008). With the goal of designing
a better college-level nursing program to help future nurses develop coping skills and
encourage internal LOC, Karayurt and Dicle studied this linkage at a Turkish university.
The study found that internal LOC develops over time as students move into their third and
37
fourth years of college, with seniors demonstrating highest internal LOC. The researchers
added that the use of problem-solving design in most classes may have contributed to this
development of LOC (Karayurt & Dicle, 2008).
Internal LOC has also been associated with assertiveness, since persons who believe
they control their destiny and control events are more likely to push for change (Bozorgi,
2009). Libert et al. (2007) compared the communication skills of doctors with internal LOC
and external LOC. The results showed that those with internal LOC communicated more
with the patients and their relatives.
Graduate Students and Locus of Control
Researchers have examined LOC to determine the likelihood of college student
attending graduate school, both undergraduates and those already in graduate school (Hulse
et al., 2007; Nordstrom & Segrist, 2009). Researchers wanted to learn whether the right
students were pursuing graduate studies and what differentiated them from those who did
not attend (Nordstrom & Segrist, 2009). Nordstrom and Segrist (2009) examined students
in a psychology course and found that internal LOC was a better predictor of student
intentions to pursue graduate school than GPA or consumer orientation, when compared to
the students with external LOC. These students with internal LOC also succeeded in
graduate school because they were motivated to conduct research studies. Nordstrom and
Segrist (2009) further posited that students with internal LOC participated more in class
and possessed a mindset that indicated they were much more likely to succeed in graduate
school.
In the context of graduate education, comparable to college-level studies,
researchers sought to examine the degree to which cognitive and non-cognitive factors led
38
to student success. Viewed as reliable predictors of success, cognitive factors are measured
by tests and critical thinking analyses. On the other hand, non-cognitive factors are
measured by personality and psychological tests (Hulse et al., 2007). Hulse et al.
administered a State-Trait Anxiety Inventory and the Rotter LOC scale to graduate nurses
at several Texas universities. The study found that cognitive factors were not generally
predictors of positive outcomes, and that solicitude and LOC could be more influential
factors (Hulse et al., 2007). In Hulse et al.’s study, however, external LOC was the factor
associated with academic success. The researchers explained this outcome by noting that
the nursing program took place in a military context. In this regard, external LOC helped
students meet the demands of graduate school in terms of overcoming isolation,
collaboration, taking control of research, establishing mentoring relationships, and
organizing and monitoring one’s education on one’s own (Hulse et al., 2007).
Impact of Technology on University Students
Previous researchers documented correlations between optimal technology
integration and various personality characteristics of users within the interactions between
self-concept and technology use (Barzegar 2011; Benson & Mekolichick, 2007; Bozorgi
2009; Cassidy, 2007; Whipp & Lorentz, 2009). Benson and Mekolichick (2007) studied
how the self-concepts of undergraduate students and teachers affected their degree of
comfort with using digital technology. The theoretical basis of that study was symbolic
interactionism, which argues that as people assemble symbols in their interactions with
others, those symbols express self-concept (Benson & Mekolichick, 2007).
Identity theory further argues that one’s sense of self is determined by how tightly
or loosely an individual connects him- or herself to other social structures and norms. In
39
their study, Benson and Mekolichick (2007) posited that students and teachers would be
more likely to use technology if such use accorded with their definitions of themselves as
either faculty members or students. These researchers confirmed that more technology
usage is linked to student academic role identity, increased degrees of self-efficacy and
higher levels of usability of these technologies (Benson & Mekolichick, 2007).
This strong relationship between the academic role identity and computer use suggested
that technology use is effective only when mediated by particular student characteristics.
Technology and Constructs Related to Locus of Control
In 2009, Bozorgi studied the relationship between college student LOC, frequency
of academic technology use, and academic achievement. Related research indicated that
increased website usage does in fact improve student final grades, and that students with
internal LOC tend to use the technology more frequently; however, the institutions of
higher education often ignore the relevance of these factors. Regardless, these factors
explain that LOC has been associated with academic performance (Bozorgi, 2009). In the
study of education, students with internal LOC may attribute both successes and failures to
their own behavior, which may later cause pressure and the expectation of future failure.
Nonetheless, in general, internal LOC has been found to be associated with academic
success and external LOC with academic failure (Bozorgi, 2009). LOC has also been linked
to learning style, such that external LOC limited students from adopting new styles, while
internal LOC encouraged a strategic approach to learning. Deep and surface learning styles
are also related to internal and external LOC (Bozorgi, 2009).
40
Web-based Learning and University Students with Internal Locus of Control
Wang and Lin (2007) employed social cognitive theory in order to explain how self-
regulation of learning influences college student learning in an online environment. The
theory defines the self-regulated learning as “students’ active involvement in self-
motivation and the use of appropriate learning strategies to pursue self-established goals”
(Wang & Lin, 2007, p. 601). The researchers also asserted that self-efficacy, expectancy,
and beliefs on task importance are also relevant to learning. To target student’s lack of
focus and confidence in online environments, self-regulation learning guidelines have been
proposed as a way to improve learning. Wang and Lin demonstrated how attention to social
cognitive theory in the design of Networked Portfolio, or NetPorts, can improve student
learning. They found that self-efficacy and collective efficacy improved student learning
behaviors as well as the quality of feedback and their learning strategies. As such, the
results confirmed social cognitive theory as it applied to web-based learning, indicating that
students with motivation employ better learning strategies and use feedback more
appropriately (Wang & Lin, 2007). This study positioned LOC as a correlate of self-
regulation.
Web-based Instruction and Locus of Control in Graduate Students
In order to circumvent the one-size-fits all approach to evaluating the utility of web-
based instruction (WBI), Bozorgi (2009) recommended qualitative research to understand
the learning approach in individual students. Bozorgi conducted a study with four of his
students in an instructional systems technology course to determine how LOC interacted
with their course work. Results showed that students with internal LOC were more aware
of managing course work, took a strategic approach to learning, and were better at asking
41
questions. The researcher found that the suitability of WBI for students is variable; for
example, a large number of students with external LOC enrolled in a WBI instructional
systems technology course but performed less satisfactorily than those in a traditional brick
and mortar class. As a result, Bozorgi (2009) attempted to determine if WBI was best suited
for students with internal LOC, and the findings confirmed that LOC is an important factor
in learners’ perceived outcomes in a WBI environment.
Conclusion
Bozorgi’s (2009) study examined the issue of web-based learning and whether or
not it improved learning in students with internal LOC. The literature review established
that in many cases, technology improved learning in settings ranging from classroom use of
handheld devices to web-based environments (Bozorgi, 2009; Mouza, 2008; Singer, 2008;
Wang et al., 2009). However, the literature on technology integration is also moving from
an instrumental or deterministic orientation toward a more critical approach to integration
whereby the characteristics of the teacher and student mediate the impact of technology on
learning. As a result of difficulty with student learning technology and graduate courses,
research on the impact of technology and learning is incomplete.
Internal LOC has also been found to improve student outcomes through the
undergraduate years (Janssen & Carton, 1999; Karayunt et al., 2008), and it serves as a
predictor of future success in graduate school (Nordstrom & Sergist, 2009). Some case
studies provided evidence that technology integration and the use of web-based instruction
are particularly helpful for graduate students with internal LOC. These studies
conceptualized internal LOC as one of the key elements of student autonomy and college
success. These studies showed that students with internal LOC are better able to negotiate
42
and manage the complexities of college or graduate school life, become autonomous
learners, ask more questions in class, and achieve better outcomes.
Furthermore, additional literature has emerged addressing the impact of technology
on learning, which sought to discover correlations between various student characteristics
that can influence student success. The study results indicated that students with strong
self-regulation skills benefitted the most from technology integration in their classrooms.
The role of internal LOC in learning has been established in the literature, but the degree to
which internal LOC helps students benefit optimally from technology integration demands
further research.
43
CHAPTER 3. METHODOLOGY
This researcher employed logistic regression to seek a statistically significant
relationship between the predictor variables (type of LOC: internal/external and frequency
of website use) and student grades. Therefore, this study involved the use of two predictor
variables, frequency of use of website and LOC, and one criterion variable, student grades.
The variable student LOC was categorized into two types, internal or external. Grades
appeared in the form of letter grades, and frequency of use divided into three categories:
high, low, and medium use (Chanchary et al., 2008).
Second, to address RQ2, a crosstab analysis examined whether a statistically
significant relationship existed between students with internal LOC and those with external
LOC and frequency of course website use. The value of Pearson chi-square helped the
researcher determine whether or not the null hypothesis must be rejected. The results
confirmed that confirm no relationship exists between internal LOC/external LOC and
frequency of website usage; the alternative hypothesis was that a relationship did exist. The
null hypothesis would only be rejected in the event of a probability of .05 or lower,
indicating that the findings were due to chance. These hypotheses tests determined the
extent to which the findings could be due to chance. In other words, the test determined
whether the relationship existed and would most likely happen again. If it happened due to
chance, then the probability that the strength of relationship would appear in a similar
manner would be very low. The chi-square tests if the observed sample frequencies are
significantly different from the expected frequencies described in the null hypothesis.
The response rate needed was calculated using G*Power calculator. G*Power
calculated a sample size of 107 responses necessary for validity of the study. Total
44
responses received were 137 of which only 112 of them were valid. The 112 valid surveys
slightly exceeded the necessary number of surveys.
Research Design
This correlational study employed statistical methods to analyze the student data
gathered from Rotter’s (1966) LOC survey, student final grades, and course website log
files to determine any relationship between the frequency of technology usage in a face-to-
face classroom and student final grades as mitigated by locus of control. The student
participants accessed Rotter’s (1966) LOC survey via a link they received in an e-mail
invitation that took them to SurveyMonkey. This survey measured the student LOC level
and determined if they should be placed in either the internal or external LOC group. This
instrument has been used and validated many times, including in recent studies (Adeagbo,
2011; Bozorgi, 2009; Ogunyemi, 2013; Savage Grainge et al., 2013). The student final
grades and log file information were released by the university. The log file showed how
often a student logged in and used the classroom website. The researcher did not teach the
course used in this research.
Research Questions and Hypotheses
The researcher employed logistic regression to examine whether a statistically
significant relationship existed between the predictor variables (type of LOC:
internal/external and frequency of website use) and student grades. The following research
questions and hypotheses guided the study:
Is there a significant relationship between frequency of course website usage, type of LOC,
and student grades?
Ho1 [Null hypothesis for RQ1]. There is no relationship between frequency of course
45
website usage, student grades, and type of LOC (as the predictor variable).
Ha1 [Alternative hypothesis for RQ1]. There is a statistically significant relationship
between frequency of course website usage, student grades, and type of LOC (as the
predictor variable).
RQ2. Is there a difference in the frequency of course website use between students with
internal LOC and those with external LOC?
Ho2 [Null hypothesis for RQ2]. There is no significant difference in the frequency of course
website usage between students with internal LOC and external LOC.
Ha2 [Alternative hypothesis for RQ2]. There is statistically significant difference in the
frequency of course website usage between students with internal LOC and external
LOC.
Variables
The researcher employed chi-square to seek a statistically significant relationship
between the predictor variables (type of LOC and frequency of website use) and student
grades. This regression analysis examined whether or not technology improves student
grades if deployed and used. The final grades of the students were provided by the
university (Bozorgi, 2009).
The predictor variable, frequency of technology use, is commonly included in
research of this type. Studies have shown that students with internal LOC used technology
more frequently than students with external LOC (Bozorgi, 2009; Chitty et al., 2009). The
frequency of use of the course website by students were measured via the LMS logs, a
method used by Bozorgi (2009). The frequency of website usage of each student was
recorded as logs in the website’s Learning Management System (LMS). Sheard et al.
46
(2003) explained how online learning environments keeps the records of student interaction
in a log file within a database system. These log files determines student website usage
based on the number of times a student logged into the course website (Sheard et al., 2003).
Chanchary et al. (2008) also conducted a study on LMS activity logs of 60 college students
enrolled in a computer science or statistics course in a university in Bangladesh. Their
study analyzed student logs on a Learning Management System to investigate any
relationship between student frequency of course website usage and their overall
performances. Results confirmed that students with low access earned poor grades.
The mitigated predictor variable, LOC (internal/external), is a psychological and
personality variable. LOC is expected to play a role in the adaptation and use of technology
by students (Bozorgi, 2009). Student LOC level was measured via responses to the Rotter
(1966) LOC survey.
Sample
This study’s sample was selected to fill a gap in the literature. The sample for this
study was drawn from the entire population of graduate students from one university; the
students had successfully completed the course Economics for Decision Making during the
period fall semester of 2013 to fall semester of 2015. Using G*power sample size
calculator, the researcher determined that 107 participants were necessary for this study
(see Appendix A). Out of 137 responses received, 112 of them were valid, which slightly
exceeded the number of surveys necessary.
Target Population
The participants for this study were all graduate students at a university who had
successfully passed the course Economics for Decision Making between the semesters of
47
fall 2013 to fall 2015 prior to completing the survey. This required core course counts
towards a master’s degree in business, economics, or accounting; therefore, the university
offers multiple sections of the course each semester. Each section contains 19 to 27
students, and all instructors require the same textbook. The total target population for this
study was 1,003 students.
Sampling Frame
Appendix A shows that the effect size of X is .15. The inclusion criteria were as
follows:
1. The student must have taken and completed the specific course within the last two
years.
2. The student must agree to complete the LOC survey.
3. The student must consent that his/her LMS login sessions data and final grade may be
released by the university to the researcher.
4. The student must complete the study.
Exclusion from the sampling frame occurred when one of the following existed:
1. The student dropped out of the class or left the university.
2. The student did not wish to participate in the survey or any part of the study.
Sample Method
The sample population were graduate university students that had taken the course
Economics for Decision Making. After the researcher received permission from the
university, students received an e-mail that invited them to participate in the research. The
e-mail also included an informed consent and explained to the students that the researcher
would be using their LMS logs, final grades, and the LOC information to conduct this
48
study. The consent form and e-mail provided a brief description of the research as well. If
the students agreed to participate, then they were offered a link within the e-mail, which
directed them to the SurveyMonkey site and the LOC survey. This approach had been used
by other researchers in accordance with Capella University’s quality and ethical standards
(Davis, 2011; Post, 2008).
Sample Size Power Analysis
The sample for this study was drawn from the entire population of graduate students
who had taken the course Economics for Decision Making and completed the course from
fall of 2013 semester to fall of 2015 semester term. Appendix A shows that the effect size
of X was .15. A researcher can utilize different approaches that would yield similar if not
the exact same results. However, since Capella’s School of Business and Technology
prefers the use of G*Power, the researcher selected that method of choice. The entire,
1,003, student population (the total population of graduate students at the university with
the specific criteria explained in this paper) were approached for participation in the study.
Instrumentation/Measures
The data collection for RQ1 included the frequency of course website usage as
documented by LMS logs after the university granted the researcher access to that data with
participants’ permission (Bozorgi, 2009; Chanchary et al., 2008). The data collection for
RQ1 also included the student’s final grades, which were provided to the researcher by the
university. If a student wished to participate in the study they also needed to permit the
researcher to view and study the details of their LMS data such as final grade and
frequency of login required by the researcher (Bozorgi, 2009).
49
The data collection for RQ2 included the following: (a) frequency of use of website
measured by LMS logs and (b) student LOC measured by Rotter’s (1966) survey. The
SurveyMonkey website administered this instrument with the advantage of effectively
eliminating the risk of researcher bias and conflict of any interest due to the fact that the
researcher did not have any direct contact with the students. The researcher required no
permission to use Rotter’s (1966) Locus of Control Scale because the survey is in the
public domain. The students received the invitation to participate, which also included the
informed consent form and a link to the survey (Bozorgi, 2009; Chitty et al., 2009; Gifford
et al., 2006). The focus of the survey was to assess LOC as the researcher utilized this
information to demonstrate how LOC can impact frequency of course website technology
usage and to what extent this could impact student grades (Bozorgi, 2009).
Field Testing
The researcher conducted a field test with the input of three experts. The experts
were invited via e-mail to take the Rotter (1966) survey and evaluate its appropriateness for
this study. All field testers recommended the instrument for the purpose of this study.
Therefore, according to the expert’s experience and feedback, the researcher concluded this
instrument was the right tool for the purpose of this research. This field test helped the
researcher get a sense of the functionality and design of the survey. No changes to the
instrument were recommended by any of the experts.
Data Collection
Procedures for the study proceeded as follows: Once the students accessed the
SurveyMonkey site, Rotter’s LOC questionnaire was available to them to complete. The
students were required to include their student identification number on the LOC
50
questionnaire to help the researcher map the LOC results to their LMS logs. The student’s
ID numbers were replaced with a code to further protect the identity of the student
participants. Rotter’s (1966) LOC questionnaire contains 29 forced-choice items. The
researcher examined whether or not the students LOC had any relationship with the
frequency of their technology usage by comparing the LOC information to the LMS log
files to see if the students with internal LOC used the course website more frequently when
compared to the students with external LOC. The researcher further examined to verify if
increased usage of technology affected the grades positively.
The LOC survey was appropriate because it has been used by many researchers
investigating how the personality factor LOC can impact student success (Alias et al., 2012;
Barzegar, 2011; Bozorgi, 2009; Hadsell, 2010). The sample criteria were determined
utilizing a sample size calculator to achieve a 95% confidence level with a 5% margin of
error and a total population of 1,003 students.
Data Analysis
Variable Operationalization
Frequency of website use was operationalized to be the number of times students
logged into the course website throughout the semester. The source of this data was the
LMS report obtained from the university. Based on the values reported, frequency of usage
was coded as low, medium, or high. Frequency was measured as a categorical, ordinal
variable (Chanchary et al., 2008). Student final grade could take on values of A, B, C, D, or
F. The source for the student final grade was also the university; these grades were
organized by student ID number. Those students with grades of Incomplete, Withdraw or
Audit were not included in the analysis. Final grade was a categorical, ordinal variable.
51
LOC could take on values of external or internal. One point was assigned for each
of the following responses: 2.a, 3.b, 4.b, 5.b, 6.a, 7.a, 9.a, 10.b, 11.b, 12.b, 13.b, 15.b, 16.a,
17.a, 18.a, 20.a, 21.a, 22.b, 23.a, 25.a, 26.b, 28.b, 29.a. The other six questions were
assigned a score of 0 as they were responses to the six distracter questions. A score of less
than eight was categorized as internal LOC. Since the cutoff value was nine, a score less
than nine put students in the internal LOC group and a score higher than nine placed the
student in the external LOC group. Students were divided according to their LOC results as
internal or external. LOC was a nominal, categorical variable. A logistic regression was
used to analyze the survey results for RQ1. A chi-square with crosstabs method of bivariate
analysis was employed to analyze the survey results for RQ2.
Analysis
RQ1 was analyzed using logistic regression to see if a statistically significant
relationship existed between the predictor variables (type of LOC and frequency of website
use) and student grades. The group was defined by the LOC predictor, so actually two
groups existed, each consisting of one predictor (IV) and one criterion (DV).
RQ2 was analyzed using chi-square with crosstabs since both variables were
categorical. An alpha level of .05 was assumed. Chi-square yielded information about
whether a statistically significant relationship existed between the variables—external
LOC and frequency of website usage and internal LOC and frequency of website usage. In
addition, if the chi-square result were significant, and since one of the variables was
nominal, gamma was run as a measure of association to test strength of the relationship.
Since chi-square was a non-parametric test, no transformations were required. SPSS
Version 20 software was used to analyze the data. The level of significance was assessed at
52
greater than .05. The surveys missing data for any of the predictor or criterion variables
were eliminated from the analysis.
Data Cleaning
The mitigated predictor variable, LOC, could be either internal or external (Rotter,
1966). Grades are means by which student achievement is measured, as well as a fair way
to display student academic success (Allen, 2005; Whitmer et al., 2012). In this research,
the grades appeared the form of letter grades ranging from A to F, and frequency of use
was divided into three categories of high, low, and medium use (Chanchary et al., 2008).
This study also used the date and time of student system access. This data was
mapped with student identification numbers in the LMS logs. Date and time of the access
was needed to verify whether the login was successful. Therefore, some cleaning of the log
file was required in order to remove all unsuccessful logs. In their study, Chanchary et al.
(2008) had the number of logins categorized into three different standards: “Low (0 to less
than median value), Med (median) and High (above median)” (p. 3). Other studies
involving LMS logs have also used a similar approach (Bozorgi, 2009; Gürpinar, Zayim,
Özenci, & Alimoğlu, 2009; Macfadyen & Dawson, 2010). In order to obtain more accurate
results, the median website usage value was not considered (Chanchary et al., 2008). The
current study also used the same technique, in addition to use of the SPSS statistical tool, to
analyze the data.
Validity and Reliability
Reliability and validity were determined by a panel of experts. The focus of the
survey was to evaluate LOC, and the researcher utilized the student LOC information to
53
demonstrate how LOC can impact frequency of course website usage and to analyze to
what extent student grades were impacted by LOC (Bozorgi, 2009).
Ethical Considerations
The researcher had received permission from the institutional review board (IRB) of
both establishments, Capella University and the institution where the study was conducted,
as well as the consent from the students that allowed the release of this information to the
instructor. The student’s information and LOC results were saved using password-protected
Symantec cloud-based storage. This information was accessible only by the researcher.
The password will be changed every three months by the researcher to ensure privacy. The
password information is not available to anyone else. The student names and information
will never be published anywhere, and therefore the privacy of all participants remains
protected. To avoid any violation of student privacy and FERPA, a code name was
developed for each individual student. The randomized nature of this research minimized
any potential threats to internal validity. Moreover, testing and instrumentation threats were
also minimized due to usage of statistically reliable and valid instruments. Additionally, use
of a unique group that consisted solely of a random sample of graduate students who had
already completed the course minimized any risks associated with conflict of interest. The
use of SurveyMonkey effectively mitigated researcher bias and conflict of interest as the
researcher did not have any direct contact with any of the students.
54
CHAPTER 4. RESULTS
This chapter describes the results of the data analysis. The chapter covers an
overview of the study, restatement of the research questions and the research hypotheses,
followed by the study’s population and sample. The result of power analysis using
G*power sample size calculator reported 107 as the minimum sample size (Appendix A),
and the study’s sample was drawn from the entire population of graduate students, at a
university, who had successfully completed the course Economics for Decision Making
during the period of fall semester of 2013 through fall semester of 2015. The instrument
used in this study was the Rotter (1966) Locus of Control (LOC) survey. The LOC survey
is a 29 item questionnaire established by the American psychologist and faculty member at
the Ohio State University and the director of clinical psychology at the University of
Connecticut, Julian Rotter. The Locus of Control survey is used for identifying influential
factors that impact social learning, in this case student LOC level.
The data analysis for this research study involved final course grades as the criterion
variable, frequency of course website usage as the predictor variable, and Locus of Control
(internal or external) as the mitigated predictor variable. Summary of research analysis
results are presented in detail, and the conclusion section answers all research questions
concisely.
Overview of the Study
This study employed a non-experimental, correlational, quantitative, self-
administered online survey, the Locus of Control Scale (Rotter, 1966). The purpose of this
study was to investigate the relationship between frequency of student course website usage
and student achievement as mitigated by students personal characteristic, internal or
55
external LOC. The research aimed to correlate the criterion variable grades to predictor
variable course website usage (frequency), to examine whether technology would improve
student grades if deployed and used. The mitigated predictor variable LOC
(internal/external) demonstrated student psychological and personality variables that played
a role in the adaptation and use of technology.
Research Questions and Hypotheses
This study was designed to gather and analyze data to answer the following research
questions:
RQ1. Is there a significant relationship between frequency of course website usage, type of
LOC, and student grades?
Ho1. [Null hypothesis for RQ1]: There is no relationship between frequency of course
website usage, student grades, and type of LOC (as the predictor variable).
Ha1 [Alternative hypothesis for RQ1]. There is a statistically significant relationship
between frequency of course website usage, student grades, and type of LOC (as the
predictor variable).
RQ2. Is there a difference in the frequency of course website use between students with
internal LOC and those with external LOC?
Ho2 [Null hypothesis for RQ2]. There is no significant difference in the frequency of
course website usage between students with internal LOC and external LOC
Ha2. [Alternative hypothesis for RQ2]: There is statistically significant difference in the
frequency of course website usage between students with internal LOC and external
LOC
56
Data Collection
The survey was administered to students at a local university. The students were
graduate students from the following departments: Accounting, Business Administration,
Supply Chain Management, Finance, Leadership and Management, Public Administration,
Economics, E-Commerce, International Business, Health Administration and Marketing.
Potential respondents agreed to participate in the survey by consenting via clicking
a link they received in an e-mail. The researcher used the results from the power analysis,
as discussed in Chapter 3 and shown in Appendix A, to determine the target number of
respondents needed for this study.
The sample suggested by the G*Power results was 107 participants. The Rotter
(1966) survey was completed by 137 students. However, out of 137 submitted surveys,
only 112 were complete; therefore, the researcher conducted the analysis with 112 samples,
which is still slightly more than the 107 suggested by the G*Power software. The
university’s college of business and public management is very popular with Chinese
student citizens. These students submit their applications from China and upon acceptance
they travel to the US for the duration of their program; therefore, the participants were 94%
Chinese students enrolled on an F-1 visa.
As far as their gender, there were almost equal number of male and female (51%
female, 49% male). 1% of the participant students were foreign students from Japan that
had been through the same university application process as the Chinese students. Same
criteria applied to the 1% Taiwanese students. In addition, 1% of the participating students
were from various parts of the Middle East such as Iran, Lebanon and Syria. As far as
student racial characteristics these students were 1% were Hispanic, 1% White non-
57
Hispanic, and 1% African American, 1% White (Anglo), and the rest were Asians (96%).
Table 1 demonstrates that these groups had very close to equal, if not exactly, number of
males and females. After sorting the students, as observed in table 1, according to their
demographic characteristics it became clear that the students were from only two continents
of Asia and America. The students from the continent of Asia were form China, Japan,
Taiwan, and different parts of the Middle East. The remaining of the students were all from
the American continent.
The students from the continent of Asia were all immigrant students between the
ages of 20-25. The African-American students were also between the ages of 20-25.
Whereas the American and Hispanic students were between the ages of 27-42. The
variables LOC, frequency of website usage, and grade did not have a significant difference
among students of various age, race, gender, country of origin and continent.
Looking at the data, it was particularly noticeable among the Chinese students as
they were the larger population, that the variable LOC, frequency of website usage and
their grade did not have any significant difference between the Chinese Males, and Females
and their age.
This observation, however, was not as obvious when looking at other nationalities
as their population was not as large. A larger sample of these students are required to
determine that. The table below summarizes the demographic findings for this study.
58
Table 1. Participant Demographics
Continent Race Country
of Origin
Total
Participants
Male Female Age (Under 25)
Asia Asian Chinese 94% 49% 51% 100%
Japanese 1% 50% 50% 100%
Taiwanese 1% 48% 52% 100%
White –
non
Hispanic
(Middle
Eastern)
Unknown 1% 50% 50% 100%
America Hispanic Mexico
and Puerto
Rico
1% 47% 53% 100%
African-
American
USA 1% 50% 50% 100%
White –
non
Hispanic
(Anglo)
USA 1% 45% 55% 100%
Total 100%
Population and Sample
To reach this study’s framed population, the researcher used aspects of both
purposive sampling and random sampling by employing SurveyMonkey to randomly
access only the students who met the study’s inclusion criteria. This study’s population
consisted of graduate students from a university who had completed the course
Economics for Decision Making between the semesters of fall 2013 to fall 2015.
Details of Analysis and Results
Two research questions and their corresponding null and alternative hypotheses
were derived from this study’s focus on the relationship between technology usage and
grades as mitigated by LOC.
59
Research Question 1 and Hypothesis 1
RQ1. Is there a significant relationship between frequency of course website usage, type of
LOC, and student grades?
Ho1 [Null hypothesis for RQ1]. There is no significant relationship between frequency of
course website usage, student grades, and type of LOC (as the predictor variable).
Ha1 [Alternative hypothesis for RQ1]. There is a statistically significant relationship
between frequency of course website usage, student grades, and type of LOC (as the
predictor variable).
Results: Frequencies
The findings for frequencies of type of LOC, website usage, and student grades
have been analyzed and are illustrated and clarified in several tables. Table 2, titled
Frequency of Types of Locus of Control demonstrates the types of Locus of Control for the
student participants. Participants that agreed to the consent completed the Rotter (1966)
LOC survey that was placed on a SurveyMonkey site for participant access. The scoring
from the 29-item questionnaire placed the students in either external LOC or internal LOC
group. The results reported that majority of participants had internal locus of control
(70.3%), while those with external locus of control were 21.6% of the total sample. The
data for 9 participants were incomplete and were not included in the study.
Table 2. Frequency of Types of Locus of Control
Type of Locus of Control Frequency Percentage
Internal 78 70.3
External 24 21.6
Total 102 91.9
Missing 9 8.1
60
Overall Total 111 100
Table 5, Frequency of Different Levels of Website Usage, demonstrates the
frequency of website usage for the student participants. This data was from the logs
captured by the Learning Management System and represents the number of times student
logged into the website. In a study by Chanchary et al. (2008) frequency of use was divided
into three categories of high, low, and medium use (Chanchary et al., 2008). Similar scaling
was utilized in other in other studies (Bozorgi, 2009). An example of the Learning
Management System logs and how the system administrators viewed it is presented below.
Note that the IP addresses are marked with letter X and student information such as
student IDs, and other data that could directly identify a specific student have been
substituted with codes as seen in the table below.
Table 3. Learning Management System Data Example
Access Time IP Address Name
11/9/2015, 23:08 xxx.xxx.xxx.xxx Student A
11/19/2015, 22:21 xxx.xxx.xxx.xxx Student B
11/18/2015, 21:05 xxx.xxx.xxx.xxx Student C
11/10/2015, 18:17 xxx.xxx.xxx.xxx Student D
11/10/2015, 11:05 xxx.xxx.xxx.xxx Student Z
This study categorized the frequency of website usage to three categories of High,
Medium and Low in such a way that the students that logged in and accessed the website
between 0 to 9 times throughout the entire semester were considered to be Low frequency
usage students. Students that accessed the website between 10 to 20 times per semester
were considered Medium frequency website users. And the students that logged in 21 times
61
or more during the semester were placed in the High website usage category. A similar
approach was taken by other researchers (Bozorgi, 2009; Chanchary et al., 2008). For
further clarification see table below:
Table 4. Grade Scale sample
Usage Level Logins through the semester
High 21+
Medium 10-20
Low 0-9
As discussed in the previous chapter, the LMS logs were cleaned up since the
Learning Management System data normally includes unrelated and ambiguous entries.
Additionally, multiple logins within a short period of time such as a minute were not taken
into consideration as it was assumed that the student had difficulty accessing the website
due to external factors such as temporary server issues, electricity shortage, system shut
down, the student network problems or computer issues (Chancahry et al., 2008). The LMS
logins also include the name of files that were requested by the students. The time-stamped
information for all downloads, or any other activities such as student access to an online
Quiz, an exam or clicking the link on an instructional video clip existed within the LMS
logs. However, similar to Bozorgi (2009), this study also did not take into account the type
of activity the student was involved in.
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As discussed in Table below, the majority of participants (51.4%), were identified
as Medium website users and therefore had logged in and accessed the website between 10
to 20 times per semester.
Thirty-six of the participants (32.4%), which is less than third of the participating
students, either did not login to the website at all or logged in less than 9 times in one
semester. These were the Low frequency website users.
Only eighteen of these participants (16.2%) were High website frequency users and
utilized the course website over 20 times in one semester.
Table 5. Frequency of Different Levels of Website Usage
Levels of Website Usage Frequency Percentage
Low 36 32.4
Medium 57 51.4
High 18 16.2
Total 111 100.0
Missing 36 32.4
Overall Total 57 51.4
Table 6 is titled Frequency of Grades and demonstrates the number of instances of
occurrences of different grades the students received from their instructor. The table also
shows the percentage of each of those instances for all student participants. This
information was provided by the university through logs that are available on the Learning
Management System. The researcher had received permission from the university as well
as the consent from the students that allowed the release of this information to the
instructor. The majority of participants (64.9%) received an A for their final grade.
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In this study all student participants’ grades were collected and analyzed. The
results showed that thirty-six of the participants (32.4%) received an average course grade
of B and only 3 (2.7%) of them received a grade of C. None of the participants received a
grade below C or failed the class. Table below clarifies the results further.
Table 6. Frequency of Grades
Course Grade Frequency Percentage
A 72 64.9
B 36 32.4
C 3 2.7
D 0 0
F 0 0
Total 111 100.0
Missing 0 0
Overall Total 111 100.0
Most students (70.2%) had internal locus of control. Also most students (51.4%) used the
websites between 10 to 20 times throughout the semester. Lastly, all student grades were
collected through LMS which reported the majority (64.9%) received a final course grade
of A.
Results: Logistic Regression
The researcher conducted a logistic regression analysis to answer Research
Question 1 and to see if a statistically significant relationship existed between frequency of
website usage and student grade as mitigated by the predictor variables type of LOC. The
group was defined by the LOC predictor, so two groups existed, each consisting of one
predictor (IV) and one criterion (DV). Results from the logistic regression rejected Ha1,
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and showed that type of locus of control, internal or external is not a mitigating factor in the
relationship between frequency of website usage and student grades.
And therefore, increased frequency of course website technology usage of students
did not significantly predict student grades. Based on the lack of significant results, the
null hypothesis was retained for the first question. The p-value was compared to .05 to
determine if the overall model was statistically significant. In this case, the model was not
statistically significant because the p-value was greater than .05. The LOC and frequency of
website usage’s ability to predict grades was not statistically significant.
Table 7. Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 1 Step 3.628 3 .304
Block 3.628 3 .304
Model 3.628 3 .304
After further analysis of the results from tables 2, 5 and 6 this study’s additional
discovery demonstrated a relationship between LOC and grades. Since, according to the
study results, the frequency of website usage did not play a role in student grades, the
impact of other factors and variables need to be studied.
In conclusion, there was no significant relationship between LOC and frequency of
course website use, but new findings demonstrated a significant relationship between LOC
and student final course grade.
For clarification purposes the identified relationships were marked as numbers 1 and
2 in the black arrows in Figure 2 below:
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Figure 2. Theoretical framework depicting relationships between the variables II.
Locus of Control was perceived to be the mitigating factor in the relationship
between the frequency of course website usage and student final grades; however, as
demonstrated using the analysis and as observed in Figure 2, Locus of Control is not the
mitigating factor for this relationship. In fact, study findings suggest that Locus of Control
itself impacts the variable student frequency of course website usage as well as the variable
student grades directly. The researcher conducted additional statistical analyses that is
shown later in the chapter.
The results from tables 2, 5 and 6 show that the majority of student participants
were internal Locus of Control and fell within the Medium course website usage level
category. These students completed the course with a final grade of A. It is also shown that
there exists a direct relationship between internal Locus of Control and student frequency
of course website usage; however, when looking at table below, it becomes clear that
student final grades were not impacted by frequency of website use. In other words, the
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students utilized the course website technology more did not receive a higher final grade in
the course.
Table 8. Summary of Results
Variable in Relationship Exists? Result
LOC Yes Grade
LOC Yes Frequency of use
Frequency of use No Grade
Research Question 2 and Hypothesis 2
RQ2. Is there a difference in the frequency of course website us between students with
internal LOC and those with external LOC?
Ho2 [Null hypothesis for RQ2]. There is no statistically significant difference in the
frequency of course website usage between students with internal LOC and external
LOC.
Ha2 [Alternative hypothesis for RQ2]. There is statistically significant difference in the
frequency of course website usage between students with internal LOC and external
LOC.
In order to respond to this research question, the data regarding frequency of course
website use was analyzed. The method employed for data analysis was chi-square with
crosstabs. The reason that chi-square with crosstabs were the method utilized for the
analysis was because both variables were categorical. An alpha level of .05 was assumed.
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The Chi-square analyses yielded information about whether or not a statistically
significant relationship existed between the variables external Locus of Control and
frequency of course website usage as well as variables internal Locus of Control and
student frequency of course website usage.
The results showed that the internal Locus of Control group students used the
course website technology more frequently than the external Locus of Control group
students. Thus the researcher rejected the null hypothesis for RQ2 and accepted the
alternative hypothesis Ha2. This study found that the students with internal Locus of
Control were the largest group in the highest frequency of website usage category and
comprised 15% of the entire sample.
In contrast, students with external Locus of Control were the largest group in the
lowest frequency of the course website usage category. These students composed 18% of
the entire sample. The results showed that the external Locus of Control group students did
not log into the Learning Management System to utilize the course website technology as
often as the internal Locus of Control students. The analysis demonstrated that Ha2 was
accepted.
Both types of Locus of Control student groups, internal LOC and external LOC,
contained large numbers of students who were found to be in the lowest frequency of
website usage category. Additionally, students with internal Locus of Control also
composed the group with highest percentage in the medium frequency of course website
usage category (47%). The results from the analysis showed support for Ha2, and are
summarized in the Table below:
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Table 9. Frequency of Website Usage and Internal or External Locus of Control
Locus of Control Frequency of Website Usage
Type (Percentage) Total
Low Medium High
Internal 15
(14.7%)
48
(47%)
15
(14.7%)
78
(77.45%
)
External 18
(17.65%)
6 (5.88%) 0 (0%) 24
(23.53%
)
Total 33
(32.04%)
54
(52.94%)
15
(14.7%)
102
(100%)
Note. Percentages were calculated as part of total sample.
These study analysis demonstrated that the Pearson chi-Square value was less
than .05. Table 10 below demonstrates the results obtained from the Pearson chi-square
analysis. The test outcomes further clarified that the results were significant. In other
words, the researcher accepted the study alternative hypothesis Ha2 and confirmed that
type of Locus of Control, internal or external, significantly influenced the variable
frequency of course website usage. In this study the students with internal Locus of Control
logged in to the course website more frequently and utilized the course website technology
significantly more than the students with external Locus of Control.
Table 10. Results of Chi-Square Tests I
Value Df Asymp. Sig.
(2sided)
Pearson Chi-Square 26.887a 2 .000
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Likelihood Ratio 28.153 2 .000
Linear-by-Linear
Association 23.262 1 .000
N of Valid Cases 112
Additional Analysis
Based on the results of the statistical analyses exploring LOC as the mitigating
factor in the relationship between frequency of website use and student grades, the
researcher conducted additional analyses that explored the relationship between LOC and
student grades and focused on running a crosstab correlation between LOC and student
grades in order to engage in a Chi-square analysis. The results are in the table below.
Table 11. Results of Chi-Square Tests II
Value Df Asymp. Sig.
(2sided)
Pearson Chi-Square .380a 1 .538
Likelihood Ratio .374 1 .714
Linear-by-Linear
Association .376 1 .540
N of Valid Cases 102
As seen in table above, Pearson chi-Square value was greater than .05, indicating
that the results were not statistically significant. This demonstrated a relationship between
LOC and student grades although not strong.
Summary of Results
The results showed that no statistically significant relationship existed between
course website usage frequency and student grades, thus resulted approval of Ho1. There is
no statistically significant relationship between frequency of course website usage, student
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grades, and type of LOC. In addition, when examining the data to answer RQ2, the results
showed that the internal LOC group used the website technology more frequently than the
external LOC group, which proved Ha2. There is a statistically significant difference in the
frequency of course website usage between students with internal LOC and external LOC.
These results confirmed the findings from a research conducted by Bozorgi (2009).
In Bozorgi (2009) the focus of the study was to evaluate student LOC using Rotter’s (1966)
survey and utilize student LOC information to demonstrate whether or not LOC impacted
frequency of course website usage and to analyze to what extent student grades were
impacted by the frequency of technology use (Bozorgi, 2009). Bozorgi also confirmed that
the student grades were impacted by the student personality factor LOC in such a way that
students with internal LOC derived higher grades than the students with external LOC.
Conclusion
In summary, these findings demonstrated that no statistically significant relationship
existed between course website usage frequency and student grades as mitigated by LOC.
However, the study did prove that the frequency of course website usage was correlated to
LOC and that the students who had internal LOC used the website significantly more often
than the students with external LOC. These finding proved in RQ2 that the internal LOC
group used the website technology more frequently than the external LOC group, which
confirmed Ha2.
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CHAPTER 5. DISCUSSION, IMPLICATIONS, RECOMMENDATIONS
This chapter synthesizes the results of this study, discusses the research
implications, demonstrates the significance of the study’s outcomes to the scholarly body of
knowledge, and makes recommendations for future research. The chapter offers a
restatement of the problem, the study paradigm, theoretical framework prompting the
research, and its two overarching research questions with their corresponding null and
alternative hypotheses.
RQ1. Is there a significant relationship between frequency of course website usage, type of
LOC, and student grades?
Ho1 [Null hypothesis for RQ1]. There is no significant relationship between frequency of
course website usage, student grades, and type of LOC (as the predictor variable).
Ha1 [Alternative hypothesis for RQ1]. There is a statistically significant relationship
between frequency of course website usage, student grades, and type of LOC (as the
predictor variable).
RQ2. Is there a difference in the frequency of course website use between students with
internal LOC and those with external LOC?
Ho2 [Null hypothesis for RQ2]. There is no significant difference in the frequency of course
website usage between students with internal LOC and external LOC.
Ha2 [Alternative hypothesis for RQ2]. There is statistically significant difference in the
frequency of course website usage between students with internal LOC and external
LOC.
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Summary of the Results
This study found no evidence to indicate a statistically significant relationship
between student grades and student frequency of course website usage. The studies
conducted by Barzegar (2011) and Bozorgi (2009) yielded different findings than this
study. The current study rejected the null hypothesis for RQ1.
This study’s results indicated that LOC had a direct relationship with the student
frequency of website usage, which aligned with Ha2. The students who were categorized as
internal LOC, using Rotter’s (1966) survey, used the course website technology
significantly more than the students with external LOC. Therefore, the results for RQ2
accepted the alternative hypothesis. Additional analysis demonstrated that although no
relationship was found between student frequency of course website usage and student
grades, taking into consideration the mitigating variable Locus of Control, the RQ1
hypothesis were rejected. New findings suggested a direct relationship between Locus of
Control and Student grades.
Statement of the Problem
The problem that prompted this quantitative study was the limited number of
studies that assessed the impact of student frequency of technology usage in the form of a
course website, in a face-to-face classroom, on student academic outcomes. Most of these
studies did not consider student personality and self-efficacy factors such as locus of
control (LOC); therefore, the results of these studies are incomplete (Tatar et al., 2008;
Whipp & Lorentz, 2009).
The results of the few existing studies that did look into all, or at least two out of
three of these variables (grades, LOC, and course website usage), also appeared to be
74
biased. For example, Bozorgi (2009) selected her sample from students from various
college level classifications. Bozorgi (2009) studied 198 freshman, sophomore, junior and
senior college students majoring in English. Although her findings confirmed this specific
study’s null hypothesis for RQ1, the LOC factor in students is known to vary over time and
with the age or personality of the student, and social factors such as college level
classification can influence and change LOC orientation (Ruhanshi, 2014; Schultz &
Schultz, 2017). This study’s RQ2, however, did not confirm the findings of Barzegar
(2011) and Bozorgi (2009) that students with internal LOC earned statistically higher
grades than students with external LOC.
Significance of the Study
This study’s significance resides in its contribution to the field of education
technology in that it addressed the gap in the literature about the relationship between
technology usage and student achievement by looking into the students personality and
psychological variable locus of control (LOC) (Barzegar, 2011; Bozorgi, 2009; Chitty et
al., 2009). Barzegar (2011) conducted a research on Iranian students in a university in Iran.
His study showed that students with internal LOC earned higher final grades.
Barzegar (2011) did not look at the frequency of course website usage. Chitty et al.
(2009) on the other hand conducted a study on the impact of student personality factor,
Locus of Control, on adaptation and usage of course website technology by these university
students. Chitty et al. (2009) demonstrated that LOC indeed influenced the adaptation of
technology and increased of technology usage by the students. However, that study did not
look at LOC as a mitigating factor to student final grades.
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The Chitty et al. (2009) only demonstrated a direct relationship between the
variables of LOC and course website usage and that the internal LOC students used the
course website more often when compared to the external LOC students. Bozorgi (2009)
was the only one among these researchers that analyzed all these 3 variables of Locus of
Control, frequency of course website usage and student final grade. He was the only one
among these researchers to consider variable LOC and observe it as a mitigating factor.
Bozorgi (2009) showed that the Iranian university students with internal LOC had
higher rate of course website usage and as a result higher grades. This study was based on
all these 3 studies mentioned above and used the same LOC instrument. This study
disapproved Bozorgi (2009) that stated LOC was a mitigating factor in the relationship
between course website usage and student grade. However, this study was in accordance
was Chitty et al. (2009) as it proved that student LOC significantly impacts course website
usage frequency.
This current study had a discovery that was not a direct part of the study but
happened to be aligned with Barzegar (2011) and similarly suggested that LOC and student
grade had direct relationship, confirming that students with internal LOC used the course
website more often than the external LOC students. This study also looked at one additional
study conducted by Chanchary et al. (2008), that study tested the relationship between
student frequency of technology usage Table below summarizes the comparison of their
findings, the relationship between the investigated variables, and whether or their
hypothesis were accepted or rejected.
76
Table 12. Result Comparison to Other Studies
Study
LOC &
Grade
LOC &
Frequency
Frequency
& Grade
Frequency & Grade
as
Mitigated by LOC
Barzegar
(2011)
Bozorgi
(2009)
Chitty et al.
(2009)
Chanchary
et al. (2008)
Tested &
Accepted
Not
Tested
Not
Tested
Not
Tested
Not
Tested
Not
Tested
Tested &
Accepted
Not
Tested
Tested &
Accepted
Tested &
Accepted
Not
Tested
Not
Tested
Not
Tested
Not
Tested
Tested &
Accepted
Not
Tested
Safavi
(2016)
New
Finding
Tested &
Accepted
Tested &
Rejected
Tested &
Rejected
This research study’s results may help future students improve their learning
outcomes. The students will be able to understand their personality factors that according to
this study result in higher grades Studies have shown that LOC can be influenced by certain
factors such as practice, socioeconomic factors, and age (Ruhanshi, 2014; Schultz &
Schultz, 2017). Further research will be required to identify solutions that could aid the
students with external LOC in their adaptation and use of technology.
A better understanding of the relationship between the personality factor LOC and
student grades will help refine and actualize the general conclusion in the literature that
how and why Locus of Control improves college and graduate-level student’s outcomes in
practice (Alias et al., 2012; Bailenson et al., 2008; Bozorgi, 2009; Hadsell, 2010).
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This study contributed to research into the micro- and meso-dimensions of
technology usage and how it impacted student academic achievement by taking into
consideration the LOC personality factors of students.
Research focusing on the issue of the link between LOC and learning outcomes in a
technology-enriched classroom is insufficient, especially at the graduate school level (Alias
et al., 2012; Bozorgi, 2009). The researcher had access to a large sample and was able to
access the necessary data. Therefore, analysis of the results of this study further clarified
the answers to the research questions and thus rejected the relationship between technology
usage and student grades as mitigated by the personality factor LOC. The study found a
relationship between LOC and technology usage and therefore accepted Ha2. The research
discovered a new relationship that was not specified in this study’s hypothesis, which
confirmed Barzegar (2011) and confirmed a relationship between LOC and student grades.
Further study is required to research if the study’s data, understanding student personality
factors such as LOC, can contribute to student retention and improved curriculum building
when employing certain technology versus others and ultimately student success.
Literature Review Overview
This study was guided by underlying assumptions based on existing studies of the
impact of LOC on student use of technology and whether or not that impact significantly
contributed to improved grades. According to Singer (2008), Wang et al. (2009), and
Mouza (2008), researchers increasingly suggested that technology improves student
motivation, engagement, and enjoyment of lessons. Bozorgi’s (2009) study examined the
issue of web-based learning and whether or not it improved learning in students with
internal LOC. The literature review established that in many cases technology improves
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learning, in settings ranging from classroom use of handheld devices to web-based
environments (Bozorgi, 2009, Mouza, 2008, Singer, 2008).
However, the literature on technology integration is also moving from an
instrumental or deterministic orientation towards more of a critical approach to integration
whereby the characteristics of the teacher and students mediate the impact of technology on
learning. As a result of difficulty with student learning technology and graduate courses,
research on the impact of technology and learning needs further study. Accordingly,
additional literature has emerged on impacts of technology on learning, and researchers
seek to discover correlations between various student characteristics ranging from self-
efficacy to self-regulation, generally finding that students with strong self-regulation skills
benefit the most from technology integration (Alias, 2012; Bozorgi,
2009). The impact of internal LOC on learning has been established in the literature;
however, the degree to which internal LOC helps students benefit from technology
assimilation in their classrooms requires additional research.
Internal LOC has also been found to improve student outcomes through the
undergraduate years (Barzegar, 2011; Bozorgi, 2009; Chitty et al., 2009) and becomes a
predictor of future success in graduate school (Nordstrom & Sergist, 2009). Some case
studies provided evidence that technology integration and the use of web-based instruction
are particularly helpful for graduate students with internal LOC. In these studies, internal
LOC emerges as one of the key elements of student autonomy and college success. These
studies have shown that students with internal LOC are better able to negotiate and manage
the complexities of college or graduate school life, become autonomous learners, ask more
questions in class, and achieve better outcomes.
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Students as Digital Natives
The results of this study sparked the researcher to explore other, more recent research on
university students and educational technology. Research on students as digital natives
were directly related to this study’s results. Often it is assumed by the instructors that
students will all use education technology to the fullest. However, education technology is
not Facebook or Instagram so students are not more into using education technology than
reading a textbook. In a study conducted by Jones et al. (2010), using a survey completed
by first year undergraduate students found that first year student minorities did not use the
technologies available to them, or used the technology in a manner that was not in
accordance with the expectation of the course and as utilized by the digital natives and Net
generation (Jones, Ramanau, Cross, & Healing, 2010). A study conducted by Margaryan,
Littlejohn, and Vojt, (2011) on the frequency of technology use of university students
suggested that students tend to use only a limited range of standard and established
technologies offered by their schools. Their study found that, the immigrant students did
not use the classroom and course technologies as much as the native students unless they
were enrolled in a Technology based discipline such as Engineering or Computer Science.
(Margaryan, et al., 2011). Therefore, “we must go beyond simple dichotomies evident in
the digital native debate to develop a more sophisticated understanding of our students’
experiences of technology” (Bennett & Maton, 2010, p. 10).
According to Bennett and Maton (2010) the new generation, also known as the
digital natives, are deeply involved in utilizing digital technologies in their everyday life in
which their major part is their education, however, the existing education system has not
been able to fully attend to their needs (Bennett & Maton, 2010). The findings from this
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research, it’s new discoveries, as well as the introduced concepts of these later researches
on native students and minorities technology usage habits prompt new areas of study that
are described in the Recommendations for Future Studies section.
Methodology
The researcher employed logistic regression to examine whether a statistically
significant relationship existed between the predictor variables (type of LOC:
internal/external and frequency of website use) and student grades. Therefore, this study
involved the use of two predictor variables, frequency of use of website and LOC, and one
criterion variable, student grades. The variable student LOC was categorized into two
groups: internal or external LOC. Grades appeared in the form of letter grades and
frequency of use was divided into three categories of high, low, and medium use
(Chanchary et al., 2008).
Second, to address RQ2, a crosstab analysis allowed the researcher to examine
whether a statistically significant relationship existed between students with internal LOC
and those with external LOC and frequency of course website use. The value of Pearson
chi-square helped the researcher determine whether or not the null hypothesis must be
rejected. The results confirmed no significant relationship existed between internal
LOC/external LOC and frequency of website usage. The null hypothesis would only be
rejected if there were a probability of .05 or lower that the findings were due to chance.
These hypotheses tests set the limit to which the findings could have occurred due
to chance. Which means, the test determined if the relationship existed and that it would
most likely occur again. If it had occurred by chance, then the likelihood that the strength
of relationship would appear in the same way would be very low. The chi-square tests
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whether the observed frequencies are significantly different from the expected null
hypothesis frequencies.The necessary response rate was calculated using G*Power
calculator. G*Power calculated a sample size of 107 responses necessary for validity of the
study. Total responses received were 137 of which only 112 of them were valid. The 112
valid surveys slightly exceeded the number of surveys necessary.
Discussion of the Results
In summary, the findings found no statistically significant relationship between
course website usage frequency and student grades. However, the study did prove that the
frequency of course website usage was related to LOC and that the students with internal
LOC students significantly used the website more often than the students with external
LOC.
These findings proved in RQ2 that the internal LOC group used the website
technology more frequently than the external LOC group, which confirmed Ha2. There is a
statistically significant difference in the frequency of course website usage between
students with internal LOC and external LOC.
These results confirmed the findings from research conducted by Bozorgi (2009),
which found that student grades were impacted by the student personality factor LOC and
that students with internal LOC derived higher grades than students with external LOC;
however, this study’s findings found no relationship between the variables student LOC
and grades. This study’s results did not align with Barzegar’s (2011) findings of a
significant relationship between student LOC and student grades. In his study, however,
Barzegar (2011) did not consider the variable frequency of technology usage.
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Assumptions and Limitations
Every effort was made to mitigate this study’s limitations by employing a research
design supported with extant literature and aligned with the study’s purpose.
Nevertheless, several limitations were inherent in this study’s research design.
Assumptions
Methodological assumptions. Quantitative methodology relies on objective data
void of subjective considerations. The choice of a quantitative methodology provided the
opportunity to determine variable-based correlations between frequency of use of
technology and student achievement as mitigated by LOC.
Other assumptions. This researcher assumed that the students surveyed would
give an honest appraisal of their expectations and outcomes derived from the website in the
course. It was also assumed that students with internal LOC would be more responsive to
the questionnaires than those with external LOC.
Limitations
The use of SurveyMonkey was essential to reaching this study’s sample population,
and the site is a well-known survey system with documented support features. However, it
is important to recognize the disadvantages that are common to online survey applications.
The use of online survey systems has been subject to limitations related to accuracy of self-
reported data, as some participants are likely to respond inaccurately to the questionnaire to
save time. Other possible influences on the participants, such as speed of the Internet, can
also impact the respondent’s attitude (Maronick, 2009).
83
Other limitations included: (a) The study was limited to graduate students taking a
specific course; (b) Gender, culture, age, and race of participants were taken into account in
this research; (c) The number of internal and external LOC students were unequal; (d)
Differences in the information taught in the classroom may have existed; (e)
Inconsistency may have resulted from the methods of the different instructors who taught
the class; (f) Differences in website content may have existed, although this is less likely
because content was usually material downloaded from the publisher’s site; (g) If one
instructor taught the class and graded the work of all students, the grading criteria,
technique, and offer of extra credit could have constituted a limitation.
Recommendations for Further Study
This study rounded out the limited extant literature investigating a relationship
between student technology use in form of a course website and student grades with the
mitigating factor of locus of control (LOC). Additional literature has developed in the
research on impacts of technology on learning, which sought to discover correlations
between various student characteristics ranging from self-efficacy to self-regulation and
generally finding that students with strong self-regulation skills benefitted the most from
technology integration. The role of internal LOC on learning has been established in the
literature, but the degree to which internal LOC helps students benefit from technology
integration elicits further research.
This research may help future students improve adaptation to courseroom
technology and its use. The students will understand their personality variable which deals
with adaptation and usage of technology. (a) Further research will be required to identify
solutions that could aid the students with external LOC in their adaptation and use of
84
technology. (b) Further research is required to investigate factors that can change locus of
control to encourage students to use course environment technology more often. (c) This
research demonstrated how LOC can impact technology usage; however, further research is
required to determine whether or not technology usage has a direct relationship with
student outcomes. (d) Future research could employ courses taught by multiple instructors
in case a particular instructor is generous with grading or offers extra credit. (e) Future
research could look at how the student final grade was calculated and whether or not
additional course work was offered, for extra credit, to the students that did not earn a
higher grade. An investigation on the teaching instructor grading style and teaching
methods is recommended prior to the study. (f) The future studies could conduct similar
research using classes from one instructor that passes the criterial for using a set grading
rubric for the assessments. None of these classes had a set rubric for the assessments and
this factor could impact the study outcomes. (g) Future studies can also investigate the
impact of various types of activities that the students participate in most while using the
course website, in their grades. And whether or not student frequency of website use or
LOC is a factor when student selects the activity they prefer to participate in more in the
course website. (h) Further research should examine if LOC is a factor on ‘minority
student’s technology use habits vs the native students. (i) Further research can also
investigate how each groups of native students, and ‘minority students’ perceive
educational technology.
After analysis of the results from tables 1 through 3 it can be concluded that
although RQ1 was rejected by this research, this research accepted Ha2, and resulted in a
new finding which was a relationship between LOC and student grades. Therefore,
85
although there is no significant relationship between student grade and frequency of course
website technology usage, there is a significant relationship between Locus of Control and
student grade. If the frequency of website usage was not a factor in this research, then there
may be other factors that cause this relationship.
Conclusion
This chapter synthesizes the results of this study, discusses the research
implications, demonstrates the significance of the study outcomes to the scholarly body of
knowledge, and makes recommendations for future research. The chapter offers a
restatement of the problem, the study paradigm, theoretical framework that prompted the
research and its two overarching research questions.
This study found no evidence to suggest there a statistically significant relationship
between student personality factor locus of control, although studies conducted by Barzegar
(2011) and Bozorgi (2009) yielded different results. Therefore, this study’s null hypothesis
for RQ1 was not rejected.
This study confirmed that LOC, however, has a direct relationship with the student
frequency of website usage. The students who were categorized as having internal LOC
used the course website technology significantly more than the students with external LOC.
Therefore, the results for RQ2 were aligned with the alternative hypothesis.
86
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APPENDIX A. G*POWER RESULTS
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