A Structural Equation Model To Analyse The Antecedents To Students Web-Based Problem-Solving Performance

Australasian Journal of Educational Technology, 2015, 31(4).
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A structural equation model to analyse the antecedents to
students’ web-based problem-solving performance
Gwo-Jen Hwang
National Taiwan University of Science and Technology, Taipei, Taiwan
Fan-Ray Kuo
National Cheng-Kung University, Tainan, Taiwan
Web-based problem-solving, a compound ability of critical thinking, creative thinking,
reasoning thinking and information-searching abilities, has been recognised as an important
competence for elementary school students. Some researchers have reported the possible
correlations between problem-solving competence and information searching ability; however,
few studies have proposed what factors might affect students’ web-based problem-solving
performance. In this study, a five-phase web-based learning approach is proposed; moreover, a
web-based problem-solving model was developed to investigate the factors that might affect
students’ web-based problem-solving performance. To evaluate the effectiveness of the
proposed approach, an experiment was conducted by engaging 201 fifth- and sixth-graders
from three elementary schools in a series of web-based problem-solving activities. Structural
equation modelling was employed to analyse the antecedents affecting the students’ web-based
problem-solving performance. From the analysis results, it was found that task-technology fit
could be the major factor affecting the students’ intention to learn on the Web, as it highly
affected their web-based problem-solving performance.
Introduction
With the rapid spread and advancement of information technology, the 21st century has become a
knowledge economy era. People who are able to obtain knowledge will be more competitive in society.
Thus, schooling not only plays the important role of imparting knowledge to students, but also cultivates
the students’ abilities of collecting data, extracting information from the data, and applying the collected
information to deal with upcoming challenges and problems (Ates & Cataloglu, 2007; Dogru, 2008;
Francis, 2008; Hwang, Chiu, & Chen, 2015; Pimta, Tayruakham, & Nuangchalerm, 2009; Zakaria &
Yusoff, 2009). For this reason, the education policy in Taiwan puts emphasis on ten fundamental skills
incorporated in student-centred learning activities and real-life experiences (Hwang, Hung, Chen, & Liu,
2014). Among these skills, high-order thinking and problem solving are viewed as important abilities in
the current education environment. Studies have revealed several factors affecting students’
problem-solving abilities, such as intelligence, learning materials, learning methods, problem-solving
instructional strategies, and parents’ socio-economic background (Oloruntegbe, Ikpe, & Kukuru, 2010;
Zheng, 2007). Among these, learning methods and problem-solving instructional strategies are considered
as the key factors; as such, research into these issues has mushroomed (Lo, 2009; Tsai & Shen, 2009).
Researchers have found a correlation among information-searching skills, cognitive structure and
problem-solving abilities (Eisenberg & Berkowitz, 1990). For instance, Kuo and Hwang (2014) found
that web-based problem-solving ability is a process of high-order thinking, consisting of critical thinking,
creative thinking, reasoning thinking and information searching on the Internet. Raes, Schellens, Wever,
and Vanderthoven (2012) indicated that when learners are solving a problem using web resources, their
web searching performances are significantly related to their critical thinking, creative thinking and
inferential thinking abilities; moreover, such an ability to search for and use web resources is highly
related to their problem-solving performance. Bilal (2001, 2002) further indicated that the lack of
effective information searching strategies and high-order thinking abilities could influence students’
performance in solving problems on the Internet. Recently, Kantra (2014) published an article relating to
web searching tips in Time emphasising that searching for information is an essential skill nowadays.
However, most previous research concerning students’ information searching and problem-solving
abilities has focused on the difficulties encountered by students in web searching, with few of them
concentrating on the investigation of the factors influencing students’ web-based problem-solving
performance.

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Nowadays, with the rapid development of computer and Internet technologies, knowledge can be derived
from diverse sources accessible via the Internet. To cultivate students’ web-based information seeking
ability via the Internet is essential from the viewpoint of multiple intelligences (Gardner, 1993),
indicating that people are encouraged to receive diverse information from a wide range of sources, which
will allow them to identify and resolve the authentic problems they face. Web-based information seeking
can be viewed as part of digital literacy, which facilitates the ability to use digital technology,
communication tools or networks to locate, critique, summarise, evaluate, and create information (Gui &
Argentin, 2011; Kuo, Chen, & Hwang, 2014). Thus, applying the advantages of the Internet to
instructional design, especially in problem-solving instruction, is likely to improve students’
problem-solving ability. However, as the information available via the Internet is both abundant and
complex, students may easily get lost if they are not equipped with the necessary filtering, analysing and
judging abilities (Elmer-Dewitt, 1995). Thus, it is worth studying how to integrate effective instructional
approaches into web-based learning environments to improve students’ web-based problem-solving
performance. Besides, a review of previous research in learning technology shows that there is a lack of
studies examining the crucial factors that determine students’ learning performance.
Consequently, this study attempts to develop a web-based learning environment which incorporates the
problem-solving theory for assessing students’ problem-solving performance. Moreover, a model to
address the factors that affect students’ web-based problem-solving performance is proposed and
validated.
Literature review
Effectively applying problem-solving approaches can immerse students in active and investigative
learning (Sellwood, 1989). Through participation in a series of practical problem-solving activities that
may involve designing, modelling, and testing of technological solutions, it is assumed that the learner
will acquire both technical knowledge and higher-order cognitive skills. Many researchers have proposed
different approaches and procedures for solving problems. For instance, Polya (1957) proposed a heuristic
process for solving problems in mathematics that provides a mental guideline for action. The steps in
Polya’s heuristic consist of (1) understanding the problem, (2) devising a plan, (3) carrying out the plan,
and (4) looking back and checking the results and evaluating the solution. Extending Polya’s approach,
Bransford and Stein (1984) developed the IDEAL model of problem-solving: (1) identifying problems
and opportunities, (2) defining goals, (3) exploring possible strategies, (4) anticipating outcomes and
acting, and (5) looking back and learning.
Owing to the advancement and popularity of computer and network technologies, many studies
investigating the factors influencing students’ problem-solving performance and learning achievement
have been conducted in web-based learning environments in recent years (Chang, Barufald, Lin, & Chen,
2007; Crippen & Eari, 2007; Gerdprasert, Pruksacheva, Panijpan, & Ruenwongsa, 2010; Jang, 2009;
Narciss, Proske, & Koerndle, 2007). For example, Wang and Wu (2008) applied a social cognitive
perspective to explore the role of self-efficacy, student feedback behaviour, and the use of learning
strategies in problem solving and performance in a web-based learning environment. The results of their
study indicate that high self-efficacy students apply more high-level learning strategies, such as
elaborative strategies and critical thinking. The students in their study who provided elaborated feedback
also had higher self-efficacy than those who did not. Kuo and Hwang (2014) indicated that the integration
of appropriate problem-solving tasks into web-based learning environments could increase both high
graders’ problem-solving performance and intention to search for information on the Web. Keramati,
Afshari-Mofrad, and Kamrani (2011) proved that increasing student technology readiness in Iranian high
schools could improve their e-learning outcomes in an information technology (IT) course as well as their
intentions to learn on the Web. Moreover, Kurt and Gurcan (2010) pointed out that if the level of
students’ computer anxiety decreased, their intention to take web-based instruction could be increased.
Hwang and Kuo (2011, p 296) further defined “web-based problem solving” as the learning activities that
engage students in comprehending a series of questions related to a specific issue, and seeking, selecting,
abstracting, and summarising information on the Web to answer the questions. Several studies have
shown that conducting web-based problem-solving activities can not only effectively improve learning

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performance, but can also increase positive attitudes towards problem solving. For example,
Brand-Gruwel, Wopereis, and Walraven (2009) proposed a model based on information problem-solving
on the Internet for secondary school students. The results reveal that the proposed model could improve
students’ problem-solving skills and attitudes towards the course. Wijekumar (2005) applied
problem-solving processes to web-based courses for elementary school students, and found that their
achievement and attitudes were significantly improved. A study by Hwang and Kuo (2011) also showed
that web-based problem-solving activities could improve elementary school students’ competence in
using keywords and extracting proper information. Raes et al. (2012) used multiple scaffoldings as an
approach to enhance both the knowledge acquisition and metacognitive awareness of junior high school
students in web-based problem-solving activities for science courses. It can be seen that fostering
students’ problem-solving competence in web-based learning environments has become an important and
widely discussed issue (Kuo, Hwang, & Lee, 2012). Most of the studies have mainly focused on students’
learning performance in web-based problem-solving activities, while few studies have been conducted to
investigate the influential factors of students’ web-based problem-solving ability, not to mention using
high-level statistical methods (e.g., structural equation modelling) to investigate the issue.
Consequently, an investigation into this issue using a high-level statistical analysis approach would help
researchers as well as educators identify the critical factors that affect students’ web-based
problem-solving ability, and hence propose effective tutoring strategies.
Theoretical background and research model
To investigate the factors that could affect students’ web-based problem-solving competence, six factors
(i.e., Internet self-efficacy, task-technology fit, computer anxiety, technology readiness, web information
seeking, and intention) are identified and elucidated as the primary dimensions based on a review of the
relevant research and theoretical background. Moreover, a causal relationship model is constructed as
shown in Figure 1, which depicts the basic components of the research framework. This model presumes
that Internet self-efficacy (IS), task-technology fit (TT), and technology readiness (TR) will directly and
positively affect intention (IN) to adopt the proposed web-based problem-solving learning approach,
while computer anxiety (CA) will directly and negatively affect students’ intention to use the approach.
Finally, the impact of the antecedent variable (intention) on the web-based problem-solving learning
performance of students is positively hypothesised, and it is also hypothesised that web information
seeking competence (WS) directly affects the web-based problem-solving learning performance of
students. In the following subsections, the factors and hypotheses are addressed in detail.
Figure 1. The hypothesised model for the study

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Internet self-efficacy
Internet self-efficacy (IS) refers to individuals’ belief in their capability to use the Internet to organise
information to produce given results; that is, IS represents personal judgment of one’s ability to use the
Internet (Eastin & LaRose, 2000); for instance, when a person is asked to use the Internet to collect data
or resources for a given task. Studies have concluded that IS has an impact on learning performance in
online learning contexts. For example, the study by Joo, Bong, and Choi (2000) revealed that students’ IS
is related to their search outcomes on the Web, while Tsai and Tsai (2003) concluded that IS can foster
better information searching strategies and learning outcomes in Internet-based environments.
Researchers have examined the effect of IS as an external variable of the intention to use technology. For
example, several studies have reported that IS is the antecedent variable which positively influences
individual behaviours of computer use (Compeau & Higgins, 1995), surfing the Internet (Hsu & Chiu,
2004) and search engine use (Liaw, Chang, Hung, & Huang, 2006). Moreover, Liang, Wu, and Tsai
(2011) found that IS could accurately predict nurses’ actual behaviour or intention to take part in
web-based continuing professional development (CPD). Thus, it is reasonable to assume that there is a
relationship between IS and the intention to learn on the Web. Accordingly, the following hypothesis is
proposed:
H1: Internet self-efficacy has a positive effect on the usage intention of adopting the web-based
problem-solving learning approach.
Task-technology fit
Goodhue and Thompson (1995) proposed that an explanation of information system success needs to
recognise both the task for which the technology is used and the fit between the task and the technology.
Task-technology fit (TT) is defined as “the matching of the capabilities of the technology to the demands
of the task, that is, the ability of IT to support a task” (Dwyer, 2007, 1022). TT is widely used as the
explanation of technology utilisation, in contrast to the technology acceptance model (TAM), where the
emphasis is on using beliefs of “perceived usefulness” and “perceived ease of use” to predict and explain
users’ acceptance of IT (Davis, 1989). As can be seen in Figure 2, the TT model has four key constructs:
task requirements, technology functionality, which together affect the third construct, task-technology fit,
which in turn affects the outcome variable, either performance or utilisation (Dishaw & Strong, 1999).
Figure 2. The task-technology fit model (Goodhue & Thompson, 1995; p.220 )
The positive relationship between TT and actual system use has been confirmed in numerous studies
(Dishaw & Strong, 1999; Klopping & McKinney, 2004). For example, the task-technology fit model
proposed by Goodhue and Thompson’s (1995, p.219) research presumes that the variances of “task
characteristics,” “technology characteristics,” and users could directly or indirectly influence “utilisation”
and “performance impacts.” In addition, they also found that users’ own cognition and beliefs could
determine the strength of their behavioural intentions, thereafter affecting the final performance of their
actual behaviour. Accordingly, the following hypothesis is proposed:
H2: Task-technology fit has a positive effect on the usage intention of adopting the web-based

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Computer anxiety
Computer anxiety (CA) refers to an individual's anxiety aroused in the process of using or facing a
computer, and has significant influences on the individual’s intention to use the computer. The main
cause of this anxiety is the unfamiliarity with computers, which makes the users worried that they might
appear clumsy in front of others or that their ignorance might cause damage to the computer (Chuo, Tsai,
Lan, & Tsai, 2011).
Doronina (1995) further summarised eight causes of CA, namely (1) individuals’ fear of causing damage
to the computer and worry that they may make mistakes, (2) a sense of uneasiness which results from the
individual’s feeling of ignorance or awkwardness when interacting with a computer, (3) a fear of
computers which reflects the user’s fear of technology or math, (4) individuals’ concern that radiation
might affect them and cause their health to deteriorate when they work in front of a computer, (5)
individuals’ fear of new or unfamiliar things leading to fear of computer technology, (6) individuals’
worry that the computer will threaten or limit the development of their intelligence, (7) individuals’
feeling that the time to use the computer is so insufficient that they will fail to take care of relevant
matters within the pre-scheduled time, thus causing a state of psychological uneasiness, and (8) some
individuals considering themselves inferior to the computer in intellectual development and being made
to give up opportunities of independent thinking.
CA is associated with negative perceptions of computers, problems using them, and avoidance of the
technology (Igbaria & Iivari, 1995). Igbaria, Parasuraman, and Baroudi (1996) indicated that CA is
negatively related to computer usage, perceived usefulness, perceived enjoyment, and social pressure.
Moreover, several studies have shown that CA has direct effects on intentions to use technology (Van
Raaij & Schepers, 2008). Accordingly, the following hypothesis is proposed:
H3: Computer anxiety has a negative effect on the intention to adopt the web-based problem-solving
learning approach.
Technology readiness
Technology readiness (TR) refers to people’s propensity to embrace and use new technologies to
accomplish goals in their home life and at work (Liljander, Gillberg, Gummerus, & van Riel, 2006;
Parasuraman, 2000). Based on the theory of diffusion of innovation (Rogers, 1962), Parasuraman’s
concept of TR focuses on the relationship between TR and intention to adopt technology-based products
and services. The more ready one is to embrace new technology, the higher the possibility that one will
intend to use technology-related products or services.
Research findings about the influences of these individual difference variables on technology acceptance
are both limited and inconsistent (Dabholkar & Bagozzi 2002). Fortunately, Parasuraman (2000)
proposed the concept of TR, which can gauge both users’ attitudes towards technology and other
demographic attributes. In particular, TR incorporates people’s feelings of optimism, innovativeness,
discomfort, or insecurity about the use of technology in the determination of a person’s readiness to use
technology. Lai and Chong (2007) found that vocational high school students who have a strong level of
TR would have strong intentions to use e-learning systems and positive attitudes towards e-learning. Lin,
Shih, and Sher (2007) proposed a framework that integrates TR into the TAM, indicating that TR could
directly affect learners’ intention to use technology and indirectly affect learning performance. TR is a
crucial factor that contributes to the success of any technology-enriched learning program. Thus, the
following hypothesis is proposed:
H4: Technology readiness has a positive effect on the intention usage of adopting the web-based

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Web information seeking
Web information seeking (WS) is different from IS, in that it refers to information seeking as a process
driven by humans’ need for information so that they can interact with the Internet environment,
emphasising communication and the needs, characteristics, and actions of information seekers
(Marchionini, 1997). For instance, it refers to a person searching for the information he/she needs through
the Internet when solving any problem. Studies have argued that WS facilitates the enhancement of
students’ academic performance through a series of information seeking activities on the Internet; that is,
locating, comparing, and assessing the searched results from the Internet could enhance student learning
performance (Hwang & Kuo, 2011; Kuo, Hwang, Chen, & Chen, 2012; Zhu, Chen, Chen, & Chern,
2011). For example, knowledge acquisition can be achieved by either viewing or searching for
information, and multiple results then call for the need to make comparisons or aggregations of the data
and concepts. Viewing or searching in Internet communities of interest may expose students to different
ways of learning or different methodologies; thus, the student’s general learning capability is improved
via socialisation (Marchionini, 2006). Zhu et al. (2011) indicated that the academic performance of 295
vocational high school students was affected by WS ability within the web-based learning activities. The
learning, in turn, is reflected in the students’ academic performance. Based on the above discussion, the
following hypothesis is proposed:
H5: Web information seeking has a positive effect on students’ problem-solving performance.
Intention
Intention (IN) refers to an individual’s subjective likelihood of performing a specified behaviour, and is
the major determinant of actual usage behaviour (Ajzen, 1985; Ajzen & Fishbein, 1980).
Many studies have focused on the investigation of the relationship between learners’ intention to use
technology and their learning achievement, finding a positive effect existing between these two variables.
For example, Watjatrakul (2011) found that university students’ intention to use a mobile messaging
service would directly affect their learning outcomes in a mobile learning model. Lau and Woods (2008)
conducted an empirical study of learning object acceptance in a multimedia learning environment, and
found that perceived usefulness positively affected behavioural intention to use multimedia;
subsequently this intention directly affected the participants’ learning effectiveness in the multimedia
learning environment. Lim, Lee, and Nam (2007) found that the intention to use e-learning is one of the
positive determinant variables affecting the academic effectiveness of students. Accordingly, the study
assumes that the students’ intention to adopt the web-based problem-solving learning approach will
positively affect their learning effectiveness. Thus, the following hypothesis is proposed:
H6: Intention has a positive effect on students’ web-based problem-solving performance.
Research method
Experimental design
Figure 3 shows the web-based problem-solving approach, which is a five-phase learning cycle adapted
from Eisenberg and Berkowitz’s (1996) big six method consisting, sequentially, of task definition,
information seeking strategies, location and access, use of information, synthesis, and evaluation. The
five phases of the web-based problem-solving approach are proposed and described in detail in the
following sections.

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Figure 3. Learning cycle of the web-based problem-solving model
Prior knowledge construction
Based on the meaningful learning theory (Ausubel, 1962), in order for acquisition of new knowledge to
take place and to be meaningful, prior knowledge needs to be activated by means of an introductory
session. Thus, the first phase of the learning cycle is to introduce the prior knowledge concerning the
issue that the students are going to inquire into on the Internet. For example, a conservation issue
concerning white dolphins is proposed by the teacher who needs to introduce the background information
about the white dolphin and why it is regarded as an endangered species.
Keyword adopted
An inquiry is a process during which individuals augment knowledge, resolve doubt, or solve a problem
(Hodson & Hodson, 1998). Acquiring knowledge from the Internet to solve problems has become a
widely used learning approach in schools. Researchers have indicated that the competence of adopting
appropriate keywords based on the problem is important in seeking information on the Internet;
moreover, without proper guidance or training, children may have difficulty in determining the
keywords (Bilal, 2002). Thus, the second phase of the learning cycle is to assist students in clarifying the
problem, identifying the relevant concepts of the problem, and even linking or converting to other
concepts.
Information identified
Finding the right information is an essential prerequisite of solving any problem. Even though the
students may find the relevant Web pages, they cannot be certain of the correctness of the data. Thus,
the third phase of the learning cycle is to guide the students to identify the relevant web pages, to look
for accurate solutions, and to critique and compare the advantages and disadvantages of the proposed
solutions before solving the problem.
Information abstracted
During the process of retrieving information from the web pages, it is an important ability for students to
judge what pages need to be conserved and how to abstract useful information for solving the problem.
Thus, the fourth phase of the learning cycle is to scaffold students to develop the information abstraction
ability. During the scaffolding process, different detailed levels of step-by-step hints are given to the
students to help them abstract information from the searched web pages based on their learning levels
(Campione & Brown, 1985); moreover, the scaffolding is decreased and eventually removed as the
students’ performance improves.

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Thinking elaborated
When the students have experienced a series of inquiry questions, more knowledge concerning a
specific issue will have been constructed. They are therefore likely to have their own solutions based on
a cognitive scheme. Thus, the fifth phase of the learning cycle is to ask the students to freely express
their own ideas on the issue. This phase also offers an opportunity to other students for reflection on
what they have done. Such a learning context is more likely to refine students’ cognitive processing,
which is consistent with Piaget’s (1970) theory of cognitive development, and also with the theory of
social constructivism (Vygotsky, 1978).
In this study, there are five sets of constructive questions for problem solving proposed by the researcher
and instructors based on the current social issues addressed in the social studies course. These issues
include nuclear power, water shortages, renewable energy, the greenhouse effect, and the garbage
disposal problem.
Experimental procedure and participants
The participants for this study were 201 fifth- and sixth-graders from three elementary schools in
southern Taiwan, aged 11–12 years. The experiment was conducted over a period of 5 weeks, and
consisted of four stages, namely the pretest, learning system orientation, the experiment, and the
post-test stage. The listed sequence and timing of the experimental procedure are shown in Figure 4. The
first stage, the pretest, was conducted for 40 minutes to measure the students’ initial problem-solving
ability, followed by an 80-minute orientation of the Meta-Analyzer learning system. When the students
were familiar with the learning system, three sets of social science issues for web-based problem-solving
activities were investigated over a period of six classes (240 minutes). Finally, the students’
problem-solving ability was measured again, and they were asked to complete the multi-dimension
affective questionnaires, totaling another 120 minutes.
Figure 4. The experimental procedure of the study
Tools
Assessment of problem-solving ability
The assessment of problem-solving ability draws upon the test items proposed by Zhan and Wu (2007,
p.58). The assessment consists of 10 items for evaluating the students’ ability in terms of “awareness of
problems,” “identification of the nature of problems,” “recognition of factors related to the problems,”
“identification of more information needed to solve the problems” and “determination of solutions.” The
perfect score for the assessment is 20. The higher the score a student obtains, the better problem-solving
ability the student presents. To ensure inter-rater reliability, four senior social studies teachers from four
elementary schools were involved in the rating. Moreover, before the formal rating of the experiment, 32
non-experimental high-grade students participated in the test in an attempt to verify the correlation among
the raters. Measured via Pearson correlation analysis, the inter-rater reliability of the pretest has a
Cronbach’s α value of 0.915, showing high consistency between the ratings of the various teachers

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(Cohen, 1988).
Research instruments for the structural equation modelling
The causal relationship model in the study was measured with a questionnaire survey structured as
follows: the first part consists of students’ personal and academic data, while the second part includes
seven dimensions for assessing the corresponding latent variables of the proposed model. For these seven
dimensions there are 36 items in total, consisting of 5 for the Internet self-efficacy (IS) dimension
developed based on the scale proposed by Eastin and LaRose (2000), 5 items for the task-technology fit
(TT) dimension developed by Goodhue (1998), 5 items for the technology readiness (TR) dimension
originating from the measure developed by Parasuraman (2000), 5 items for the computer anxiety (CA)
dimension originating from the measure proposed by Lloyd and Gressard (1984), 5 items for the Web
information seeking (WS) dimension originating from the measure developed by Choo, Detlor, and
Turnbull (1998), 5 items for the intention (IN) dimension developed by Davis (1989), and 6 items for the
problem-solving learning effectiveness (LE) dimension originating from the measure proposed by Gagne,
Briggs, and Wager (1992). Descriptions and an example item for each construct are shown in Table 1.
Table 1
Description and example items for each construct
Construct
(abbrev.)
Description Example items
IS The degree to which individuals
judge their capabilities to use the
Internet in diverse learning
situations
• I can search for information by using
keywords.
• I can surf a website by inputting its address.
TT The matching of the IT capability to
the demands of the task
The questions of Meta-Analyzer can guide
me to deeply understand the meaning of
social issues.
CA An individual’s anxiety aroused in
the process of using or facing a
computer
• I will feel anxious when operating a
computer.
• I will be afraid of computer classes.
TR People’s tendency to embrace and
employ new technologies to achieve
goals in their home life and at work
• I won’t worry about information leakage
when transmitting messages on the Internet.
• I believe that information I get on the Web
is safe and correct as long as I keep
checking.
WS The degree to which an individual
prefers to use the Internet to seek
information
• I prefer to search for information I need
from the Internet.
• I am used to finding out information by
surfing the Internet when solving problems.
IN The degree to which a learner
intends to adopt the proposed
learning context
• I would like to apply this kind of learning
mode to other subjects.
• I would like to recommend this kind of
learning mode to other class peers.
LE An individual’s web-based
problem-solving achievement and
learning achievement.
• I believe this kind of learning mode could
enhance my keyword-adopting and
information recognising abilities.
• I believe this kind of learning mode could
raise my abilities in analysing and solving
problems.
These seven latent variables are measured via a 5-point Likert scale, ranging from 1 (strongly disagree) to
5 (strongly agree). Moreover, the items have been modified and adapted via an iterative personal
interview process with two senior social studies teachers and one expert to verify the completeness,
wording, and appropriateness of the instrument and to confirm its content validity. The Cronbach's α
values of the seven dimensions are 0.87, 0.81, 0.82, 0.90, 0.84, 0.85 and 0.86, respectively.

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The web-based learning environment
The web-based learning system, Meta-Analyzer, was developed by Hwang, Tsai, Tseng, Lin, and Tsai
(2007) to assist teachers in tracing and analysing learners’ information-searching behaviours for a series
of questions related to some specific issues. It has been used in a number of studies as a tool to analyse
the online information-searching behaviours of learners of various levels in a range of subject areas
(Chiou, Hwang, & Tseng, 2009; Hwang, Chen, Tsai, & Tsai, 2011; Hwang, Tsai, Tsai, & Tseng, 2008;
Tseng, Hwang, Tsai, & Tsai, 2009). As searching behaviours are not the main concern of this study, the
data of the learners’ web-based searching behaviours are not used or reported in this paper.
To efficiently conduct the problem-solving or searching process via Meta-Analyzer, four constructive
inquiry-based questions are given to learners to search for information online for relevant learning units.
Each question needs to be answered properly via the process of appropriate keyword adoption,
information selection, summarisation, and integration, as shown in Figure 4. The searching behaviours of
the students, including the keywords they use, the browsed pages, the time spent browsing the web pages
and the user behaviours on the Web, are recorded on the server for cognitive quality analysis based on
Bloom’s (1956) cognitive levels, which are useful for educators to adjust their teaching strategies.
Figure 4. Framework of the web-based searching behaviour analysing system
The student interface consists of three operation areas: the question and answer area is located on the left
side of the browser; the information searching area is located on the upper-right side; and the web pages
found by the search engines are given on the lower-right side. To answer the questions, the students can
input keywords to search for information, and then browse the web pages that might be relevant to the
topic, as shown in Figure 5.

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Figure 5. Student interface of Meta-Analyzer
Results
Assessment of problem-solving ability
Applying the problem-solving ability assessment developed by Zhan and Wu (2007), a paired samples
t-test was employed to compare the test scores of the students before and after the experiment, as shown
in Table 2. It was found that the students’ post-test scores of problem-solving ability assessment
significantly improved in comparison with their pretest scores (t = 12.27, p < .001). This result implies
that the five-phase learning approach could be helpful to the students in terms of improving their
problem-solving ability.
Table 2
Paired samples t-test for students’ problem-solving ability
N Mean SD t-value
Pretest 201 11.78 3.43
12.27***
Post-test 201 14.82 2.68
***
p < .001
Measurement validation
To ensure the quality of measuring potential factors that might affect the web-based problem-solving
performance of the students, a statistical analysis was performed to evaluate convergent validity, such as
the individual item reliability, construct reliability (CR), and average variance extracted (AVE) of the
questionnaire by taking the indicators (i.e., the questionnaire items) as measures of latent variables
(Fornell & Larcker, 1981). The measurement model was evaluated by employing the partial least square
(PLS) and the confirmatory factor analysis (CFA) based on three criteria suggested by Fornell and
Larcker (1981), namely:

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(1) All indicator factor loadings should be significant and exceed 0.5.
(2) Composite reliabilities should exceed 0.7.
(3) AVE by each construct should exceed the variance due to measurement error for the construct
(i.e., AVE should exceed 0.5).
The Cronbach’s α values, shown in Table 3, indicate that each construct exhibits strong internal
reliability, as all the standard factor loading values in the CFA of the measurement model exceed 0.5 and
are significant at p = 0.001. In addition, the composite reliabilities of the constructs range from 0.84 to
0.92, and the AVE, ranging from 0.51 to 0.69, is greater than the variance due to measurement error.
Therefore, all three conditions for convergent validity are met, indicating good internal consistency
(Fornell & Larcker, 1981).
Table 3
Results of convergent validity of constructs
Constructs Composite
reliability (> 0.7)
AVE (> 0.5) Factor loading
(> 0.5)
Measurement
error
IS 0.90 0.65 0.89 0.33
TT 0.84 0.51 0.82 0.48
CA 0.85 0.53 0.84 0.42
TR 0.92 0.69 0.92 0.31
WS 0.85 0.53 0.84 0.44
IN 0.87 0.56 0.87 0.41
LE 0.88 0.56 0.88 0.40
Note: IS = Internet self-efficacy, TT = task-technology fit, CA = computer anxiety, TR = technology
readiness, WS = web information seeking, IN = intention, LE = learning effectiveness.
Discriminant validity assesses the extent to which a concept and its indicators differ from another concept
and its indicators (Bagozzi, Yi, & Philipps, 1991). According to Fornell and Larcker (1981), the
correlations between items in any two constructs should be lower than the square root of the average
variance shared by items within a construct. As shown in Table 4, the square root of the variance shared
between the construct and its items is greater than the correlations between the construct and any other
construct in the model, satisfying Fornell and Larcker’s (1981) criteria for discriminant validity. All
diagonal values exceed the inter-construct correlations, and thus the results confirm that our instrument
has satisfactory construct validity.
Table 4
Correlation matrix and discriminant validity of constructs
Constructs Mean SD IN LE IS TT CA TR WS
IN 4.14 .43 0.748
LE 4.26 .63 0.193 0.748
IS 3.86 .51 0.111 0.134 0.806
TT 4.03 .40 0.148 0.185 0.098 0.714
CA 2.09 .52 -0.120 -0.152 -0.090 -0.115 0.728
TR 4.16 .57 0.157 0.223 0.109 0.183 -0.168 0.831
WS 4.06 .41 0.114 0.191 0.086 0.128 -0.108 0.201 0.728
Note: All correlations (off-diagonal elements) are significant at p < 0.05. Diagonal elements (in italics
and bold) are square roots of AVE. IN = intention, LE = learning effectiveness, IS = Internet
self-efficacy, TT = task-technology fit, CA = computer anxiety, TR = technology readiness, WS = web
information seeking.
Structural model validation
A model is said to fit the observed data to the extent that the covariance matrix it implies is equivalent to
the observed covariance matrix (Hoyle, 1995). That is, the covariance matrix is a way of describing the
relation between a collection of variables. A single covariance value describes the relationship between
two variables. It is a tool for estimating the possible error in a numerical value and for predicting a

412
numerical value. However, there are no recommended measures of model fit. In this study, seven
common indices of fit with the values that are recommended in the literature (Hair, Anderson, Tatham, &
Black, 1998; Hu, Chau, Sheng, & Tam, 1999) were employed to assess the model; that is, “≧.8” for
goodness-of-fit index (GFI) and adjusted goodness of fit index (AGFI), “≧.9” for normed fit index
(NFI), comparative fit index (CFI) and non-normed fit index (NNFI), “≦.05 for root mean square
residual (RMSR), and “.05-.08” for root mean square error of approximation (RMSEA). The commonly
used measures of model fit, based on the results of an analysis of the structural model, are summarised in
Table 5. As seen in Table 4, all goodness-of-fit statistics are in the acceptable ranges, with the exceptions
of GFI (0.782) and AGFI (0.756), which are sensitive to sample size, but are close enough to the
recommended value of 0.8. On the whole, the various fit indices indicate that the research model has a
good model fit.
Table 5
Assessment of structure model fit
Model fit indices χ2
/df GFI AGFI NFI CFI NNFI RMSR RMSEA
Recommended value ≦3 ≧.8 ≧.8 ≧.9 ≧.9 ≧.9 ≦.05 .05-.08
Obtained 1.82 .782 .756 .957 .980 .978 .019 .061
Supported Yes Close Close Yes Yes Yes Yes Yes
Hypothesis testing
Figure 5 shows the resulting path coefficients of the research model. Five out of six hypotheses were
supported by the data. Among the external variables, technology readiness did not significantly influence
intention (β = -0.08, p > .05). However, Internet self-efficacy, task-technology fit and computer anxiety
were significant influences on intention with β values of 0.35 (p < .001), 0.55 (p < .001) and -0.19 (p <
.05) respectively. These four variables together explain 54% of the variance of intention (R2
= 54%), and
among these four variables, task-technology fit is the most influential factor for intention. Moreover, web
information seeking and intention were significantly influential factors for problem-solving performance
with β values of 0.36 and 0.65, respectively. Problem-solving performance in the study is jointly
predicted by intention (β = 0.65, p < .001), and web information seeking (β = 0.36, p < .001) and these
two variables together explain 56% of the variance in problem-solving performance (R 2
= 56%). Among
these two variables, intention is the most influential factor for problem-solving performance. Table 6
summarises the results of the hypothesis test.
Figure 5. Standardised path coefficients of the research model

413
Table 6
Inferred values of the research model
Hypotheses From --> to Standardised path (β) t-value Supported
H1 IS -> IN 0.35***
4.36 Yes
H2 TT -> IN 0.55***
5.04 Yes
H3 CA -> IN -0.19*
-2.165 Yes
H4 TR -> IN -0.08 -.95 No
H5 WS -> LE 0.36***
5.14 Yes
H6 IN -> LE 0.65***
7.77 Yes
*
p < .05, ***
p < .001
Standardised causal effects
The present study mainly aims to identify the main determinants of elementary school students’
problem-solving performance in web-based learning contexts.
Bollen (1989) mentioned that latent variables have three kinds of effect, namely direct effect, indirect
effect and total effect, which are employed to explain the relationship among the latent variables. A
coefficient linking one construct to another in the path model represents the direct effect of a determinant
on an endogenous variable. An indirect effect reflects the impact a determinant has on a target variable
through one or more other intervening variables in the model. A total effect on a given variable is the sum
of the respective direct and indirect effects.
Internet self-efficacy with a total effect of 0.35 has a medium significant effect on intention. These four
determinants account for approximately 54% of the variance in intention to adopt the proposed learning
mode, with direct effects of 0.35, 0.55, -0.19 and -0.08, respectively. This is largely due to the effects
contributed by task-technology fit and Internet self-efficacy, thus stressing the importance of the
relationship between these two variables. However, for technology readiness, this endogenous variable
shows no significant effect on intention compared to the other three endogenous variables, as shown in
Table 7.
Table 7
Summary of direct, indirect and total effects of the causal model
Dependent latent
variables
Independent latent variables Standardised estimates
Direct Indirect Total
Intention
(R2
= 0.56)
Internet self-efficacy 0.35***
- 0.35***
Task technology fit 0.55***
- 0.55***
Computer anxiety -0.19*
- -0.19*
Technology readiness -0.08 - -0.08
Web-based
problem-solving
performance
(R2
=0.54)
Internet self-efficacy - 0.23***
0.23***
Task-technology fit - 0.36***
0.36***
Computer anxiety - -0.12*
-0.12*
Technology readiness - -0.05 -0.05
Web information seeking 0.36***
- 0.36***
Intention 0.65***
- 0.65***
*
p < .05, **
p < .01, ***
p <.001
Discussion and conclusions
Web-based learning environments have become one of the most prominent instructional delivery
alternatives in schools today. This study proposes a five-phase learning approach to promote students’
web-based problem-solving performance; moreover, a theoretical model based on the problem-solving
theory for investigating the key determinants of students’ performance in the learning context is proposed.
The analysis results using structural equation modeling approach provide strong evidence for the

414
theoretical validity of each construct of learning performance. The estimate of 0.54 for the intention
construct (R2
= 54%) for these paths provides good support for the hypothesised impact of Internet
self-efficacy, task-technology fit and computer anxiety on the dependent variable, intention. In addition,
the estimate of 0.56 for the web-based problem-solving performance construct (R2
= 56%) denotes that
web-based problem-solving performance as perceived by learners is both directly and indirectly mediated
by the intention construct. Therefore, as a whole, the model has strong explanatory power for student
problem-solving performance within the proposed online learning context.
The significant path coefficients and the value of the R2
increase our confidence in the hypothesis testing
results and provide support for the association with problem-solving performance in the web-based
learning context. The results show that problem-solving performance is affected by interaction with
cognitive and social environmental factors, which ensures that technology alone does not cause learning
to occur. This corresponds to the perspective of the social cognitive theory, that is, human behaviours
have reciprocal interactions between cognitive, behavioural and environmental influences (Bandura,
1986).
The empirical results of this study indicate that intention is the most significant determinant of student
problem-solving performance in a web-based learning context. Internet self-efficacy, task-technology fit,
and computer anxiety provide an indirect contribution to problem-solving performance via the above
determinants; in contrast, web information seeking provides a direct contribution to problem-solving
performance. Accordingly, when students become more confident and capable of learning within the
proposed web-based learning context, and are more accustomed to the problem-solving context, they will
likely anticipate more benefits from the use of the proposed learning system, and foster positive intention
to use the learning system, such that, overall, the system will be more helpful to the problem-solving
performance of the students. The contributions and implications of the study are presented in the
following.
Task-technology fit is inferred to be the biggest determinant among the antecedent constructs of
problem-solving performance. That is, intention is the strongest predictor of learners’ problem-solving
performance, followed by task-technology fit, web information seeking, and Internet self-efficacy
predictors. The intention-learning achievement link has been validated in academic research (Lau &
Woods, 2008; Lim et al., 2007; Watjatrakul, 2011), and its revalidation in the web-based learning context
further attests to the robustness of this association. Furthermore, intention may be the key factor in
explaining the students’ acceptance of the web-based learning model because it is the strongest predictor
of web-based problem-solving performance (reaching 56% of variance). Among the latent variables
influencing intention, task-technology fit is identified in this study as the first determinant of intention.
Task-technology fit is the same critical cognitive belief as perceived usefulness, which is consistent with
previous studies (Goodhue, Klein, & March, 2000; Vessey, 1991), implying that problem-solving
activities can be conducted appropriately by means of web-based information searching systems such as
Meta-Analyzer to guide students to answer constructive questions about real-life social issues, whereby
students’ fundamental problem-solving concepts can be effectively developed. This also implies that the
better the technology meets the learning task characteristics, the more the learning design can improve
students’ learning performance and intention to use the technology. Moreover, task-technology fit is also
identified as having the biggest indirect effect on problem-solving performance among the latent variables
of Internet self-efficacy, task-technology fit, computer anxiety, and technology readiness.
Internet self-efficacy, followed by task-technology fit, is the second significant predictor (β = 0.35, p
< .001) of the intention construct. This prediction shows that the given web-based learning activities
required the students to adopt keywords, search for related information, and identify and abstract
essential information for answering the questions. Such a web-based problem-solving activity also
promoted the students’ confidence and competency in using the Internet when solving problems. Thus,
based on this result, it can be suggested that educators organise pre-class learning activities for the
promotion of using the Interne. For example, how to adopt keywords based on questions, how to copy
and paste relevant information from searched pages, how to organise and abstract essential information,
can all be taught and practised. That would increase students’ confidence and intention to participate in
web-based learning activities.

415
Similarly, web information seeking followed by intention is the second significant predictor (β = 0.36, p
< .001) of web-based problem-solving performance. This construct differs from Internet self-efficacy in
that it emphasises the degree of a learner's preference to use the Internet for seeking information. This
prediction shows that the given web-based learning activities required the students to search for
information to answer the questions. The students would get used to a series of web-based
problem-solving activities and gain better learning performance. Thus, this implies that it can be
suggested that educators manage more web-based learning classes for students so that the students’
web-based problem-solving performance can be enhanced. Accordingly, their digital literacy can also be
improved (Kuiper, Volman, & Terwei, 2009; Savage et al., 2010).
On the other hand, technology readiness is the only insignificant predictor (β = -0.08, p > .05) of the
intention construct. This suggests that the students had a positive propensity to use the technology
service, which is inconsistent with previous studies (Lai & Chong, 2007; Lin et al., 2007). This could be
due to the fact that the students were accustomed to the network environment in which they would trust
the safety and convenience brought by the proposed web-based problem-solving model.
Although this study provides insights into what determines students’ problem-solving performance in the
proposed web-based learning model, it has several limitations representing chances for future research.
First, further research may be needed to examine the effect of individual characteristics (e.g., gender) in
the learning performance of web-based learning via multi-group analysis. In recent years, the importance
of gender difference in the Internet self-efficacy domain has been emphasised. For example, Gefen and
Straub (1997) first investigated gender differences in perceptions of usefulness and ease of use of e-mail,
and intention to use it. Later, Venkatesh and Morris (2003) conducted a series of studies on the
moderating effect of gender on various relationships between constructs in the TAM. Tsai, Liang, Hou,
and Tsai (2012) found that there would be an effect from the interaction between different search contexts
and gender on students’ online searching strategies. Thus, gender difference can be considered in studies
focusing on the investigation of what determinants produce learning differences between male and female
learners. Second, the model was validated using sample data gathered from three elementary schools in
Taiwan. The fact that the participants come from one country limits the generalisability of the results.
Other samples from different countries and cultures should be collected to confirm and refine the findings
of this study. Third, studies have indicated that students who adopt collaborative learning strategies could
have better learning achievement than those who use individual learning strategies (Kuo, Hwang, Chen, et
al., 2012; Lazakidou & Retalis, 2010; Neo, 2003). Few studies have investigated what factors could affect
student performance in group activities. Thus, future research could investigate the factors affecting
individual students’ web-based learning performance in a collaborative learning context.
Acknowledgements
This study is supported in part by the National Science Council and the Ministry of Education under
contract numbers of NSC 101-2511-S-011 -005 -MY3, NSC 102-2511-S-011 -007 -MY3 and U19604.
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Corresponding author: Gwo-Jen Hwang, gjhwang.academic@gmail.com
Australasian Journal of Educational Technology © 2015.
Please cite as: Kuo, F-R. & Hwang, G-J. (2015). A structural equation model to analyse the antecedents
to students’ web-based problem-solving performance. Australasian Journal of Educational
Technology, 31(4), 400-420.

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