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1. An activity-theoretical approach to investigate
learners’ factors toward e-learning systems
Shu-Sheng Liaw a,*, Hsiu-Mei Huang b,1
, Gwo-Dong Chen c,2
a
General Education Center, China Medical University, 91 Shiuesh Road, Taichung 404, Taiwan, ROC
b
Department of Management Science, National Taichung Institute of Technology, 129, Sec. 3,
Saming Road, Taichung 404, Taiwan, ROC
c
Department of Computer Science and Information Engineering, National Central University, No. 300,
Jung-da Road, Chung-li, Tao-yuan, Taiwan 320, ROC
Available online 20 March 2006
Abstract
The Internet and World Wide Web have provided opportunities of developing e-learning systems.
The development of e-learning systems has started a revolution for instructional content delivering,
learning activities, and social communication. Based on activity theory, the purpose of this research
is to investigate learners’ attitude factors toward e-learning systems. A total 168 participants were
asked to answer a questionnaire. After factor analysis, learners’ attitudes can be grouped four
different factors – e-learning as a learner autonomy environment, e-learning as a problem-solving
environment, e-learning as a multimedia learning environment, and teachers as assisted tutors in
e-learning. In addition, this research approves that activity theory is an appropriate theory for
understanding e-learning systems. Furthermore, this study also provides evidence that e-learning
as a problem-solving environment can be positively influenced by three other factors.
Ó 2006 Elsevier Ltd. All rights reserved.
Keywords: E-learning; Activity theory; Problem solving; Multimedia instruction; Learner autonomy; Teacher as
assisted tutor
0747-5632/$ - see front matter Ó 2006 Elsevier Ltd. All rights reserved.
doi:10.1016/j.chb.2006.02.002
*
Corresponding author. Tel.: +886 4 2205 3366x1927; fax: +886 4 2203 3108.
E-mail addresses: ssliaw@mail.cmu.edu.tw (S.-S. Liaw), hmhuang@ntit.edu.tw (H.-M. Huang), chen@csie.
ncu.edu.tw (G.-D. Chen).
1
Tel.: +886 4 2219 6609.
2
Tel.: +886 3 4226062x35230.
Computers in Human Behavior 23 (2007) 1906–1920
Computers in
Human Behavior
www.elsevier.com/locate/comphumbeh

2. 1. Introduction
The World Wide Web (WWW/Web) is nothing short of the world’s library. Easy to use,
update, and universal in its availability, it is the driver of the knowledge economy, and also
a natural vehicle for learning; especially for e-learning (electronic learning). In general,
e-learning refers to the use of Internet technologies to deliver a broad array of solutions
that enhance knowledge and performance. In addition, e-learning is based on the follow-
ing three fundamental criteria (Rosenberg, 2001): first, e-learning is networked, which
makes it capable of instant updating, storage/retrieval, distribution, and sharing of the
instruction or information; second, it is delivered to the end-user via a computer using
standard Internet technology; and, third, it focuses on the broadest view of learning that
goes beyond the traditional paradigms of training.
Learning is a phenomenally complex process, and much more complex than the stim-
ulus–response connections envisioned by psychologists. Researchers have argued for
learning to be situated in rich contexts because useable and robust knowledge can be
appropriated by engaging in tasks and situations that are authentic (Collins, 1996). More-
over, learning which begins in rich, situated contexts those can be transferred if intentions
to decontextualize or generalize knowledge constructions or meaning-making across situ-
ations are made. From activity theory point of view, meaningful learning is an active,
intentional, conscious, constructive, and socially medicated practice that includes recipro-
cal intention–action–reflection activities (Jonassen, 2002).
In e-learning systems, learning activities are based on learner autonomy and interac-
tive learning actions; in addition, learning instruction is based on multiple media and
ill-structured formats. Furthermore, e-learning also offers cooperative learning opportu-
nities. Based on activity theory, the purpose of this study is to examine learners’ atti-
tudes toward e-learning systems. Indeed, understanding learners’ attitudes toward
e-learning systems is necessary to ensure that e-learning stands the best possible chance
to succeed.
2. Research development
2.1. Activity theory
A broad range of educational research has shown that it is not possible to understand
fully how people learn if learner variables are left out. Therefore, human relations and
resources are likely a center of learning theories recently. In other words, understanding
the relations among individuals, instruction, and social communication is a new educa-
tional paradigm. Activity theory is a cross-disciplinary framework for studying different
forms of human practices, factoring in the processes of context as developmental process,
both at the individual and social levels at the same time, including the use of artifacts
(Kuutti, 1997). Activity theory claims activity and consciousness are the central mecha-
nisms of learning because conscious learning and activity (performance) are interactive
and interdependent (Jonassen, 2002).
Activity theory is a form of sociocultural analysis that focuses on the activity system
as the unit of analysis, rather than the learner. Activity systems are collective human con-
structions that are not reducible to discrete individual actions (Leont’ev, 1972). Vygotsky
(1978) points out that mind emerges through interactions with others and environment,
S.-S. Liaw et al. / Computers in Human Behavior 23 (2007) 1906–1920 1907

3. mediated by artifacts, signs, and language. Through a process of internalization of exter-
nal activity, artifacts affect the kinds of mental processes learners develop (Hung &
Wong, 2000). An activity system is any system of ongoing, object-directed, historically
conditioned, dialectically structured, and tool-mediated human interactions (Russell,
1997).
Activity theory contains interacting components and is organized to accomplish the
activities of the activity subsystems (Engestrom, 1987; Jonassen, 2002). Interacting compo-
nents include subject, tools, object, outcome, division of labor, community, and rules (see
Fig. 1).
In an activity theory, the subject means the individual or group of members engaged in
the activity. Objects in activity theory are artifacts those produced by the system. Tools are
that the subject uses them for acting on the object. Rules operate in any context or com-
munity refers to the explicit regulations, policies, and conventions that constrain activity
as well as the implicit social norms, standards, and relationships among members of the
community (Jonassen, 2002). The community consists of the individuals and subgroups
that focus at least some of their effort on the object. Division of labor refers both to
the horizontal division of tasks between cooperating members of the community and also
to the vertical division of power and status (Engestrom, 1999).
The exchange subsystem includes three components: subject, rules, and community.
The exchange subsystem engaged the subject and rules that constrain the activity and
the community with the subject interacts. The exchange of individual, social, and culture
norm in any work community also determines the nature of the work culture and the
climate for those who involved in any activity system.
The distribution subsystem includes three components: subject, rules, and community.
The distribution subsystem ties the object of activity to the community by defining a divi-
sion of labor. According to the community characteristics and the outcome expectations,
labors are divided to be achieved in the community work through the object.
Tools
Subject
Object
Rules Community Division
of Labor
Fig. 1. Activity theory.
1908 S.-S. Liaw et al. / Computers in Human Behavior 23 (2007) 1906–1920

4. 2.2. E-learning systems
The Internet and the Web are incredibly popular at public domains and they have pro-
vided unprecedented opportunities of conducting e-learning for learning and training pur-
poses. The development of e-learning has revolutionized content delivering and social
communication in learning activities. Essentially, e-learning systems should include multi-
ple formats and various resources. It needs to also support collaboration, offer interaction,
implement Web-based activities as part of learning framework, and assist both novices
and experts (Liaw & Huang, 2000). With these characteristics, e-learning is capable of cre-
ating a wealth of online and distance learning opportunities to users that is not readily
available in textbooks or faculty lectures. Rosenberg (2001) stated that e-learning had
the following benefits. First, it lowers costs. Second, its content is more timely and depend-
able. Third, it is a just-in-time learning approach. Fourth, it builds universal communities.
And finally, it provides an increasingly valuable learner service.
2.3. Factors of designing e-learning systems
Learning is the retention and transfer of knowledge to new and different situations. Of
all the educational technologies that have exhibited great potential, e-learning appears to
be the most promising. Essentially, the e-learning system predominantly provides informa-
tion, not instruction or other pedagogical supports for learning (Liaw & Huang, 2003;
Vosniadou, 1996). On its own, it is completely incapable of supporting knowledge con-
struction, or providing a context for learning, and the kind of learning communities that
schools have always nurtured. When designing an appropriate e-learning system, the fol-
lowing three factors should be considered: learning models, instructional structure, and
learning metacognition (Liaw, 2003; Vosniadou, 1996).
According to learning models, a great deal of learning performance requires the execution
of complex principles for processing instruction or activities. Learning to do things, such as
developing computer skills, involves the acquisition and refinement of complex motor skills
which become faster, more accurate, and more automatic with the accumulation of experi-
ence and expertise. In addition, learning to solve educational problems requires the attain-
ment and development of many learning principles and procedures which in turn, make it
possible to devise and execute learning activities or solutions (Vosniadou, 1996).
E-learning is delivered to the end-user via a computer using standard Internet technology.
It also offers an ‘‘anywhere and anytime’’ earning environment. Based on the characteris-
tics of e-learning, a user has more opportunities to be an active and self-regulatory learner.
For example, in an e-learning system, learners can control learning time and learning steps
by themselves. Thus, based on learners’ perspectives, e-learning is a learner autonomy
environment.
In essence, the major functions of the teacher are: informing the learner of the objec-
tives, presenting stimuli, increasing his/her attention, helping the learner recall what he/
she has previously learned, providing conditions that will evoke performance, determining
sequence of learning activities, and prompting/guiding the learning process (Joyce & Weil,
1996). From these points of view, teachers are assisted tutors for student’s learning. In gen-
eral, e-learning systems provide various assisted functions, such as teacher-made online
instruction, online conference, online help and suggestions, online examination, and online
monitoring. All these functions offer opportunities for teachers to be assisted tutors.
S.-S. Liaw et al. / Computers in Human Behavior 23 (2007) 1906–1920 1909

5. An instructional structure deserves more attention because an effective one will help
learners to create their own knowledge. Essentially, learning processes are influenced
not only by the nature of the perceptual stimuli but also by the nature of individuals’
expectations, based on prior knowledge and past experience. Therefore, an appropriate
instructional structure can enhance learners’ knowledge construction from their short-
term memory to their long-term memory (Atkinson & Shiffrin, 1971).
Essentially, e-learning offers both multimedia ill-structured and well-structured instruc-
tional information. When an individual constructs his/her own knowledge, information is
first deposited in short-term memory; then transferred to long-term store (Atkinson &
Shiffrin, 1971). Based on dual-coding theory (Butler & Mautz, 1996), two separate systems
can work independently or together for verbal and imagery processing. In addition, when
information coding in both systems, it is easier to retain than information coded only in a
verbal or imagery system. Hence, multimedia instructional formats are more helpful for
individual learning than text-only formats.
The concept of metacognition, first introduced by Flavell (1976), has become quite
fashionable in contemporary cognitive psychology. Metacognition includes ways of help-
ing learners gain understanding about how knowledge is constructed and the conscious
control of tools for doing so (Joyce & Weil, 1996). It refers to both the knowledge about
learners’ cognitive processes and the control over these processes (Veenman, Prins, & Elsh-
out, 2002). Learning metacognition signifies the gradual increase in a learner’s own active
and conscious control of instruction or content processing strategies during learning pro-
cessing. Brown (1987) stated that metacognition denoted the understanding of knowledge
– an understanding that could be reflected in either effective use or overt description of the
knowledge in question.
Metacognitive knowledge consists of ‘‘knowledge of cognition in general as well as
awareness and knowledge of one’s own cognition’’ (Anderson et al., 2001, p. 29). It
includes identifying strategies to perform tasks, understanding the demands of various
tasks, and knowing one’s capabilities for accomplishing them. Thus, metacognitive knowl-
edge refers to knowledge about the interplay between individual characteristics, task char-
acteristics and available strategies in a learning situation (Flavell, 1979). In addition,
metacognitive skills concern the self-regulatory activities actually being executed by a lear-
ner in order to structure the problem solving process (Veenman et al., 2002). As e-learning
systems usually provide collaborative, interactive, and network systems, they offer a learn-
ing environment to improve learners’ problem-solving capabilities and thinking skills.
Thus, based on considerations for designing e-learning systems, learning models consti-
tute a learner autonomy environment with teachers as assisted tutors, instructional struc-
tural should support multimedia content that include ill-structured and well-structured
formats, and learning metacognition consists individual problem-solving capabilities
and thinking skills. Fig. 2 presents factors of designing an e-learning system.
2.4. Research hypotheses
In general, e-learning systems possess these characteristics (Liaw & Huang, 2000, 2003).
First, they offer a multimedia environment. Second, they integrate various kinds of infor-
mation and construct information bases. The multiple mixed-media nodes in an e-learning
system can be instantly called up in a consistent manner, irrespective of the structure of
information or resource. Third, e-learning systems support interactive communication
1910 S.-S. Liaw et al. / Computers in Human Behavior 23 (2007) 1906–1920

6. whereby users have full control over their own learning situations and this high-level inter-
action gives them dynamic control of information. Fourth, they support networks for
accessing information. An e-learning system goes beyond static Web pages and page link-
ing, by creating truly interactive networks with information exchange between users and
servers. These nonlinear and random-access networks point out a new direction aside from
the conventional step-by-step concept. And fifth, they provide a cross-platform environ-
ment, allowing for the systems to be executed independently on various computer operating
systems. In an e-learning system, information and resources from around the world can be
accessed by anyone from anywhere in the world as long as he/she has a computer with an
Internet connection.
Based on characteristics of e-learning, this study proposes the following hypotheses:
Hypothesis 1A: Based on learners’ attitudes, e-learning systems are a learner autonomy
environment.
Hypothesis 1B: Based on learners’ attitudes, e-learning systems are environments where
teachers are assisted tutors.
Hypothesis 1C: Based on learners’ attitudes, e-learning systems are a problem-solving
learning environment.
Hypothesis 1D: Based on learners’ attitudes, e-learning systems are a multimedia learn-
ing environment.
As designing e-learning programs, mastering problem-solving techniques means acquir-
ing strategies that make users in an active and self-regulatory way manage learning so as to
develop a memory structure for constructing their own knowledge and problem-solving
capability. In addition, multimedia instructional formats are more helpful for enhancing
learners’ knowledge construction from their short-term memory to their long-term mem-
ory. Moreover, teachers could be helpful tutors for assisting learners when they solve
problems. In other words, the factor of learning models and the factor of instructional
Factors of designing
e-learning systems
Learning
models
Instructional
structure
Learning
metacognition
• Multimedia instruction • Problem solving
• Learner
autonomy • Thinking skills
• Teachers as
assisted tutors
• Ill-structured content
• Well-structured
content
Fig. 2. Factor considerations of designing e-learning systems.
S.-S. Liaw et al. / Computers in Human Behavior 23 (2007) 1906–1920 1911

7. structure have positive effects on the factor of learning metacognition. Fig. 2 shows these
correlations. Thus, this study proposes the following hypotheses:
Hypothesis 2A: E-learning as a problem solving learning environment that can be pre-
dicted by the factor of e-learning as a learner autonomy environment.
Hypothesis 2B: E-learning as a problem solving learning environment that can be pre-
dicted by the factor of teachers as assisted tutors in e-learning system.
Hypothesis 2C: E-learning as a problem solving learning environment that can be pre-
dicted by the factor of e-learning as a multimedia learning environment.
3. Research design
3.1. Building an e-learning system
This study builds an e-learning system in order to examine the hypotheses. The e-learn-
ing system is a Web-based system; thus, it can be plugged into Web browsers when
students use it as a learning assisted tool. The major functions of the e-learning system
include multimedia instructional content, group discussion function (synchronous com-
munication), blackboard function (asynchronous communication), tutor monitoring func-
tion (teacher’s monitoring and helping), and online examination function. There are five
purposes for building the e-learning system. First, based on the e-learning system, it sup-
ports nonlinear networks to access information; and instructional formats are ill-struc-
tured and networked. Secondly, it offers a multimedia environment where instructional
contents are driven by multimedia. Third, it supports interactive communication and ran-
dom-access networks, thus activities are performed cooperatively and collaboratively.
Fourth, based on the system, it provides a cross-platform environment where experts
and teachers can monitor learning activities. And fifth, it supports interactive communica-
tion and cross-platform environments that accommodate learning activities. Fig. 3 shows
the e-learning system.
3.2. Participants
In this study, questionnaire construction and administration for assessing individual
perceptions toward computer and Web-based environments included six major steps
(Gall, Borg, & Gall, 1996; Liaw, 2000). The six major steps in carrying out a research
study using a questionnaire included: (a) defining research objectives, (b) selecting a sam-
ple, (c) designing the questionnaire format, (d) revising the questionnaire for improving
content validity, (e) writing a cover letter and distributing the questionnaire, and (f) ana-
lyzing questionnaire data. Based on these six steps, the questionnaire survey may have
more accepted reliability and content validity.
The survey was conducted on 171 students in a university in central Taiwan who take
either ‘‘Introduction to Computer Science’’ or ‘‘Introduction to Computer Network’’
courses. After using the e-learning system for six weeks, all participants were asked to
answer a questionnaire that included demographic information and two different compo-
nents (computer and Internet experience, attitudes toward the e-learning system). The
questionnaires, including a cover letter were distributed to participants during class. All
subjects were asked to respond to the questionnaire immediately and their responses were
1912 S.-S. Liaw et al. / Computers in Human Behavior 23 (2007) 1906–1920

8. guaranteed confidentiality. Questionnaires with missing responses were eliminated (three
missing responses) in this study. A total of 168 responses were collected of which 77 belong
to male students and 91 to female students.
3.3. Instruments
For improving questionnaire’s content validity, two associate professors were invited
for reviewing questionnaire items to assess what were essential questions. A three-point
likert scale was assessed to each questionnaire item. The three-point likert scale included
1 as ‘‘It is not necessary to ask the question’’, 2 as ‘‘It is useful, but not essential to ask the
question’’, and 3 as ‘‘It is essential to ask the question’’. Essentially, we deleted the items
that got a one-point score, revised the items that got a two-point score, and kept the items
that got a three-point score. Ten undergraduate students were invited to examine which
items were unclear. Each item was assessed by a three-point likert scale. The three-point
likert scale included A as ’’Unclear question’’, B as ‘‘Needed modification’’, and C as
‘‘Clear question’’. Usually, the items that had an A-point were deleted, the items that
had a B-point were revised, and the items that had a C-point were kept for the survey.
The data for this study was gathered by means of a questionnaire. The questionnaire
included three major components: (a) demographic information, (b) computer and Inter-
net experience, and (c) attitudes toward the e-learning system. The details of this question-
naire are described as below.
Fig. 3. The e-learning system.
S.-S. Liaw et al. / Computers in Human Behavior 23 (2007) 1906–1920 1913

9. Demographic information: The demographic component covered gender, the field of
study, and experiences of using e-learning systems.
Computer and Internet experience: In this component, participants were asked to indi-
cate whether they had experience using Web browsers, e-mail, and word processing pack-
ages, as well as coding Web pages. These four questions are all 7-point likert scales (from 1
which means ‘‘no experience’’ to 7 which means ‘‘highly experienced’’).
Attitudes toward the e-learning system: Participants were asked to indicate their atti-
tudes toward the system. These 15 questions were all 7-point likert scales (from 1 which
means ‘‘strongly disagree’’ to 7 which means ‘‘strongly agree’’).
4. Results
According to experience of using e-learning systems, in this research, 73 students do not
have any e-learning course, 80 students have one e-learning course, nine students have two
e-learning courses, and six students have three or more e-learning courses before. Descrip-
tive statistics (means (M) and standard deviations (SD)) of computer use and experience
were shown in Table 1. The alpha reliability of attitudes toward search engines as a learn-
ing tool (Table 2) was to be highly accepted (a = 0.92). The high alpha reliability gives a
support for questionnaire content reliability.
Table 1
Descriptive statistics of Computer and Internet Experience (from 1 which means ‘‘no experience’’ to 7 which
means ‘‘highly experienced’’)
Variables M SD
Experience using Web browsers 4.92 1.70
Experience using e-mail 5.46 1.41
Experience using word processing packages 4.89 1.48
Experience coding Web pages 3.11 1.50
Table 2
Means, SD and item-total correlations (from 1 which means ‘‘strongly disagree’’ to 7 which means ‘‘strongly
agree’’)
No. Items M SD r*
1 I can learn actively in the e-learning system 4.77 1.43 0.57
2 The e-learning system improves my thinking skills 5.04 1.39 0.69
3 The e-learning system enhances my problem-solving skills 5.42 1.23 0.75
4 The e-learning system provides various aspects to solve problems 5.46 1.16 0.62
5 I like colorful pictures in e-learning instruction 6.04 1.09 0.62
6 I like learning videos in e-learning instruction 5.90 1.21 0.66
7 I like the teacher’s help and suggestions in the e-learning system 4.89 1.48 0.60
8 I like the animated e-learning instruction 5.86 1.15 0.60
9 I like the teacher’s voice and image in the e-learning system 4.51 1.60 0.60
10 I have more opportunities to create my own knowledge in the e-learning system 5.62 1.24 0.75
11 The hypertext e-learning instruction can enhance my learning motivation 4.70 1.39 0.66
12 I can discuss actively with others in the e-learning system 5.35 1.26 0.49
13 I like the teacher’s online multimedia instruction in the e-learning system 4.65 1.54 0.60
14 I can read the e-learning instruction actively 5.15 1.44 0.68
15 I can find information actively in the e-learning system 5.34 1.43 0.68
r*, corrected item-total correlation.
1914 S.-S. Liaw et al. / Computers in Human Behavior 23 (2007) 1906–1920

10. 4.1. Factor analysis
A principal component analysis (PCA) was conducted on factorial structure. Generally
speaking, factor analysis requires two stages: factor extraction and factor rotation (Kach-
igan, 1991). The primary purpose of the first stage is to make an initial decision about the
number of factors underlying a set of measured variables or items. The goal of the second
stage is to statistically manipulate the results to enable the factors to be more interpretable
and to make final decisions about the number of underlying factors.
Essentially, deciding how many factors should be retained for more detailed analysis is
a crucial event for factor analysis; and Eigenvalues, percentage of variance explained, and
scree plots are useful assisting tools for this purpose. When a component’s eigenvalue was
greater than 1 and the percentage of variance was greater than 6.7%, this component could
be retained for factor extraction. Four eigenvalues were greater than 1 and the percentage
of variance were larger than 6.7%. Varimax and Oblimin rotations were conducted to cor-
rect the artifact. After rotation analyses, the Varimax rotation made it easier to interpret
factor structure than Oblimin rotation did. Varimax (orthogonal) rotations were used for
4-factor extraction. In general, Varimax aims to maximize the sum of variances of squared
loadings in the columns of the factor matrix (Green, Salkind, & Akey, 1997). The rotation
converged in six iterations for Varimax rotation. After Varimax rotation, the rotated
eigenvalues and percentages of variance were presented in Table 3.
Using a minimum factor structure coefficient criterion of 0.53, six items had uniquely high
saliency with component 1, three items with component 2, three items with component 3, and
three items with component 4. The 4-factors (as shown in Table 4) were titled: e-learning as a
learner autonomy environment, e-learning as a problem-solving environment, e-learning as
a multimedia learning environment, and teachers as assisted tutors in e-learning.
4.2. Correlations and regressions
The Pearson correlation coefficients among the variables are presented in Table 5. The
bi-variate relationships indicated that many of the variables significantly correlated with
each other.
Concerning analytic strategy for assessing the predictive model, multiple regression
analysis is an appropriate multivariate analytical methodology for empirically examining
sets of relationships in the form of linear causal models. The results of stepwise multiple
regressions for the path associated with the variables were presented in Table 6. The
regression analysis was performed to check the effects of predicted variables (e-learning
as a learner autonomy environment, e-learning as a multimedia learning environment,
and teachers as assisted tutors in e-learning) on e-learning as a problem-solving environ-
ment. The results showed all three independent variables could predict e-learning as a
Table 3
Total variance explained and percentage of variance by varimax rotation
Component Eigen value Percentage of variance Cumulative percentage
1 3.14 20.90 20.90
2 2.77 18.49 39.39
3 2.40 16.03 55.42
4 2.33 15.53 70.95
S.-S. Liaw et al. / Computers in Human Behavior 23 (2007) 1906–1920 1915

11. Table 4
Factor structure
No. Items Factor coefficient
Factor: e-learning as a learner autonomy environment
1 I can learn actively in the e-learning system 0.70 0.51 0.03 0.01
10 I have more opportunities to create my own knowledge
in the e-learning system
0.53 0.40 0.35 0.28
11 The hypertext e-learning instruction can enhance my learning motivation 0.73 0.20 0.28 0.16
12 I can discuss actively with others in the e-learning system 0.68 0.16 0.32 0.23
14 I can read the e-learning instruction actively 0.65 0.33 0.24 0.20
15 I can find information actively in the e-learning system 0.74 0.35 0.01 0.18
Factor: e-learning as a problem-solving environment
2 The e-learning system improves my thinking skills 0.23 0.75 0.23 0.29
3 The e-learning system enhances my problem-solving skills 0.26 0.73 0.39 0.22
4 The e-learning system provides various aspects to solve problems 0.25 0.68 0.l8 0.23
Factor: e-learning as a multimedia learning environment
5 I like colorful pictures in e-learning instruction 0.15 0.44 0.64 0.15
6 I like learning videos in e-learning instruction 0.24 0.29 0.79 0.16
8 I like the animated e-learning instruction 0.24 0.01 0.80 0.24
Factor: teachers as assisted tutors in e-learning
7 I like the teacher’s help and suggestions in the e-learning system 0.14 0.38 0.25 0.59
9 I like the teacher’s voice and image in the e-learning system 0.15 0.21 0.15 0.89
13 I like the teacher’s online multimedia instruction in the e-learning system 0.24 0.14 0.17 0.83
Table 5
Correlation analysis
Variables 2 3 4 5 6 7 8
1. Experience using Web browsers 0.63**
0.51**
0.23**
0.29**
0.16*
0.18**
0.02
2. Experience using e-mail 0.58**
0.29**
0.32**
0.27**
0.31**
0.11
3. Experience using word processing packages 0.43**
0.37**
0.27**
0.23**
0.15
4. Experience coding Web pages 0.35**
0.27**
0.13 0.16*
5. E-learning as a learner autonomy environment 0.66**
0.60**
0.54**
6. E-learning as a problem-solving environment 0.62**
0.57**
7. E-learning as a multimedia learning environment 0.54**
8. Teachers as assisted tutors in e-learning
*
Correlations is significant at the p < 0.05 (2-tailed).
**
Correlations is significant at the p < 0.01 (2-tailed).
Table 6
Regression results for predicted path relationships
Dependent variable Independent variables b R2
change p
E-learning as a
problem-solving
environment
E-learning as a learner autonomy environment 0.38 0.44 0.000
E-learning as a multimedia learning environment 0.28 0.08 0.000
Teachers as assisted tutors in e-learning 0.23 0.03 0.001
1916 S.-S. Liaw et al. / Computers in Human Behavior 23 (2007) 1906–1920

12. problem-solving environment (F(3,163) = 67.21, p < 0.0005, R2
= 0.55). These three pre-
dictors have 55% contribution for predicting e-learning as a problem-solving environment.
5. Discussions
Based on the statistical results, this study provides evidence that Hypothesis 1A, 1B,
1C, and 1D are all appropriate hypotheses. From factor analysis, learners’ attitudes
toward e-learning systems can be classified into four different factors – e-learning as a lear-
ner autonomy environment, e-learning as a problem solving environment, e-learning as a
multimedia learning environment, and teachers as assisted tutors in e-learning. In general,
when designing e-learning systems, these factors – e-learning as a learner autonomy envi-
ronment and teachers as assisted tutors in e-learning – can be viewed as variables of learn-
ing models; in addition, e-learning as a problem-solving environment can be taken as a
variable of learning metacognition; furthermore, e-learning as a multimedia learning envi-
ronment can be regarded as a variable of instructional structure.
In addition, Table 6 shows that Hypothesis 2A, 2B, and 2C are all highly accepted.
These results deduce that learner’s active learning, multimedia instruction, and teachers
as assisted tutors are all positive factors for enhancing learners’ problem-solving skills.
In other words, based on learners’ attitudes, learning models and instructional structure
could positively influence individual learning metacognition in e-learning systems. In this
research, e-learning as a learner autonomy environment has is the main contributor (44%)
for predicting e-learning as a problem-solving environment.
From Table 5, learners’ experience using e-mail and word processing packages have posi-
tive significant correlations with e-learning as a learner autonomy environment, e-learning as
a multimedia learning environment, and e-learning as a problem-solving environment. In
addition, inter-correlations among four attitudes factors (e-learning as a learner autonomy
environment, Teachers as assisted tutors in e-learning, e-learning as a multimedia learning
environment, and e-learning as a problem-solving environment) are all highly accepted
(all correlations bigger than 0.50). In other words, these four attitude factors show signifi-
cantly positive effects on one another. This evidence also offers a recommendation when
designing e-learning systems, these four factors should be considered at the same time.
6. Implication
Based on the statistical results, this research may provide the following implications:
first, implication of activity-theoretical e-learning systems; second, implication of attitude
model toward e-learning systems.
6.1. Implication of activity-theoretical e-learning systems
Learning activities include complex cognitive and social processes that necessarily inter-
act with the world around it. E-learning systems provide opportunities for learners to com-
municate the real world and to search interdisciplinary domains. Activity theory provides
an alternative lens for analyzing learning processes and outcomes that captures more of
the complexity and integration with the context and community that surround and sup-
port it. Based on statistical results and activity theory, the factor of multimedia instruction
can be viewed as a kind of tools, the factor of learner autonomy is a kind of rules, the
S.-S. Liaw et al. / Computers in Human Behavior 23 (2007) 1906–1920 1917

13. factor of teachers assisting cab be viewed as a division of labor, and the factor of e-learn-
ing as a problem-solving environment is an object in activity theory. Fig. 4 shows the
activity theoretical based e-learning.
In trying to use e-learning for creating activity systems, the future research should
investigate the effects of the system. Essentially, each technology has its particular charac-
teristics for training and learning purposes, based on the characteristics of e-learning, it is
hoped that activity-theoretical approach of e-learning systems could have positive effects
for learners to empower their problem-solving skills.
6.2. Implication of attitude model toward e-learning systems
Based on the regression analysis, this study proposes an attitude model toward
e-learning systems (as presented in Fig. 5). From Fig. 5 learning models and instruc-
tional structure are significant positive factors to affect learner’s learning metacognition.
Learner
Multimedia
e-learning
instruction
E-learning as a
problem-solving
environment
Learner autonomy E-learning
Community
Teachers as
assisted tutors
Fig. 4. Activity theoretical e-learning.
Learning models (such
as learner autonomy,
teachers as assisted tutors)
Learning metacognition
(such as problem
solving and thinking
skills)
Instructional structure
(such as ill-structured or
well-structured
multimedia content)
Fig. 5. The attitude model for e-learning.
1918 S.-S. Liaw et al. / Computers in Human Behavior 23 (2007) 1906–1920

14. Based on this research, individual autonomy and teacher’s assist both are critical vari-
ables in e-learning systems. In addition, multimedia instructional content is also a major
variable when designing e-learning systems. Furthermore, this research presents that
e-learning systems can be a useful problem-solving environment. Future research should
examine this attitude model for understanding more learners’ attitudes toward e-learn-
ing systems.
7. Conclusion
In light of learners’ attitudes, this demonstrates that e-learning systems can be designed
as an environment for improving learner autonomy, multimedia instructional content and
problem-solving skills, with teachers as assisted tutors. From the activity theory viewpoint,
individuals actively construct their knowledge within social realms. In other words, learn-
ers are not to passively accept information by mimicking the wording or conclusions of
others, but instead need to encourage themselves in internalizing and reshaping or trans-
forming information through active consideration (Brook & Brook, 1993). Additionally,
based on the dual-coding theory, when a learner processes information through both ver-
bal and imagery functions, these multi-model approaches to education are thought to be
particularly effective for accommodating learners with diverse styles and preferences for
learning (Butler & Mautz, 1996).
With teacher’s helping, learners have more opportunities to develop complex cognitive or
metacognitive skills, such as breaking a topic down into subtopics, organizing diverse infor-
mation, and formulating a point of view. These metacognitive learning activities are effective
in developing higher-order thinking skills, such as defining problems, judging information,
solving problems, and drawing appropriate conclusions (Laney, 1990). Moreover, mastery
of viewpoints, immediate knowledge, individualized learning, as well as expert instruction
and monitoring are all major parts (Joyce & Weil, 1996). Indeed, teachers’ assistance and
supervision are crucial in ensuring that learners master instructional objectives.
In summary, to ascertain that e-learning has the best possible chance to succeed, this
research provides evidence that learners’ active learning, multimedia instruction, prob-
lem-solving skills, and teachers as assisted tutors are critical factors for designing e-learn-
ing systems. Furthermore, in e-learning systems, a learner’s problem-solving skills can be
influenced by any of the above factors. In trying to use e-learning systems as a learning
tool for assisting students’ learning activities, future research should investigate the effects
of these attitude factors.
Acknowledgements
This study was partially supported by the National Science Council and China Medical
University, Taiwan, ROC. Project Nos. NSC93-2524-S-039-001, NSC94-2520-S-039-001,
NSC94-2524-S-008-002 and CMU94-050.
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