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1. An expert system for improving web-based problem-solving ability of students
Gwo-Jen Hwang a,⇑
, Chieh-Yuan Chen b
, Pei-Shan Tsai c
, Chin-Chung Tsai a
a
Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, 43, Sec.4, Keelung Rd., Taipei, 106, Taiwan
b
Department of Information and Learning Technology, National University of Tainan, 33, Sec. 2, Shulin St., Tainan city 70005, Taiwan
c
Graduate Institute of Engineering, National Taiwan University of Science and Technology, 43, Sec.4, Keelung Rd., Taipei, 106, Taiwan
a r t i c l e i n f o
Keywords:
Expert systems
Web-based learning
Problem-solving ability
Information technology-applied
instructions
Information-searching strategies
a b s t r a c t
Although previous research has demonstrated the benefits of applying the Internet facilities to the learn-
ing process, difficulty in using this strategy has also been identified. One of the major difficulties is owing
to the lack of an online instructional environment that can advise the students in using the Internet facil-
ities to solve problems. In this paper, an innovative approach is proposed, and it develops the knowledge
base of an expert system by analyzing the online problem-solving behaviors of the teachers. Conse-
quently, the expert system works as an instructor to assist the students in improving their web-based
problem-solving ability. To demonstrate the innovative approach, two experts are asked to evaluate
the performance of the expert system. Experimental results show that, the novel approach is able to pro-
vide accurate and constructive suggestions to students in improving their problem-solving ability.
Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction
The rapid progress in information and communication technol-
ogies has motivated efforts towards integrating web-based learn-
ing activities into the curriculum (Chang, 2001; Chu, Hwang,
Tsai, & Chen, 2009; Huang & Lu, 2003; Tsai, Liu, Lin, & Yuan,
2001; Tsai & Tsai, 2003, most of them are too outdated). In the past
decade, considerable work has been conducted on the use of Inter-
net as a distance-learning tool (Hwang, 2003; Hwang, Tseng, &
Hwang, 2008; Tseng, Chu, Hwang, & Tsai, 2008; Tseng, Su, et al.,
2008). One of the greatest benefits of web-based learning activities
is to allow students to participate in learning as active and self-
directed participants (Tsai, 2001).
Web-based learning activities often involve information
searching tasks since the learning environment is connected with
information sites worldwide. The popularity of accessing web infor-
mation has raised various educational issues, including the strate-
gies of information-seeking and use, the skill of processing web
information, the roles of teachers in educating and training, and
the development of new environments that facilitate teachers to ob-
serve and analyze the information-seeking behaviors of students in
web-based learning environments (Bilal, 2000; Tseng, Hwang, Tsai,
& Tsai, 2009; Zaphiris, Shneiderman, & Norman, 2002).
In the past decade, many studies (e.g. Bilal, 2000; Poindexter &
Heck, 1999; Tsai & Tsai, 2003) have been conducted to analyze the
learning behaviors of students in using search engines to collect
information for problem-solving. Researchers have indicated that,
it appears to be difficult for novice users to search information
effectively and efficiently on the Internet (Dias, Gomes, & Correia,
1999; Marchionini, 1995); therefore, training novice users to use
search engines to collect information for problem solving has been
recognized to be an important and challenging issue (citation).
Song and Salvendy (2003) further emphasized the importance
of reusing individual web browsing experiences; that is, the
knowledge and experiences of the expert-level users could be very
helpful to those novice users. Although there are some tools that
can record students’ problem-solving behaviors of using search en-
gines (citations), it is quite impossible for the teachers to give per-
sonalized suggestion to each individual student for improving the
web-based problem-solving skills of the students. This paper pro-
poses an innovative approach to elicit and analyze knowledge
and experiences concerning web-based problem-solving strategies
from teachers. The knowledge-based system is then employed to
advise individual students for improving their problem-solving
skills on the Internet.
2. Background and rationale
Artificial Intelligent has been applied to the development of
intelligent systems for several decades (citation). To develop an
intelligent monitoring system for improving the stability and reli-
ability of Internet service systems, it is important to know how
intelligent behaviors of human experts can be simulated by a com-
puter system. Most of the systems perform intelligent behaviors by
eliciting knowledge from a group of domain experts (Chu & Hwang,
0957-4174/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved.
doi:10.1016/j.eswa.2011.01.072
⇑ Corresponding author. Tel.: +886 915396558, fax: +886 6 3017001.
E-mail addresses: gjhwang@mail.nutn.edu.tw (G.-J. Hwang), m09505010@
stumail.nutn.edu.tw (C.-Y. Chen), D9622305@mail.ntust.edu.tw (P.-S. Tsai), cctsai@
mail.ntust.edu.tw (C.-C. Tsai).
Expert Systems with Applications 38 (2011) 8664–8672
Contents lists available at ScienceDirect
Expert Systems with Applications
journal homepage: www.elsevier.com/locate/eswa

2. 2008). An inference program is then invoked to make reactions to
the real time situations based on the expertise in the constructed
knowledge base.
Expert systems are such intelligent systems constructed by
obtaining the knowledge from human experts and coding it into
a form that a computer may apply to similar problems. Expert
knowledge is a combination of a theoretical understanding of the
problem and a collection of heuristic problem-solving rules that
experience has shown to be effective in the domain. In the past
decades, expert systems have been applied to a variety of prob-
lem-solving applications, such as decision making, designing, plan-
ning, monitoring, diagnosing, and training activities (Buchanan,
1985; Liebowitz, 1997; Mahaman, Passam, Sideridis, & Yialouris,
2003; Zhou, Jiang, Yang, & Chen, 2002).
The successful cases of the expert system approach can not only
demonstrate the benefits of applying expert system approach to
coping with medical diagnosis problems, but also depict the diffi-
culty of applying it. In building an expert system, the critical bot-
tleneck is to obtain the knowledge of the special domain from
the domain experts, which is called knowledge acquisition. Several
methods and systems have been proposed to cope with this prob-
lem, e.g., MDRG (Hwang, Chen, Hwang, & Chu, 2006) and KAMET
(Chu & Hwang, 2008). Most of these methods and systems were
proposed to deal with the acquisition of domain knowledge by
interviewing with the experts; however, in developing an expert
system to advise the students for improving their web-based prob-
lem-solving ability, it is difficult for the teachers to address the
‘‘exact rules’’ for solving the problems. Consequently, it becomes
an interesting and challenging issue to construct a set of rules for
describing the problem-solving strategies of the teachers by
observing their online behaviors. The following sections describe
an innovative approach, which is utilized to cope with this problem
by analyzing the essential factors that affect the web-based prob-
lem-solving ability of students and mining the association relation-
ships among those factors.
3. Innovative approach for development the web-based
problem-solving advisor
Fig. 1 shows the model for developing the web-based problem-
solving advisor, which is a rule-based expert system. To construct
the knowledge base of the expert system, an online problem-
solving behavior recording system is used to record the
web-searching behaviors of experienced teachers. The records of
the teachers are then analyzed by a data-mining scheme to gener-
ate a set of rules that can be used to give suggestions to students.
3.1. Problem-solving behavior recorder
To record and analyze the web-based problem-solving behav-
iors of teachers and students, a web-based environment, Meta-
Analyzer, has been implemented ( Hwang, Tsai, Tsai, & Tseng,
2008; Hwang, Tseng, et al., 2008). While the users log in the learn-
ing environment, a list of predefined topics to be investigated will
be displayed. Once the user selects a topic, an information-search-
ing interface for problem solving is depicted, as shown in Fig. 2.
The interface consists of three operation areas: the question and
answer area are located on the left side, the information searching
area is located on the upper-right side, and the web pages returned
from the search engines are given on the lower-right side of the
window. To answer the question, the student can input keywords
to search for information, and then browse the web pages that
might be relevant to the topic. The entire user portfolio, including
the keywords, the browsed web pages and the user behaviors on
the web will be recorded in the server for further analysis. In addi-
tion, a set of control buttons is listed at the top of the window,
which provides several useful functions for information searching,
such as bookmark insertion/deletion/browsing and system
demonstration.
Fig. 3 shows the interface for browsing the information-search-
ing portfolio of individual students. The presented information in-
cludes the answer to each question, the web pages browsed, and
the browsing time for each web page, etc. The ‘operation’ column
records the behaviors of each learner, where 1 means ‘input key-
words’, 2 means ‘browsing web pages’, 3 means ‘insert web page
to bookmark list’ and 4 means ‘remove the web page from the
bookmark list’.
3.2. Quantitative parameters for describing the web-based problem-
solving behaviors
In building knowledge-based systems, knowledge acquisition is
known to be a critical bottleneck and has become an important re-
search issue. Most of previous investigations on knowledge acqui-
sition employ grid-like or table-like structures to represent the
elicited knowledge (Hwang et al., 2006); nevertheless, the
Expert
System
Knowledge
Base
Data Mining
Mechanism
Suggestions
Problem-Solving
Behavior Recorder
Web-Searching
Behaviors of
Teachers
Students
Students
Teachers
Teachers
Web-Searching
Behaviors of
Students
Web-based
Problem-Solving
Advisor
Fig. 1. Model for developing web-based problem-solving advisor.
G.-J. Hwang et al. / Expert Systems with Applications 38 (2011) 8664–8672 8665

3. knowledge concerning behaviors or strategies is difficult to be de-
scribed in this way. That is, to construct the knowledge to repre-
sent the web-based problem-solving behaviors or strategies of
domain experts (teachers), it is important to propose new knowl-
edge acquisition methodologies.
To address this problem, a set of detailed quantitative indica-
tors, called ‘‘Web Problem-Solving Measure’’, is proposed based
on the six indicators suggested by Lin and Tsai (2005) and the user
on-line behaviors attained from Meta-Analyzer. The new quantita-
tive indicators for answering each question are given as follows
(Hwang, Tsai, et al., 2008; Hwang, Tseng, et al., 2008):
Number of different keywords used for answering a question.
Number of attempts for answering a question.
Total Time for web page selection.
Number of browsed and non-adopted pages.
Total Time for surveying web pages before browsing the first
adopted page.
Number of adopted pages.
Total Time for browsing the adopted pages.
Number of revisited and adopted pages.
Total Time for browsing the revisited and adopted pages.
Number of revisited but not adopted pages.
Total Time for browsing the revisited but not adopted pages.
Number of marked and adopted pages.
Number of marked but not adopted pages.
Number of revisions made on the answer.
These quantitative indicators are helpful to the instructors in
understanding the web-searching behaviors and ability of the
students. For example, if the indicator ‘‘Number of adopted pages’’
is equal to one, it indicates that the student only referred to one
web page for answering the question; that is, the student only cop-
ied and pasted data to answer the question without making any
comparison to judge the quality of the information. In this perspec-
tive, the student may not be considered as having good web-
searching ability.
In order to build a reference model for evaluating problem-
solving abilities, the searching behaviors of the students are
categorized into three aspects; that is, keyword-adopting ability,
relevant information-selecting ability and data abstraction ability,
as shown in Table 1. Each of the aspects is relevant to several
quantitative indicators defined in the previous subsection. This
way of categorization is similar to that utilized by Hwang, Tseng,
et al. (2008) and Hwang, Tsai, et al. (2008).
3.3. Construction of the knowledge base
This study attempted to analyze on-line problem-solving
behaviors of experienced teachers to build a set of rules for helping
students to improve their problem solving abilities. Sixty-seven
teachers from elementary and junior high schools participated in
the experiment, including 29 females and 38 males. Each teacher
was asked to answer a search task that contains four questions
to experience the problem-solving process with the assistance of
Meta-Analyzer:
(1) How many nuclear power plants are there in Taiwan?
Where are they located?
(2) What is the scientific principle of using nuclear power?
Fig. 2. Example of using Meta-Analyzer to collect information for problem solving.
8666 G.-J. Hwang et al. / Expert Systems with Applications 38 (2011) 8664–8672

4. (3) What are the advantages and disadvantages of nuclear
power?
(4) Do you agree to develop nuclear power in Taiwan? Why?
Questions 1–3 are categorized into the ‘‘fact-finding search
task’’, and question 4 is categorized into the ‘‘argument-based
search task’’. A fact-finding task is one that requires a single,
straightforward answer. An argument-based task is the one that
requires a fluid answer. Table 2 shows the statistical results of
the quantitative indicators on 67 teachers.
By employing factor analysis, it is possible to investigate the
number of various subgroups and to identify what these subgroups
Fig. 3. Teacher interface for browsing the information-searching portfolio.
G.-J. Hwang et al. / Expert Systems with Applications 38 (2011) 8664–8672 8667

5. theoretically characterize. The factor analysis results show that the
eigenvalues of three factors (i.e., relevant information-selecting
ability, question-answering ability and keyword-adopting ability)
are all greater than 1.00 (i.e., 3.40, 1.99 and 1.13) and account for
59.24% of variance, as shown in Table 3. An item is retained only
when the corresponding load is greater than 0.40 on the relevant
factor or less than 0.40 on non-relevant factor. Thus, the initial
14 items were then reduced to 11 items. The internal reliability in-
dex (alpha coefficients) of the three factors are 0.77, 0.64, and 0.56,
respectively; moreover, for the complete item set, the alpha coeffi-
cient is 0.76, acceptable for statistical analysis.
We further conducted correlation analyses between the three
factors and teachers’ search task scores, as shown in Table 4. It
indicates that the teachers with higher fact-finding search task
scores tended to have more quantitative indicators in factor 2
and factor 3 than lower fact-finding search task scores (p .01).
Also, the teachers with higher argument-based search task scores
tended to have more quantitative indicators in factor 2 (p .01).
Table 1
Aspects and quantitative indicators of web-based problem-solving ability.
Num. Quantitative indicators (for answering each question) Aspects
I1 Number of different keywords used Initial stage (keyword-adopting ability)
I2 Number of attempts for answering a question
I3 Total time for web page selection
I4 Number of browsed and non-adopted pages Recursive stage (relevant information-selecting ability)
I5 Total time for surveying web pages before browsing the first adopted page
I10 Number of revisited but not adopted pages
I11 Total time for browsing the revisited but not adopted pages
I13 Number of marked but not adopted pages
I6 Number of adopted pages Answer stage (data abstraction ability)
I7 Total time for browsing the adopted pages
I8 Number of revisited and adopted pages
I9 Total time for browsing the revisited and adopted pages
I12 Number of marked and adopted pages
I14 Number of revisions made on the answer
Table 3
Rotated factor loadings and Cronbach’s a values for the three factors (subscales) of quantitative indicators (n = 67).
Items Factor 1 Factor 2 Factor 3
Factor 1: relevant information-selecting ability a = 0.77
I5: Time for surveying web pages before browsing the first adopted page 0.79
I10: Number of revisited but not adopted pages 0.76
I11: Time for browsing the revisited but not adopted pages 0.75
I4: Number of browsed and non-adopted pages 0.65
I13: Number of marked but not adopted pages 0.58
Factor 2: question-answering ability a = 0.64
I6: Number of adopted pages 0.83
I7: Time for browsing the adopted pages 0.76
I12: Number of marked and adopted pages 0.53
Factor 3: keyword-adopting ability a = 0.56
I3: Time for web page selection 0.83
I2: Number of attempts for answering a question 0.64
I1: Number of different keywords used for answering a question 0.51
Eigen-value 3.40 1.99 1.13
% of variance 30.95 18.05 10.25
Overall a = 0.75, total variance explained is 59.24%.
Table 2
Descriptive Statistics of the quantitative indicators (n = 67).
Quantitative indicators Mean SD
I1: Number of different keywords used for answering a question 1.03 0.43
I2: Number of attempts for answering a question 1.60 0.79
I3: Time for web page selection 64.62 59.65
I4: Number of browsed and non-adopted pages 1.83 1.54
I5: Time for surveying web pages before browsing the first adopted page 135.48 93.13
I6: Number of adopted pages 0.96 0.47
I7: Time for browsing the adopted pages 88.30 64.89
I8: Number of revisited and adopted pages 0.32 0.40
I9: Time for browsing the revisited and adopted pages 14.59 24.41
I10: Number of revisited but not adopted pages 0.54 0.73
I11: Time for browsing the revisited but not adopted pages 17.48 31.84
I12: Number of marked and adopted pages 0.28 0.38
I13: Number of marked but not adopted pages 0.12 0.25
I14: Number of revisions made on the answer 0.00 0.00
8668 G.-J. Hwang et al. / Expert Systems with Applications 38 (2011) 8664–8672

6. Teachers’ search task scores were correlated to many of the search
strategies, which somewhat supported the validity of using Meta-
Analyzer.
Moreover, as those experienced teachers are experts of on-line
problem solving. If students want to get high task scores in the
fact-finding search task, they must have more quantitative indica-
tors in factor 2 and factor 3 as the teachers did. If students want to
get high task scores in the argument-based search task, they must
have more quantitative indicators in factor 2 as the teachers did.
Therefore, it could be useful to analyze the web-searching behav-
iors of the teachers and build some rules for assisting the students
in improving their on-line problem-solving performance.
By applying the association rule-mining algorithm (Tseng
Hwang, 2006), 32 association rules are obtained for Factor 1 and
Factor 2, where Mi is the mean of quantitative indicator Ii. Those
rules are then checked and revised by two experienced teachers
such that more detailed suggestions can be given in the conclusion
part of individual rules.
RULE_01: IF (I12 P 0) AND (I12 M12) AND (I6 P 0) AND
(I6 M6) AND (I7 P 0) AND (I7 M7)
THEN [It is concluded that the student only referred to a few
numbers of different web pages for answering the question.
The system will suggest the student to refer to more web
pages, to mark the relevant web pages, and to spend more
time in reading the contents.]
RULE_02: IF (I12 P 0) AND (I12 M12) AND (I6 P M6) AND
(I7 P 0) AND (I7 M7)
THEN [It is concluded that the student referred to many dif-
ferent web pages for answering the question, but forgot to
mark the relevant ones; moreover, the student did not spend
enough time in reading the contents. Therefore, the system
will remind the student to mark web pages that are relevant
to the question, and spend more time in reading the
contents.]
RULE_03: IF (I12 P 0) AND (I12 M12) AND IF (I6 P 0) AND
(I6 M6) AND (I7 P M7)
THEN [It is concluded that the student only referred to few
web pages for answering the question, although he/she
spent lots of time in reading the contents of those web pag-
ers. The system will suggest the student to refer to more web
pages, and to mark the relevant ones.]
RULE_04: IF (I12 P 0) AND (I12 M12) AND IF (I6 P M6) AND
(I7 P M7)
THEN [It is concluded that the student referred to many web
pages for answering the question, and spent enough time in
reading the contents. However, he/she forgot to mark the
web pages that are relevant to the question. Therefore, the
system will remind the student to mark the web pages.]
RULE_05: IF (I12 P M12) AND (I6 P 0) AND (I6 M6) AND
(I7 P 0) AND (I7 M7)
THEN [It is concluded that the student only referred to few
web pages for answering the question, and did not spend
enough time in reading the contents. The system will sug-
gest the student to refer to more web pages, and spend more
time in reading the contents.]
RULE_06: IF (I12 P M12) AND (I6 P M6) AND (I7 P 0) AND
(I7 M7)
THEN [It is concluded that the student referred to many web
pages for answering the question, but did not spend enough
time in reading the contents. Therefore, the system will sug-
gest the student to spend more time in reading the contents
of web pages.]
RULE_07: IF (I12 P M12) AND IF (I6 P 0) AND (I6 M6) AND
(I7 P M7)
THEN [It is concluded that the student only referred few web
pages for answering the question. The system will suggest
the student to refer to more web pages, and to mark the rel-
evant ones.]
RULE_08: IF (I12 P M12) AND IF (I6 P M6) AND (I7 P M7)
THEN [It is concluded that the student referred to many web
pages for answering the question, and also mark the relevant
ones; moreover, the student has spent enough time in read-
ing the contents of web pages. Therefore, the system will
praise and admire the student.]
RULE_09: IF (I13 P 0) AND (I13 M13) AND (I4 P 0) AND
(I4 M4) AND (I5 P 0) AND (I5 M5)
THEN [It is concluded that the student has paid his/her
entire attention to the question. Therefore, the system will
praise and admire the student.]
RULE_10: IF (I13 P 0) AND (I13 M13) AND (I4 P M4) AND
(I5 P 0) AND (I5 M5)
THEN [It is concluded that the student browsed too many
web pages that are irrelevant to the question. The system
will suggest the student to check the use of keywords in
searching relevant web pages and pay more attention to
the question.]
RULE_11: IF (I13 P 0) AND (I13 M13) AND (I4 P 0) AND
(I4 M4) AND (I5 P M5)
THEN [It is concluded that the student spent too much time
in reading the contents of the web pages. The system will
suggest the student to spend less time in reading the con-
tents of those web pages that are irrelevant to the question.]
RULE_12: IF (I13 P 0) AND (I13 M13) AND (I4 P M4) AND
(I5 P M5)
THEN [It is concluded that the student browsed too many
web pages that are irrelevant to the question. The system
will suggest the student to check the correctness of the key-
words using in searching relevant web pages and pay more
attention to the meaning of the question]
RULE_13: IF (I13 P M13) AND (I4 P 0) AND (I4 M4) AND
(I5 P 0) AND (I5 M5)
THEN [It is concluded that, although the student browsed
few web pages in answering the question, he/she has
marked some irrelevant web pages. The system will remind
the student to avoid marking the relevant web pages.]
RULE_14: IF (I13 P M13) AND (I4 P M4) AND (I5 P 0) AND
(I5 M5)
THEN [It is concluded that the student browsed too many
web pages that are irrelevant to the question, and also
marked the relevant ones. The system will suggest the stu-
dent to check the correctness of the keywords using in
searching relevant web pages, pay more attention to the
question and don’t mark the relevant web pages.]
Table 4
Inter-correlation matrix of the three factors and tasking scores (n = 67).
Factor 1 Factor 2 Factor 3 Fact-
finding
search
task scores
Argument-
based search
task scores
Factor 1 –
Factor 2 0.25**
–
Factor 3 0.23 0.41**
–
Fact-finding
search
task scores
0.21 0.49**
0.33**
–
Argument-based
search task scores
0.10 0.33**
0.13 0.39**
–
**
p .01
G.-J. Hwang et al. / Expert Systems with Applications 38 (2011) 8664–8672 8669

7. RULE_15: IF (I13 P M13) AND (I4 P 0) AND (I4 M4) AND
(I5 P M5)
THEN [It is concluded that the student spent too much time
in reading the contents of the web pages that are irrelevant
to the question, and also marked the irrelevant ones. The
system will suggest the student to spend less time in reading
the contents of those web pages that are irrelevant to the
question and avoid marking the relevant ones.]
RULE_16: IF (I13 P M13) AND (I4 P M4) AND (I5 P M5)
THEN [It is concluded that the student browsed too many
web pages that are irrelevant to the question, spent too
much time in reading the irrelevant contents, and marked
many irrelevant web pages. The system will suggest the stu-
dent to check the correctness of the keywords using in
searching relevant web pages, pay more attention in reading
the question, spend less time in reading the contents of
those web pages that are irrelevant to the question, and
avoid marking the relevant web pages.]
RULE_17: IF (I10 P 0) AND (I10 M10) AND (I11 P 0) AND
(I11 M11)
THEN [It is concluded that the student only revisited few
web pages that are irrelevant to the question, and spent less
time in reading the irrelevant contents. Therefore, the sys-
tem will praise and admire the student.]
RULE_18: IF (I10 P M10) AND (I11 P 0) AND (I11 M11)
THEN [It is concluded that the student revisited too many
web pages that are irrelevant to the question, although the
time spent for browsing each irrelevant web page is not
long. The system will suggest the student to revisit fewer
web pages that are irrelevant to the question.]
RULE_19: IF (I10 P 0) AND (I10 M10) AND (I11 P M11)
THEN [It is concluded that the student only revisited few
web pages that are irrelevant to the question, but spent
too much time in reading the irrelevant contents. The sys-
tem will suggest the student to spend less time in reading
the contents of those web pages that are irrelevant to the
question.]
RULE_20: IF (I10 P M10) AND (I11 P M11)
THEN [It is concluded that the student revisited too many
web pages that are irrelevant to the question, and spent
too much time in reading the irrelevant contents. The sys-
tem will suggest the student to spend less time in revisiting
the irrelevant web pages and reading the irrelevant
contents.]
For Factor3, Twelve rules are generated, where Q = 0 for fact-
finding task and Q = 1 for argument-based task.
RULE_21: IF (I3 P 0) AND (I3 M3) AND (I2 P 0) AND (I2 M2)
AND (Q = 0)
THEN [It is concluded that the student attempted few fre-
quencies of different keywords and did not spend enough
time in selecting web pages to browse in the fact-finding
task. The system will suggest the student to try more search
Fig. 4. Illustrative example of providing learning suggestions to a student.
8670 G.-J. Hwang et al. / Expert Systems with Applications 38 (2011) 8664–8672

8. operations by using different keywords, and to select more
web pages to browse.]
RULE_22: IF (I3 P M3) AND (I2 P 0) AND (I2 M2) AND (Q = 0)
THEN [It is concluded that the student did not spend enough
time in selecting web pages to browse in the fact-finding
task. The system will suggest the student to browse more
abstracts in selecting web pages.]
RULE_23: IF (I3 P 0) AND (I3 M3) AND (I2 P M2) AND (Q = 0)
THEN [It is concluded that the student attempted few fre-
quencies of different keywords in the fact-finding task. The
system will suggest the student to try more search opera-
tions by using different keywords.]
RULE_24: IF (I3 P M3) AND (I2 P M2) AND (Q = 0)
THEN [It is concluded that the student have made enough
tries with different keywords and spent enough time in
selecting web pages to browse in the fact-finding task.
Therefore, the system will praise and admire the student.]
RULE_25: IF (I1 P 0) AND (I1 M1) AND (Q = 0)
THEN [It is concluded that the student used few different
keywords in the fact-finding task. The system will suggest
the student to try more different keywords and avoid using
entire question statement as keywords.]
RULE_26: IF (I1 P M1) AND (Q = 0)
THEN [It is concluded that the student used enough different
keywords in the fact-finding task. The system will praise and
admire the student, and remind him/her to avoid using the
entire abstracts as keywords.]
RULE_27: IF (I3 P 0) AND (I3 M3) AND (I2 P 0) AND (I2 M)
AND (Q = 1)
THEN [It is concluded that the student have made enough
tries with different keywords and have spent enough time
in selecting web pages to browse in the argument-based
task. Therefore, the system will praise and admire the
student.]
RULE_28: IF (I3 P M3) AND (I2 P 0) AND (I2 M) AND (Q = 1)
THEN [It is concluded that the student spent lot of time in
selecting web pages to browse in the argument-based task.
The system will suggest the student to improve the effi-
ciency in selecting the web pages to browse.]
RULE_29: IF (I3 P 0) AND (I3 M3) AND (I2 P M) AND (Q = 1)
THEN [It is concluded that the student have made enough
tries with different keywords in the argument-based task.
The system will suggest the student to check the correctness
of the keywords used in searching relevant web pages.]
RULE_30: IF (I3 P M3) AND (I2 P M2) AND (Q = 1)
THEN [It is concluded that the student have made enough
tries with different keywords and spent lot of time in select-
ing web pages to browse in the argument-based task. The
system will suggest the student to check the correctness of
the keywords used in searching relevant web pages and
pay more attention to the problem-solving task.]
RULE_31: IF (I1 P 0) AND (I1 M1) AND (Q = 1)
THEN [It is concluded that the student have made enough
tries with plenty of keywords in the argument-based task.
The system will praise and admire the student while
reminding him/her to avoid using the entire abstracts as
keywords.]
RULE_32: IF (I1 P M1) AND (Q = 1)
THEN [It is concluded that the student have made enough
tries with plenty of keywords in the argument-based task.
The system will remind the student to avoid using the entire
abstracts as keywords.]
Based on these rules, learning guidance can be provided to indi-
vidual students after they participate in the problem-solving activ-
ities. Fig. 4 shows an illustrative example of providing learning
suggestions to a student.
4. Experiment and evaluation
To evaluate the performance of the expert system, two domain
experts were invited to test the accuracy of the suggestions given
by the systems. Both the experts have two-year experiences in
designing and conducting web-based teaching learning activities.
Thirty-two cases were evaluated by applying the expert system.
It was found that the accuracy of suggestions made by the systems
is rather high (99.4% and 92.2%); therefore, we conclude that the
innovative approach can correctly provide learning guidance to
students for most of the cases.
Furthermore, a questionnaire survey has been conducted to col-
lect the opinions from 108 students who have experienced the
learning environment, as shown in Table 5. It can be seen that
80–90% of the students gave an ‘‘Agree’’ or ‘‘Strongly Agree’’ feed-
back to those questionnaire items. It is interesting to know that
93% of the students agreed that the learning suggestions were
helpful to them and nearly 83% of the students would like to rec-
ommend the learning environment to their teachers or classmates,
implying that most students enjoyed participating in the learning
activity.
5. Conclusions
Recent studies have shown the importance of investigating the
web-based information searching behaviors of students. Classify-
ing students’ web-based information searching strategies is helpful
to the teachers in realizing the learning problems of individual stu-
dents such that more effective instructional strategies can be
developed accordingly (Tsai Tsai, 2003). Researchers also indi-
cated that students who are well trained in using online searching
and evaluating strategies are likely to develop more accurate and
in-depth understanding of certain topics. That is, it is important
to help students develop more sophisticated approaches to
Table 5
The item results of the questionnaire survey.
Questionnaire item Strongly disagree
(%)
Disagree
(%)
No comment
(%)
Agree
(%)
Strongly agree
(%)
1. Meta-Analyzer is easy to use 1 2 15 33 49
2. Meta-Analyzer is helpful to me in making me focus on the
problems to be coped with
0 3 18 32 47
3. Meta-Analyzer is helpful to the me in realizing the problems to be coped with 1 2 16 35 46
4. The learning suggestion is helpful to me in improving the information seeking
ability
0 1 6 36 57
5. I would recommend Meta-Analyzer to my teachers 1 2 14 32 51
6. I would recommend Meta-Analyzer to my classmates 1 5 12 29 53
7. Using Meta-Analyzer in enjoyable 2 3 12 31 52
G.-J. Hwang et al. / Expert Systems with Applications 38 (2011) 8664–8672 8671

9. enhance Internet-based learning. Therefore, it becomes an impor-
tant issue to develop an online training mechanism to assist stu-
dents in improving their web-based information searching skills.
This study analyzes the information searching behaviors of
teachers who are experienced in using search engines to find infor-
mation for problem solving and generate expert rules accordingly.
Those rules are then used by an expert system for advising stu-
dents based on their information searching records. Moreover,
experimental results show that the innovative approach is able
to accurately provide constructive suggestions to the students.
From the findings of this study, it can be demonstrated that the
training about web-based information searching ability can be
more effective with the help of a well-designed expert system. In
the near future, we attempt to apply the innovative approach to
various technology-enhanced learning activities for science and so-
cial science courses in different levels of schools.
Acknowledgement
This study is supported in part by the National Science Council
of the Republic of China under Contract Nos. NSC 98-2511-S-011-
008-MY3 and NSC 99-2511-S-011-MY3.
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