The effects of self-regulated learning strategies and system satisfaction regarding learner's performance in e-learning environment

The effects of self-regulated learning strategies and system
satisfaction regarding learner’s performance in e-learning
environment

Jong-Ki Lee
Kyungpook National University,
mirae91@knu.ac.kr

Abstract

An e-learner’s characteristics are very important variables with regards to
educational performance and the e-learning environment.
This study suggests a research model, based on a successful e-learning model,
which presents the relationship between e-learner’s self-regulated learning strategies
and the quality perception in LMS (learning management systems). This research
model focuses on self-regulated learning strategies and satisfaction with the learning
environment.
This learning environment consists of a learning management system, learning
content, and interaction that are provided by e-learning. Especially, this study suggests
that e-learner’s self-regulated learning strategy is very important in e-learning
performance. The validity of the model will be shown empirically.

Keywords

e-learning, LMS, self-regulatory efficacy, self-regulated learning strategy


















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Introduction
The sloan consortium online learning survey report (2004) showed that online
learning is at historically high levels and has not reached a plateau in its annual growth
rate of approximately 20%. The report also revealed that a majority of institutions of
higher education say online learning is just as good as traditional classroom instruction,
and it is believed that online learning will experience the same relative improvements as
face-to-face instruction.
Many researchers stay on an exploratory study regarding explanations of
variations in e-learning effectiveness (Wang, 2003). The tendency in educational
engineering in introducing theoretical variables which explain e-learning effectiveness is
insufficient except for a few information systems (Piccoli et al., 2001). Moreover, this
approach of putting together information systems and educational engineering is rarely
observed.
This research investigates the theoretical background of pedagogical e-learning. It
closely examines the relationship between information systems success models and e-
learning. In addition, it suggests as well as verifies new research models that assess or
evaluate e-learning effectiveness, based on models of educational engineering
variables and information systems, which can be verified theoretically or empirically.
Moreover, this study suggests that a learner’s self-regulated learning strategy is a very
important variable which is related to the e-learner’s scholastic performance.

Theoretical background

Self-directed learning
The teaching-learning method in distance learning is assumed to be self-directed
learning (SDL), which is supported by the educational philosophy of constructivism.
According to constructivism theory, e-learning is an active information process because
knowledge generation is accomplished through individual experience, maturity and
interaction with one’s environment. Due to this point of view, the educational philosophy
of constructivism is distinguished from objectivism in that the learner is regarded as a
passive recipient of information (Rovai, 2004).
Learning performance in regards to e-learning is possibly lower than a crammed
educational style based on objectivist educational philosophy, with the exception of a
strategic approach relating to the efforts and studies for the pleasure of the self-learner.
The SDL teacher is available as an assistant and guide for learning, not as a unilateral
knowledge source and messenger (Lee et al., 2008).
Learners take the lead in self-regulated learning for the development of a total
learning process that involves problem perception, adoption, and assessment of
alternatives (Kang, 1999). Learners play the same roles that the producers do by
organizing or re-organizing knowledge like a consumer, by selecting knowledge and
using it practically (Maddux & Johnson, 1997; Westera & Sloap, 1998).
E-learning must be considered as one of many SDL strategies. The reason is
that an e-learner attends a lecture only to register the time, place, subject, and to alter
the order of attending lectures. Proper monitoring of the learner is difficult in
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comparison with the off-line education already being used, not only because the
learning progress method of evaluation is being altered, but because personal meetings
with the teacher are also no longer part of the process. Therefore, it is important to
manage one’s ability to organize self-learning time, process information, plan data, and
control data.

Self-regulatory efficacy and self-regulated learning strategy
According to the cognitive psychology theory, self-regulatory efficacy (SRE) is
defined as the efficacy of well-performed self-regulatory mechanisms such as self-
observation, self-judgment, and self-response. Self-observation is defined as
intentionally concentrating on various aspects. It provides information based on
evaluating one’s work as well as setting goals, relates to self-regulatory functions in
order to assess the degree of task progress, and leads to behavior changes. Self-
judgment compares the learner’s goals to his or her present performance. It is
determined by affinity or negativity according to a criterion adapted and based on
judgment, character of goal, and significance of self-achievements based on one’s
purpose (Bandura, 1986).
In addition, self-response is explained by motivating oneself to change and control
behavior according to the satisfaction of goal progress. The learner’s independent
assessment of self-regulated learning ability is called self-regulatory efficacy (SRE;
Bong, 1998). Self-response is also measured by one’s self-confidence in mastering a
subject as high as a SRE’s learner is willing to attain. Confidence promotes learning
performance through the promotion of individual goals such as traditional education
psychology (Bandura, 1997).
Self-regulated learning is required for academic performance completed through
SDL. Self-regulated learning is defined as a learner’s intended effort toward learning
subjects (Corno & Mandinach, 1983). In addition, it is a systematic management
process regarding one’s own thoughts, emotions, and behavior regarding one’s
personal goals and achievements (Schunk, 2000).
In regards to self-regulated learning strategies, Zimmerman and Martinez-Pons
(1988) reported on the correlation between self-regulated learning strategy and the
person’s grades in Mathematics and English. As well, Zimmerman, Bandura, and
Martinez-Pons (1992) researched the causes and consequences of studying roles to
self-efficacy. Zimmerman and Bandura (1994) also reported on the correlation between
self-efficacy and self-regulated learning strategy. Zimmerman and Martinez-Pons
(1986) developed an integrated strategy, based on a self-regulated learning strategy,
including self-testing, organizational transformation, goals and planning, pursuing
information, recording and checking, structured environment, strength, demonstration
and memory, seeking help, and reviewing strategy. Their opinions are very important in
e-learning environment.
According to self-regulated learning principles, the learner uses the strategic
relationship between self-regulation and learning to reach his or her chosen self-
learning goal, and to develop, revise, and complement the learning strategy via self-
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feedback. Therefore, the learner must make a constant effort to sustain learning
motivation (Zimmerman, 1990).
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Information systems success model and learning management systems
DeLone and McLean (1992) suggested the information systems success (ISS)
model that is measured through six dimensions: system quality, information quality, use,
user satisfaction, individual impact, and organizational impact. Furthermore a perceived
usefulness (PU) suggested by Davis (1989) with technology acceptance model (TAM).
And, Seddon and Kiew (1997) replaced the perceived usefulness with use concept.
Information quality is defined as a quality of system outputs of the product, and
the usage and user satisfaction is defined as the recipients’ interaction of information
and information system product (Shannon and Weaver, 1949; Mason 1978; DeLone
and McLean, 1992; Lee J. K. and Lee W. K., 2007).
The effects on e-learning are measured with an ISS model because it is also one
of the information systems. The e-learning success model (ELS; Lee, 2004) evaluates
e-learning effectiveness based on an ISS, constructivism, and self-regulatory efficacy.
The learning management system (LMS) is applicable to the information process
system that processes learning content and supports all matters related to other
learning. Learning content is the product created through LMS in the ELS model. The
interaction between teacher and students is applicable to the human service process in
the ELS model (Lee, 2004).
In student learning, LMS can be a critical factor in e-learner satisfaction, because
the subjects can be given through e-learning system. In addition, LMS discharges its
transmission duties through a variety of learning content and unique forms are offered
for each and every lesson. From a traditional view point, this is similar to the logic that
classrooms and educational facilities transfer educational content having an effect on
learner’s satisfaction is not related to attending lectures of a given subject (Lee, 2004).
Learning content has different qualities according to each lecturers or producer’s
ability or character. Therefore, learning content is an important assessment factor and it
is a direct criterion in deciding a learner’s satisfaction, unlike learning management
systems. It is similar logic that direct factors decide learner’s satisfaction in the case of
traditionally learned content. A learner may request human services to resolve a
difficulty, an inconvenience or a technical problem that can occur when using the
system, because LMS is one of many information systems. Of course, every e-learning
organization should have a department that can resolve technical problems and sustain
management separately (Lee, 2004).
Learners as users appeal to the teacher even for support for technical problems
such as the usage of LMS, in addition to guidance and help about learning content.
Therefore, teachers play a more important role than the staffs of a general information
system department. This is similar to differences between serving staff and a head cook
regarding the production of food. Guest confronts the general staff with their problems
regarding food, and they resolve it (Lee, 2004).
In addition to an LMS is described as a highly generalized model (Chin-Ping Chu
et al., 2004), consisting of seven parts: a tracking service; a delivery service; a learner
profile service; a course management service; a content management service; a
test/assessment service; and a sequencing service. These services provide the
functionality of learner/learning tracking, content delivery, course import/export, and
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content sequencing. The tracking service takes a learner request and traces the
learning status of the learner, while the delivery service delivers the learning content to
the learner.
Research model and hypothesis
Research model
This study suggests that a research model as in Figure 1. It is a modification of
an information systems success model, and it considers an information system’s
attributes and self-regulated learning attributes. In addition, it supports education
engineering in e-learning. This model is composed of independent variables, such as
perceived usefulness regarding LMS, satisfaction of learning content (ICQ, IRQ), and
interaction between teacher and learner (SQ). Also, self-regulated learning strategy has
an effect on the learner’s performance. The dependent variable is the academic
performance of the learner after experiencing e-learning.
information
contextual quality
H5
information
H6
representational
satisfaction
real record
quality
performance
H4
H3
H1
service quality
H2
perceived
self- regulated
usefulness
learning strategy

Figure 1 research model



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Hypothesis
ISS model and self-regulated learning strategy
According to consumer behavior theory, satisfaction is measured through a
customer’s response regarding fulfillments, and customer judgment regarding products
or services. Satisfaction also includes the fulfillment of one's performance (Oliver and
Swan, 1989). For judgment, fulfillment of one's performance is required as a reference
that is to be compared with a standard. References are needed to be compared with
results or outcomes in order to judge satisfaction (Au, et al., 2002).
In an information system, consumers or customers of consumer behavior theory
refer to users who utilize the system directly, unless they have a technical background
(DeLone and McLean, 1992; Au et al., 2002). Similar to consumer behavior theory, an
end-user’s satisfaction is a user’s attitude toward a specific computer application system
which they utilized (Doll and Torkzadeh, 1988). Satisfaction can also be justified by the
perceived or emotional assessment regarding fulfillment level referring to experienced
performance via the information system (Au et al., 2002). E-learning is also regarded
as an information system. E-learning satisfaction should correlate with end user based
on information system satisfaction. Learning is compared with a traditional, brick and
mortar course. Satisfaction for e-learning and assessment of the information system are
compared with consumer behavior theory. Traditionally, in the field of information
systems, it has been assumed that user’s information system satisfaction obtained a
higher level of performance than that of unsatisfied users (Bailey and Pearson, 1983).
It is inferred that for e-learning, a learner’s satisfaction will be positively related to
academic performance. In self-regulated learning, the learner uses a strategic
relationship between self-regulation and learning in order to reach his chosen self-
learning goal, and to develop, revise, and complement the learning strategy via self
feedback. The learner must make a constant effort to sustain learning motivation
(Zimmerman, 1990). From these points of view, the following hypothesis can be put
forth:
Hypotheses-1 (H1): A learner's self-regulated learning strategy in e-learning will
be positively related to the scholastic performance of an e-learner.
Quality of the e-learning environment.
According to the information systems success model, system quality measures
the information system process itself and it’s effect on user satisfaction (DeLone and
McLean, 1992). System quality implies accuracy and efficiency according to the
communication theory based information systems success model (DeLone and
McLean, 1992). With regard to information system theory, system quality is based on
how easily a user can deal with the system (Doll and Torkzadeh, 1988; Rai et al., 2002).
It is acknowledged that system quality in information systems success model is
substituted for perceived ease of use (Seddon and Kiew, 1997; Rai, et al., 2002).
Perceived ease of use can be justified as the perception of how much effort is needed in
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using a system, which is an important variable in attitudes toward information systems
(Davis, 1989; Davis et al., 1989).
LMS is one of many information systems used by learners. Perceived usefulness
for LMS has an effect on satisfaction toward a learning environment. Therefore, this
study suggests the following hypotheses:
Hypotheses-2 (H2): A learner's perceived usefulness toward a learning
management system will be positively related to e-learner satisfaction.
In addition, e-learning environmental satisfaction includes LMS, learning content
and the service quality of interaction. Also the satisfaction is estimated by perceived
usefulness and perceived ease of use.
Hypotheses-3 (H3): A learner's assessment of the service quality of interaction
between a professor and learner will be positively related to e-learner satisfaction.
However, information quality is defined whether or not it is agree with the
processing business. Also, information quality is composed of contextual quality,
representational quality, and other components (Lee et al., 2002; Lee J. K., 2004).
Furthermore, the content representational quality and contextual quality of LMS are very
important variables in regards to e-learner satisfaction levels. Therefore, this study
suggests the following hypotheses:
Hypotheses-4 (H4): A learner's assessment for content representational quality
will be positively related to e-learner satisfaction.
Hypotheses-5 (H5): A learner's assessment for content contextual quality will be
positively related to e-learner satisfaction.
In addition, Successful SRE learners will be concerned with the substance and
quality of the learning content more than unsuccessful SRE learners. Lower SRE
learners will be interested in easily accessible information and focused understanding.
Lower SRE learners will prefer methods about a given learning content (Lee, 2004).
According to Thatcher and Pamela (2002), personal innovativeness in information
technology has an effect on computer self-efficacy. Also, Gatian (1994), there is a
powerful relationship among user satisfaction, decision-making performance, and
efficiency. Also, in the information systems success model of DeLone and McLean
(1992), satisfaction was an effective variable regarding working efficiency or the
decision-making level. Therefore, this study suggests the following hypotheses:
Hypotheses-6 (H6): A learner's satisfaction will be positively related to the
scholastic performance of an e-learner.

Research methods

Students enrolled in e-learning courses at Daegu University responded to a poll
in the first semester of 2005. The participating students took cyber courses in 3
different subjects offered at the above Universities, and 230 copies of an analysis
questionnaire were collected. The survey was conducted from the 25th April to the 9th
May 2005.
The analysis was designed with PLS (partial least square) method and used PLS
Graph 3.0 software (Chin, 1998). PLS requirements for sample size are nor strict. The
reason is that the approaches because it is based on components (Chin, 1998).
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Sample distributions are illustrated in Table 1.

Table 1 Demographics
Item
Frequency
Percent (%)
Male
121
52.6
Gender
Female
109
47.4
Freshman
10
4.3
Sophomore
62
27.0
Grade
Junior
54
23.5
Senior
104
45.2
Cultural science
93
40.4
Social science
79
34.3
Major
Natural science
30
13.0
Engineering
24
10.4
Art and physical
4
1.7
First
145
63.0
E-learning
2 times
50
21.7
career
3 times
25
10.9
4 times and above
10
4.4
Cultural subjects
36
15.7
Classification Subject of special study
145
63.0
of course
Subject of
study
5
2.2
professorship
2 subjects and above
44
19.1
Less than 1 hour
18
7.8
Computer
1-2 hours
96
41.7
use time
2-6 hours
94
40.9
(1day)

6-12 hours
19
8.3
12 hours and above
3
1.3
Less than 1 hour
5
2.2
e-learning
1-2 hour
122
53.0
time
3-4 hour
80
34.8
(1 week)

5-7 hour
17
7.4
8 hours and above
6
2.6

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This study used the Likert 5-point scale and used the following measurement
tools.
For perceived usefulness of LMS, it is used five edited items suggested by Davis
et al. (1989). In the case of information quality (ICQ, IRQ) for learning content, used
eight items suggested by Lee et al. (2002). Regarding service quality, it is used seven
items suggested by Kettinger and Lee (1997). For e-learner satisfaction, it is used three
items suggested by Wang (2003), for concerning scholastic performance, used one item
according to the e-learner’s academic real record.
Measurements are illustrated in Table 2.

Table 2 Measurement
Variables
Item
Researchers
Perceived usefulness
5
Davis et al.(1989)
Information
3
Lee et al.(2002)
representational quality
Service quality
7
Kettinger & Lee(1997)
Information contextual
5
Lee et al.(2002)
quality
Self-
4
Zimmerman et al.(1986)
regulated learning strategy
Satisfaction
3
Wang(2003)
Real record
1
Real Record
Analysis
Construct reliability is proven as shown in Table 3, and correlations of latent
variables are shown in Table 4. The suggested measure model is estimated as a good
discriminate validity. Each hypothesis is accepted with the exception of H6. Figure 2
shows each analysis result.

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