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THE RELATIONSHIP AMONG SELF-REGULATION, INTERNET USE, AND
ACADEMIC ACHIEVEMENT IN A COMPUTER LITERACY COURSE
DISSERTATION
Presented to
The Faculty of the Graduate School
Southern University and A&M College
In Partial Fulfillment
Of the Requirements for the Degree of
Doctor of Philosophy
In
Science/Mathematics Education
By
SungHee YangKim
July 2009
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i
Dedication
To my family, Thomas and MooKean Yang
ii
Acknowledgement
Acknowledgements are given to the Chair, instructors, and the staffs of the
Department of Computer Science of Southern University and A & M College for
allowing their department to participate in this study. Also, a special appreciation is
extended to Ms. Marilyn Gray for her assistance in the data collection process, the
participants who volunteered, and permission-granting authorities at this higher-
education institution for their support.
A humble appreciation is extended to those individuals who are not mentioned
specially for their moral, emotional, and financial supports, and their encouragement
during the research and subsequent writing of this dissertation.
Finally, a special thanks to my doctoral committee members, Dr. Juanita Bates,
Dr. Lynn Loftin, Dr. Joseph Meyinsse, Dr. Ebrahim Khosravi, and Dr. Nigel Gwee. Also
the other faculty members and staff, Dr. Moustapha Diack, Dr. Luria Stubblefield, Dr.
Exyie C. Ryder, and Ms. Zenobia Washington, of the Science/Mathematics Education
Department (SMED) of Southern University for their excellent instruction, unbiased
mentoring, and continuous feedback.
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TABLE OF CONTENTS
Page
List of Tables……………………………………………………………………………...v
List of Figures...…………………………………………………………………………..vi
Abstract ……………………………………………………………………………...…..vii
CHAPTER I. INTRODUCTION…..………….………………………………....1
Background of the Study………………………………………….1
Statement of the Problem………………………………………….2
Significance of the Study………………………………………….3
Research Questions………………………………………………..5
Assumptions…………...…………………………………………..6
Limitation of the Study...………………………………………….6
Definitions of Terms…………………………..…………………..6
CHAPTER II. REVIEW OF RELATED LITERATURE………………………...8
Theoretical Background: Social Cognitive Theory….……………8
Self-regulation………………...…………………….…....10
Self-regulated Learning Strategies….……………….…...16
Self-efficacy …………………..…………………….…...19
Test Anxiety…………………...…………………….…...20
The Studies related to Self-regulated Learning .……………...…22
Constructs of Self-regulation....…………………….…....22
Self-regulated Learning Strategies and Motivational
iv
Factors……………………………………………………25
Internet Use……………………...….……………….…...............32
Computer Literacy.……………...….……………….…...............38
Review of Studies Connecting Self-regulated Learning,
Internet Use, and Academic Achievement…….………………....43
Self-regulated Learning Strategies for Academic
Achievement......................................................................43
Self-regulation and Internet Use………………………....49
Directions of Study…………..…….……………….……………56
CHAPTER III. METHODOLOGY………………………………………………58
Participants……………………………………………….………58
Instruments...………………………..…………................………59
Demographic Survey………………...…………...……...59
Internet Use Questionnaire..….……...…………...……...59
Internet Use software………………...…………...……...59
Self-regulated learning……….………………….………60
Academic Achievement…..……………………..………61
Procedure………………………………………………………...62
Data Analysis.………………………………………… ………...63
CHAPTER IV. RESULTS..………………………………………………………65
Description of Demographic Information ……………….………66
Description of Self-reported Computer and Internet
Use Survey.………………………………………………………68
Statistical Analysis of the MSLQ……………………………......70
Construct Validity of the Scores on the MSLQ Scales…. 70
v
Statistical Analysis of Internet Use……….…..………….………78
Statistics of Academic Achievement..………….………..………81
Correlations between Self-regulation, Internet Use, and
Academic Achievement……………….…………...…………….83
Correlations between Factors, Internet Use, and
Academic Achievement………………………………….….86
Summary of Results..…………..……………..………………….86
CHAPTER V. CONCLUSION.………………………………………………….89
Findings………………………………....……………….………90
Implications……………………………………………………...94
Future Studies……..…….……..……………..………………….95
Summary of Conclusion..…………..……………...…………….97
REFERENCES………………………….……………………………………………….98
APPENDICES………………………………………………………………………….110
Appendix A. Demographic Information………………………111
Appendix B. Internet Use Questionnaire……………………..112
Appendix C. Motivated Strategies for Learning
Questionnaire………..……………………114
Self-efficacy…………………………………………….114
Test Anxiety…………………………………………….114
Metacognitive self-regulation……….………………….115
Appendix D. Consent Form…………………………………..116
Appendix E. VITA……….…………………………………..118
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LIST OF TABLES
Tables Page
1. Description of Demographic Information……………………………………..……....67
2. Computer and Internet Use Self-report Survey (Hours per Week).………………....69
3. Primary Use of the Web………………………………………………………………69
4. Time Periods of Internet Access………………………………………………………70
5. Ratings of the MSLQ………………………………………………..………………...71
6. Sorted Factor Loadings of the MSLQ (Item 1-20)…..………………………………..74
7. Factor Analysis of Participants‟ Rating Items from the MSLQ (1-20) – Rotation …...76
8. Bivariate Correlations among Factors and the Selected Scales of the MSLQ ……......77
9. Statistics for the Participants‟ Ratings on the MSLQ Selected Scales …….…………77
10. Results of the Three Selected Scales of the MSLQ………………….………………79
11. Data for Each Participant Internet Usage for Three Class Days ……………….……80
12. Data from Three Class Days of Internet Use Averaged for One Class Period….…....82
13. Data of Course Grade, Average Score, Content and Skill Achievements…………...83
14. Bivariate Correlations between the MSLQ, Internet Use and Grades……………….85
15. Bivariate Correlations between Factors, Internet Use, and Academic
Achievement…………………………………………………..………………87
vii
LIST OF FIGURES
Figures Page
1. The relationships between the three major classes of determinants in triadic
reciprocal causation………………………..........……...………………………………9
2. Triadic forms of self-regulation………………………………………………………13
3. Cyclical phases of self-regulation……………………………………………………..15
viii
ABSTRACT
This research was a correlational study of the relationship among self-regulation,
students‟ nonacademic internet browsing, and academic achievement in an undergraduate
computer literacy class. Nonacademic internet browsing during class can be a distraction
from student academic studies. There has been little research on the role of
self-regulation on nonacademic internet browsing in influencing academic achievement.
Undergraduate computer literacy classes were used as samples (n= 39) for measuring
these variables. Data were collected during three class periods in two sections of the
computer literacy course taught by one instructor. The data consisted of a demographic
survey, selected and modified items from the GVU 10th
WWW User Survey
Questionnaire, selected items of the Motivated Strategies for Learning Questionnaire, and
measures of internet use. There were low correlations between self-regulation and
academic grades (r= .18, p > .05) and self-regulation and internet use (r= -.14, p > .05).
None of the correlations were statistically significant. Also, there was no statistically
significant correlation between internet use and academic achievement (r= -.23, p >.05).
Self-regulation was highly correlated to self-efficacy (r= .53, p < .05). Total internet
access was highly correlated to nonacademic related internet browsing (r= .96, p < .01).
Although not statistically significant, the consistent negative correlations between
nonacademic internet use with both self-regulation and achievement indicate that the
internet may present an attractive distraction to achievement which may be due to lack of
self-regulation. The implication of embedded instruction of self-regulation in the
computer literacy course was discussed to enhance self-regulated internet use. Further
ix
study of interaction of self-regulated internet use and academic achievement is
recommended.
1
CHAPTER I
INTRODUCTION
Background of the Study
Undergraduate students often face difficulties in their studies. A different
environment and curriculum in the university may cause them to succumb to stress if they
have not acquired certain strategies to overcome obstacles that they encounter. Many
universities offer various kinds of support to help students achieve their academic goals.
Two examples of such support are the availability of computer rooms and access to the
internet (Malaney, 2004). Universities often require a computer literacy course to ensure
that all students develop skills and knowledge in computer and internet use. This
technology is helpful and necessary (Niemczyk & Savenye, 2001); however, internet
access and/or computer use may not assure academic success.
In a computer literacy course, students learn how to use the internet for research
and communication and how to use various computer software applications. In a recent
survey, students noted that they took the computer literacy course because the content
would be helpful and attractive (Niemczyk & Savenye, 2001). Although learning the
uses of a computer can be valuable, many students are undisciplined and browse the
internet from site to site accomplishing very little (Ebersole, 2000).
A computer literacy course teaches the use of computer tools (Ocak & Akdemir,
2008), but not how to regulate and motivate learning for academic success. Computer
literacy requires students to be motivated with positive attitudes in order to be successful
2
in using the computer (Saparniene, Merkys, & Saparnis, 2005). In order to achieve
academic success, students must be able to self-regulate their learning by using
motivation and learning strategies (Niemczyk & Savenye, 2001).
Additionally, understanding the interaction between the students‟ learning
strategies, motivation, and technology can provide insight into helping students improve
academic achievement (Hargis, 2000). The student‟s own motivation and learning
strategies allow time management and efficient use of resources to achieve academic
success (Terry, 2002). Effective uses of the strategies as well as the optimal use of
strategies for learning are important in learning course materials and achieving goals
(Zimmerman, 2000). Also, Niemczyk and Savenye (2001) found that self-regulated
learning strategies are related to the course grade in a computer literacy course.
Self-regulation may be especially important in computer literacy because the
temptation for browsing the many thousands of web sites can overwhelm an
undisciplined student.
Statement of the Problem
The purpose of the study is to investigate undergraduate university students‟ self-
regulated learning, internet use, and academic achievement while enrolled in a computer
literacy course. Computer literacy is a required course for students at universities to
develop and support academic achievement. Students learn to use the internet for
research and study and to enhance academic performance and learning outcomes.
Students must be able to self-regulate their internet use to maximize academic
achievement.
3
Significance of the Study
This research is to investigate the relationship between self-regulation and internet
use, and the relationship between internet use and the course achievements. Self-
regulated learning is defined as “the strategies that students use to regulate their cognition
as well as the use of resource management strategies that students use to control their
learning” (Pintrich, 1999, p. 459). Internet use measured for this study refers to as
nonacademic internet browsing. Academic achievement includes computer skills, use of
applications, and the terminology in a computer literacy course.
Self-regulated learning is interpreted using social cognitive theory which focuses
on the personal, behavioral, environmental influences (Zimmerman, 1989, 2001). Self-
regulated learning in the social cognitive view was assumed to be the reciprocal
influences among person, behavior, and environment (Bandura, 1997). Subprocesses of
self-regulation are self-observation, self-judgment, and self-reaction (Zimmerman, 2001).
Internet use provides many opportunities for education. Application of self-regulation to
internet usage can enhance the benefits offered by the internet.
Self-regulated learning is a theory which has been applied and investigated in
many areas especially in academic learning. Research in self-regulated academic
learning areas include student grades, university classes, computer use, internet use, web-
based courses, mathematics, language of literature, science, nutrition, accounting, and
agriculture (Zimmerman, 1989; Zimmerman & Schunk, 2001; Zimmerman, 2001).
Considerable research has demonstrated a positive relationship between self-regulation
and academic achievement. The measurements for self-regulated academic learning are
also developed in many ways: self-reporting questionnaires (Pintrich, Smith, Garcia, &
4
McKeachie, 1991; Wolters, Pintrich, & Karabenick, 2003); structured interviews
(Zimmerman & Martinez-Pons, 1986, 1988); and teachers‟ ratings (Zimmerman &
Martinez-Pons, 1988; Winne & Perry, 2000).
Studies about internet use and academic achievement were examined using the
motives of internet use (Choi, Watt, Dekkers, & Park, 2004), attitude of the students‟
internet use (Ebersole, 2000), online time management (Terry, 2002), supporting tools for
self-regulatory skills in Web-based learning environment (Niemi, Nevgi, & Virtanen,
2003), the advantages for self-regulated learners on the internet (Hargis, 2000), and
Internet uses and technology (Young, 2001; Reisberg, 2000). The results of these studies
showed that improved computer skills, better time management, and more positive
attitudes of internet use improved academic achievement.
While internet use among students positively influences academic learning
(Zenon, 2006), there is research indicating a negative influence of internet use (LaRose,
Lin, & Eastin, 2003). Due to the unregulated world wide internet system, users can
access any site if there is no control system. Internet users have to self-regulate internet
use by their own volitional strategies. Students who have deficient self-regulation on the
internet misuse or abuse the internet, and their learning is interrupted. Internet addiction
is interpreted within the social cognitive view as a deficiency of self-regulation (LaRose,
Mastro, & Eastin, 2001).
The students learn to use computers and the internet during computer literacy
courses for the purpose of learning skills and for improving academic achievement. If
students misuse their tools or are unable to efficiently utilize the tools, the aided
technology is no longer helpful. Self-regulation helps student utilize the internet and
5
computer to achieve academic goals. Self-regulation plays the key role in the learning
process and in regulating internet use. Students achieve their own goal if they control
and manage their tools with regulated learning. This research is to examine the relations
among self-regulated learning, nonacademic internet use, and academic achievement in
the computer literacy course.
Research Questions
This study focuses on three questions.
Question 1
Is self-regulated learning linearly correlated with student performance outcomes
in a computer literacy class?
Question 2
Do self-regulated learners abstain from nonacademic browsing the internet during
computer literacy classes?
Question 3
Is internet browsing during computer literacy classes correlated with academic
success?
The participants in the present study were students in a college computer literacy
course. Metacognitive self-regulation, self-efficacy, and test anxiety were measured by
utilizing two sections the Motivated Strategies and Learning Questionnaire (MSLQ)
developed by Pintrich, Smith, Garcia, and McKeachie (1991). The variables were course
grade, number of nonacademic websites visited during a class periods, and scores on the
three sections of the MSLQ, self-efficacy, test anxiety, and metacognitive self-regulated
6
learning strategies. These variables were analyzed to ascertain relationships among the
variables.
Assumptions
There were four assumptions of this research. Students answered honestly to the
items of self-regulation, and internet use instruments and the demographic survey. The
students of the two classes had equal learning skill and ability. The students had equal
skills of computer and internet use. The students who participated in this research
represented the whole university except students, who completed a course offered by the
Department of Computer Science other than CMPS 105, or students who completed a
course offered by the college of the students major, or students who passed a computer
literacy test.
Limitation of the study
The study involved students enrolled in a one semester computer literacy course
in a public university located in the southern United States. While students were from the
various majors, their performances were measured in the computer literacy course only.
The performance abilities of using the computer and internet were individually different.
This study was limited to the measurement of the internet use which was different from
the computer use.
Definitions of Terms
This research is related to academic achievement, internet browsing, and self-
regulation. The definitions of the terms are stated for the study.
Academic Achievement: Grade in the computer literacy course.
7
Internet Browsing: The action of using the internet for nonacademic purposes measured
during the computer literacy class.
Motivation: Activation to action. Level of motivation is reflected in choice of courses
of action and in the intensity and persistence of effort (Bandura, 1994).
Self-efficacy: People‟s beliefs about their capabilities to produce effects as described by
Bandura (1977a).
Self-regulation: Self-generated thoughts, feelings, and actions that are planned and
cyclically adapted to the attainment of personal goals (Zimmerman, 2000).
Test Anxiety: Emotionality component which refers to “affective and psychological
arousal aspects of anxiety” (Pintrich, Smith et al., 1991).
8
CHAPTER II
REVIEW OF RELATED LITERATURE
Social cognitive theory introduced the concept of self-regulated learning. Self-
regulation was recognized and described in the mid-1980s in the education literature to
indicate the way students became masters of their own learning processes (Pintrich, 1995;
Zimmerman, 1989, 1990, 2000; Zimmerman & Martinez-Pons, 1986, 1988, 1990;
Zimmerman & Schunk, 2001). This chapter provides the rationale for using social
cognitive theory as the intellectual foundation for the current research. The sections
below discuss key issues regarding learning practices through a literature review of the
theories, concepts, and research findings. As such, it helps establish the theoretical
framework used for exploring the relationship among three variables -- self-regulation,
internet use, and academic achievement. It is organized into four sections: (a) the
theoretical background -- social cognitive theory; (b) self-regulated learning factors; and
(c) internet use and computer literacy; and (d) the literature connecting self-regulated
learning, internet use, and academic achievement. Essentially, this chapter serves as a
review of the related literature as well as a guide for understanding the larger goals of this
research project.
Theoretical Background: Social Cognitive Theory
From its roots in behaviorism, social cognitive theory (SCT) offers the extended way for
studying and learning practices. SCT offers a complex, multi-faceted theoretical
framework which explains how people acquire and maintain certain behavioral patterns,
while also providing the basis for intervention strategies (Bandura, 1997). An important
9
breakthrough in the study of learning came in the 1960s and 1970s (Bandura, 1977b).
Bandura (1989b) pioneered the successor to social learning theory by introducing
cognitive elements. Bandura used a triad of determinants as shown in Figure 1. These
determinants of learning were person, environment, and behavior at the apices of a
triangle. Equally important, Bandura posited that learning depended on the interaction of
these determinants. As Pajares (2002) notes, “for example, how people interpret the
results of their own behavior informs and alters their environments and the personal
factors they possess which, in turn, inform and alter subsequent behavior.”
Figure. 1 The relationships between the three major classes of
determinants in triadic reciprocal causation. Source: Social
Foundations of Thought and Action (Bandura, 1986)
Because there was a constant interaction between those three determinants and
each one of them could affect the other, it was referred to as a triadic reciprocality.
Personal factors can influence and change the environment and behavior; the
environment can affect and change the person and the person‟s behavior; and the
behavior can influence and change personal factors and the environment. Here, it is
10
important to note the stark implications of Bandura‟s theory. The learner is viewed as
thoroughly integrated with the learning environment.
Bandura further draws out important human capabilities or processes that derive
from the relationship of the three determinants. He states:
In the social learning view, people are neither driven by inner forces nor buffeted
by environmental stimuli. Rather, psychological functioning is explained in terms
of a continuous reciprocal interaction of personal and environmental
determinants. Within this approach, symbolic, vicarious, and self-regulatory
processes assume a prominent role. (1977b, p. 11)
Symbolic capabilities permit people to “extract meaning from their environment,
construct guides for action, solve problems cognitively, support forethoughtful courses of
action, gain new knowledge by reflective thought, and communicate with others at any
distance in time and space” (Pajares, 2002). Symbolic activities permit people to model
their behavior. Vicarious capabilities permit people to learn without the difficulties of
trial and error. Self-regulatory capabilities, which will be discussed in greater detail
below, permit self-directed changes in behavior.
Self-regulation. Zimmerman (1989, 2000) expanded and developed Bandura‟s
social cognitive theory by applying it specifically to the field of education. As self-
regulated learning has been shown to be effective in the field of education (Boekaerts,
1999), Zimmerman‟s contribution to SCT is particularly relevant to this study since it
further developed the concept of self-regulation. For Zimmerman (1989, 1990) self-
regulation (as restated by Schunk 1994, p. 1) can be defined as “the process whereby
11
students activate and sustain cognitions, behaviors, and affects, which are oriented toward
the attainment of goals.”
In his later research, Zimmerman redefined and expanded self-regulation. As he
stated (2000, p. 14) self-regulation consists of “self-generated thoughts, feelings, and
actions that were planned and cyclically adapted to the attainment of personal goals.” He
also recognized that the quality and presence of actions and covert processes depended on
one‟s beliefs and motives.
In summary, Zimmerman offered guidance regarding the planned and cyclical
nature of goal attainment. However, his conceptualization gives little detail of the role of
the contextual features in the environment during the self-regulated learning processes to
attain goals.
Pintrich defined self-regulated learning as “the strategies that students use to
regulate their cognition as well as the use of resource management strategies that students
use to control their learning” (1999, p. 459). Pintrich also defined self-regulation as “an
active, constructive process whereby learners set goals for their learning and then attempt
to monitor, regulate, and control their cognition, motivation, and behavior, guided and
constrained by their goals and the contextual features in the environment” (2000, p. 453).
Pintrich (2000) describes the importance of the environmental influences on the study of
self-regulation through his definition.
Wolters, Pintrich, Karabenick (2003) reviewed three areas of self-regulation
strategies; cognition, motivation, and behavior and defined self-regulation based on three
assumptions. They proposed that the assumptions in any model of self-regulation
include: (a) learners are active and constructive participants in the learning process; (b)
12
learners can monitor, control, and regulate their environmental features and certain
aspects of their own cognition, motivation, and behavior; and (c) cognitive, motivational,
and behavioral self-regulatory activities are mediators among person, context, and
eventual achievement.
Zimmerman suggested three elements that must be present for a student to utilize
self-regulated learning: (a) students‟ self-regulated learning strategies; (b) self-efficacy
perceptions; and (c) goal commitment (1989). Self-regulated learning strategies, defined
by Zimmerman (1989, p. 329), refer to “actions and processes directed at acquiring
information or skill that involve agency, purpose, and instrumentality perceptions by
learners.” Self-efficacy refers to people‟s beliefs about “their capabilities to organize and
execute courses of action required to attain designated types of performance” (Bandura,
1986, p. 391). Students‟ goal commitments are required for them to be self-regulated and
those academic goals such as grades, social esteem can vary extensively in nature and in
time of attainment.
Zimmerman (1989) retains Bandura‟s (1977b) triadic form to illustrate the role of
self-regulated learning. That is, the key elements include the person, the environment,
and behavior. However, his model adds new levels of complexity. Zimmerman‟s
innovation entails the overlay of feedback loops and the consequent strategies resulting
from the learning process itself.
Through the distinction among personal, environmental, and behavioral
determinants of self-regulated learning, Zimmerman (1989) viewed self-regulated
learning assuming reciprocal causation among three influence processes. Zimmerman
argues that self-regulated learning occurs “to the degree that a student can use personal
13
(i.e., self) processes to strategically regulate behavior and the immediate learning
environment” (p. 330). Students‟ strategies emerged from the learning process as means
to control behavior and the environment, and covert self-regulatory processes.
Zimmerman (2000) adapted the triadic reciprocality indicating self-regulation
shown in Figure 2. The figure includes behavioral self-regulation, environmental self-
regulation, and covert self-regulation. The figure illustrates how self-regulation interacts
with the three social cognitive determinants of behavior, the person, and the environment.
A person can use self-regulation through behavior to adjust the environment, such as
organizing materials for study or turning off the TV. The interaction of the behavior with
the environment in turn supports the person.
Figure. 2 Triadic forms of self-regulation. Source: Attaining
self-regulation: A social cognitive perspective (B. J.
Zimmerman, 2000). In M. Boerkaets, P. R. Pintrich, & M.
Zeidner (Eds.), Handbook of Self-regulation. San Diego
Academic Press.
14
One example of self-regulation could be a student checking his/her homework,
which gives information on the correctness of his/her work and, through enactive
feedback, determines if he/she should repeat checking the homework. The student‟s
action to check homework was “initiated personally and implemented through use of
strategies, and enactively regulated through perceptions of efficacy” (Zimmerman, 1989,
p. 330). Zimmerman referenced Carver and Scheier‟s (1981) research to state that self-
efficacy worked as a controller through a feedback loop to regulate which strategies
should be used to acquire knowledge and skill. The student‟s behavior of checking
homework was due to the student‟s self-efficacy and a feeling that he/she controlled the
learning. Self-regulation was the action of the student to use strategies to take control of
his/her learning by controlling self, environment, and behavior.
Environmental self-regulation strategy is students‟ proactive use of an
environmental management strategy (Zimmerman, 1989). Zimmerman gave an example
of a student who arranged a study area for completing school work. The student
controlled the environment by eliminating noise, or arranging adequate lighting or a place
to write. Zimmerman found that once the student perceived the effectiveness of this
environmental setting in assisting learning, the student repeated this behavior and
modified the environment for successful learning. This was carried reciprocally through
an environmental feedback loop. Zimmerman insisted that self-regulated learning
strategies are only those that come under the influence of key personal processes such as
self-efficacy perceptions or goal setting.
Student uses covert self-regulation strategies by monitoring and adjusting the
cognitive and affective state (Zimmerman, 1989). Zimmerman suggested that when a
15
student uses an elaboration strategy such as integrating and connecting new information
with prior knowledge, the use of strategies is reciprocally regulated through a covert
feedback loop. For instance, when a student learns a new word, booklet, he/she recalls
the word book and easily associates the meaning of the new word booklet.
However processing those self-regulated learning strategies raises a key issue
(Zimmerman, 1998). This is how those processes are structurally interrelated and
cyclically sustained. In Figure 3, Zimmerman (2000) illustrates the interrelated structures
of self-regulation processes and sustained cyclical phases of those processes in social
cognitive perspectives. Self-regulation is described as cyclical because the feedback
from prior performance is used to make adjustments during current efforts. From a social
cognitive view, self-regulatory processes fall into three cyclical phases: forethought,
performance and volitional control, and self-reflection processes.
Figure. 3 Cyclical phases of self-regulation. Source: Self-
regulated learning: From teaching to self-reflective practice
(Schunk, D. H. & Zimmerman, B. J, 1998). New York:
Guilford. Copyright 1998 by Guildford Press.
16
Forethought is defined as “influential processes that precede efforts to act and set
the stage for it.” In the forethought phase, students set goals and plan strategies. Also
self-motivational beliefs such as self-efficacy and goal orientations adoption, increase the
value of self-regulatory skills. Forethought influences performance or volitional control.
Performance or volitional control involves processes that occur during physical efforts
and affect attention and action. Performance and volitional control phase includes self-
control and observation. The phase helps students to focus on the task and optimize their
effort. Also the phase helps students track specific aspects of their own performance, the
conditions that surround it, and the effects that it produces. The process of performance
and volitional control influences self-reflection processes which influence a student‟s
response to that experience. Self-reflection phase helps students to evaluate their
performance and attribute causal significance to the results. Also, the phase affects
forethought processes as reflecting the achievement outcomes which strengthen self-
efficacy. Consequently forethought influences performance or volitional control which
affects self-reflection and self-reflection influences forethought.
Self-regulated Learning Strategies. Zimmerman and Martinez-Pons (1986,
1988) constructed self-regulated processes through structured interview with high school
students. The self-regulated learning strategies were self-evaluating, organizing and
transforming, goal-setting and planning, seeking information, keeping records and
monitoring, environmental structuring, self-consequating, rehearsing and memorizing,
seeking social assistance, reviewing tests, reviewing textbooks, and preparing for class or
further testing.
17
Pintrich, Smith, Garcia, and McKeachie (1991) developed and constructed the
Motivated Strategies for Learning Questionnaire (MSLQ) designed to measure fifteen
constructs in the areas of cognition, behavior, and motivation. It categorizes the
strategies in the area of cognition and behavior such as rehearsal, elaboration,
organization, critical thinking, metacognitive self-regulation, effort regulation, peer
learning, and help seeking. The MSLQ is discussed in details later. The learning
strategies are all important for learning, but, metacognitive self-regulation is critical.
Metacognition refers to “the awareness, knowledge, and control of cognition” and the
processes of metacognitive self-regulatory activities are “planning, monitoring, and
regulating” (1991, p. 23). Students plan, monitor, and regulate their cognition. Planning
activities are “goal setting and task analysis,” and monitoring activities are “tracking
one‟s attention as one reads, and self-testing and questioning”. Regulating refers to “the
fine-tuning and continuous adjustment of one‟s cognitive activities”.
The Inventory of Metacognitive Self-Regulation (IMSR) was developed by
Howard, McGee, Shia, and Hong (2000b) from studies with other instruments in order to
more clearly define the factors involved with metacognitive self-regulation relevant to
problem solving. They reported in a literature review that the subcomponents of
metacognitive self-regulation appear to be knowledge of cognition, problem
representation, subtask monitoring, evaluation, and objectivity. The IMSR was
developed to measure the knowledge and regulation of cognition. The MSLQ was
developed to measure the awareness and control of cognition.
Pintrich (1995, p. 7) stated that “self-regulated learning is a way of approaching
academic tasks that students learn through experience and self-reflection.” Pintrich
18
assumed that “students can learn to be self-regulated” (p. 8). Pintrich also suggested that
“self-regulation is controllable, is appropriate to the college context, and is teachable” (p.
8). Furthermore, Pintrich believed that self-regulation fits well with the notion that
students contribute vigorously to their learning and are active recipients of information.
Zimmerman (1989, p. 329) describes self-regulated learners as the students who
“personally initiate and direct their own efforts to acquire knowledge and skills rather
than relying on teachers, parents, or other agents of instruction.” The self-regulated
learner is aware of his/her own efforts to accomplish the intended outcome. This
awareness makes an „effective learner‟ as one who recognizes the relationships between
the different learning strategies and the social and environmental outcomes (Zimmerman
& Martinez-Pons, 1988). The self-regulated learner can effectively regulate his/her
behaviors (Pintrich, 2000; Zimmerman, 1995) through environmental influences or by
covert self-regulation or internal processes such as intrinsic motivation. Effectiveness of
self-regulation is determined by the quality and quantities of student‟s own self-
regulatory strategies (Zimmerman, 2000).
Weinstein, Husman, and Dierking (2000) suggest a slightly different view of an
effective learner in a review of the literature on the self-regulation interventions focusing
on learning strategies. They suggest that the effective learner should know when the
learning strategies would be effective or not. Knowing the proper learning strategies and
how to use them is important, but, knowing the appropriate situation in which to apply
the strategies is more important.
McKeachie (2000) discusses five elements for becoming an effective learner in
each of the possible ways of learning such as reading, listening, observing, talking, and
19
writing. Those elements are: (a) motivation; (b) a knowledge base that provides a
conceptual structure for further learning; (c) skills for further learning; (d) strategies for
efficient learning; and (e) metacognitive strategies. McKeachie notes that metacognitive
strategies include planning, self-monitoring, and self-regulation. All are highly important
to the other four elements and important for becoming an effective life long learner.
Metacognitive strategies include having organized and conceptual knowledge, having
skills for learning, maintaining the knowledge, and planning, monitoring, and regulating
learning.
Self-efficacy. As a factor of self-regulation, the role of self-efficacy is critical.
Learners who have strong self-efficacy perform well, not only due to their actual learning
ability, but also due to the internal interactions of ability with self-efficacy (Bandura,
1977a). Bandura noted that humans could think and regulate actions (1977b, 1989a,
1997), and also that they could regulate their behavior by integrated feedback. Human
cognitive processes regulate behavior which in turn can affect academic achievement.
Students with high self-efficacy are motivated to use self-regulated learning
strategies to monitor, regulate, and control their own learning. In the social cognitive
view, self-efficacy refers to perceptions about one‟s capabilities to organize and
implement actions necessary to attain designated performance of specific tasks
(Zimmerman, 2000). Self-efficacy influences achievement behaviors such as choice of
task, persistence, and skill acquisition (Schunk, 2000, p. 109). Students‟ self-efficacy
beliefs influence their choices and manipulation of learning environments (Zimmerman).
Self-efficacy is a key motivational factor affecting self-regulated learners‟ use of
better strategies and monitoring for achieving academic learning. The beliefs of self-
20
efficacy determine how people feel, think, motivate themselves, and behave (Bandura
1994). Self-efficacy includes judgments of one‟s capability and confidence of one‟s
skills to complete the task (Pintrich, Smith, Garcia, & McKeachie, 1991). Goal progress
and attainment raise learners‟ self-efficacy and can lead to their adopting new more
difficult goals (Schunk, 2001). Schunk points out that high self-efficacy learners achieve
mastery goals and the mastery goals enhance learners‟ academic performance. Self-
efficacy correlates positively to productive use of self-regulatory strategies (Zimmerman
& Martinez-Pons, 1990). The learners who have high self-efficacy were more likely to
use self-regulated learning strategies than the learners who have low self-efficacy
(Bandura, 1994).
Test Anxiety. Like self-efficacy, test anxiety is one of the motivational factors in
self-regulated learning. Test anxiety is an emotionality component of motivation which
describes as “affective and psychological arousal aspects of anxiety” (Pintrich, Smith,
Garcia, & McKeachie, 1991, p. 15). In their literature review, Pintrich and DeGroot
(1990) viewed that test anxiety has many different effects on the students performance.
They proposed that test anxiety might be related to the three components of self-regulated
learning in different ways. Test anxiety interferes with students‟ use of self-regulated
learning strategies such that when test anxiety is high, students are not able to maintain
and achieve their intended goals (Garcia, 1995). However, Garcia identified test anxiety
as a motivational component.
Garcia (1995) investigated how student emotions affect learning and reported that
emotions were often used to support the use of self-regulated learning strategies. The
motivational strategies used were defensive-pessimism and self-handicapping to manage
21
affective outcomes. In other words, the students use their emotions as motivation to
accomplish goals through application of learning strategies. Garcia describes how
control of the emotions could be used to regulate cognitive, metacognitive, and resource
management strategies. Defensive pessimism and self-handicapping both are used to
manage perceived shortcomings. Defensive-pessimism is used to anticipate and to
manage the affective consequences of success and failure. Learners who have low
expectations often fail to pass a test. But defensive pessimism works causing the learner
to prepare in advance and makes failure less likely to happen. Anxiety works causing the
learner to prepare for the test by increasing efforts. The student‟s anxiety can often
provide the learner motivation to prepare and expend efforts for success. Defensive
pessimism does not always require perceptions of high self-efficacy and competence
(Garcia). Self-regulated learning may also arise from concerns about the lack of self-
efficacy and lack of competence in defensive pessimism. Self-handicapping is another
strategy of anxiety. The self-handicappers who show low effort and outcomes attribute
the failure to the lack of effort. Self-handicappers who expend high effort and achieve
high outcome attribute successful outcomes to greater effort. These self-handicappers are
those people afraid if they study and then fail, that the failure is due to their low ability.
Whereas if they do not study and fail, it is due to lack of studying. The learners attribute
success to effort alone and do not recognize their own competence or ability.
In the theoretical background reviewed, self-regulation is integral to students‟
learning and achievement. Self-regulation is based on the social cognitive theory and is
comprised of self-regulated learning strategies, self-efficacy and goal commitments. The
studies related to self-regulated learning are discussed in the next section.
22
The Studies related to Self-regulated Learning
Constructs of Self-regulation. As discussed before, self-regulated learning is
comprised of learning strategies and motivation (Pintrich, Smith, Garcia, & McKeachie,
1991). Students utilize learning strategies based on motivation such as self-efficacy and
test anxiety. Many measures of self-regulation have been developed (Zimmerman &
Martinez-Pons, 1986). The constructed self-regulated learning strategies and motivation
have been utilized in many fields. One particular area is education.
Zimmerman and Martinez-Pons (1986) developed a structured interview to assess
high school students‟ use of self-regulated learning strategies. The hypothesis was that
students selected from a high achievement track in a suburban public school would
display greater use of self-regulation strategies than students chosen from lower
achievement tracks. The achievement level of students was evaluated using multiple
sources of information. The sample was randomly selected and consisted of 80 tenth
graders; 40 were designated as a high achievement track and 40 as a lower achievement
track. Students were assigned to the achievement tracks according to information derived
from multiple sources such as entrance test scores, grade point average prior to entering
high school, and teachers‟ and counselors‟ recommendations.
After identifying 14 classes of self-regulated behavior and including a label
“other”, Zimmerman and Martinez-Pons (1986) defined each category as stated. For
example, a category, self-evaluation, was defined as “statements indicating students-
initiated evaluations of the quality or progress of their work” and gave an example as “I
check over my work to make sure I did it right.” They also identified six learning
contexts such as “in classroom situations,” “at home,” “when completing writing
23
assignments outside class,” “when completing mathematics assignments outside class,”
“when preparing for and taking tests,” and “when poorly motivated.” Students were
interviewed to assess their self-regulated learning strategies used for class work,
homework, and study for each learning context. The interviewer was unaware of the
students‟ achievement levels. After coding, students‟ responses were assigned to one of
the fourteen self-regulated learning strategies or to a non-self-regulated learning strategy
referred to as “other.”
Zimmerman and Martinez-Pons (1986) found that the high achievement students
used self-regulated learning strategies more frequently than the lower achievement
students. High achieving students were distinguished mostly by their use of teachers or
adults (35 %) and peers (50 %) as sources of social support compared to low achieving
students who used adults (8 %) and peers (23 %). The low achieving students are less
likely to seek support. This study is important because it gives an optimistic view that
human achievement is heavily dependent on the use of many of the same strategies.
Pintrich, Smith, Garcia, and McKeachie (1991) developed the Motivated
Strategies for Learning Questionnaires (MSLQ) for several years to assess college
students‟ motivational orientations and their use of learning strategies for a college
course. The early instrument was used for students to evaluate the effectiveness of their
class, “Learning to Learn” at the University of Michigan. The instrument was revised on
the basis of the results from statistical and psychometric analyses, including internal
reliability coefficient computation, factor analyses, and correlations with academic
performance and aptitude measures. The formal development of the MSLQ was funded
by the National Center for Research to Improve Postsecondary Teaching and Learning
24
(NCRIPTAL). After three times of data collections and analyses, the final version of the
MSLQ was presented. The construct “motivation” includes six components and the
construct “learning strategies” includes nine components. One of metacognitive self-
regulation items is “during class time I often miss important points because I‟m thinking
of other things.” They offered feedback for student to determine his/her own strengths
and weaknesses. Detailed information is described later in this chapter.
Howard, McGee, Shia, and Hong (2000b) developed an instrument which could
be used extensively in classrooms across the country to help teachers identify self-
regulatory strengths and weaknesses for students aged 12 to 18 years. The instrument
was developed in two phases. First, the existence of the factors was confirmed. The
factors were identified as knowledge of cognition and regulation of cognition in the
context of problem solving and extended understanding of regulatory skills related to
planning, monitoring, and evaluating. They conducted a study using two instruments
with a sample 339 students aged 10-19 years old distributed into three groups from three
different cities. One instrument was the Junior Metacognitive Awareness Inventory
(Jr.MAI) developed by Dennison, Krawchuk, Howard, and Hill (1996) and the other was
How I Solve Problems (HISP) developed by Fortunato, Hecht, Tittle, and Alvarez (1991).
Items were analyzed and some items were removed. They utilized a principle component
method using a varimax factor rotation to extract the most independent constructs.
Repeated factor analyses were utilized and the final five factors which were stated
previously accounted for 42.7 percent of the sample variance.
In phase two, Howard, McGee, Shia, and Hong (2000b) created the IMSR
specific to metacognitive awareness and regulatory skills in the context of problem
25
solving. They administered the revised inventory to a sample of 829 students in grades 6-
12. The sample was 80 percent Caucasian. The overall inventory demonstrated a
reliability of Cronbach alpha= .935. They conducted an exploratory principle component
factor analysis using a varimax rotation. The five factors revealed eigenvalues over 1.12
which accounted for 51.6 percent of the variance. Reliability ranged from alpha= .720 to
alpha= .867.
Howard, McGee, Shia, and Hong (2000b) concluded that knowledge of cognition
was an important factor of the IMSR as well as of the Jr.MAI. The five metacognitive
and self-regulatory constructs were relevant to problem solving. They observe that the
implications of their research suggest that teacher professional development teams should
begin providing teachers with a set of tools and training resources to help promote
students self-regulation for students in their class rooms. They also noted that the
information would be important to teachers who are concerned not only about what
students learn but also about how they learn it.
Self-regulated Learning Strategies and Motivational Factors. In a correlational
study of motivational orientation and self-regulated learning and academic performance,
Pintrich and DeGroot (1990) recruited 173 seventh graders from eight science and seven
English classrooms. The sample was 57.8 percent girls. Academic performance data
were obtained from classroom assignments. Self-efficacy was positively related to
cognitive engagement and performance. Test anxiety was not significantly related to the
use of cognitive strategies (p > .05) or self-regulation (p > .05) but test anxiety was
negatively correlated to self-efficacy (r= -.34). The results showed that self-regulation,
self-efficacy, and test anxiety were the best predictors of performance. If students have
26
decreased test anxiety, students‟ performance and learning skills are increased. Pintrich
and DeGroot concluded that the motivational beliefs alone were not sufficient for
successful academic performance. They also observed that the self-regulated learning
components seemed to be more directly related to performance, suggesting that students
must have “will” and “skill” to be successful in the classroom.
Weinstein, Husman, and Dierking (2000) developed a program to assess how self-
regulated learning influences academic achievement. The program provided learners
with awareness of the range of learning strategies and skills the students have available.
The researchers measured effects of the course on students‟ GPAs and continued to track
the student achievement over a five-year period while at the university. Fifty-five percent
of the students who did not take the course graduated within five years. Seventy-one
percent of the students who took the course graduated within five years. Weinstein,
Husman, and Dierking measured student data for one semester using the Nelson Denny
Reading Test and the Learning and Study Strategies Inventory (LSSI) to measure
students‟ skill, will, and self-regulation components of the Model of Strategic Learning
developed by Weinstein in 1994. Weinstein, Husman, and Dierking concluded that the
16 percent difference is an exciting finding that supports the long-term retention effects
of an intervention in learning strategies. Weinstein, Husman, and Dierking found that the
students took advantages of learning when the program of self-regulation intervention
focused on learning strategies. The students who attended the program had higher GPAs.
Weinstein, Husman, and Dierking suggested that directions for research in self-regulation
should be towards the investigation of the changing nature of learning in computer and
distance learning environments.
27
Utilizing the IMSR, Howard, McGee, Hong, and Shia (2000a) examined
metacognitive self-regulation in relation to problem solving in computer-based science
inquiry. The sample for the study consisted of 1,163 students from grades 5th
to 12th
across the U.S. representing a cross-section of socioeconomic backgrounds and
urban/suburban/rural categorizations. The ethnic groups were Caucasian (80.5 %), Asian
American (10.2 %), African American (5.4 %), Hispanic or Latino (2.8 %), and other
(3.8 %). The participants were categorized according to high and low levels of
metacognition and high and low levels of aptitude using the top and bottom 28 percent.
Treatment groups used the software program, The Astronomy Village, on an average of
20 instructional periods. They analyzed the data using two 2x2 ANOVAs. The IMSR
factors were statistically significant predictors of content of understanding and problem
solving. Content understanding depended on knowledge of cognition (p < .01), problem
representation (p < .01), and objectivity (p < .01). Problem solving depends on
knowledge of cognition (p < .05), problem representation (p < .01), evaluation (p < .05),
and objectivity (p < .01). High levels of metacognitive self-regulation compensated for
low overall GPA. The results demonstrated that the variables, knowledge of cognition,
objectivity and problem representation, are important for success in both content
understanding and problem solving. Evaluation is also one of the predictors of problem
solving. To confirm the importance of those variables, they recommended that the
variables should be more broadly subjected to academic or experimental examination.
Subtask monitoring was not an important predictor for content understanding or problem
solving. High levels of metacognitive self-regulation compensated for low overall
aptitude in regard to problem solving.
28
Bembenutty (2006) examined the relationship among teachers‟ self-efficacy, self-
regulated learning, and academic performance. The study consisted of 63 secondary
education teachers who enrolled in a classroom management course. Bembenutty
defined homework self-efficacy as “individuals‟ beliefs in their capabilities to organize
and execute the courses of actions required to produce any given assignment or self-
initiated academic work” (p. 5). The teachers answered questionnaires on self-efficacy,
homework self-efficacy, and self-regulation. There were two examinations on content,
one was a practice non-graded test and the other was a graded final test. The internal
consistency reliability, Cronbach alpha, for self-efficacy was .95, for homework self-
efficacy was .81, and for self-regulation of learning was .90. Self-efficacy was correlated
with homework self-efficacy (r= .34) and self-regulation (r =.34). Self-regulation was
correlated to homework self-efficacy (r=.37) and performance on the practice non-
graded test (r= .41). Scores on the final graded test were correlated to the practice non-
graded test (r=.42). The findings of the path analysis suggested an indirect influence
between teachers‟ self-efficacy and academic achievement via homework self-efficacy
and self-regulation. The final grade was directly influenced by the non-graded test.
Based on the results, Bembenutty concluded that teachers needed to reflect on how to
learn, how to teach for goals, and how to increase self-efficacy which is important in self-
regulated learning. The findings of this study support previous studies by Bembenutty
(2004), Boekaerts (1999), Pajares (1996), and Schunk (1996). Schunk and Ertmer (2000)
suggest that enhancing self-efficacy for students is imperative because self-efficacy plays
a key role in the academic learning and performance.
29
Pintrich, Smith, Garcia, and McKeachie (1991) reported that test anxiety was
negatively correlated to self-efficacy (-.37) and final grade (-.27) suggesting that the self-
efficacy and test anxiety were important to learning outcomes. When they developed the
instrument to measure motivation and learning strategies, they examined the relationships
among motivation and self-regulated learning strategies. Test anxiety was negatively
correlated to metacognitive self-regulation (-.24). They suggested that training in the use
of effective learning strategies and test taking skills were required to reduce the degree of
anxiety.
Bembenutty, McKeachie, Karabenick, and Lin (1998) examined the relationship
between test anxiety and self-regulation on students‟ motivation and learning in a study
of 429 college students enrolled in an introductory course. The students were questioned
using the MSLQ. For the purpose of analysis, three test anxiety groups were created by
dividing the distribution into low (N= 121), medium (N= 154), and high (N= 154). A
MANOVA was used in the analysis of the multivariate relationships of the predictors test
anxiety and self-regulation, with the dependent variables motivation and use of self-
regulated learning strategies (rehearsal, metacognition, elaboration, organization, and
time management, help-seeking). Results showed that test anxiety had an effect only on
the self-regulated learning strategies of rehearsal (p<.001), organization (p<.001), and
help-seeking (p<.05). An ANOVA using predictors test anxiety and self-regulation with
final grade as the dependent variable showed that the main effects of test anxiety and
self- regulation had a statistically significant effect on course grade (p<.001) and (p<.01)
respectively. The interaction between test anxiety and self-regulation was not statistically
significant (p<.05). They suggested that these results required researchers to reconsider
30
the interaction between self-efficacy and self-regulation. For future research, they
suggested assessing the effects of test anxiety on self-regulation.
Artino (2008) reviewed studies of academic motivation and self-regulation during
the 1995 - 2007 period and suggested practical guidelines for online instructors. He
noted that online learning shifts control from the instructor to the learner. Online
learning doubled from 2002 to 2006 (Allen & Seaman, 2007). On the issue of self-
regulation and motivation, Artino highlighted the associated instructional implications for
online teachers. In these empirical studies, students‟ self-efficacy and task value matter
in their academic achievement. His other finding concerned influence of student
collaboration. That is, when students collaborate and seek help from others, they tend to
experience greater success.
Artino (2008) suggests several guidelines for the instructor of online learning.
First, the instructor assesses components of students‟ self-regulation and supplies
individualized feedback. Second, the instructor provides students with individualized
differential support on the basis of the weaknesses or strengths. Third, the instructor
develops and supports students‟ self-efficacy. Fourth, the instructor clarifies task
relevance and designs online activities to produce interest. Fifth, the instructor utilizes
peer models and encourages collaboration and co-regulation.
In conclusion, Artino (2008) insisted that the learners‟ academic motivation and
self-regulation are important. Artino mentioned that self-regulated learning “has been
studied in traditional classrooms as a means of understanding how successful students
adapt their cognition, motivation, and behavior to improve learning.”
31
Vrugt and Oort (2008) developed and tested a model of effective self-regulated
learning. They focused on the major models of self-regulation and found agreement
among theorists that self-regulated learning involves goal setting, metacognition, and the
use of cognitive and metacognitive strategies. By connecting a student‟s present state
and his/her future state, achievement goals play an important role in performance.
The participants were 952 first-year psychology students who enrolled in the
Introduction to Psychology course. Vrugt and Oort administered effort regulation of the
Motivated Strategies for Learning Questionnaire (Pintrich, Smith, Garcia, and
McKeachie, 1991) to group the participants into effective or less effective self-regulated
learners. They also assessed students‟ achievement goals by using the achievement goals
questionnaire (Elliot & Church, 1997). To measure metacognition, they used the
Awareness of Independent Learning Inventory (AILI) (Elshout-Mohr, Meijer, van
Daalen-Kapteijns, & Meeus, 2004). They used the MSLQ to assess students‟ study
strategies. They used exam scores from the introductory psychology course to measure
students‟ achievement.
The results from the effective self-regulators showed that the use of metacognitive
(r= .15) and resource strategies (r= .20) had a positive effect on the exam scores and the
use of the surface cognitive strategies (r= -.13) had a negative effect on the exam scores.
Low effective self-regulators showed that metacognitive strategies (r= .20) and resource
management strategies (r= .23) also had a positive effect on the exam scores and the
surface cognitive strategies (r= -.15) had a negative effect on the exam scores.
Effort investment was positively related to the pursuit of the three achievement
goals, metacognition, and the use of study strategies. Vrugt and Oort (2008) suggested
32
administering interviews to gather data about students‟ engagement in metacognitive
activities during task performance and use of behavioral observation of effort and
strategy use based on log data.
Based on the findings presented in the prior section, self-efficacy is positively
correlated to metacognitive self-regulation. Test anxiety is negatively correlated to
metacognitive self-regulation. Consequently, those factors are keys to self-regulation for
academic learning and achievement. While the internet has various functions which
attract users, self-regulation may now be required for anyone who facilitates the internet
and the computer.
Internet Use
Day, Janus, and Davis (2005) sought to assess computer use since 1984 and
internet use since 1997. The report was based on the data collected and published by the
U.S. Census Bureau in the October 2005 supplement. They provided information about
the characteristics of households and people who had and had not adopted the use of
computers and the internet at home, school, and work.
In 1997, comparative data showed that 36.6 percent of American household had a
personal computer and 18 percent had internet access. In 2003, about 62 percent (70
million) of American households had a personal computer and over 54 percent had
internet access. Those percentages of internet access increased more than three times by
the end of the sixth year.
First, in 2003, in the survey of computer and Internet use for children (N= 61,897)
ages 3 to 17 years showed that 86.3 percent of children who had a computer at home used
the computer at home. Also 63.5 percent of children who had internet access at home
33
used the internet at home (N= 40,923). Moreover 86.4 percent of children enrolled in
school used the computer at school and 43.1 percent of children enrolled in school used
the internet at school (N= 56,588). Over 86 percent of children used a computer
anywhere and over 56 percent of children used internet anywhere. There was no
difference in computer use between genders at home (male 86. 0 %, N= 23,886 and
female= 86.6 %, N= 22,860) and at school (male= 83.2% and N= 29,046; female= 83.6%
and N= 27,542). Also there was no difference in internet use between gender at home
(male= 62.4 % N= 20,900 and female= 64.6 %, N= 20,023) or between gender at school
(male= 42.0% N=29,046, female= 44.3% N= 27,542).
Children in grades 9 to 12 (64.3 %) used the computer and internet more than
children in grades 5 to 8 (53.1%), grades 1 to 4 (29.7 %), or less than grades 1 (10.9 %).
There was no difference in people‟s educational attainment of households of computer
use and Internet use at home and school. The educational attainments of households
contribute to the use of the internet at school (less than high school graduate= 31. 4 %,
high school graduate= 41.9 %, college or associate degree= 45.2 %, Bachelor‟s degree=
47.9 %, and Advanced degree= 50.7 %).
Second, in 2003, the information on computer and internet use for young adults
age 18 to 24 years (N= 27,404) showed that 90.5 percent of those who had a computer at
home used the computer at home. Also 89.2 percent who had internet access at home
used the internet at home (N= 16,438). Over 38 percent of those employed (N= 17,086)
used a computer at work and over 23 percent used the internet at work. Over 89 percent
enrolled in school (N= 11,937) used a computer at school and 70.9 percent of those
34
enrolled in school used the internet at school. Moreover 75.4 percent used a computer
anywhere (N= 27,404) and 70.6 percent used an internet anywhere (N= 27,404).
Of 13,222 young adults who were enrolled in college in 2003, 94.4 percent had a
computer at home (N= 11,214) and used the computer at home. Ninety-two percent of
those who had internet access (N= 10,270) at home used the internet at home. Fifty
percent of those in college and employed (N= 7,575) used the computer at work and 31.4
percent used the internet at work. Over 85 percent of the college enrolled young adults
used the computer at school and 67.2 percent used the internet at school. Ninety-two and
eight percent of the college enrolled young adults used the computer anywhere and 87.7
percent used the internet anywhere.
Third, from the survey of computer and internet use in 2003 for the adult
population 18 years and older, 49.5 percent of black households (N= 24,482) and 46.7
percent of Hispanic (N= 26,565) had a computer at home. These rates were somewhat
lower than the rates of other races (White= 68.0 %, N= 175,230; Asian= 76.5%, N=
9,023). Over 78 percent, who was black and had a computer, used the computer at home.
In 1984, women used the computer at home at a lower rate (42.8%) than men (63.1 %),
but in 2003 at a higher rate (men= 81.5 %, women= 83.5%).
Adults, 18 years and older, who used the internet in 2003, 54 percent used it for
email, 46.5 percent for information on products or services, 21.5 percent for playing
games, and 3.9 percent for taking an online course.
The main reasons adults did not use the internet at home were as follows: (a) no
interests or needs (39.4 %); (b) high costs (23.3 %); (c) no computer or an inadequate
35
computer (23 %); (d) lack of skills (4.5 %); (e) lack of time (2.3 %); (f) other places to
access (2.1 %); and (g) concern that children will access inappropriate sites (0.9 %).
Based on the 2005 survey, Wells and Lewis (2006) reported that nearly 100
percent of the sample of 1,205 public schools had internet access facilities. Eighty-nine
percent of the schools used the internet to provide material for instructional planning at
the school level. Eighty-three percent of the schools offered school or district level
professional development for teachers. The teachers learned how to integrate the use of
the internet into curriculum. Eighty-seven percent of those schools used the internet to
provide assessment results and data for teachers to use to individualize instruction. Wells
and Lewis also reported the ratio of students to instructional computers with internet
access by dividing the total number of students in all public schools by the total number
of instructional computers with internet access in all public schools. The ratio in 1998
was 12.1 to 1 and the ratio in 2005 was 3.8 to 1. The decreased ratio over 7 years showed
that more of the public school teaching and learning activities were involved in the use of
the computer and the internet. The increased use of the computer and the internet provide
more opportunities and information for teaching and learning in the public school.
However, the teachers and the parents were concerned about students‟ access to
inappropriate material on the internet. The protections used for students‟ access to the
internet were as follows: (a) monitoring by teachers or other staff; (b) blocking or
filtering software; (c) written contract that parents have to sign; (d) written contract that
students have to sign; (e) monitoring software; (f) honor code for students; and (g)
intranet. In 2005, 99 percent of public school used blocking or filtering software to
protect students from inappropriate material on the internet.
36
Malaney (2004) surveyed undergraduate students‟ computer and internet use from
1988 to 2003. From those surveys, the critical turning points were in 1996 and in 2003.
In 1996, 94.3 percent of students used the computer, but 34.8 percent of students said that
they used the computer everyday. By 2003, all the undergraduate students said they used
the computer and the internet daily. Twenty-two percent of the students in 1988 owned
their own computers and by 1997 ownership increase to 45.1 percent. Malaney also
examined students‟ spending time on the computer and the internet. In 2003, 98 percent
of the students spent at least some time on the internet and the students estimated that
they spent 28.36 hours per week using the internet. Students used an instant messenger
on an average 10.63 hours per week and checked email an average of 2.35 hours per
week. The question related to the amount of time students used the computer and the
internet is no longer important because presently the students use them at high frequency.
Researchers are now concerned about students‟ effective use and efficient time
management of the computer and the internet. Unlike Lindros and Zolkos‟ (2006)
positive view of internet use, Malaney found that students lost their focus from the
original task during internet use. Only 6.5 percent of those surveyed said that they
“never” lost their focus. Over half of the students reported that it was difficult to stop
using the internet and over 30 percent failed to succeed in stopping to use the internet.
Malaney also found that the students did not feel that their internet use was negatively
impacting their lives. She suggested that the anticipating and describing potential
problems is necessary to prepare the solutions.
Johnson and Johnson (2006) asked ninety-three college students about their
internet experience such as if they used the internet prior to this course which utilized
37
synchronous and asynchronous computer mediated communication modes. The choices
were never, rarely, daily, weekly, and monthly. Over 40 percent of the students
responded that they used the internet and visited websites daily, prior to the course. Over
35 percent of the students answered that they used the internet and visited websites
weekly, prior to the course. Over 31 percent of the students reported they communicated
online prior to the course. About 70 percent of the students answered that they never or
rarely played online games prior to the course. Students who used computer-mediated
communication tools selected real chat and an asynchronous discussion. Students who
preferred synchronous chat had more experience than students who preferred
asynchronous discussion. Additionally, students learned when they preferred real chat,
synchronous computer mediated communication, rather than asynchronous discussion.
Caskey (2009) investigated internet use of 241 academically talented middle
school students. The major internet activities of the students on a weekly basis averaged
10 or more hours playing games (66 %), chatting with friends or family (55.6 %), and
looking up information important to them (61. 3 %). Caskey stated that education could
not keep up with the internet in increased productivity, creative expression, and
innovation. Therefore, Caskey noticed the challenge of integrating internet with the
activities in classroom. The activities for the young were creating, interacting,
collaborating, sharing, and exchanging information, original ideas, and artifacts across a
connected, distributed environment. In addition, Caskey noted the need for opportunities
for modeling and guiding students on appropriate internet behaviors and ethical uses for
students to be motivated of the current education which has a low speed of development
compared to a high speed of internet development.
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From the studies on internet and computer use, many of those uses take precious
time of students and affected the students‟ views negatively. The negative points are
elaborated by Bugeja (2005) such as the social interaction crisis, negative impacts on
education, accessing personal information, and social displacement. Lindros and Zolkos
(2006) refuted the negative views of Bugeja reviewing studies related to positive
influences of the internet. While email or internet addicted use takes away students‟
educational time, technology, such as educational tools or online library databases,
enhance students‟ education experiences. Lindros and Zolkos suggest that families and
communities should decide the best time and place to use technology.
Because of the broadened internet and computer use in almost every field of
endeavor such as education, commerce, manufacturing, most educational institutions
have employed courses related to technology such as computer literacy or information
technology literacy to. Thus, computer skills are now requirements for the students to
graduate from the institutions.
Computer Literacy
Computer literacy usually includes the knowledge and abilities to use applications
such as word processors, spreadsheets, databases, presentation tools, graphical web
browsers, and operating systems (Dunsworth, Martin, & Igoe, 2004; Ocak & Akdemir,
2008; Shelly, Cashman, & Vermaat, 2004). The most widely used applications taught in
computer literacy course are Microsoft (MS) Office Word, Excel, Access, PowerPoint,
Internet Explorer, and Windows operating systems. One of the MS Office textbooks
written by Shelly, Cashman, and Vermaat (2004) introduces those applications
graphically with practical examples to enhance computer literacy. The book includes
39
Window XP and MS Office 2003. Window XP was one of Windows operating systems
and included lessons on managing windows, file management, storage, input, and output.
Managing Windows includes lessons such as displaying the start menu, opening and
closing a window, maximizing and minimizing the window, etc. MS Office 2003
included MS Office Word 2003, Excel 2003, Access 2003, and PowerPoint 2003. MS
Office Word 2003 is “a full-featured word processing program that allows users to create
professional looking documents and revise them easily” (Shelly, Cashman, & Vermaat, p.
WD4). MS Office Excel 2003 is “a powerful spreadsheet program that allows users to
organize data, complete calculations, make decisions, graph data, develop professional
looking reports, publish organized data to the web, and access real-time data from Web
sites” (Shelly, Cashman, & Vermaat, p. EX4). MS Office Access 2003 is “a powerful
database management system (DBMS) that functions in the Windows environment and
allows students to create and process data in a database” (Shelly, Cashman, & Vermaat,
p. AC4). MS Office PowerPoint 2003 is “a complete presentation graphics program that
allows users to produce professional-looking presentations” (Shelly, Cashman, &
Vermaat, p. PPT4). Users can develop announcements, letters, memos, resumes, reports,
fax cover sheets, mailing labels, news letters, etc. Internet Explorer was released in 1995
and upgrade to version 8 in 2009. Internet Explorer was designed to view a broad range
of web pages. The features of Internet Explorer 7 were tabbed browsing, quick tabs, tab
groups, streamlined interface, advanced printing, instant search box, favorites center,
RSS feeds, and page zoom. For example, tabbed browsing feature is viewing multiple
sites in a single browser window and easily switch from one site to another through tabs
at the top of the browser frame. Even though the features of Internet Explorer supported
40
limited open web standards, the usage share of the web browser was over 66 percent in
2009 (Net Applications, 2009). The ability and knowledge of the use of the internet web
browsers were the skills to enhance computer literacy. Computer literacy in the present
study is referred to as the knowledge and ability to use computers and technology
efficiently.
Dunsworth, Martin, and Igoe (2004) questioned what kind of computer skills
should be taught and how the skills should be taught to the beginners. They evaluated a
3-credit computer literacy course for undergraduates having 14 three-hour weekly
meetings providing knowledge about computers, computing, and application skills in
using MS Office software. Eleven instructors, 329 students, and a course coordinator
answered a 26 item-survey through Blackboard or a pencil-based survey. Student
assessments included online quizzes, online midterm exams, and hands-on final exams.
The hands-on final exam assessed students‟ knowledge of how to use MS Office Word,
Excel, and PowerPoint. The usefulness of the computer skills and helpfulness of
strategies were determined by analyzing students test scores. The usefulness of the
contents was for the application skills on an average of 2.44, ranging from 0, least agreed,
to 3, most agreed; and for the concept of knowledge on an average of 2.26. The helpful
strategies instructors used such as projects, in class activities, and handouts were helpful
on an average of 2.53. Otherwise, cooperative group work, online discussion forums, and
reading textbook were rated lower with and average of 1.48. Students and instructors
rated PowerPoint, and Internet and World Wide Web as the most useful content of the
course. Students commented that the quizzes were too exhaustive and did not evaluate
the skills learned but only the amount of memorization without using the application
41
software. Additionally students‟ achievement in the hands-on final exam score was
higher than the online multiple-choice tests. From those comments and results,
Dunsworth, Martin, and Igoe recommended that the hands-on tests would be better for
assessing the application skills. Dunsworth, Martin, and Igoe emphasized on more in-
class and hands-on activities in teaching facilitated by an appropriate students-instructor
ratios. After evaluating the course, Dunsworth, Martin, and Igoe concluded that
computer literacy was a good general studies course for the students,
As computer literacy is important for college students (Dunsworth, Martin, &
Igoe, 2004), it is also important for younger students and the teachers in elementary and
middle, and high school to learn computer literacy. Ocak and Akdemir (2008)
investigated the use of computer applications of 63 primary school science teachers to
find their perception of the integration of computer applications and their level of
computer literacy. They showed how computer literacy played a part in the instruction of
science. Half of the teachers encouraged students to use computer applications in science
classrooms. Around a fourth of the teachers indicated that they used traditional methods
since they did not know how to apply computer applications in instruction. About 55
percent of the teachers reviewed internet sources periodically for use in instruction.
Computer literacy was statistically correlated to the frequency of computer use (r= .871)
and the perceptions of the integration of computer applications (r= .717). Teachers‟
computer applications were word processing, spreadsheets, database programs, graphic
and drawing programs, desktop publishing, presentation programs, educational CDs,
email, internet, and others. Roughly 40 percent of the teachers‟ level and frequency of
use of email and internet were very high.
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Wecker, Kohnlet, and Fischer (2007) studied the use of computers and the
internet in an inquiry learning science environment. Using a sample of 37 secondary
school students, they investigated the role of computer literacy in the classroom with the
web-based inquiry scientific environment and analyzed the learners‟ patterns of media
use. They found that the students less familiar with computers acquired more knowledge.
The finding could alleviate some of the worry of the knowledge gap due to the digital
divide between the learners with a low level of computer literacy and those with a higher
level of computer literacy. Wecker, Kohnlet, and Fischer worried that one of their study
limitation, the two days duration, was too short. The short duration could not support a
broad generalization.
Through their literature review of the studies related to interactive
communication, Tatković and Maja (2005) viewed today‟s society as changed into a
multimedia society and continues to change moving toward new forms of
communication. While students are surrounded by the new technologies such as TVs,
DVDs, PCs, mobile phones, and the internet, Tatković and Maja viewed the internet as
the most commonly used media above all others. In their study, 77 percent of the sample
(N= 130) attending pre-school teaching department answered that the use of
contemporary communication media in the process of education does not have a negative
influence in terms of a students-teacher relationship. Ninety-five percent answered that a
PC presentation was the best communication media in teaching. Tatković and Maja
suggest that teachers need to update their skills and enhance the ability to apply the new
media in the process of education.
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Recognizing the decline in enrollment of computer science programs, scholars in
information technology faced the challenge of attracting more students into computing
disciplines. In a study, Perez and Murray (2008) noted that Information Technology
Literacy was important in higher education. They developed an Information Technology
Literacy course as a service to the institution. They pilot-tested the course for one
semester and then offered the course the following two years. They found that 67 percent
of 24 professors strongly agreed that “all students should be required to take an
information technology literacy course.” The students who enrolled in the course were
majoring in areas such as business, education, English, health and humanities, science
and mathematics, etc. Perez and Murray concluded that after taking an information
technology literacy course, a number of students changed their major to computer science
or information systems.
From the review, internet use also influences students‟ learning and academic
achievement. The studies related to self-regulation, internet use, and academic
achievements are discussed in the next section.
Review of Studies Connecting Self-regulated Learning, Internet Use, and Academic
Achievement
Self-Regulated Learning Strategies for Academic Achievement. Computer
literacy has become a required skill by most universities. Courses are offered which are
designed for instructing students in computer and internet usage. Research in this area
has focused on the importance of students‟ attitudes towards the computer, the
relationship between students‟ learning strategies and computer literacy, the influence of
learning goals, and self-evaluation on college students‟ achievement outcomes during
computer skill learning.
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Schunk and Ertmer (1998) examined the influence of learning goals and self-
evaluation on college students‟ achievement outcomes during introductory computer skill
learning. The research addressed the three cyclical phases as shown in Figure 3.
Students were provided with goals to pursue during subsequent learning as part of the
forethought phase. To assess performance control, students self-reported perceived
competence and use of self-regulatory strategies. Students evaluated their learning
progress during self-reflection. The student goal was to learn the computer skill
application HyperCard. The sample participants were mostly female undergraduate
education majors. The participants were randomly assigned to one of four conditions.
One group had learning goals without self-evaluation; another group had learning goals
with the self-evaluation component. A third group had performance goals without self-
evaluation, and the fourth had performance with self-evaluation. Data were collected
during three Hyper Card sessions with pre- and post-tests. The pre- and post-testing
measured the level of self-regulation, self-efficacy, and achievement. For the computer
skill learning, students who were provided with learning goals showed greater self-
efficacy for successfully performing computer based tasks and more use of self-
regulatory strategies than those provided with performance goals. Self-evaluation of
learning progress was measured at the end of each of three laboratory sessions. The
results indicated that self-evaluation with learning goals provided increased benefits to
student learning as compared to those without self-evaluation.
While motivation and attitudes are the keys for learning on the computer,
Saparniene, Merkys, and Saparnis (2005) questioned how different attitudes toward
computers can lead to different levels of computer literacy. Attitudes are formed in the
45
process of experience and may change due to the internal and external factors.
Saparniene, Merkys, and Saparnis focused on the relationship between emotion and
motivation with a computer. They suggested that students who had personal problems
might not have a motive to study computer subjects well, but, if they had more positive
feelings such as enthusiasm, pleasure, satisfaction, these emotions could help to
accomplish difficult tasks and achieve good academic results. Their study was designed
to determine if students‟ attitudes toward computers were correlated with computer
knowledge and skills. One thousand four participants from four universities and five
high schools and colleges in Lithuania were surveyed in a study of computer literacy.
Nine hundred seventeen of those responded to a computer literacy test consisting of 19
theoretical questions and 24 practical questions. The Cronbach alpha reliability estimate
on the theoretical part was 0.73 and on the practical part the Cronbach alpha was 0.90
indicating fairly reliable scores. The scale mean of theoretical part of the test was 9.7
with a maximum score of 19, and the scale mean of practical part was 25.4 with a
maximum score of 48. The 917 participants were divided into three groups based on
attitudes toward computers as indicated by the initial survey. Thirty-three and a half
percent of the participants were in the positive group, 46.5 percent were neutral, and 20.1
percent were negative in their attitudes toward computers. A factor analysis of the initial
survey produced five factors which were interpreted as: (a) computer as a hobby and an
object of admiration; (b) computer as a source of fatigue, stress, and dissatisfaction; (c)
indifference to the computer; (d) dissociation from computer enthusiasts and fanatics; and
(e) computers as a factor of improvement and education. The mean factor score for each
group was calculated and plotted on a standardized scale of the survey. Students, who
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had positive attitudes toward computers, showed higher scores on the factors
“improvement and education” and “computer as a hobby and object of admiration”.
Students with negative attitudes about computers, showed higher scores on the factors
“computers as a source of fatigue, stress, and dissatisfaction,” “indifference to the
computer,” and “dissociation from computer enthusiasts and fanatics.” Those with
neutral attitudes scored between the other groups on all factors.
Using the standardized scores from the computer literacy test, the groups were
compared and within each group mean standardized scores were computed for each
gender. For the students with neutral attitudes, the mean for males was 0.6 and for
females was -0.2. In the positive group, the mean standard score for males was 0.9, and
for females was near zero. For students with negative attitudes the standard score for
males was 0.5 and for females -0.7. Saparniene, Merkys, and Saparnis (2005) concluded
that computer literacy requires students to be motivated with positive attitudes in order to
be successful using the computer.
Niemczyk and Savenye (2001) studied the relationship of students‟ motivation
and self-regulated learning strategies to enhance performance in an undergraduate
computer literacy course. There were 291 participants of which 193 were females and 98
were males. Most of participants were majoring in 26 disciplines including education (27
%), communication (18 %), or broadcasting (11 %) with an average age of 22 years. The
computer literacy course was comprised of a lecture class and lab. The three surveys,
“Strategies Used for Learning in a computer literacy course”, the MSLQ, and extra multi
format questions related to students habits were responded to by the participants. Sets of
analyses were conducted including multiple regression analysis. The data were collected
47
at the end of the semester during lab sections. The average course grade is 2.73 (A= 4 to
D= 1). The mean of self-efficacy for learning performance is 5.32, test anxiety 3.87,
metacognitive self-regulation 4.01 ranging from 1 to 7. Self-efficacy was correlated to
the course grade (r= .30) and nine percent of the variation in course grade explained by
differences in self-efficacy of the students. Over 79 percent of the students thought that
they were responsible for their success in learning. They thought that their study
schedule (24 %) and discipline (24 %) helped them become a better learner. Around 50
percent of the students thought that reading a text book and taking notes were the
methods to use to study for this class and other course. Students noted that they took the
computer literacy course because they thought the content would be helpful and
attractive. Other reasons for taking this course were that the course is required for
academic major (80 %), improves their academic skills (73 %), fits into their schedule
(73 %), and improves their career prospects (70 %). In order to achieve academic
success, they insisted that students must be able to self-regulate their own learning by
using motivation and learning strategies. They found that self-regulated learning
strategies are related to course grade in a computer literacy course. They suggested
further research such as the longitudinal examination of the relationships between the
motivation and learning strategies that are proven to be most effective. The second
suggestion is the interactions of the significant variables to determine the interplay and
influence they may have on students learning and performance.
Saparniene, Merkys, and Saparnis (2006) also studied the impact of the
psychological factors on the quality of computer literacy. The psychological factors were
attention, intelligence, emotional-motivational relationship with computer, learning
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strategies, computer stress, etc. Saparniene, Merkys, and Saparnis (2006) concluded that
attention was statistically more strongly related to computer literacy while intelligence
had a weaker relationship with computer literacy.
Askar and Davenport (2009) studied the factors related to Java programming self-
efficacy among first year engineering students who enrolled in an introductory Java
programming course. The scores of Java programming self-efficacy of these students,
whose family uses the computers, were higher than those students whose family did not
use computers. The correlation between computer skills and Java programming self-
efficacy was statistically significant (r= .592, p < 0.01) among first year engineering
students enrolled in an introductory Java programming course. Computer skills included
chat, e-mail, word processing, spreadsheet, PowerPoint, web-design, database and
programming.
Winters, Greene, and Costich (2008) stated that computer-based learning
environments (CBLEs) present important opportunities for fostering learning. However,
studies have shown that students have difficulties when learning in these environments.
To better understand the positive and negative influences of CBLEs, self-regulated
learning models help identify which specific self-regulated learning processes are
associated with learning, how different learner and task characteristics may be related to
students‟ self-regulated learning, and how aspects of self-regulated learning can be best
supported in CBLEs.
Self-regulated learning models received a great deal of attention in CBLEs
research. As posited by Winters, Greene, and Costich (2008), the self-regulated learning
model shows that individuals effectively plan, monitor, and control their learning.
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Importantly, learners can manage their environments. Their results indicate that students
adapted their self-regulated learning processes to web-based learning and learners and
task characteristics influenced their processes. Self-efficacy for self-regulated learning
has been shown to relate positively to other beliefs critical to academic success when
using CBLEs.
Winters, Greene, and Costich (2008) found from several studies that “nearly one
third of the reviewed studies did not include any type of measure of student learning” and
that several studies “included learning outcomes, but, did not find significant differences
between experimental groups on those outcomes” (p. 440).
In the research and theoretical literature, there are few studies on self-regulation,
internet use, and academic achievement. Instead, the majority of the research focused on
computer-based e-learning or online learning.
The research appears to indicate that computer literacy learning or computer
related learning is influenced by high motivation such as positive attitudes. Self-
evaluation with learning goals and interactive learning strategies are also keys in
computer literacy learning. One other aspect of computer literacy course is the use of the
internet. The next section is a discussion of self-regulation and internet use.
Self-regulation and Internet Use. Ebersole (2000) reported that low achieving
students are more likely to use the WWW for easy access to entertainment when bored,
but that high achieving students are more likely to use the WWW for access to
supplemental learning materials otherwise unavailable. Hargis (2000) also viewed that
understanding the interaction between the students‟ learning strategies, and motivation
and technology can provide insight into helping students improve academic achievement.
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Consequently, Hargis examined the effect of self-regulated learning on internet usage.
Hargis insisted that research about self-regulation should be done to maximize the new
educational tools, internet use. Jakubowski and Dembo (2002) also insisted that students
either have or should develop their own learning strategies in order to be successful
college students in the college environments. Terry studied that a student‟s own
motivation and learning strategies allow time management and efficient use of resources
to achieve academic success (2002). Zenon found that internet use among students
positively influences their academic learning (2006).
The purpose of this research is to determine if there are relationships between
self-regulated learning and academic achievement, between self-regulated learning and
internet use, and between internet use and academic achievement. Self-regulated
learning has been shown to play a key role in academic learning and performance.
Students might not meet their academic goals if internet browsing is not focused and
efficient. Self-regulation in learning can also apply to internet use in order for students to
be successful in academic fields. Unfocused internet browsing might be unregulated
internet use which comes from deficiency of self-regulation. Students who use the
internet efficiently and properly are self-regulated internet users.
The Internet is used for instruction and learning in classrooms (Day, Janus, &
Davis, 2005) although, there are problems with internet use such as internet addiction or
unregulated internet use (LaRose, Mastro, & Eastin, 2001). When students use the
internet academically or non-academically, both can influence their learning positively or
negatively. Nonacademic internet browsing refers to the use of the internet which is not
related to academic work. It includes all other uses which are not related to the learning
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in a computer literacy course. Those uses include playing games, banking, shopping,
chatting, gambling, looking up things, and emailing.
Young (1996) viewed internet addiction as an effect of internet misuse and
studied a classified group of 396 dependent internet users and 100 non-dependent internet
users by interview, online, telephone, and mail. The study identified that the dependent
group used the internet on an average 38.5 hours per week compared to non-dependent
group who used the internet an average of 4.9 hours per week. Over 80 percent of the
dependent group used the internet less than 1 year but over 70 percent of the
non-dependent group used the internet over 1 year. Over 78 percent of the dependent
internet users spent time for chatting, multi-user games, and news groups while over 79
percent of the non-dependent internet users spent their time on email, www, and database
search. The problems of the internet use were usually academic, financial, and
occupational. The result revealed that even though the students had a strong research
tool, they experienced academic problems due to surfing irrelevant web sites. Despite the
negative impacts of the dependence on the internet, 54 percent of the dependent internet
users did not have the intention to reduce their time spent on the internet. With pointing
out the limitations and bias of the sample, Young suggested that non-dependent internet
users may not recognize their internet addiction and they feel no need to diagnose their
status of internet use.
Young (2001) observes that discussions of internet use behavior help expand
students‟ understanding of the implications of the new technology. There were many
risks such as free and unlimited internet access, unstructured time, freedom from parental
control, no online monitoring, escaping from the academic stress, etc. Additionally
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Young describes the effects of the internet addiction. Those reactions were declining
grades, less investment in relationship with friends, general irritability when off-line, and
lying about the time they spent online. Young suggests that educating administrators and
faculty on the dynamics of internet abuse can raise awareness and help prevent addiction
throughout the campus system. Implementing resident life educational programs is to
address students‟ internet addiction. Encouraging students to seek counseling when
internet-triggered problem arise is one of solutions. Additionally, the importance of their
participation in the social clubs or organizations the campus offers can be emphasized.
Finally, the counselors can discuss cyber-behavior to help expand students understanding
of the implications of the internet and computer.
Like Young, Reisberg (2000) also found that 10 percent of the internet users who
participated in the study and met the criteria of the internet addiction and over 90 percent
of those were men. The majors of the internet dependence were computer science,
chemistry, physics, math, and engineering. Internet-dependent students spent an average
229 minutes per day on the internet. Additionally Reisberg suggested that the colleges‟
action should be to find a way to monitor or restrict the amount of the time of students‟
internet use because internet dependence could lead to class absence and social isolation
or other severe outcomes.
LaRose, Mastro, and Eastin (2001) investigated the understanding of internet
usage in a correlational study with undergraduate students. They assumed learners
actively sought out the internet in a goal directed way. The goal directed way provided
them with the means of gratifying a wide variety of needs. They described gratification
such that the learners chose the easier ways rather than difficult ones when the learner
53
had a choice. One hundred seventy-one undergraduate students from an introductory
communications class were asked to keep a diary of the total time and purpose of their
internet use. Students also responded to a self-reporting questionnaire about expected
outcomes of their internet activity. The questionnaire had nine constructs of expected
outcomes of internet usage which were: activity outcomes; pleasing sensory outcomes;
novel sensory outcomes; social outcomes; negative outcomes; internet self-efficacy; self-
disparagement; self-slighting; and perceived addiction. There were positive correlations
between internet usage and the expected positive outcomes such as activity outcomes (r=
.48), pleasing sensory outcomes (r= .37), novel sensory outcomes (r= .32), and social
outcomes (r= .39). There were negative correlations between internet usage and the
expected negative constructs such as negative outcomes (r= -.16), self-disparagement (r=
-.48), and self-slighting (r= -.46). Internet addiction was positively correlated with
internet usage (r= .65). Internet self-efficacy was highly correlated to internet usage (r=
.65). Internet use was predicted, using a multiple regression with the predictors, internet
self-efficacy (b= .652), perceived addiction (b= .411), activity outcomes (b = .208), and
self-disparagement (b= -.144) at an alpha level of p < .05. The results suggest that users
with high self-efficacy access the internet confidently, and users with perceived addiction
use the internet more than others. They interpreted the results to mean that a person with
internet addiction is deficient in internet self-regulation.
Another factor influencing internet use is anxiety. Scealy, Philips and Stevenson
(2002) proposed that internet usage was different among students and was influenced by
an individual‟s personality. Through the literature review, they found that anxious people
were less likely to use the internet for information searches and were particularly less
54
likely to utilize web pages if they were poorly designed. They examined how personality
such as anxiety and shyness moderated internet usage. The 177 participants for their
study were recruited from Monash University and the general public from local libraries
and Internet cafes. Their results showed that the participants‟ average use of the internet
per month was for email (11.30 hours), work/study (9.58 hours), and buying products
(0.83 hours). Shy males were likely to use internet for recreation/leisure. Males with
high academic attainment were more likely to use the internet for banking/paying bills.
Scealy, Philips, and Stevenson concluded that internet use was not predicted by shyness,
anxiety, gender and academic attainment.
Choi, Watt, Dekkers, and Park (2004) examined the understanding of the motives
behind internet usage and the users‟ attitudes about the internet, social values, and
relational involvement with the internet in a correlational study. There were 1,344
participants from three countries in the study. An online survey was translated and
conducted in the three countries: US, Netherlands, and South Korea. The results of
internet use were that 99 percent of Korean, 85 percent of the Netherlands, 52 percent of
US users had broadband access. Attitudes of expectation and positive evaluation of the
internet were associated with internet use and motives. The eight factors examined were:
seeking information, online companionship, diversion, self-improvement, escapism, self-
expression, peer pressure, and offline companionship. The third factor “diversion” was
described as passing time, having fun, relaxing or finding excitement. The fourth factor
was self-improvement which included gaining respect from people, not falling behind in
the future, or developing an interest in new things. Escapism, amusement or self-
expression, peer pressure, and offline companionship were the rest of the factors. All
55
eight factors explained 66 percent (r2= .658) of the variance in internet usage. Students
were seeking information (r2= .153) and online companion (r
2= .14). They concluded
that when students were motivated to use the internet, they gained satisfaction which kept
the students returning.
The motives for internet use are diverse. Internet users have their own motives.
People spend time on the internet for study and enjoyment. However, those that do not or
cannot regulate themselves may not recognize that they spend an inordinate amount of
time accomplishing very little.
LaRose, Lin, and Eastin (2003) examined unregulated internet usage. They
described internet usage as how long or how many hours on an average students use the
internet. They proposed that unregulated internet usage might be the result of a
deficiency of self-regulation (LaRose & Eastin, 2002). They hypothesized that deficient
internet self-regulation is positively related directly/indirectly with internet habit strength,
self-reactive incentives, internet self-efficacy, depression, or self-reactive outcome
expectations. They noted that students were monitoring, judging, and adjusting their
behaviors when using the internet. They viewed unregulated internet usage as the
product of a deficiency or a low level of self-regulation and that self-regulation can be
increased through learning. The participants were collected from 465 students in three
introductory communications classes. The reported average time spent on the internet on
a weekday was 89 minutes and on a weekend day was 69 minutes. The results showed a
direct relationship between internet usage and deficient internet self-regulation (r= .45)
and internet self-efficacy (r= .38). They called it unregulated internet usage when the
56
users do not manage their internet use time, or they have a problem using the internet
productively.
The studies of internet usage suggest that if students are deficient in the use of
self-regulated strategies, their use of the internet can negatively affect academic
achievement regardless if they are novice or expert users. Unregulated internet use can
be changed into regulated internet use when students are guided by goals and objectives
during internet use. Students can be self-regulated learners by using learning strategies.
Students can be motivated to use self-regulated learning strategies by clear learning goals
and objectives.
Directions of Study
During the past decades, school, home, and public places offer internet connection
and internet use is tremendously increased from in any place and any time. Over 89
percent enrolled in school aged over 18 to 24 years in 2003 used the internet at school
(Day, Janus, & Davis, 2005). In 2005, 100 percent of public school has internet access
facilities. In 1994, 34.8 percent of undergraduate students used computer and in 2003,
the undergraduate students used the computer and internet daily (Wells and Lewis, 2006).
As self-regulated learning has been shown to be effective in the field of education
(Boekaerts, 1999), it is very helpful to the teachers, students, and educators who use the
internet for learning. Research on self-regulation should be done to maximize the new
educational tools, internet use (Hargis, 2000). The internet is a useful tool for education
but, there must be research on how students can learn to use this tool for maximum
learning efficacy and efficiency and avoid many misuses of internet.
57
Through out the review of self-regulation based on social cognitive theory (Bandura,
1977b, 1986), self-regulation strategies, self-efficacy, and test anxiety were important in
academic achievement and internet use (Bembenutty, 2006; Pintrich & DeGroot, 1990;
Saparniene, Merkys, & Saparnis, 2005; Zimmerman, 1989; Zimmerman & Martinez-
Pons, 1986). Self-efficacy and test anxiety play special roles in students becoming self-
regulated internet users (Bembenutty, McKeachie, Karabenick, & Lin, 1998; Scealy,
Philips, & Stevenson, 2002). Self-regulated learning is important in internet use
(LaRose, Mastro, & Eastin, 2001; LaRose, Lin, & Eastin, 2003). However, most
research was designed to understand the relations between self-regulation and academic
achievement (Niemczyk & Savenye, 2001) or self-regulation and internet use. There
were no published studies on the relationships among self-regulation, internet use, and
academic achievement in a computer literacy course. Consequently, the present research
on self-regulation and nonacademic internet use focuses on the strategies of self-efficacy,
test anxiety, and metacognitive self-regulation in a computer literacy course.
58
CHAPTER III
METHODOLOGY
The present research is a correlational study to assess the relationship among the
variables measuring self-regulation, internet use, and academic achievement. This
methodology section includes a description of the design of the study, the sample, the
instruments, procedures, data analysis, and the pilot study. The research questions to be
addressed are:
1. Is self-regulated learning correlated with student performance outcomes in a
computer literacy class?
2. Do self-regulated learners abstain from nonacademic browsing the internet during
computer literacy classes?
3. Is internet browsing during computer literacy classes correlated with academic
success?
Participants
The student participants were enrolled in a comprehensive university in the
southeastern United States. The university offers two-year, four-year, graduate,
professional, and doctoral degree programs which are fully accredited by the Southern
Association of Colleges and Schools. The university offers bachelor's degrees in 40
areas, master and doctoral degrees in more than 25 academic disciplines. Approximately
9,000 students are enrolled each year at the urban campus. A large proportion of the
students commute to the university.
59
For this research, two computer literacy classes taught by a single instructor were
selected. However, only two of the sections were used in the study. Since computer
literacy is a requirement of the university, students could either take a computer literacy
course or pass an examination for the computer literacy requirement. Students majoring
in computer science take a more advanced computer course than the computer literacy
course for other students. The large majority of the university students complete the
computer literacy course offered by the Computer Science Department. The computer
literacy classes provide a representative sample of the student body of the university with
the exception of those students who complete a course offered by the college of the
student major department or those who pass a computer literacy test. Approximately 30
students are enrolled in each class. The sample was 39 students who participated in this
study.
Instruments
Demographic Survey. Demographic information was surveyed by using an 8-
item questionnaire slightly modified from Graphic, Visualization, & Usability Center‟s
(GVU) WWW User Survey Questionnaire (1998) and the part of the Motivated Strategies
for Learning Questionnaire (MSLQ) in Appendix A.
Internet Use Questionnaire. This instrument was slightly modified from GVU
10th
WWW User Survey Questionnaire in order to fit in with the research environment.
This research questionnaire for internet use is in Appendix B. One measure of internet
browsing was measured by using a 6-item self-report questionnaire.
Internet Use Software. A second measure of nonacademic internet browsing was
generated from interpretation of data from computer student use in a computer literacy
60
course. A free software program, WinSpy®, was downloaded and installed in each
computer. The WinSpy® index.dat viewer read the contents of index.dat files in each
computer. Windows used index.dat files to organize data on the system. The data were
gathered from computers indicating when the users were online and offline, which web
sites were visited, and how long the user used the internet. The data were reported daily
for one week for each class.
Self-Regulated Learning. Level of students‟ self-regulated learning was
measured by 20 items chosen from the 81-item Likert Scale questionnaire, MSLQ,
generated by Pintrich, Smith, Garcia, and McKeachie (1991) shown in Appendix C.
There were no norms developed since the MSLQ is designed to be used in
individual courses (Winne & Perry, 2000). Individuals might report different levels of
motivation or use different strategies depending on the course taken (Pintrich, Smith et
al., 1991). Winn and Perry suggested the development of local norms for the different
courses or instructors at the institution.
The MSLQ manual provided a description for each scale and information for
calculating relevant statistics such as internal reliability coefficients, means, standard
deviations, and zero order correlations with a final grade for each item and scale
(Pintrich, Smith, Garcia, & McKeachie, 1991). Pintrich, Smith et al. used sample data
generated from 380 students of whom 356 were from a university, and 24 were from a
community college. The data included 37 classrooms and 14 subject domains. The
internal consistency reliability Cronbach alpha for the subscales ranged from 0.52 to
0.93. Internal consistency reliability is relevant when participants complete a
questionnaire with several items measuring one subscale or construct. Internal
61
consistency is high if each item correlates with other items on the same construct, i.e. if
all items are measuring the same thing such as self-regulation (Graziano & Raulin, 2004,
p. 89). Determinining the range for (robust) Cronbach alpha is called the internal
consistency or the internal consistency reliability of the test.
The MSLQ subscale on metacognitive self-regulation was used by the researcher
in a pilot study with 68 computer literacy students. The internal consistency reliability
Cronbach alpha was 0.78 which compared favorably with the internal reliability of 0.79
reported by Pintrich, Smith, Garcia, and McKeachie (1991).
A pilot study was performed with five computer literacy classes to assess which
items provided the best measurement of the variables. The selected items were then used
to reveal the relationships between self-regulated learning and internet use and between
internet use and academic achievement. This pilot study was performed using only the
part of the MSLQ concerning meta-cognitive self-regulation and one-item of internet use.
The instrument consisted of a 13-item Likert scale including meta-cognitive self-
regulation, one factor subscale of MSLQ, and internet use. The results of the study
guided the researcher in selecting only the metacognitive self-regulation questionnaire
items scales from the MSLQ questionnaire.
The questionnaire items for self-efficacy and test anxiety were selected based on
the studies related self-regulation and academic achievement. The importance of self-
efficacy and test anxiety is described in the literature review.
Academic Achievement. Students‟ academic achievement was measured by
using the grades from the computer literacy course. This course provided an introduction
to computers and their uses. In addition, students were made aware of computer
62
applications in the home, education, and industry. An introduction to application
software and its uses included, but were not limited to, word processing, spreadsheets,
databases, multimedia, email, and internet.
Instructional methods included discussion, lecture, required labs, visual aids, oral
reports, independent study, computer assisted instruction, and other methods as
determined by the instructor. General course requirements were class participation,
test/quizzes, examinations, hands-on computer performances, projects, reports, library
assignments, research papers, and other requirements as determined by the instructor.
Examinations were departmental so the questions were the same for all students
who took the course. The grade was determined by four exams which accounted for 50
percent of the course, and laboratory assignments for the remaining 50 percent. The
grading scale was based on a 10 percent range for each grade: A, 90-100 percent; B, 80 –
89 percent; C, 70 – 79 percent; D, 60 – 69 percent; F, below 60 percent.
Procedure
Eight weeks into the term, Participating students signed consent forms (See
Appendix D) to allow the researcher to access the course grades, use the data of internet
use from the computer, and administer and use the responses to MSLQ and internet use
questionnaires. The student names were confidential for all data collected. Participants
responded to a demographic survey, the MSLQ, and the GVU on nonacademic internet
browsing. In addition, student computers recorded numbers of websites visited during
class and the time spent on each site. The researcher accessed these data and recorded
time spent on nonacademic sites for each student. Only three of the eight class days of
63
participants‟ history of internet use were analyzed. The researcher slightly modified the
MSLQ demographic data survey to obtain general information.
Each respondent was asked to rate each item of the MSLQ on a 1-7 response scale
with 1 indicating “not true at all” to 7 indicating “always true.” The scale was odd-
numbered having a middle value, 4, which is labeled “neutral.” The reversed items had
an opposite meaning from the overall direction of the scale. The response values of these
items were reversed before summing for the total. If the respondent gave a 1, the
researcher made it a 7; 2= 6; 3= 5; 4= 4; 5= 3; 6= 2; and 7= 1. Academic achievement
was an ordinal scale using A, B, C, D, and F from highest to lowest.
Data Analysis
This research measured self-regulation, internet use, and academic achievement.
The research null hypotheses were the following:
H0 1: There is no linear relationship between students‟ self-regulation and academic
achievement.
H0 1: There is no linear relationship between students‟ self-regulation and internet
use during computer literacy course.
H0 1: There is no linear relationship between students‟ internet use and academic
achievement.
The students‟ self-reports data of MSLQ and the internet use produced interval
variables. The data were analyzed to determine if there was a correlation between self-
regulation and internet use. The Pearson product-moment correlation coefficient (r) was
used for the interval scales.
64
The variables for academic achievement data were ordinal data so that the
Spearman rank-order correlation (ρ) was used to analyze the correlation between internet
use and academic achievement.
The correlation coefficients can range from -1 to +1, which -1 or +1 means a perfect
negative or positive linear relationship among variables respectively. A zero correlation
means no linear relationship between the variables. Correlations were interpreted based
on the direction and size of the correlation coefficients, effect size, and the statistical
significance at alpha= .05.
65
CHAPTER IV
RESULTS
The purpose of this study was to investigate the relationships between self-
regulated learning, internet use, and academic achievement of college students while
enrolled in a computer literacy course. Computer literacy is a required course for
students at universities to develop and support academic achievement. Students learn
how to use the internet for research and study. Students must be able to self-regulate
their internet use to focus on academic achievement. The concerns of this research were
(a) whether self-regulated learning is correlated with student performance outcomes in
computer literacy classes; (b) whether self-regulated learners abstain from nonacademic
internet browsing during computer literacy classes; (c) whether internet browsing during
computer literacy classes is negatively correlated with student performance outcomes in
computer literacy classes.
Each student who participated in the study was enrolled in one of two sections of
a computer literacy course taught by the same instructor. Each class contained no more
than 30 students. Students answered questions regarding demographic information and
internet use and the Motivated Strategies for Learning Questionnaire (MSLQ). In the
present study, the MSLQ included only the scales of self-efficacy, test anxiety and
metacognitive self-regulation. The following null hypotheses were tested:
H0 1: There is no linear relationship between students‟ self-regulation and academic
achievement in a computer literacy course.
66
H0 2: There is no linear relationship between students‟ self-regulation and internet
use during a computer literacy course.
H0 3: There is no linear relationship between students‟ internet use and academic
achievement in a computer literacy course.
Eight weeks into the term, students responded to the demographic questionnaire, a
self-report survey of internet and computer use, and the MSLQ. Two weeks later
students were assessed for internet use. At the beginning of the data collection phase the
temporary internet files on the computers in the computer literacy course classroom were
deleted. The data were collected during three class periods in the two sections. The
students‟ amount of internet use was measured by accessing computer internet history in
the temporary internet files. The software program, WinSpy®, collected and stored the
visited sites for each student‟s computer. Each student‟s internet activity was identified
by the instructor and time of class day. Access to the internet was quantified and
organized by counting the number of mouse clicks opening new pages and categorizing
the clicks by browsing, course related, and school web sites. The number of visited web
site domains and mail checking was also counted. Academic achievement was measured
by the students‟ total points for the course and the course grade. Data from students who
withdrew from the course or who attended fewer than three days during data collection
were not included.
Description of Demographic Information
The details of the demographic information are described in Table 1. Thirty-nine
of 60 students met the criteria for data collection. Over 60 percent of the sample was
female. Most of the students were sophomores rather than freshman. The computer
67
Table 1
Description of Demographic Information
Description n Percent
Gender
Male 15 38.5
Female 24 61.5
Classification
Sophomore 31 79.5
Junior 8 20.5
High School Graduate Year
2008 1 2.6
2007 17 43.6
2006 8 20.5
2005 or before 3 7.7
Missing 10 25.6
Major
Criminal Justice 7
17.9
Mass Communication 8
20.5
Nursing 8 20.5
Psychology 4 10.3
Rehabilitation 2 5.2
Speech Therapy 2 5.2
Other 8 20.4
N= 39
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literacy course is for students with little computer experience. Science, technology,
engineering and mathematics students generally do not take this course since most have
computer experience. College majors represented in Table 1 reflect this fact.
For the semester during the research, five participants were enrolled in 3 to 13
credits, 22 were enrolled in 14 to 16 credits, and 12 were enrolled in 17 or more credits.
Five participants had taken computer literacy before.
Description of Self-reported Computer and Internet Use Survey
The findings of the self-report survey of computer and internet use are shown in
Table 2. The participants reported that they worked with computers on an average of
15.66 hours per week. They reported that they studied for the computer literacy course
on an average of 4.67 hours per week, played and had fun on the computer for on an
average of 5 to 10 hours per week, used the computer to study and do research on an
average of 5 to 10 hours per week, and checked email on an average of 5 to 10 minutes
per class.
The details of uses of the internet are shown in Table 3. The table shows the
percentage of participants who responded to the question about what they used the
internet for.
The self-reports of internet access during the computer literacy classes are shown
in Table 4. The table shows that most of the students, 61.5 percent, accessed internet
after finishing their lab, but, 20.5 percent were on the internet during the instructor‟s
lecture.
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Table 2
Computer and Internet Use Self-report Survey (Hours per Week)
Description Mean Median SD Minimum Maximum
Hours on the Computer 15.66 14 11.530 2 48
Fun and Play + 2.69 3 1.301 1 6
Study and Research + 2.69 2 .832 1 4
Use Internet during This
Course for Fun or Email
Checking (Minutes per
Class) *
2.67 2 1.034 1 5
N= 39
+ denotes item which ranges 1 to 6, 1= less than 1 hours; 2= 1 to 5 hours; 3= 5 to 10 hours; 4= 10 to
20 hours; 5= 21 to 40 hours; 6= over 40 hours.
* denotes item which ranges 1 to 5, 1= less than 1 minutes; 2= 1 to 5 minutes; 3= 5 to 10 minutes; 4=
10 to 20 minutes; 5= over 20 minutes.
Table 3
Primary Uses of The Internet
Primary Uses n Percent
Education 27 69.2
Shopping/Gathering Information 22 56.4
Entertainment 32 82.1
Work/Business 14 35.9
Communication (not including emails) 25 64.1
Gathering Information for personal needs 21 53.8
Wasting Time 17 43.6
N= 39
n is the number of responses reported for each category
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Table 4
Time Periods of Internet Access
Description of Assess Period n Percent
During Lecture 8 20.5
During Lab 9 23.1
After Finishing Lab 24 61.5
When Bored 7 17.9
Before Instruction 3 7.7
N= 39
n is the number of responses to each category
Students reported problems during internet access. When asked what problems
they have when accessing the internet, over 66 percent responded that the speed was too
slow.
Statistical Analysis of the MSLQ
Construct Validity of the Scores on the MSLQ Scales. The MSLQ questionnaire
included only the selected scales of self-efficacy, test anxiety, and metacognitive self-
regulation. Students‟ responses to the MSLQ items are shown in Table 5. For example,
item 9 stated “When I take a test I think about how poorly I am doing compared with
other students.” As highlighted in Table 6, participant 10 rated item 9 as “7.” This
means item 9, was “always true,” whereas, participant 15 rated item 9 as “2” meaning,
for this participant, item 9 was more likely “not true at all.” Each item is a statement to
be rated from 1 to 7, 1 being “not true at all,” to 7 “always true.”
71
Table 5
Ratings of the MSLQ
Item
Parti.
1
2
3
4
5
6
7
8
9
10
11
12
13
14®
15
16
17
18®
19
20
1 7 7 7 7 7 7 7 7 1 1 1 1 1 7 6 6 1 7 6 7
2 7 5 5 7 6 6 6 6 4 5 2 2 2 7 7 6 7 3 3 6
3 6 6 7 5 5 6 6 6 1 6 7 2 1 1 1 6 6 6 6 6
4 3 6 6 6 6 5 6 4 1 1 2 2 2 1 5 6 5 4 4 3
5 6 5 5 6 5 7 6 5 5 6 1 4 6 7 7 7 6 4 6 7
6 6 4 3 6 5 6 7 7 5 5 7 5 4 7 6 7 6 6 7 7
7 7 5 7 6 7 7 7 7 6 6 6 4 1 5 7 7 7 7 7 7
8 5 2 2 2 2 5 3 2 5 1 5 3 3 6 3 3 5 5 3 3
9 5 5 5 5 5 5 5 6 4 4 7 6 6 3 5 5 7 5 4 4
10 7 7 7 7 7 7 7 7 7 6 7 7 7 4 4 3 5 7 4 4
11 7 7 7 7 7 7 7 7 1 3 7 1 1 3 6 2 6 4 6 6
12 7 3 5 3 6 6 7 7 1 4 5 4 4 7 6 6 4 4 6 5
13 6 6 6 6 6 6 6 6 1 1 4 2 2 6 6 6 6 6 6 6
14 5 3 4 5 3 6 5 5 6 6 6 4 4 4 2 3 3 6 5 6
15 4 6 5 6 6 6 6 6 2 4 2 5 6 6 6 6 5 6 5 5
16 5 5 5 5 5 6 6 6 2 2 3 3 2 6 6 6 6 5 5 5
17 3 4 4 4 4 3 4 2 7 5 6 6 7 1 4 4 4 3 4 7
18 7 7 7 7 7 7 7 7 1 4 3 1 7 7 7 7 7 7 3 7
19 4 3 4 4 4 3 4 3 4 3 5 3 3 6 6 5 4 4 5 4
20 6 4 5 4 5 7 6 5 4 3 4 5 5 7 5 5 4 6 4 5
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Table 5
Ratings of the MSLQ
Item
Parti
1
2
3
4
5
6
7
8
9
10
11
12
13
14®
15
16
17
18®
19
20
21 5 5 5 5 5 7 5 6 3 3 3 3 2 6 5 5 5 4 6 5
22 6 7 7 6 7 7 7 7 6 5 7 6 7 7 4 7 7 3 6 7
23 7 7 7 7 7 7 7 7 1 4 7 1 1 7 7 7 1 7 4 7
24 7 7 7 7 7 7 7 7 1 1 1 1 1 7 7 7 7 7 7 1
25 4 3 3 3 5 5 4 4 4 4 6 5 5 5 4 4 4 4 3 4
26 5 3 5 4 5 7 5 7 3 4 3 1 1 5 6 6 5 2 5 4
27 7 7 7 7 7 7 7 7 1 4 5 6 4 4 6 4 5 5 5 6
28 7 7 7 7 7 7 7 7 3 4 7 5 4 5 5 4 5 4 4 4
29 5 7 7 7 6 7 7 7 1 3 4 1 1 6 5 6 2 7 7 6
30 6 5 7 5 5 6 6 5 6 5 6 5 5 3 5 4 5 3 4 4
31 4 5 5 4 5 7 4 6 1 2 5 2 1 7 6 6 4 6 6 6
32 5 3 6 4 5 6 6 6 2 3 2 2 2 6 6 5 4 7 4 5
33 7 7 6 6 6 7 5 3 3 4 2 5 4 4 5 5 5 5 5 3
34 6 5 7 4 7 7 7 7 1 1 6 6 7 5 6 3 3 7 6 7
35 7 6 7 7 6 7 7 7 1 5 7 1 2 5 7 7 7 7 7 7
36 5 4 5 4 6 6 5 6 6 6 7 5 5 6 6 4 6 4 5 6
37 6 4 7 5 3 6 7 6 2 5 7 2 1 5 6 6 6 7 5 5
38 7 7 7 7 7 7 7 7 1 5 7 1 1 1 7 6 6 6 7 7
39 6 5 6 6 6 6 6 6 5 4 3 2 2 2 6 6 6 2 5 5
N=39. Number of Items = 20. Parti: Participants ®: Reversed Rated Item
The ratings range from 1 to 7 where 1 means „not true at all‟ and 7 means „always true.‟
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Items 1-8 were related to the self-efficacy, items 9-13 were related to the test anxiety,
and items 14-20 measured measure students‟ metacognitive self-regulation. The MSLQ
items for metacognitive self-regulation were reduced from 12 to 8 items due to time
constraints.
Factor analysis was used to assess the construct validity of the selected MSLQ
scales of self-efficacy, test anxiety, and metacognitive self-regulation. The participant
ratings on the selected 20 items of the MSLQ were tested for sampling adequacy using
the Kaiser-Meyer-Olkin (KMO) statistics. The KMO assesses if there is some latent
structure in the data. It is referred to as the factorability of R. Small values indicate that
the correlations between pairs of variables cannot be explained by other variables
therefore, a factor analysis would be inappropriate. The KMO statistic was calculated as
0.602 which is greater than 0.6 indicating a factor analysis is reasonably appropriate
(Field, 2005).
A factor analysis utilizing principal components extraction was performed on the
selected 20 items of the MSLQ using the responses from the 39 participants. The factors
were chosen based on the eigenvalue greater than 1 and scree plots. These criteria
yielded five factors accounting for 70.3 percent of the variance. Sorted factor loadings of
the selected 20 items are shown in Table 6. The rotated component matrix used Varimax
with Kaiser Normalization is shown. The Varimax rotation enhanced interpretability of
the factors. The highest structure coefficient for each factor was 0.887 for Factor I, 0.920
for Factor II, 0.801 for Factor III, 0.714 for Factor IV, and 0.817 for Factor V.
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Table 6
Sorted Factor Loadings of the MSLQ (Item 1-20)
Item
Factors
I II III IV V
Item5 0.887 0.080 0.111 -0.076 -0.027
Item 2 0.879 -0.068 -0.047 -0.109 -0.013
Item 3 0.868 -0.220 -0.130 0.064 -0.037
Item 7 0.849 -0.087 0.163 0.243 -0.073
Item 4 0.832 -0.129 0.103 0.006 0.158
Item 8 0.724 -0.180 0.252 0.336 -0.099
Item 1. 0.716 -0.026 0.190 0.227 0.036
Item 6 0.709 -0.085 0.262 0.143 -0.181
Item 13 -0.025 0.920 -0.104 0.028 0.021
Item 12 -0.089 0.844 -0.291 0.139 0.044
Item 9 -0.367 0.603 -0.137 0.256 0.409
Item 19 0.300 -0.439 0.219 0.410 -0.053
Item 14 -0.042 0.147 0.801 -0.091 0.191
Item 16 0.185 -0.294 0.758 0.084 0.201
Item 15 0.407 -0.170 0.519 -0.147 0.129
Item 11 0.012 0.058 -0.525 0.714 0.029
Item 10 0.044 0.312 -0.036 0.677 0.459
Item 20 0.198 0.066 0.257 0.663 -0.196
Item 17 0.156 -0.022 0.104 0.078 0.817
Item 18 0.315 -0.206 0.108 0.218 0.587
Item 10 0.044 0.312 -0.036 0.677 0.459
Items considered salient to a factor were those with structure coefficients greater than
absolute .43.
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The five factors which were identified using items with structure coefficients over 0.43
are in Table 7. Factor I included 8 items, Factor II included four items, Factor III
included 4 items, Factor IV included 3 items, and Factor V included 4 items.
Factor I was interpreted as “computer literacy course confidence.” The related
items asked about the students‟ confidence in their capabilities with computers,
confidence in making excellent grades, understanding complex material, and excellence
in completing assignments or tests.
Factor II was interpreted as “personal emotional reaction to tests in computer
study.” The factor was saturated with the items concerning emotions such as thinking
about failing a test, feeling uneasy and upset, having a fast heart beat, and trying to
determine which concept they don‟t understand.
Factor III was interpreted as “the students‟ perceptions of monitoring their own
work for success.” The factor included items about the students‟ thinking of the
consequences of failing, missing important points due distractions, and about asking
themselves questions to assure understanding and then going back and to try to figure out
something confusing.
Factor IV was interpreted as “integrating prior knowledge to the present work.”
The factor was correlated with items concerning students‟ thinking about consequences
of failing, thinking about items on other parts of the test they couldn‟t answer when they
took a test, and setting goals for themselves to direct their activities in each study period.
Factor V was interpreted as “self-checking skills for the class and the instructor.”
The factor was correlated with the students‟ determinations of changing the study method
to fit the course requirements and instructor‟s teaching style, checking their recognition
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Table 7
Factor Analysis of Participants’ rating Items from the MSLQ (1-20) -Rotation
Factor Items (Varimax Rotation) Rotation
Eigenvalue
Cumulative %
of Variance
Factor I 1, 2, 3, 4, 5, 6, 7, 8 5.866 29.332
Factor II 9, 12, 13, 19 2.522 41.942
Factor III 11, 14, 15, 16 2.250 53.194
Factor IV 10, 11, 20 2.017 63.280
Factor V 9, 10, 17, 18 1.603 70.295
Note. Extract Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 6 iterations.
of their reading for class but not knowing what it was all about, and thinking about the
items on the other parts of the test they couldn‟t answer.
Bivariate correlations of the student factor scores for Factors I, II, III with student
scale totals are shown in Table 8. The correlation coefficients suggest construct validity.
Factor I (student confidence) was correlated with the scale score of self-efficacy (r= .974,
p<.01). Factor II (emotional reactions to testing) was moderately correlated to the scale
for test anxiety (r= .760, p<.01). Factor III (student monitoring for success) was
moderately correlated to the scale items of metacognitive self-regulation (r= .727, p<.01).
These correlations support the construct validity of the selected scales in the MSLQ.
Descriptive statistics for the participants‟ ratings on the selected scales of the
MSLQ are shown in Table 9. MSLQ internal consistency reliability Cronbach alpha was
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Table 8
Bivariate Correlations among Factors and The Selected Scales of the MSLQ
Scale Factor
Factor I Factor II Factor III Factor IV Factor V
Self-efficacy .974** -.111 .125 .117 -.029
Test Anxiety -.123 .760** -.309 .480** .247
Metacognitive Self-regulation .399* -.297 .727** .353* .021
N= 39.
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Table 9
Statistics for the Participants’ Ratings on the MSLQ Selected Scales
Description Self-
efficacy
Test Anxiety Metacognitive
Self-regulation
Total Score
of MSLQ
Mean 45.97 18.26 36.79 101.03
Median 45 17 37 101
SD 8.62 7.17 5.75 12.71
Range 33 29 20 58
Minimum 23 5 27 68
Maximum 56 34 47 126
Quartile (Participants)
for the Scale
1st (10) 41 13 33 93
2nd
(10) 45 17 37 101
3rd
(10) 54 23 41 110
4th
(9) 56 34 47 126
Number of Items 8 5 7 20
Item Mean 5.75 3.65 5.26 5.05
N= 39
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0.735 for the total of the three selected scales; for self-efficacy, 0.93; for test anxiety,
0.78; and for metacognitive self-regulation, 0.61.
The results of the three selected scales of the MSLQ are shown in Table 10.
Participants‟ mean on the self-efficacy scale is 5.75. The highest rated item on the self-
efficacy scale was 6.26 which asked if the participant was “expecting to do well in this
class.” The lowest rated item of the scale asked if the participant “understood the most
difficult material” with a mean of 5.23. Participants‟ mean on the test anxiety scale is
3.65. The highest rated item on the test anxiety scale was 4.74 which asked if the
participant thought “of the consequences of failing.” The lowest rated item of the scale
asked if the participant thought “about the items on other parts of the test I can‟t answer”
with a mean of 3.05. Participants‟ mean on the metacognitive self-regulation scale is
5.26. The highest rated item on the metacognitive self-regulation scale was 5.49 which
asked the participant if “When confused about something I‟m reading for this class, I go
back and try to figure it out.” The lowest rated item of the scale asked if the participant
tries “to change the way I study in order to fit the course requirements and instructor‟s
teaching style” had a mean of 5.05.
Statistical Analysis of Internet Use
Data for each participant‟s internet usage for three class days are shown in Table
11. Participants‟ history of internet use was analyzed for three of the eight days
collected. The participants‟ login ID was the same for each individual. Since individual
participants had no personal login ID or password, it was assumed that the participants
started and finished the class period within the designated time on the same computer. In
addition the instructor noted if anyone moved to a different computer.
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Table 10
Results of the Three Selected Scales of the MSLQ
Scale Item Mean SD
Self-Efficacy Item 1. 5.77 1.202
Item 2. 5.23 1.530
Item 3. 5.74 1.371
Item 4. 5.46 1.393
Item 5. 5.64 1.267
Item 6. 6.26 1.019
Item 7 6.00 1.124
Item 8. 5.87 1.436
Test Anxiety Item 9. 3.79 2.064
Item 10. 3.05 1.609
Item 11. 4.74 2.099
Item 12. 3.33 1.896
Item 13 3.33 2.156
Metacognitive
Self-regulation
Item 14. 5.26 1.788
Item 15 5.49 1.374
Item 16 5.33 1.383
Item 17 5.05 1.572
Item 18 5.18 1.571
Item 19 5.13 1.239
Item 20 5.36 1.478 N= 39.
The ratings range from 1 to 7 where 1 means „not true at all‟ and 7 means „always
true.‟
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Table 11
Data for Each Participant Internet Usage for Three Class Days
Participants Number of Internet
Access Clicks
Number of Internet
Browsing Clicks
Number of Course
Related Clicks
Number of College
Web Clicks
1 27 12 0 15
2 7 7 0 0
3 31 27 4 0
4 9 7 1 1
5 11 2 9 0
6 30 25 5 0
7 24 20 4 0
8 115 115 0 0
9 55 32 16 7
10 61 46 15 0
11 54 54 0 0
12 51 43 8 0
13 87 74 12 1
14 67 41 6 20
15 21 9 9 3
16 23 17 4 2
17 59 52 7 0
18 32 32 0 0
19 26 20 2 4
20 10 4 5 1
21 13 5 6 2
22 14 0 14 0
23 38 31 3 4
24 45 44 1 0
25 19 6 12 1
26 33 19 14 0
27 11 6 4 1
28 34 10 24 0
29 93 66 17 10
30 10 7 3 0
31 90 75 15 0
32 28 18 9 1
33 78 65 13 0
34 16 11 5 0
35 2 0 2 0
36 48 39 3 6
37 69 49 17 3
38 41 28 13 0
39 9 0 9 0
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For the measure of internet use, the middle 50 minutes out of the 80 class minutes
were examined. This was done to insure that the computer was utilized during class
instruction. Because of the difficulty of measuring the minutes of internet use by the
participants, the number of mouse clicks was chosen instead. Internet use history was
categorized as the total number of internet access clicks and details of internet access.
The total number of internet access clicks included the number of non-course related
internet browsing clicks, course related clicks, and college web clicks. The details of
internet access included the number of visited web sites and clicks of mail checking.
Data for participants‟ measured history of internet use during class is shown in Table 12.
Students clicked the internet an average of 13 times with the mode of 9 per 50 minutes
class time. The median of the internet access clicks was 32 times in 50 minutes of class
time. There was a statistically significant correlation between the total number of internet
access clicking and non-course related internet browsing clicking (r= .964, p < .01) as
would be expected. Students visited on an average of over 2 websites per 50 minutes
class time. These web sites may involve multiple clicks.
Statistics of Academic Achievement
The data for academic achievement are based on content learning and computer
skills and are shown in Table 13. Assessment for learning content material was from the
sum of the scores on four exams and assessment of computer skills was from four lab
scores. The academic achievement score included both the content material and
computer skills plus bonus points. The mean of the participants‟ academic grades was
2.9 as the grade range was from 1 to 4 with 0 as a grade of an F, 1 as a grade of a D, 2 as
a grade of a C, 3 as a grade of a B, and 4 as a grade of an A.
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Table 12
Data from Three Class Days of Internet Use Averaged for One Class Period (50
minutes), N = 39 Participants
Internet Clicks during Class
Time
Total Percent Mean per
Participant
SD Range
Number of Internet Browsing
Clicks
344 69 9 8.65 38
Number of Mail Checking
Clicks
28 6 1 1.24 5
Number of Course Related
Clicks
97 19 2 2.01 8
Number of College Web
Clicks
28 6 1 1.43 7
Total Number of Internet
Access Clicks
497 100 13 9.30 38
Total Number of Web Sites
Visited
92 - 2.35 1.55 7
The mean score was 80.74 percent of total points possible. The scores of
participants who withdrew the course were not included in the study. No student failed
the course. The percentage score for computer skills was 29.11 percentage points higher
than content learning scores on an average. The correlation coefficient between scores
for computer skills and content learning was 0.402 (p<.05).
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Table 13
Data of Course Grade, Average Score, Content and Skill Achievements
Description Percent Mean SD Range Minimum Maximum
Course Grade
(4.0 Scale)
2.90 .852 3 1 4
A (n= 10) 25.6
B (n= 17) 43.6
C (n= 10) 25.6
D (n= 2) 5.2
F (n= 0) 0
Content Achievement
Percent
53.54 8.092 39.50 40 79.50
Skill Achievement
Percent
82.65 13.354 54.50 43.50 98
Mean Difference
between Content and
Skill Achievements
-116.46 50.120
t= -14.5, Sig.= .000
Average Score
Percent
80.74 9.233 46 57 102
Correlations between Self-regulation, Internet Use, and Academic Achievement
The bivariate correlation matrix between each of the three subscales of the
MSLQ, self-efficacy, test anxiety, metacognitive self-regulation, self-reported computer
time and internet use, computer history recorded internet use, and academic achievement
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are shown in Table 14. The correlation between the constructs self-efficacy and
metacognitive self-regulated learning was statistically significant (r= .53, p < .01) as
might be expected. Note that self-efficacy was also correlated with course grade (r= .48,
p < .01) and skill achievement (r= .46, p < .01). Self-regulation was not highly correlated
with any of the other variables as was stated by the null hypotheses.
The second research question regarding the correlation between self-regulation
and internet use showed a low negative correlation (r= -.14, p > .05) which was not
statistically significant. Self-efficacy also showed a low negative correlation with
internet use (r= -.26, p > .05). Neither correlation was statistically significant, but, it is
interesting to note that both correlations are negative, as might be predicted, i.e., students
who are self-regulated and efficacious would not be expected to spend a lot of time and
effort in nonproductive activity during class. Correlations between test anxiety and the
number of visited websites and the number of course related clicks were not statistically
significant. But test anxiety was negatively correlated with visited websites (r= -.24, p >
.05) and positively correlated with course related clicks (r= .22, p > .05). Test anxiety
showed statistically significant correlations with self-reported computer use for fun and
play (r= .35, p < .05), and self-reported computer use for research and study (r= .33, p <
.05).
The third research question regarding the correlation between internet use and
academic achievement showed a low negative correlation which was not statistically
significant (r= -.23, p> .05). This finding may be explained by the more highly correlated
(r= .60, p< .01) self-reported hours on the computer and the computer hours for fun and
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Table 14
Bivariate Correlations between the MSLQ, Internet Use and Grades
Description 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1. Self-efficacy _ -.20 .53* -.06 .20 -.09 .11 -.26 -.26 .11 .06 .13 .48* .46* .27
2. Test Anxiety _ -.31 -.00 .07 .35* .33* -.07 -.11 -.24 .22 .04 .02 -.08 -.18
3. Self-regulation _ -.07 .09 -.22 .02 -.17 -.14 .10 -.07 .02 .18 .17 .15
4. Hrs. on Computer _ -.15 .60** .07 .17 .11 .19 .21 .15 .04 .07 .16
5. Hrs. of Study _ .01 .27 -.24 -.20 -.15 -.01 -.15 -.08 -.11 -.11
6. Computer for Fun _ .25 .23 .20 -.06 .19 .30 -.05 .01 -.07
7. Study on Computer _ -.25 -.25 -.34* .08 .27 -.12 -.13 -.20
8. Number of Clicks _ .96** .60** .29 .07 -.23 -.19 -.22
9. Internet Browsing _ .56** .11 .08 -.27 -.23 -.23
10. Visited Web Sites _ .29 -.01 -.00 -.00 .03
11 Course Related _ -.16 .10 .17 -.17
12. Mail Checking _ .12 -.08 -.33*
13. Course Grade _ .85** .67**
14. Skill _ .40*
15. Content _
N= 39.
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
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play. Computer use for study and research was negatively correlated to the number of
visited web sites (r= -.34, p<.05). Mail checking was also negatively correlated to
content achievement (r= -.33, p < .05), but showed no correlation with skills achievement
(r= -.08, p> .05). Study hours for this course was significantly correlated to the credits
taken for semester (r= .33, p < .05).
Correlations between Factors, Internet Use, and Academic achievement
The correlations between the five factors and self-reported total hours per week of
computer and internet use, class time internet use measured by numbers of clicks, and
academic achievement are shown in Table 15. The factor analysis was used to assess the
reliability of the MSLQ scales. The three selected scales of the MSLQ and the first three
factors showed high correlations of 0.974, 0.760 and, 0.727 (p < .01). Factor I, which
include the same items as self-efficacy, was correlated to grades (r= .47, p<.01) and skill
achievement (r= .46, p<.01). This correlation compares favorably with the scale score
self-efficacy with grades and skills achievement. None of the five factors were
significantly correlated to other variables related to the internet use.
Summary of Results
This study is an effort to find the effects of self-regulation and internet use on
academic achievement for college students in a computer literacy course. Demographic
data, internet use questionnaires and internet history, and the selected items from the
MSLQ scales measuring self-efficacy, test anxiety, and self-regulation were collected and
analyzed by using SPSS. Bivariate correlations between measures of self-regulation,
internet use, and academic achievement were used to reveal any relationships. Factor
analysis was utilized to assess the construct validity in the MSLQ selected scales and the
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Table 15
Bivariate Correlations between Factors, Internet Use, and Academic Achievement
Description Factor
I
Factor
II
Factor
III
Factor
IV
Factor
V
Hours of Work with Computer -.07 -.15 -.18 .10 -.04
Hours of Study with Computer .26 .15 .01 -.13 .17
Credits taking for Semester -.28 .14 -.19 .33* -.29
Computer Use for Fun and Play -.11 .22 -.22 .26 -.09
Computer Use Study and Research .13 .38* .06 .11 .18
Number of Internet Access Clicks -.25 -.21 -.25 .02 -.25
Number of Internet Browsing Clicks -.26 -.24 -.22 -.03 -.12
Number of Visited Web Sites .11 -.30 -.13 -.07 -.22
Number of Course Related Clicks .06 .12 -.11 .13 .02
Number of Mail checking Clicks .10 -.03 -.11 .17 -.16
Course Grade .47** .06 .10 .04 .07
Skill Achievement .46** .08 .15 -.12 -.03
Content Achievement .23 -.03 .18 -.07 -.17
N= 39.
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
factor scores supported the validity of those measures. Neither factor scores nor the
scores on the MSLQ scales showed statistically significant correlations with
self- regulation and academic achievement, self-regulation and internet use. There was
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no statistically significant correlation between internet use and academic achievement.
For each of the three research questions, the null hypothesis was not rejected.
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CHAPTER V
CONCLUSION
This study of correlation between internet usage and academic achievement
focuses on the effects of self-regulation based on Bandura‟s (1986) social cognitive
theory. Triadic forms of self-regulation are shown in Figure 2. Metacognitive self-
regulation is labeled “covert self-regulation” in that the students regulate their own
learning processes such as reviewing material or connecting ideas (Zimmerman, 1989).
Research has shown that self-regulation has a positive influence on academic
achievement in many fields (Niemczyk & Savenye, 2001; Saparniene, Merkys, &
Saparnis 2005; Schunk & Ertmer, 1998; Weinstein, Husman, & Dierking, 2000;
Zimmerman & Martinez-Pons, 1986). McKeachie (2000) and Pintrich (1995) reported
that students can learn and be taught self-regulated leaning strategies. The current study
investigated if self-regulation was related to effective internet use and academic
achievement in a computer literacy course. LaRose, Lin, and Eastin suggested that
unregulated internet use was from a deficiency of self-regulation (2003). The students
who participated in this study responded to the Motivated Strategies for Learning
Questionnaire (MSLQ) composed of the selected scales to measure self-efficacy, test
anxiety, and metacognitive self-regulation. These variables were correlated with internet
usage to see if the correlations conform to the predictions based on the theory. The
results suggest that self-regulation does influence students‟ internet use and the internet
use influences students‟ academic achievement. In the sample of 39 college students in
the computer literacy courses, no statistically significant correlations were observed and
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the three null hypotheses were not rejected, but all the correlations of academic
achievement were positive.
Findings
There was no statistically significant correlation between metacognitive self-
regulation and academic achievement and the null hypothesis was not rejected. However,
the correlations of self-regulation with all the measures of academic achievement were all
positive (course grade, r= .19 p > .05; skills, r = .17 p > .05; content, r = .15 p > .05).
Pintrich, Smith, Garcia, and McKeachie (1991) reported a correlation of .30 for the self-
regulation scale with course grades in a university (N= 380). The finding is consistent
with the other research in which Niemczyk and Savenye (2001) reported the low
correlation (r = .11, p > 05) between metacognitive self-regulation and computer literacy
course grade. Self regulation is only possible for those students with self-efficacy in that
they feel they can be successful students through controlling their own behavior. As
might be expected, there was a statistically significant correlation between self-regulation
and self-efficacy (r= .53, p< .05). Pintrich, Smith et al. reported a correlation of .46 for
the self-regulation scale with self-efficacy. Self-efficacy had a correlation with academic
achievement (r = .48, p < 05) and with skills (r = .46, p < .05). Pintrich, Smith et al.
reported a correlation of 0.41 for the self-efficacy scale with course grades. The finding
was consistent with other research in which the high achieving students used self-
regulated learning strategies more than low achieving students (Zimmerman & Martinez-
Pons, 1986).
The selected 20 items of the MSLQ questionnaire in the present study were eight
items for the scale measuring self-efficacy, five items for the scale measuring test
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anxiety, and seven items for the scale measuring metacognitive self-regulation. A factor
analysis using a Varimax rotation produced five factors explained seventy percent of the
total variance. All of items from the self-efficacy scale loaded on Factor I with structure
coefficients ranging from 0.887 to 0.709. Of the Items identified in the MSLQ as
measuring the construct test anxiety, the items loaded on the Factor II with structure
coefficients ranging from 0.920 to .439. Of the MSLQ items for the metacognitive self-
regulation scale, the items loaded with structure coefficients ranging from .801 to .519.
The last two factors were not interpretable and were not used. The first three factors
accounted for fifty-three percent of total variance and shared high structure coefficients
with other constructs. A correlation of participants‟ factor scores and their totals for each
scale showed strong correlations with the first three factors supporting construct validity
of the scales of the MSLQ. The most highly related construct is self-efficacy with Factor
I, test anxiety with Factor II, and metacognitive self-regulation with Factor III. Some
MSLQ items did not show high correlations with the three identified factors. Those were
item 10, item 11, and item 9. This may indicate that the items may have multiple
interpretations by the participants in this sample. Item 9 interpreted as test anxiety and
regulating. Item 11 interpreted as metacognitive self-regulation and planning. Item 10
interpreted as planning and regulating.
The factor analysis using the sample scores shows that the construct for self-
regulation was not as distinct as a single factor as self-efficacy. Five factors were
identified and interpreted using the participant responses on the three selected scales.
Factor I was self-efficacy, Factor II was test anxiety, and Factor III, IV, V were aspects of
self-regulation. Factor III was interpreted as monitoring, Factor IV was planning, and
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Factor V was regulating. Factor III showed a correlation of 0.727 with the MSLQ self-
regulation scale which would support construct validity of that scale, but it was lower
than the other two scales. It is interesting to note that these three Factors III, IV, and V
are the components of self-regulation as discussed by Pintrich (2000), but not separated
in the scales of the MSLQ instrument. This diffusion of the self-regulation construct may
explain the lower correlations in the current study.
Test anxiety was negatively correlated with self-efficacy (r= -.20, p > .05), self-
regulation(r = -.31, p > .05), and content achievement (r= -.18, p > .05). These findings
were consistent with the other research in which the decreased test anxiety helped
students to increase their self-efficacy and achievement (Pintrich & DeGroot, 1990).
The second hypothesis tested was that there was no correlation between students‟
self-regulation and internet use in a computer literacy course. As measured, 69 percent of
internet access was for nonacademic internet browsing. Even though this null hypothesis
was not rejected, a self-regulated learner was less likely to access the internet during a
computer literacy class (r= -.17, p > .05).
A surprising result was that students who had high test anxiety clicked more
course related internet sites (r= .22, p > .05) and visited fewer nonacademic sites (r= -.24,
p > .05). Test anxiety unusually acts as a motivational tool to use the internet for course
related goals as Garcia suggested (1995). Students who had test anxiety prepared their
work in advance to make failure less likely (Garcia). Test anxiety was correlated with the
hours of computer use for fun and research (r= .35, p < .05) and for study and research
(r= .33, p < .05). Test anxiety leads students to use the computer in both ways. Test
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anxiety plays not only as a motivational strategy as Garcia noted (1995) but also works as
a distraction (LaRose, Mastro, & Eastin, 2001).
The third hypothesis was that there was no correlation between students‟ internet
use and academic achievement. Students‟ internet use did not show a statistically
significant influence on their academic achievement. However, the correlations of
internet access with all the measures of academic achievement were negative (course
grade, r= -.23 p > .05; skills, r= -.19 p > .05; content, r= -.22 p > .05). The correlations of
non-course related internet browsing with all the measures of academic achievement
were also all negative (course grade, r= -.27 p > .05; skills, r= -.23 p > .05; content, r= -
.23 p > .05). A statistically significant correlation (r= .60, p < .01) was found between
internet access and nonacademic internet browsing. Students who did not accomplish
skills achievement were more likely to check mail during class hours (r= -.33, p < .05)
and mail checking was six percent of the total internet access. Those findings support the
contention that internet access and internet browsing does influence students‟ grades.
Also, these findings are supported by the work of LaRose, Lin, and Eastin (2003) who
also reported negative influence of internet use.
In interpreting the data there are some interesting findings that may further the
goals of the research. Students who reported that the use of the computer was mainly for
study were less likely to access internet (r= -.25, p > .05) and browse internet
nonacademic related sites (r= -.25, p > .05), and visit many web sites (r= -.34, p < .05).
Whereas, students who reported they used the computer for fun and play were more
likely to access internet (r= .23, p > .05), browse nonacademic related internet sites (r=
.20, p > .05), access internet course related sites (r= .19, p > .05), and mail checking (r=
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.30, p < .05). Over seventeen percent of the students self-reported that they use internet
when they were bored.
Sixty-five percent of students in the present study, used internet mostly after
finishing a lab during computer literacy class. Also, 82 percent of students reported
entertainment as their primary usage of internet. As Niemczyk and Savenye (2001) noted
that students took the computer literacy course because the content would be helpful and
attractive, 69 percent of the internet access was internet browsing and 82 percent of the
students reported that the primary use of the internet was for entertainment. The easy
access of internet acts as an “attractive distraction” from work and may lead the students
to think about things that do not relate to the class.
Implications
This study explored the relationships between the variables self-regulation,
internet use, and academic achievement in a computer literacy course. Self-efficacy
appeared to play the major role in the computer literacy course. This study supports
Zimmerman‟s (1989) contention that only learners with self-efficacy will utilize self-
regulated learning strategies. Self-regulated learning strategies are used to modify the
environment, set goals, monitor behavior in order to succeed. As students spend time
studying on the computer, they visit web sites more and may be attracted to browsing
nonacademic internet sites. The attractive distraction of the internet can affect student
contraction and achievement. While self-efficacy is correlated to high academic
achievement, self-regulated learning strategies should be embedded in a computer
literacy course to help students deal with the distractions of the internet and to take
control of their own learning.
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Students‟ perceived test anxiety did not correlate with their internet use during
class, but did correlate with self-reported time spent on the computer for fun and play and
for study and research in this study. The three determinants formed by Bandura (1977b)
support the relation between students‟ control of their cognition and their behavior.
Behavior influences a person‟s cognition and vice versa. Niemczyk and Savenye (2001)
insisted that students self-regulation of their own learning in a computer literacy course
was of great importance. The students who are unregulated are students who have a
deficiency in self-regulation (Mastro & Eastin, 2001). According to Pintrich (1995),
students can be taught and can learn self-regulation. The self-regulated students can
control their own learning which influences their academic achievement. The instruction
of self-regulation can be embedded during class instruction so that the students can learn
how to plan and control their internet use during a computer literacy course. Self-
regulation instruction must be a useful tool during computer-based courses to enhance
academic achievement. Also students‟ strengths or weaknesses in using learning
strategies should be evaluated and improved.
Future Studies
The present study was limited by a sample derived from sophomores and juniors
who participated in a computer literacy course under a single instructor in an HBCU
University. In order to confirm the importance of the correlation between self-regulation,
internet use, and academic achievement, a large sample at several institutions should be
taken. The best approach would be a longitudinal study during class periods for a full
term. It may be difficult to generalize these results to other age groups. For example, a
freshmen or senior might have different correlations between self-regulation, internet use,
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and academic achievement. Also, in the current study students‟ majors were limited to
non-science majors, non-engineering, and non-mathematics so that internet experience
and usage may be limited in comparison.
When asked about the students‟ internet use, the question was limited to that
“when do you use internet during class,” and students answered “before class” or “after a
lab.” A more detailed study may ask about the period of time the internet is used, such
as how many minutes of the internet was used during class, and then compare the amount
time of perceived internet use and recorded internet use. There was an assumption that
the students‟ skills of the internet and the computer were the same to all, experts or
novices.
Future studies on self-regulation on internet use and academic achievement need
to explore several factors such as: (a) how students manage their environmental
resources, time on computer and internet use? (b) what kind of strategies students
elaborate for their work? and (c) when do they utilize their strategies? For the effective
learning as McKeachie (2000) stated, students should know how to use and when it is an
appropriate situation to apply their strategies. The students‟ cognition of their resources,
e.g., time, is necessary for the students to plan, control, and regulate to achieve their goals
(Pintrich, 2000). One of the responses for the reason to use the internet during class was
when they were bored; therefore students‟ proper time management will enhance the
management of their class hour.
Further study on interaction between self-regulated internet use and academic
achievement is recommended. The present study was limited to two sections of a
computer literacy course to assess students‟ internet use perception and students‟
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academic achievement. Investigating students‟ self-efficacy and self-regulation could
assist students in achieving academic goals.
In the factor analysis, there were three items with high structure coefficients
across several factors. Further refinement of the MSLQ instrument could more precisely
define the factors and enhance interpretability of results.
Summary of Conclusion
The purpose of this study has been to determine if there were correlations among
students‟ self-regulation, internet use, and academic achievement. The three null
hypotheses were not rejected although some correlations warranted further investigation.
The students‟ self-regulation negatively correlated with their internet browsing and
positively correlated with their academic achievement, and their nonacademic internet
browsing negatively correlated with their academic achievement.
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APPENDICES
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Appendix A
Demographic Information
1. Gender (Circle one) Male Female
2. What year did you graduate from high school?
3. Class level (Circle one) Freshman Sophomore Junior Senior
4. What is your major? ___________________________________
5. How many hours per week do you work with a computer?
6. How many hours a week do you study for this course?
7. How many classes are you taking this term? __________
8. How many times did you take this course? 1st 2
nd 3
rd 4
th
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Appendix B
Internet Use Questionnaire
1. Primary use of the web?
How do you primary use the Web for? (Please check all that apply)
a. Education
b. Shopping/gathering product information
c. Entertainment
d. Work/business
e. Communication with others(not including emails)
f. Gathering information for personal needs
g. Wasting time
h. Other: state________________________
2. Have Fun and Explore
How many hours per week do you use your computer for fun/play?
a. Less than 1
b. 1 to 5 hours
c. 5 to 10 hours
d. 10 to 20 hours
e. 21 to 40 hours/week
f. over 40 hours/week
3. Have research or job
How many hours per week do you use your computer for your study or research?
a. Less than 1
b. 1 to 5 hours
c. 5 to 10 hours
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d. 10 to 20 hours
e. 21 to 40 hours/week
f. over 40 hours/week
4. Frequency
How long do you use internet during this course for fun or email checking?
a. None
b. Less than 5 min
c. 5 to 10 min
d. 10 to 20 min
e. over 20 min
5. When do you access internet during this course?
a. Lecture
b. Lab
c. When finished your Lab work
d. Bored
e. Else, describe it___
6. In your opinion, what is the single most critical issue facing the internet?
a. Finding things/navigating around
b. Speed/Bandwidth
c. Else, describe it ________________________
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Appendix C
Motivated Strategies for Learning Questionnaire (MSLQ)
Self-efficacy for learning and performance.
1. I believe I will receive an excellent grade in this class.
2. I‟m certain I can understand the most difficult material presented in the readings
for this course.
3. I‟m confident I can understand the basic concepts taught in this course.
4. I‟m confident I can understand the most complex material presented by the
instructor in this course.
5. I‟m confident I can do an excellent job on the assignments and tests in this course.
6. I expect to do well in this class.
7. I‟m certain I can master the skills being taught in this class.
8. Considering the difficulty of this course, the teacher, and my skills, I think I will
do well in this class.
Test Anxiety.
9. When I take a test I think about how poorly I am doing compared with other
students.
10. When I take a test I think about items on other parts of the test I can‟t answer.
11. When I take tests I think of the consequences of failing.
12. I have an uneasy, upset feeling when I take an exam.
13. I feel my heart beating fast when I take an exam.
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Metacognitive Self-regulation.
14. During class time I often miss important points because I‟m thinking of other
things (REVERSED).
15. When I become confused about something I‟m reading for this class, I go back
and try to figure it out.
16. I ask myself questions to make sure I understand the material I have been
studying in this class.
17. I try to change the way I study in order to fit the course requirements and
instructor‟s teaching style.
18. I often find that I have been reading for class but don‟t know what it was all
about. (REVERSED)
19. When studying for this class I try to determine which concepts I don‟t understand
well.
20. When I study for this class, I set goals for myself in order to direct my activities in
each study period.
The Motivated Strategies for Learning Questionnaire ©. Copyright 1991 by The Regent
of The University of Michigan. All rights reserved.
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Appendix D
Consent Form
1. Title of Research Study: The Relationships among Self-Regulation, Internet Use
and Academic Achievement in a Computer Literacy Course.
2. Investigator: SungHee YangKim, Science/Mathematics Department Southern
University Baton Rouge, 225-771-2085, [email protected].
3. Purpose of the Research: The present investigation is an effort to assess the
relationships among self-regulation, internet use and academic achievement in a
computer literacy course. This information obtained from this study will be used in
my dissertation and will partially fulfill the requirements for the Ph. D. degree in
Science/Mathematics Education from Southern University and A&M College.
4. Procedures for this Research: You will be asked to respond to three questionnaires,
a demographic survey, self-regulation, and internet use to examine the relationship
among self-regulation, internet use and academic achievement. You were selected
because you are in computer literacy course. Additional data required for the study are
internet use during class and your class test scores and mid-term and final grades. This
survey should take approximately 15 to 20 minutes.
5. Potential Risks or Discomforts: There are no potential risks associated with this
study.
6. Potential Benefits to you or Others: The potential benefits of this study are
improved understanding of how self-regulation, internet use and academic
achievement are related.
The demographic survey, MSLQ and internet use will be conducted by SungHee
YangKim as the principle investigator under the supervision of Dr. Juanita Bates,
professor of Science and Mathematics Education at Southern University and A&M
College. In addition, I would like to collect your grades of this course and test scores
from your college.
Participation is voluntary and not related in any way to your grade in the class. You
may withdraw consent at any time without consequences. There is no right or wrong
answers to this questionnaire. A code will be assigned to all participants so that your
name and identities remain anonymous. Also your name will not be used in any
publications, reports, or presentations that might result from this study. Questions
regarding this study should be forwarded to me at 550 Lee Dr. Apt #21 Baton Rouge, LA
70808, or by email at: [email protected]. You can also contact my supervisor, Dr.
Joseph Meyinsse, at Department of Science/Mathematics Education, P. O. Box 9256,
Baton Rouge, LA 70813-9256, phone # (225) 771-2085.
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If you have questions or concerns about your rights as a research volunteer in this study,
or you want report a research-related injury, contact Sandra C. Brown, DNS, School of
Nursing, Southern University - Baton Rouge, Baton Rouge LA 70813; Voice - 225-771-
5145; Facsimile - 225-771-2349; E-mail - [email protected].
Sincerely,
SungHee YangKim
I, ____________________________, may complete the surveys, Demographic Survey,
Self-regulated Learning Strategies, and Internet use.
Information gained from this survey will be used for the sole purpose as stated above.
Student‟s Signature _______________________________ Date _______________
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VITA
SungHee YangKim earned the Bachelor of Engineering degree in electrical
engineering from Kyungpook National University, Daegu, South Korea. After
graduation, she worked at Korea Mobile Telecommunication, Inc., South Korea for four
years and then came to United States with her husband.
She received a Master of Science degree in Computer Science at Southern
University in Baton Rouge, Louisiana. She entered Science/Mathematics Education
program at Southern University Baton Rouge for her Ph. D.
Permanent Address: 550 Lee Dr. Apt # 21 Baton Rouge, LA 70808
This manuscript was typed by the author.
85
APPROVAL FOR SCHOLARLY DISSEMINATION
The author grants to the Southern University Library the right to reproduce, by
appropriate methods, upon request, any or all portions of this dissertation.
It is understood that “request” consists of the agreement on the part of the
requesting party, that said reproduction is for his personal use and the subsequent
reproduction will not occur without written approval of the author of this dissertation.
The author of this thesis reserves the right to publish freely, in the literature, at
any time, any or all portions of this dissertation.
Author__________________
Date____________________
