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1. A hybrid approach to promoting students’ web-based problem-solving
competence and learning attitude
Fan-Ray Kuo a
, Gwo-Jen Hwang b,*, Chun-Chia Lee c
a
Department of Information and Learning Technology, National University of Tainan, 33, Sec. 2, Shulin St., Tainan city 700, Taiwan
b
Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, 43, Sec. 4, Keelung Rd., Taipei 106, Taiwan
c
Department of Information Management, Fooyin University, 51 Jinxue Rd., Daliao Dist., Kaohsiung City 831, Taiwan
a r t i c l e i n f o
Article history:
Received 3 May 2011
Received in revised form
17 September 2011
Accepted 20 September 2011
Keywords:
Teaching/learning strategies
Elementary education
Applications in subject areas
Interactive learning environments
a b s t r a c t
Fostering problem-solving abilities has long been recognized as an important issue in education;
however, past studies have shown that it is difficult and challenging to find effective learning strategies
or tools for improving students’ problem-solving abilities. To cope with this problem, in this study,
a hybrid approach that integrates the cognitive apprenticeship model with the collaborative learning
strategy is proposed for conducting web-based problem-solving activities. Students’ problem-solving
performance is examined in such a hybrid learning context. Furthermore, past studies indicate that
cognitive load could affect learners’ performance; thus, the influence of cognitive load on students’
problem-solving effectiveness with this new approach is investigated in depth. The experimental results
show that middle- and low-achievement students in the experimental group gained significant benefits
from the hybrid approach in comparison with those who learned with the traditional approach.
Accordingly, a discussion of how to accommodate the needs of different learning ability groups is
provided.
Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction
Society is experiencing an information explosion; thus, schools are playing not only the role of knowledge transfer for students, but are
also a key to cultivating their information-searching abilities in quick response to a pluralistic community. It is necessary to facilitate
students’ problem-solving abilities to adapt to a future changing society through training courses concerning information retrieval and
reorganization processes. That is, students would enhance their problem-solving abilities via the thinking processes of knowledge orga-
nization and reasoning (Goldstein & Levin, 1987; Mayer, 1992). Thus, it is important to identify effective learning approaches to improve
students’ problem-solving competence, which has become an important and challenging issue. Previous studies have revealed several
influential factors on students’ problem-solving abilities, such as intelligence quality, learning materials, learning methods, problem-solving
instruction strategies, and the socioeconomic background of parents (Mustafa & Özgül, 2009; Oloruntegbe, Ikpe, & Kukuru, 2010; Zheng,
2007). Among these, learning approaches and problem-solving instruction strategies are considered as being the key factors (Harskamp
& Suhre, 2007; Lo, 2009; Tsai & Shen, 2009). Moreover, researchers have indicated that information-searching skills and problem-
solving abilities have a highly positive correlation. For example, Eisenberg and Berkowitz (1990) found significant connections between
information-searching skills, problem-solving ability and the knowledge structure of students. Bilal (2000, 2001, 2002) further indicated
that a lack of effective information-searching strategies and high-order thinking abilities would influence students’ performance in
searching for information on the Internet; however, it would be difficult for students to enhance their high-order thinking ability by only
observing and imitating the cognitive skills of teachers in a traditional learning context. In other words, a more effective learning approach is
needed to help students acquire both cognitive and metacognitive skills.
To address this issue, Brown, Collins and Duguid (1989) proposed a learning model called cognitive apprenticeship, which focuses on
acquiring thinking skills such as cognitive skills and metacognitive skills resulting in sustained participation in a community (Collins, Brown,
& Newman, 1989). Several studies have reported that the cognitive apprenticeship model is able to promote students’ high-order thinking,
* Corresponding author. Tel.: þ886 915396558.
E-mail addresses: revonkuo@gmail.com (F.-R. Kuo), gjhwang.academic@gmail.com, gjhwang@mail.ntust.edu.tw (G.-J. Hwang), dereklee@mail.nctu.edu.tw (C.-C. Lee).
Contents lists available at SciVerse ScienceDirect
Computers & Education
journal homepage: www.elsevier.com/locate/compedu
0360-1315/$ – see front matter Ó 2011 Elsevier Ltd. All rights reserved.
doi:10.1016/j.compedu.2011.09.020
Computers & Education 58 (2012) 351–364

2. cognitive skills and oral presentation abilities (Ertl, Fischer, & Mandl, 2006; Hwang, Yang, Tsai, & Yang, 2009; Schellens & Valcke, 2005).
However, in the practice of a traditional learning context, a teacher can hardly take care of all of the students in a class in the limited time,
especially middle- and lower-achieving students (Brophy, 1968; Glass & Smith, 1979; Karakaya, Ainscough & Chopoorian; 2001; Wright,
Horn, & Sanders, 1997). To cope with this problem, the collaborative learning strategy is embedded in the learning activity, so that the
high-achievement students can play the role of tutors during the learning activities. The ability to work with peers to solve problems
collaboratively has been recognized as being another important competence (Barron, 2000; Hwang, Chu, Yin, & Lin, 2008; Hwang, Tsai,
Tseng, Lin & Tsai, 2007). Numerous positive results have demonstrated the importance of collaborative learning. For example, Li (2002)
reported the effectiveness of group work in promoting students’ critical thinking skills, problem-solving skills, social skills and self-
esteem. Researchers have indicated that collaborative learning often leads to better learning outcomes than individual work (Lipponen,
Hakkarainen, & Paavola, 2004; Neo, 2003). However, researchers have also pointed out that those benefits would not automatically
happen in a collaborative learning environment unless a sound instructional design is provided (Chu, Hwang, & Tsai, 2010; Hwang, Shi, &
Chu, 2011).
In the meantime, several previous studies have argued that students’ information-searching ability has significant positive correlation
with their web-based problem-solving performance (Hwang & Kuo, 2011; Hwang, Chu et al., 2008; Hwang, Tsai, Tsai, Tseng, & Judy, 2008;
Hwang, Wang et al., 2008; Hwang, Yin et al., 2008); that is, fostering students’ information-searching ability could improve their web-based
problem-solving performance. As several studies have reported the benefit of using the web-based information-searching behavior
analyzing system, Meta-Analyzer, in guiding students to enhance their web-based problem-solving competence (Chu, Hwang, & Huang,
2010; Hwang, Tsai, et al., 2008; Panjaburee, Hwang, Triampo, & Shih, 2010), this study attempts to propose a hybrid approach combining
the cognitive apprenticeship model with the collaborative learning strategy in web-based learning activities using Meta-Analyzer. In such
a hybrid model, students can learn collaboratively with the cognitive apprenticeship strategy to improve their problem-solving perfor-
mance. An experiment has been conducted to evaluate the effectiveness of the innovative approach by comparing the learning performance
of the experimental group students who learned collaboratively using the cognitive apprenticeship strategy with those of the control group
students who learned with the conventional cognitive apprenticeship approach for problem solving.
2. Literature review
2.1. Problem-solving ability
Problem-solving ability is not only essential to work, but is also a critical skill for adapting to the environment. Sternberg (1988) indicated
that problem-solving ability should encompass six skills: (1) identifying the nature of the problem; (2) choosing problem-solving steps; (3)
choosing problem-solving strategies; (4) choosing appropriate information; (5) allocating proper resources; and (6) monitoring the
problem-solving process. Recently, many researchers have found that students’ problem-solving ability can be facilitated through the
integration of problem-solving strategies and computer technologies (Chen, 2010; Chiou, Hwang, & Tseng et al., 2009; Ferreira & Santos,
2009; Ho, Yin, Hwang, Shyu, & Yean, 2009; Kim & Hannafin, 2011; Merrill & Gilbert, 2008). For instance, Yu, She, and Lee (2010) investi-
gated the effects of two factors: the mode of problem-solving instruction (i.e. Web-based versus non-Web-based) and the level of academic
achievement (i.e. high achievers versus low-achievers) on students’ problem-solving ability and biology achievement. Their research
findings showed that Web-based problem-solving instruction had the potential to enhance and sustain the learners’ problem-solving skills
over an extended period of time. Chen and Hsiao (2010) conducted a web-based problem-solving activity to examine students’ learning
behavior and cognitive change in two music appreciation courses at a university. They found that such a learning approach promoted the
students’ learning performance and improved their higher order thinking ability. However, previous studies have also found that it is easy
for students to get lost on the Internet while searching for information to solve complex problems without assistance or scaffolding from
tutors (Hargittai, 2006; Li & Kirkup, 2007). Thus, a proposal for a learning approach by way of which students’ problem-solving ability on the
Internet can be fostered is needed.
2.2. Cognitive apprenticeship
Apprenticeship means gaining practical experience via the explanation and demonstration of practice by masters, and observation by
apprentices of specific skills and knowledge. Thus, masters play the role of knowledge carriers, while apprentices play the role of knowledge
acceptors. Brown et al. (1989) further proposed the cognitive apprenticeship theory based on Vygotsky’s zone of proximal development in
1978. Cognitive apprenticeship (CA) focuses on acquiring thinking skills such as cognitive skills and metacognitive skills resulting in sus-
tained participation in a community (Collins et al., 1989; Hsu, Hwang, & Chang, 2010; Schellens & Valcke, 2005). It provides an opportunity
for beginners or novices to observe how instructors or experts solve complex problems step by step in an authentic context in terms of
cognitive construction. Collins et al. (1989) listed six major steps for applying this model:
(1) Modeling: the experts demonstrate and explain their way of thinking for students to observe and understand;
(2) Coaching: the learners practice the methods, while the experts advise and correct;
(3) Scaffolding: through increasing the complexity of problems and decreasing the level of assistance according to the learners’ progress,
the experts progressively help the learners successively approximate the objective of accomplishing a task independently;
(4) Articulation: the learners are given opportunities to articulate and clarify their own way of thinking;
(5) Reflection: the learners compare their own thoughts with those of experts and peers;
(6) Exploration: the learners manipulate and explore the learned skills or knowledge to promote their true understanding.
Previous studies have indicated that the CA model can particularly strengthen learners’ high-order thinking abilities. For example,
Snyder (2000) developed adult high-order thinking skills with the CA model and found that the CA group learners developed better
problem-solving skills than the text-based group learners. However, previous studies have also indicated that a teacher can hardly take care
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352

3. of all of the students in a class in the limited time, especially middle- and lower-achieving students; moreover, previous research has found
that it is easy to create a high cognitive load for students when the CA model is incorporated in a complex learning activity (Renkl &
Atkinson, 2003; Sweller, 1994; Sweller, van Merriënboer, & Paas, 1998). Thus, how to reduce student’s cognitive load and promote their
problem-solving ability in a CA model learning activity is worth studying.
2.3. Collaborative learning
Through the process of collaboration and brainstorming in a group, members are able to quickly accept a large amount of new infor-
mation, and new ideas are generated to complete the task (Lipponen, 2002). However, respect for people in this competitive society is
insufficient, which also leads students to ignore the importance of collaboration in a team. For this reason, students cannot only learn the
spirit of respecting others, but can also facilitate their learning performance through the collaborative learning mechanism (Hwang, Chu,
et al., 2008; Hwang, Tsai, et al., 2008; Hwang, Wang, et al., 2008; Hwang, Yin, et al., 2008; Ontrubia & Engel, 2009; Schellens & Valcke, 2005).
As for the collaborative learning models, Learning Together (LT) is one of the well-known models (Chu, Hwang, Tsai, & Chen, 2009;
Mitnik, Recabarren, Nussbaum, & Soto, 2009; Wang & Lin, 2007), proposed by Johnson and Johnson (1987). In this study, the LT model
was adopted since it enables group members to establish positive interdependence, whereby they perceive that their success or failure lies
in their working together as a group. In addition, the LT model also enables teachers to easily observe how students think during the learning
process (Putnam, 1997). In the LT model (Johnson & Johnson, 1991), each learning group is required to complete a task and hand in a single
sheet, for which they receive praise and rewards based on the group product. Arends (2004) suggested assigning 4 to 6 students to each
cooperative learning group, such that the group members are able to share ideas with each other to reach their collective goal. Moreover,
through this heterogeneous group mechanism, low-achievement students are able to observe how high-achievement students solve
complex problems; such a learning context is also called modeling learning (Bandura, 1986). Thus, previous studies have also proved that
low-achievement students would effectively improve their problem-solving ability and reduce cognitive load via this collaborative
mechanism of heterogeneous grouping (Kalyuga, 2009; Kalyuga & Sweller, 2004; Van Merrienboer & Sweller, 2005).
2.4. Learning ability
Learning ability usually means the ability of obtaining knowledge or skills (Horn, 1976). Previous studies have indicated that the learning
achievement levels of students are likely to affect their learning outcome when participating in learning activities (Adesoji & Ibraheem,
2009; Lee, 2007; Peterson, Janicki, & Swing, 1981; Saleh, Lazonder, & Jong, 2005), implying that students’ learning achievements prior to
the learning activities could be a determinant factor that affects their learning performance during the learning process. Therefore, in this
study, “learning ability” represents the learning achievement levels of students in social science before participating in the experiment.
2.5. Web-based searching behavior analyzing system
The web-based searching behavior analyzing system, Meta-Analyzer (Chu et al., 2010; Hwang, Tsai et al., 2008; Panjaburee et al., 2010),
was developed to assist teachers in tracing and analyzing the learners’ information-searching behaviors for specific questions. To efficiently
conduct the problem solving or searching process via Meta-Analyzer, four constructive inquiry-based questions are given to facilitate
learners in their search for information online regarding relevant learning units. Each question needs to be answered properly by the
processes of appropriate keyword adoption, information selection, summarization, and integration, as shown in Fig. 1. The searching
behavior of the students, including the keywords used, the browsed pages, the time of browsed web pages and the user behaviors on the
Fig. 1. Framework of the web-based searching behavior analyzing system.
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4. web etc., are recorded completely in the server for further analysis, which is useful for educators to adjust their teaching strategy
accordingly.
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 Fig. 2.
Fig. 3 shows an illustrative example of a student’s searching portfolio recorded by Meta-Analyzer, including the student’s identification,
the student’s answer to each question, the teacher’s evaluation of each question, the time duration for each operation, and so on. Via
browsing the searching portfolios, researchers or teachers can analyze the detailed searching behaviors of the students. Past studies have
indicated that making good use of students’ portfolios cannot only facilitate their learning performance, but can also improve educators’
teaching performance (Brenda, 2004; Carchiolo, Longheu, & Malgeri, 2002; Dinham & Scott, 2003; Klenowski, 2000).
Meta-Analyzer has been recognized as an efficient tool for analyzing web-based problem-solving activities (Hwang & Kuo, 2011; Hwang,
Tsai et al., 2008; Tseng, Hwang, Tsai, & Tsai, 2009). So far, more than 1300 elementary school students have experienced Meta-Analyzer in
the past three years (Tsai, Tsai, & Hwang, 2011).
3. Research questions
The aim of this study is to propose a hybrid learning mechanism for improving students’ web-based problem-solving abilities via the
combination of the cognitive apprenticeship model and the collaborative learning strategy. Moreover, the following research questions are
investigated:
1. Is there a significant difference in both problem-solving ability and online problem-solving performance between the experimental
group and the control group? This question is investigated to see whether the students who learned collaboratively using the cognitive
apprenticeship strategy significantly outperformed those who learned with the conventional cognitive apprenticeship approach in
terms of problem-solving ability measurements and online problem-solving performance.
2. Is there an interaction effect between the two independent variables, instructional strategy and learning ability? This question will
gauge whether different achieving students’ problem-solving abilities can be affected by different instructional strategies.
Fig. 2. The Meta-Analyzer Interface.
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5. 3. Is there a significant difference in learning attitude toward the social science course between the experimental group and the control
group? This question is investigated to see whether the students who learned collaboratively using the cognitive apprenticeship
strategy significantly outperformed those who learned with the conventional cognitive apprenticeship approach in terms of learning
attitude toward the social science course.
4. Is there a significant difference in cognitive load between the experimental group and the control group? This question is investigated to
see whether the students who learned collaboratively using the cognitive apprenticeship strategy significantly outperformed those who
learned with the conventional cognitive apprenticeship approach in terms of cognitive load.
4. Methods
4.1. Participants
The study took place in an urban public school in southern Taiwan. Fifty-eight fifth-grade students (11–12 year olds) from two classes of
an elementary school participated in the experiment. The students distributed in the two classes had equivalent academic achievement via
normal class grouping after entering the school, and had also experienced information-searching lessons in the fourth grade. One teacher
taught two classes in the same computer classroom at different times of the day. This teacher had five years teaching experience. One class
was assigned to be the experimental group, while the other was the control group. The students in the experimental group learned with the
hybrid approach that combined the collaborative learning strategy and the cognitive apprenticeship strategy, while the control group
students learned with only the cognitive apprenticeship strategy.
4.2. Experimental design
Fig. 4 shows the experiment procedure of this study. The cognitive apprenticeship (CA) strategy consisted of four phases as outlined
below. CA plays an important role in facilitating middle- and lower-achieving students in constructing high-order thinking ability by
Fig. 3. Students’ online information-searching behaviors recorded in the database.
F.-R. Kuo et al. / Computers & Education 58 (2012) 351–364 355

6. observing how higher-achieving students solve complex problems. The major difference between the experimental group and the control
group is the intervention of collaborative learning in the experimental group. That is, the middle- and lower-achieving students in the
experimental group could not only observe how the teacher solved problems, but could also obtain assistance from higher-achieving peers
to complete the tasks together. On the other hand, individual students in the control group could only observe the teacher’s demonstration
and guidance without the assistance of their peers.
Moreover, seven sets of constructive questions concerning different social issues for problem-solving practice were designed by the
teacher, as given in Appendix 1. The first and the seventh question sets were used to evaluate the students’ online problem-solving ability
before and after the learning activity, respectively, while the remaining question sets were used as training cases during the learning activity.
Moreover, each set of questions contained four sub-questions which were embedded in the Meta-Analyzer system based on the social issue.
First of all, the students were given a brief introduction of a social issue to construct basic prior knowledge. Subsequently, they were
requested to work through the problem-solving process via Meta-Analyzer to construct deeper understanding of the issue. For example, one
of the issues was the “Garbage problem.” The purpose of the learning unit was to learn how to reduce the garbage problem to protect the
environment. Thus, the teacher designed four sub-questions, namely, “What are the impacts on the Earth if lots of rubbish is produced?”,
“What are three main methods of waste disposal and how do they work?”, “What are the differences among landfill, garbage incineration,
and recycling?”, and “What waste disposal method would you accept to decrease the garbage problem?”. Through a series of information-
searching processes, the students’ cognitive scheme was constructed accordingly; moreover, they became more conscious of the importance
of current social issues.
Prior to the experiment, all of the learners were given orientation and practice using the Meta-Analyzer system. The four phases of the
learning activity based on cognitive apprenticeship theory for the two groups of students are illustrated as follows:
Phase one: modeling, coaching, scaffolding
In this phase, teachers need to demonstrate how to solve problems by adopting appropriate keywords, selecting relevant web pages,
integrating information with related pages, and answering questions carefully based on the problem-solving procedure. Accordingly, both
the experimental group and the control group students were required to do the 2nd and 3rd sets of questions individually in two
consecutive weeks, while the teacher was responsible for coaching and scaffolding them at all times. After each question was completed, the
teacher articulated how the question should actually be solved in detail until the students deeply understood the process.
Phase two: coaching, scaffolding, articulation and reflection
In the second phase, the experimental group students needed to establish positive interdependence based on heterogeneous grouping
(Johnson Johnson, 1987; Yiping, Philip, John, 2000), whereby members perceive that their success or failure lies in their working
together as a group via the LT strategy within the time limit; on the other hand, the control group students were designated to complete the
questions individually. Moreover, researchers have indicated that the number of members in a group could affect students’ learning quality
when working in a collaborative learning context (Johnson Johnson). Prior research has found that a group consisting of 3–6 members is
120 Min.
160 Min.
160 Min.
160 Min.
40 Min.
80 Min.
40 Min.
Experiment group
(N=29)
Control group
(N=29)
Interviews
Introduction of Meta-Analyzer
pre-test and pre-questionnaires
post-test and post-questionnaires
Demonstration of problem-solving skills
Collaborative problem-
based learning with
coaching scaffolding
Traditional problem-
based learning with
coaching scaffolding
Collaborative problem-
based learning without
coaching scaffolding
Traditional problem-
based learning without
coaching scaffolding
Phase 1
Phase 2
Phase 3
Phase 4 Online problem-solving post-test
Fig. 4. Experiment design for the collaborative cognitive apprenticeship strategy and traditional cognitive apprenticeship strategy.
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356

7. appropriate for learning (Dillenbourg, 1999; Gros, 2001; Johnson, Johnson, Smith, 1998). Prior studies also indicate that high-achieving
students have good information-searching and problem-solving abilities (Lee, Hsieh Hwang, 2011; Hwang Kuo, 2011; Hwang, Chen,
Tsai, Tsai, 2011; Tseng et al., 2009). Thus, in the study, each group consisted of three members with different levels of achievement. A
high-achievement student usually plays the role of leader to demonstrate how he/she solves problems via appropriate keyword adoption,
information selection, summarization, and integration, during which the middle- and low-achievement members can observe and reflect.
Moreover, the middle- and low-achievement members are allowed to question the high-achievement peer at any time when conducting
problem-solving activities. After the completion of each question, the students were required to articulate their knowledge, and share how
they carried out the problem-solving processes with others. In this way, the students were able to compare their own problem-solving
portfolios with peers or teachers. Finally, the teacher commented on the students’ performance and made a conclusion about the
problem-solving process. In this phase, the students in both groups needed to complete two sets of questions (the 4th and 5th sets) in two
consecutive weeks.
Phase three: articulation and reflection
In this phase, the mechanisms of coaching and scaffolding were phased out, implying that the learning activity mainly focused on
student-centered learning without any assistance from the teacher. Once the students completed the 6th set of questions, the teacher picked
out some of them to articulate their learning process. The students in the experimental group further shared how they cooperated with their
peers during the problem-solving processes. Likewise, the students were able to compare their own problem-solving processes with those
of their peers or teachers. Finally, the teacher commented on the students’ presentations and provided a conclusion.
Phase four: self-exploration
In the final phase, the students were asked to take the online problem-solving test with the 7th set of questions. Note that both groups of
students needed to answer the questions individually. Moreover, they were asked to fill out the questionnaires and do the problem-solving
ability assessment.
4.3. Measuring tools
Problem-solving ability measurement
The Problem-solving ability measurement originating from Speeddie, Houtz, Ringenbach, and Feldhusen (1973) was used in the pre-test
and the post-test. It was designed for measuring the problem-solving ability of elementary high-grade learners. The measurement consists
of five aspects, including “being aware of the existence of the problem”, “confirming the nature of the problem”, “identifying factors related
to the problem”, “identifying necessary information for the problem”, and “deciding on a solution”. Via asking two senior social science
teachers to evaluate the answers of 50 examinees (non-subjects), its correlation coefficient r is 0.91, showing high inter-rater reliability
(Cohen, 1988).
Scale of learning attitude toward social science
The scale of learning attitude toward social science was developed by Loo and Thorpe (2005), and consists of five investigated
dimensions, including “interest in learning social studies”, “immersion in learning social studies”, “capable of learning social studies”,
“usefulness of learning social studies”, and “attitude toward problem solving,” with a four-point Likert-type scoring mechanism (1: strongly
disagree, and 4: strongly agree). A higher score represents a better attitude toward learning social studies. The Cronbach’s alphas of the
subscales are 0.88, 0.71, 0.76, 0.82 and 0.79, respectively, showing that the measurement scale has a high reliability.
Cognitive load scale
The Cognitive load scale, which was developed by Bratfisch, Borg, and Dornic (1972), was utilized in the study. The purpose of the scale is
to measure whether learners suffer from an overloaded cognitive system induced by external factors to the extent that it would affect their
academic achievement. The scale consists of two subscales, mental effort and mental load, with seven-point Likert-type response alter-
natives (7: strongly agree,1: strongly disagree). The higher the mental effort score is, the more effort they spend learning the course content.
Similarly, the higher the mental load score is, the higher pressure learners experience during the learning process. As for the reliability test,
researchers (Gimino, 2000; Paas, 1992) have demonstrated the scale’s reliability, and convergent, construct, and discriminate validity. In the
study, the reliability of the scale was examined with non-experimental subjects (n ¼ 30) and reached Cronbach’s a ¼ .87, implying that the
scale has a high reliability and sensitivity to cognitive load.
Questionnaire for collaborative learning strategy
The questionnaire design of collaborative learning refers to the definition of collaborative learning defined by Johnson and Johnson
(1984), consisting of six multiple choice statements, such as “I can get more ideas when discussing with peers”, “I feel great if working
with peers collaboratively”, “I like to learn social science courses with peers collaboratively”, “I think the collaborative learning approach
facilitates my learning,” etc., and two open-ended questions, such as “What advantages and disadvantages of the collaborative learning
approach do you think exist when learning social science courses?” The questionnaire mainly investigates the feelings of students in the
learning process, and the perceptions of the collaborative learning strategy. The score was calculated by adopting the four-point Likert-type
response alternatives (4: strongly agree, 1: strongly disagree). The higher the score is, the more positive the students’ attitudes are toward
F.-R. Kuo et al. / Computers Education 58 (2012) 351–364 357

8. the collaborative learning strategy. The content of the questionnaire was evaluated and refined twice by two senior social science teachers.
Thus, its high internal reliability was established. As for the examination of scale reliability, one non-experimental class was evaluated
before the experiment was conducted. In the reliability test, the Cronbach’s alpha of the scale reached 0.74. Nunnaly (1978) has indicated 0.7
as an acceptable reliability coefficient. Thus, the measurement scale has acceptable reliability.
Constructive questions design
To promote learners’ problem-solving ability, teachers need to design good learning strategies and good questions to be explored by the
learners based on educational theories (Graesser Person, 1994; Murphy, Mahoney, Chen, Mndoza-Diaz, Yang, 2005). Thus, a set of
questions for a teaching unit needs to be carefully designed to conform to the requirements of the problem-solving process. In the process of
designing the seven sets of questions, two senior social science teachers and one university professor joined forces to organize the questions
based on social issues and problem-solving theory. Accordingly, these questions have shown good face validity. Besides, as for inter-rater
reliability, the teachers evaluated 12 non-experimental group learners before conducting the experiment to assure a consistent score
standard. According to Pearson correlation analysis for the two raters, the statistical results show that the correlation coefficient (r) of the
first three sub-questions (knowledge-finding questions) reached 0.92 (p 0.001), while the correlation coefficient of the last sub-question
(argument question) reached 0.77 (p 0.01), implying that the assessment of online problem-solving ability with Meta-Analyzer has high
inter-rater reliability in the study.
4.4. Analysis strategy
In the present study, the Statistical Package for Social Science (SPSS 17.0) was used for data analysis, which included computation of
univariate and multivariate General Linear Model (GLM), paired-samples t-tests, and Pearson correlations. Multivariate GLM was employed
to analyze if groups’ problem-solving performance and cognitive load differed significantly from each other. Univariate GLM was used to
analyze if group learning performance differed significantly from each other due to the interaction effect of the diverse learning abilities of
the students. Paired-samples t-tests were applied to analyze if the learning attitudes of students toward social science differed significantly
before and after experiments. Pearson correlation was applied to investigate if the correlation between learning performance and learning
attitudes of the experimental group students showed a positive inclination. To evaluate the treatment effects, the effect size was calculated
for the instrument. The alpha was established a priori at the 0.05 level, as suggested in the literature.
5. Results
5.1. Analysis of problem-solving ability, online problem-solving performance and cognitive load
From the analysis of the multivariate general linear model, Table 1 shows a significant difference between the two groups for the
treatment effect on problem-solving ability (PSA; F ¼ 35.595, p 0.001), online problem-solving performance (OPSP; F ¼ 16.686, p 0.001),
and for the mental load aspect (ML; F ¼ 37.961, p 0.001) of cognitive load. The effect sizes calculated from partial eta square were 0.389,
0.230 and 0.404, respectively, which is a large effect size (Cohen, 1988).
Overall, the problem-solving ability and online problem-solving performance of the experimental group students was significantly
better than that of the control group students, as shown in Table 2, implying that the intervention of collaborative learning may facilitate
students’ problem-solving ability, which confirms the findings of previous studies (Kim Hannafin, 2011; Lazakidou Retalis, 2010; Liao
Ho, 2008; Uribe Klein, 2003). In terms of cognitive load, the mean scores of the two groups for the aspect of mental load show a middle-
and high-level load (mean score median) ranging from 4.53 to 6.10 with significant difference (F ¼ 37.961.49, p 0.001), implying that the
students would feel distracted during the learning activities. That is, the mental load of the control group was significantly higher than that
of the experimental group. Due to the cognitive apprenticeship strategy without a collaborative mechanism that was implemented with the
control group, the students were interviewed individually and expressed that they were stressed and nervous when waiting to articulate to
their peers in class what they had done. Comparatively, the students in the experimental group with the collaborative mechanism expressed
less stress regarding articulating their problem-solving process than those in the control group because they had learned how to solve
problems from their high-achieving peers.
5.2. Interaction effect between the learning ability and instruction strategy facets
In order to further assess the treatment effects of the experimental students’ problem-solving ability, the achieving level of the students
was taken into consideration to examine if an interaction effect between the two independent variables existed. From the analysis of the
Table 1
General linear model of the post-test for the students in the experimental and control groups.
Source Dependent variables F P value h2
group PSAa 35.595 0.000 0.389
OPSPb 16.686 0.000 0.230
MEc 0.039 0.845 0.001
MLd 37.961 0.000 0.404
a
Problem-solving ability (PSA).
b
Online problem-solving performance (OPSP).
c
Metal effort (ME).
d
Mental load (ML).
F.-R. Kuo et al. / Computers Education 58 (2012) 351–364
358

9. univariate general linear model, Table 3 shows that the interaction effect has a significant difference (F ¼ 3.951, p 0.001) between the
learning ability and instruction strategy in the post-test of problem-solving ability. Table 3 shows the initial result indicating a significant
difference between the two independent variables (Ability*Group; F ¼ 3.95, p 0.05), implying that there were significant interaction
effects between learning ability and instruction strategy. That is, the learning performance of students with different learning abilities could
be affected by different instructional strategies. Thus, an investigation of the simple main effect for different learning abilities needed to be
conducted, as shown in Table 4.
Table 4 shows that the results present no significant difference for high-achievement students between the two groups (F ¼ 0.16,
p 0.05). However, it shows a significant difference for the middle- and low-achievement students between the two groups (F ¼ 25.34,
p 0.001; F ¼ 37.75, p 0.001). Both groups’ effect size (h2
) of over 0.56 were also examined and showed a large effect size (Cohen, 1988),
implying that those middle- and low-achievement students who adopted the collaborative mechanism gained significantly higher learning
achievement than those who adopted the non-collaborative mechanism.
To sum up, the middle- and low-achievement students in the experimental group showed better problem-solving abilities than those in
the control group, implying that middle- and low-achievement students are able to observe and imitate how high-achievement students in
the same group solve problems. Thus, their problem-solving skills and misconceptions can be improved accordingly. However, the high-
achievement students in the two groups showed no significant difference in their improvement, implying that those students could
already reach the high end of learning effect whatever instruction strategies intervened; this is known as the ceiling effect. Research has
indicated that such a learning situation can be improved by promoting the validity and difficulty of the assessment based on the suggestions
of domain experts (Hwang, Wang, Sharples, 2007; Rachel Irit, 2002).
5.3. Learning attitude toward social science study
To investigate the students’ learning attitude toward social science when using the two different learning strategies, a questionnaire
consisting of five subscales including “interest in learning social studies”, “immersion in learning social studies”, “capability of learning
social studies”, “usefulness of learning social studies”, and “attitude toward problem solving,” were administered to the two groups before
and after the experiment. A total of 58 valid questionnaires were submitted, with a response rate of 100%. Paired-samples t-test analysis was
employed to examine the difference between the pre- and post-test for the two different groups in learning attitude toward social studies.
Table 5 shows the analysis result for the two groups, indicating that there was significant difference between the groups in the three
dimensions of “immersion in learning social studies”, “capability of learning social studies”, and “attitude toward problem solving.” The
result also shows that there was significant difference in the whole learning attitude scale, implying that the instruction strategy with
collaborative intervention effectively improves students’ learning attitude toward social studies.
Moreover, the study further investigated the correlation between the experimental group students’ problem-solving ability and learning
attitudes toward social studies. Pearson correlation analysis was employed, and the result is shown in Table 6. Table 6 shows that the two
variables display a medium positive correlation (r ¼ 0.479, p 0.01) (Cohen, 1988). This means that the higher the learning attitude toward
the social science course, the higher learning performance they gained, implying that the proposed hybrid approach can both facilitate the
learning attitude and learning performance of students.
5.4. Qualitative data collection
Qualitative data were collected to support the quantitative findings. In the context of this study, the semi-structured questions with
in-depth interviewing method were formulated based on the statistical results of the learning attitude toward social science instrument,
and these semi-structured interviews added to the accuracy and adequacy of the students’ perceptions of (1) the intention to accept the
Table 2
Pairwise comparisons of multivariate analysis for the experimental and control groups.
Dependent variable Group Mean S.E. Post hoc (LSD)
PSAa
(1) Experimental 16.207 0.372 (1) (2)
(2) Control 13.069 0.372
OPSPb
(1) Experimental 21.000 0.863 (1) (2)
(2) Control 16.017 0.863
MEc
(1) Experimental 2.707 0.124
(2) Control 2.741 0.124
MLd
(1) Experimental 4.534 0.180 (2) (1)
(2) Control 6.103 0.180
a
Problem-solving ability (PSA).
b
Online problem-solving performance (OPSP).
c
Metal effort (ME).
d
Mental load (ML).
Table 3
General linear model for the interaction effect on the post-test between learning ability and instruction strategy.
Source Type III SS Mean square F P value
Ability 18.528a
9.264 4.637 0.014
Group 59.727 59.727 29.897 0.000
Ability*Group 15.788 7.894 3.951 0.025
Error 101.886 1.998
Corrected total 425.655
a
R2 ¼ 0.761 (Adjusted R2 ¼ 0.732).
F.-R. Kuo et al. / Computers Education 58 (2012) 351–364 359

10. hybrid learning strategy in social science study; (2) the development of problem-solving skills after accepting the hybrid learning approach;
(3) the combination of practicing collaborative learning strategies; and (4) the combination of practicing the cognitive apprenticeship
model. Nine students from the experimental group were randomly selected to be interviewed by the researcher to provide further feedback
aligned to the objectives stated in the research objective section of this study.
As for the perception of intention to accept the hybrid learning approach, the question is “Do you like to learn social studies via such
a learning approach?” The feedback was that most students believed such a web-based learning strategy is positive for their learning
performance. Most of them hope to learn other courses with the same approach. In addition to learning motivation and achievement, the
teacher also found that the proposed approach enables students to construct their understanding of the course via member interdependence.
As for the perception of the development of problem-solving skills, the question was, “Is your problem-solving ability improved using
such a learning approach?” The feedback showed that the students believed the possession of problem-solving skills is important when
encountering any problem in life. After experiencing a series of problem-solving skills training with different social issues, the students had
more confidence than ever before. In addition, they believed that looking for any possible resource around them to solve the encountered
problems is easier for them than ever before.
As for the perception of the usage of the collaborative learning strategy, the question was, “How do you feel when the collaborative learning
strategy is embedded in the learning activities?” The feedback showed that the students were able to come up with more ideas via the
collaborative learning process. The brainstorming process in the group always stimulated them to think divergently, and then to focus on the
discussed problems, guided by their high-achieving peers. In addition, the teacher also found that the middle- and low-achievement students in
the experimental group made significant improvement in their keyword-adopting ability, and that more constructive ideas were generated.
For the perception of the practice of the cognitive apprenticeship (CA) model, the question was, “How do you feel when the cognitive
apprenticeship model is embedded in the learning activities?” The feedback showed that the students agreed that such a hybrid approach
could enable them to deeply understand the knowledge presented in the course. However, mental load could be increased by the inter-
vention of the articulation stage of CA. Most students indicated that articulation on the podium when facing peers made them more nervous.
Some students were even afraid of being teased if they said something wrong. Thus, such a learning context could distract them from their
peers’ explanation of what they were doing. To address the issue, the teacher found that the experimental group students had more
confidence in the articulation process than the control group students, implying that the intervention of the collaborative learning strategy
in the learning context could decrease the students’ distraction during the learning process.
6. Discussion
The quantitative findings show that integration of cognitive apprenticeship with the collaborative learning approach promotes students’
problem-solving abilities. This finding confirms the results of the previous studies that meaningful scaffolding through a collaborative
mechanism can enhance the construction of high-order thinking about a subject (Hwang Kuo, 2011; Kim Hannafin, 2011; Klenowski,
2000; Murphy et al., 2005; Uribe Klein, 2003). The qualitative findings regarding the effects of the hybrid approach revealed that the
students believed that such a learning strategy can facilitate problem-solving performance through a series of constructive questions with
modeling, coaching, scaffolding, articulation, reflection and exploration processes. Moreover, the experimental group students believed that
looking for any possible resource around them to solve real-life problems is easier for them than ever before.
Table 4
Pairwise comparisons of students of different abilities in the experimental and control groups.
Learning ability (achievement level) Exp. group Mean (S.D.) Control group Mean (S.D.) F h2
High 17.43 (1.72) 17.01 (1.01) 0.16 0.02
Middle 16.46 (1.39) 13.56 (1.24) 25.34a
0.56
Low 14.89 (2.15) 12.12 (1.62) 37.75a
0.61
a
p 0.001.
Table 5
Paired-samples t-test for learning attitude toward social science study.
Dimensions Experimental group (N ¼ 29) Control group (N ¼ 29)
Mean (S.D.) t Mean (S.D.) t
Interest in learning social studies pre-test 2.88 (0.66) 1.46 2.97 (0.57) 2.16a
post-test 3.04 (0.54) 3.16 (0.54)
Immersion in learning social studies pre-test 2.65 (0.48) 4.28b
2.89 (0.50) 2.20a
post-test 2.97 (0.74) 3.07 (0.38)
Capability of learning social studies pre-test 2.78 (0.69) 3.98b
3.05 (0.48) 1.55
post-test 3.20 (0.52) 3.22 (0.65)
Usefulness of learning social studies pre-test 3.18 (0.74) 1.15 3.40 (0.59) 0.43
post-test 3.36 (0.47) 3.44 (0.51)
Attitude toward problem-solving pre-test 3.17 (0.72) 2.50a
3.26 (0.59) 0.25
post-test 3.47 (0.52) 3.29 (0.39)
Total scale pre-test 2.90 (0.52) 3.83b
3.08 (0.43) 2.71a
post-test 3.18 (0.39) 3.22 (0.40)
a
p 0.05.
b
p 0.001.
F.-R. Kuo et al. / Computers Education 58 (2012) 351–364
360

11. The findings of the investigation on the effect of both students’ learning ability and instruction strategy simultaneously on students’
problem-solving ability showed that the effect for the middle- and low-achieving students in the experimental group was superior to that
for the same level students in the control group. This finding confirms the results of the previous studies that, through a collaborative
mechanism, high-achieving students can assist middle- and low-achieving students in improving their learning performance (Lazakidou
Retalis, 2010; Liao Ho, 2008; Lou, 2004). The qualitative findings revealed that the students were able to generate more ideas by discussing
with group members. Moreover, the teacher also found that the middle- and low-achieving students in the experimental group made
significant improvement in adopting keywords, and generated more constructive ideas through the guidance of the high-achieving
students.
However, as to the cognitive load caused in the experiment, the quantitative findings showed that the mental load of the experimental
group was significantly lower than that of the control group, implying that students would feel at ease during the learning activities through
the collaborative mechanism. In contrast, individual students in the control group would be distracted from the teacher’s interpretation
when performing the articulation phase. The qualitative data showed that articulation on the podium made them more nervous. A couple of
students were even afraid of being teased if they said something wrong. Thus, such a learning stage could distract them from their peers’
explanation of what they did. However, the teacher found that the experimental group students had more confidence in the articulation
process than the control group students did, implying that the intervention of the collaborative mechanism in the learning context could
decrease the students’ distraction during the learning process. This result confirmed previous studies, indicating that it is necessary to add
germane cognitive load to germane learning materials or instructional strategies for the construction of cognitive schemas (Miller, 1956;
Paas, Renkle, Sweller, 2003).
7. Conclusions and implications
This study aims to propose a hybrid approach combining the cognitive apprenticeship model with the collaborative learning mechanism
for conducting problem-solving learning activities on the Internet. An empirical study was performed with 58 participants distributed into
two groups using different strategies. A survey and interviews were administered to the students following the tests. A summary of the
research results is provided as follows.
The research results reveal that the assessment of the problem-solving and online problem-solving performance of the experimental
group students indicated significantly better performance than that of the control group students. In addition, the middle- and low-
achievement students in the experimental group also showed more significant improvement in performance than did those in the
control group, implying that the proposed hybrid approach can foster middle- and low-achievement students’ problem-solving abilities.
Such a result reflects the positive relationship between learning performance and learning attitude, especially for the aspects of “immersion
in learning social studies”, “capability of learning social studies”, and “attitude toward problem solving.”
As for cognitive load, the instruction embedded with cognitive apprenticeship without the collaborative learning mechanism would
bring students much more stress and even distract them from learning, whereas the experimental group students did not perceive such
a high degree of stress or distraction. In other words, the middle- and low-achievement students in the experimental group were given
chances to inspect what and how the high-achievement students did during the problem-solving process. These findings are consistent
with past research (Ertl et al., 2006; Hwang et al., 2009). Moreover, researchers assert that if only students’ learning performance is
improved, it is necessary to add germane cognitive load to germane learning materials or instructional strategies for the construction of
schemas (Miller, 1956; Pass et al., 2003).
The study concludes that the method of the integration of the cognitive apprenticeship and collaborative learning mechanism within an
online inquiry-based learning environment has great potential to promote middle- and low-achievement students’ problem-solving ability
and learning attitudes toward social science. In addition, such a hybrid approach could ease their learning anxiety via the inspection of high-
achievement peers.
As for the implications of the research results, to help students to think aloud is essential when conducting the cognitive apprenticeship
process. Thinking aloud is an effective approach for helping students learn to monitor and reflect on their comprehension of the course
content (Baumann, Seifert-Kessell, Jones, 1993). Thus, how to cultivate and encourage students to engage their prior knowledge with
a particular topic, and make their thinking visible in the process of learning is important for collaborative learning (Bell, 1998; Dickey, 2007;
Su, Yang, Hwang, Zhang, 2010).
Acknowledgments
The authors would like to thank Miss Szu-Chuang Chen, an elementary school teacher, for her assistance in conducting the experiment.
This study is supported in part by the National Science Council of the Republic of China under contract numbers NSC 99-2511-S-011-011-
MY3 and NSC 99-2631-S-011-002.
Table 6
Pearson correlation analysis of problem-solving ability and learning attitude in the experimental group.
Problem-solving ability Learning attitude
Problem-solving ability Pearson correlation 1 0.479a
Sig. (2-tailed) 0.009
Learning attitude Pearson correlation 0.479a
1
Sig. (2-tailed) 0.009
a
p 0.01.
F.-R. Kuo et al. / Computers Education 58 (2012) 351–364 361

12. Appendix 1. Seven sets of constructive questions for problem-solving ability.
Set No. Topics 1st question 2nd question 3rd question 4th question
1 Credit Card Slave How many credit card slaves are
there in Taiwan?
What leads them to become card
slaves?
What disadvantages and
advantages are there when
shopping with a credit card?
If you had a credit card, how would
you use it to avoid becoming a card
slave?
2 Renewable energy What are the three forms of power
generation used in Taiwan?
In addition to the previous
methods, what other methods are
there? Give a short introduction to
each.
What are the disadvantages and
advantages of nuclear power and
thermal power?
If you were the Minister of Energy,
what form of power generation
would you adopt, and why?
3 Greenhouse effect What countries are the world’s top
two carbon dioxide emitters?
What are the impacts on the Earth
of the emission of lots of carbon
dioxide?
What solutions can decrease carbon
dioxide emissions in life? What can
you do?
If you were the Minister of
Environmental Protection, what
would you do to lower carbon
dioxide emissions?
4 Garbage problem What are the impacts on the Earth if
lots of rubbish is produced? E.g.
water, air, soil etc.
What are three main methods of
waste disposal? How do they work?
What are the differences among
“landfill”, “garbage incineration”,
and “recycling”?
What waste disposal method would
you accept to decrease the garbage
problem?
5 Water shortage How many liters of water are used
on average per day in Taiwan?
The annual rainfall exceeds
2500 mm in Taiwan, but there is
still a water shortage, why?
Do you think the construction of
reservoirs can solve water
shortages in southern Taiwan?
What impact would they have?
What specific actions can you take
to help conserve water at school, at
home or anywhere else?
6 Falling birthrate problem Please find out the birthrate in 1979
and 2009 in Taiwan, respectively.
Currently, what is leading to the
falling birthrate problem in
Taiwan?
What industries can be affected by
the low birthrate problem?
If you were the President or
Premier, what policy would you
advocate to promote the birthrate?
7 Aging problem Please find out the population over
65 years old in 1979 and 2009 in
Taiwan, respectively.
There will be an aging society in
Taiwan. What are the potential
problems then?
What factors lead to an aging
society?
If you were the Minister of the
Interior, what steps would you take
to solve the aging problem?
F.-R.
Kuo
et
al.
/
Computers
Education
58
(2012)
351–364
362

13. References
Adesoji, F. A., Ibraheem, T. L. (2009). Effects of student teams-achievement divisions strategy and mathematics knowledge on learning outcomes in chemical kinetics.
Journal of International Social Research, 2(6), 15–25.
Arends, R. I. (2004). Learning to teaching (6th ed.). New York: Mcgraw-Hill Companies.
Bandura, A. (1986). Social Foundations of Thought and Action. Englewood Cliffs, NJ: Prentice-Hall.
Barron, B. (2000). Problem solving in video-based microworlds: collaborative and individual outcomes of high achieving sixth grade students. Journal of Educational
Psychology, 92, 391–398.
Baumann, J. F., Seifert-Kessell, N., Jones, L. A. (1993). Using think alouds to enhance children’s comprehension monitoring abilities. The Reading Teacher, 47(3).
Bell, P. (1998, April). Engaging students with scientific controversy: Using arguments to make thinking visible. Paper presented at the 1998 Annual Meeting of the National
Association for Research in Science Teaching, San Diego, CA.
Bilal, D. (2000). Children’s use of the Yahooligans! Web search engine: I. cognitive, physical, and affective behaviors on fact-based search tasks. Journal of the American Society
for Information Science, 51(7), 646–665.
Bilal, D. (2001). Children’s use of the Yahooligans! Web search engine: II. Cognitive and physical behaviors on research tasks. Journal of the American Society for Information
Science and Technology, 52(2), 118–136.
Bilal, D. (2002). Children’s use of the Yahooligans! Web search engine. III. Cognitive and physical behaviors on fully self-generated search tasks. Journal of the American Society
for Information Science and Technology, 53(13), 1170–1183.
Bratfisch, O., Borg, G., Dornic, S. (1972). Perceived item-difficulty in three tests of intellectual performance capacity. Technical Report, 29. Stockholm, Sweden: Institute of
Applied Psychology.
Brenda, J. (2004). Summative assessment of portfolios: an examination of different approaches to agreement over outcomes. Studies in Higher Education, 29, 395–412.
Brophy, J. (1968). Teacher influences on student achievement. American Psychologist1069–1077, (October).
Brown, J. S., Collins, A., Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32–42.
Carchiolo, V., Longheu, A., Malgeri, M. (2002). Adaptive formative paths in a web-based learning environment. Educational Technology Society, 5(4), 64–75.
Chen, C. H. (2010). Promoting college students’ knowledge acquisition and ill-structured problem solving: web-based integration and procedure prompts. Computers
Education, 55(1), 292–303.
Chen, F. S., Hsiao, Y. W. (2010). Using WebQuest as a creative teaching tool at a science and technology university in Taiwan. World Transaction on Engineering and Technology
Education, 8(2).
Chiou, C. K., Hwang, G. J., Tseng, J. C. R. (2009). An auto-scoring mechanism for evaluating problem-solving ability in a web-based learning environment. Computers
Education, 53(2), 261–272.
Chu, H. C., Hwang, G. J., Huang, Y. M. (2010). An enhanced learning diagnosis model based on concept effect relationships with multiple knowledge levels. Innovations in
Education and Teaching International, 47(1), 53–67.
Chu, H. C., Hwang, G. J., Tsai, C. C. (2010). A knowledge engineering approach to developing mindtools for context-aware ubiquitous learning. Computers Education, 54(1),
289–297.
Chu, H. C., Hwang, G. J., Tsai, C. C., Chen, N. S. (2009). An innovative approach for promoting information exchanges and sharing in a Web 2.0-based learning environment.
Interactive Learning Environments, 17(4), 311–323.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
Collins, A., Brown, J. S., Newman, S. E. (1989). Cognitive apprenticeship: teaching the craft of reading, writing, and mathematics. In L. B. Resnick (Ed.), Knowing, learning, and
instruction: Essays in honor of Robert Glaser (pp. 453–494). Hillsdale, NJ: Erlbaum.
Dickey, M. D. (2007). Barriers and enablers in integrating cognitive apprenticeship methods in a Web-based educational technology course for K-12 (primary and secondary)
teacher education. Research in Learning Technology, 15(2), 119–130.
Dillenbourg, P. (1999). Collaborative learning. Cognitive and computational approaches. Amsterdam: Pergamon.
Dinham, S., Scott, C. (2003). Benefits to teachers of the professional learning portfolio: a case study. Teacher Development, 7(3), 229–244.
Eisenberg, M. B., Berkowitz, R. E. (1990). Information problem-solving: the big six skills approach to library and information skills. Norwood, N. J.: Ablex Publishing Corp.
Ertl, B., Fischer, F., Mandl, H. (2006). Conceptual and socio-cognitive support for collaborative learning in videoconferencing environments. Computers Education, 47(3),
298–315.
Ferreira, D. J., Santos, L. D. (2009). Scaffolding online discourse in collaborative ill-structured problem-solving for innovation. Informatics in Education, 8(2), 173–190.
Gimino, A. E. (2000). Factors that influence student’s investment of mental effort in academic tasks: a validation and exploratory study. Los Angeles: University of Southern
California.
Glass, G. V., Smith, J. L. (1979). Meta-analysis of research on class size and achievement. Educational Evaluation and Policy Analysis, 1(1), 2–16.
Goldstein, F. C., Levin, H. S. (1987). Disorders of reasoning and problem-solving ability. In M. Meier, A. Benton, L. Diller (Eds.), Neuropsychological rehabilitation. London:
Taylor Francis Group.
Graesser, A. C., Person, N. K. (1994). Question asking during tutoring. American Educational Research Journal, 31(1), 104–137.
Gros, B. (2001). Instructional design for computer-supported colloaborative learning in primary and secondary school. Computers in Human Behavior, 17, 439–451.
Hargittai, E. (2006). Hurdles to information seeking: explaining spelling and typographical mistakes in users’ online search behavior. Journal of the Association for Information
Systems, 7(1), 52–67.
Harskamp, E., Suhre, C. (2007). Schoenfeld’s problem solving theory in a student controlled learning environment. Computers Education, 49(3), 822–839.
Ho, T. F., Yin, P. Y., Hwang, G. J., Shyu, S. J., Yean, Y. N. (2009). Multi-objective parallel test-sheet composition using enhanced particle swarm optimization. Educational
Technology Society, 12(4), 193–206.
Horn, J. L. (1976). Human abilities: a review of research and theory in the early 1970s. Annual Review of Psychology, 27, 437–485.
Hsu, C. K., Hwang, G. J., Chang, C. K. (2010). Development of a reading material recommendation system based on a knowledge engineering approach. Computers
Education, 55(1), 76–83.
Hwang, G. J., Chen, C. Y., Tsai, P. S., Tsai, C. C. (2011). An expert system for improving web-based problem-solving ability of students. Expert Systems with Applications, 38(7),
8664–8672.
Hwang, G. J., Chu, H. C., Yin, P. Y., Lin, J. Y. (2008). An innovative parallel test-sheet composition approach to meet multiple assessment criteria for national tests. Computers
Education, 51(3), 1058–1072.
Hwang, G. J., Kuo, F. R. (2011). An information-summarising instruction strategy for improving the web-based problem solving abilities of students. Australasian Journal of
Educational Technology, 27(2), 290–306.
Hwang, G. J., Shi, Y. R., Chu, H. C. (2011). A concept map approach to developing collaborative mindtools for context-aware ubiquitous learning. British Journal of Educational
Technology, 42(5), 778–789.
Hwang, G. J., Tsai, P. S., Tsai, C. C., Tseng, C. R., Judy, C. T. (2008). A novel approach for assisting teachers in analyzing student web-searching behaviors. Computers
Education, 51, 926–938.
Hwang, G. J., Yang, T. C., Tsai, C. C., Stephen J.H., Yang (2009). A context-aware ubiquitous learning environment for conducting complex science experiments. Computers
Education, 53(2), 402–413.
Hwang, W. Y., Wang, C. Y., Hwang, G. J., Huang, Y. M., Huang, S. (2008). A web-based programming learning environment to support cognitive development. Interacting with
Computers, 20(6), 524–534.
Hwang, W. Y., Wang, C. Y., Sharples, M. (2007). A study of multimedia annotation of web-based materials. Computers Education, 48(4), 680–699.
Hwang, G. J., Yin, P. Y., Hwang, C. W., Tsai, C. C. (2008). An enhanced genetic approach to composing cooperative learning groups for multiple grouping criteria. Educational
Technology Society, 11(1), 148–167.
Johnson, D. W., Johnson, R. T. (1984). Cooperative learning. New Brighton, Mass: Interaction Books.
Johnson, D. W., Johnson, R. T. (1987). Learning together and alone: Cooperative, competitive, and individualistic learning (2nd ed.). Englewood Cliffs, NJ: Prentice-Hall.
Johnson, D. W., Johnson, R. T. (1991). Learning together and alone. Boston: Allyn and Bacon.
Johnson, D. W., Johnson, R. T., Smith, K. A. (1998). Cooperative learning returns to college: what evidence is there that it works? Change, 30(4), 26–35.
Kalyuga, S. (2009). Knowledge elaboration: a cognitive load perspective. Learning and Instruction, 19, 402–410.
Kalyuga, S., Sweller, J. (2004). Measuring knowledge to optimize cognitive load factors during instruction. Journal of Educational Psychology, 96, 558–568.
F.-R. Kuo et al. / Computers Education 58 (2012) 351–364 363

14. Karakaya, F., Ainscough, T. L., Chopoorian, J. (2001). The effects of class size and learning style on student performance in a multimedia-based marketing course. Journal of
Marketing Education, 23(2), 84–90.
Kim, M. C., Hannafin, M. J. (2011). Scaffolding problem solving in technology-enhanced learning environments (TELEs): bridging research and theory with practice.
Computers Education., 56(2), 403–417.
Klenowski, V. (2000). Portfolio: promoting teaching. Assessment in Education, 7(2), 215–236.
Lazakidou, G., Retalis, S. (2010). Using computer supported collaborative learning strategies for helping students acquire self-regulated problem-solving skills in math-
ematics. Computers Education., 54(1), 3–13.
Lee, H. (2007). Instructional design of web-based simulations for learners with different levels of spatial ability. Instructional Science, 35, 467–479.
Lee, C. I., Hsieh, Y. C., Hwang, G. J. (2011). A study of an inquiry-based learning approach on college students’ online problem-solving abilities. International Journal on Digital
Learning Technology, 3(4), 1–27.
Li, Q. (2002). Exploration of collaborative learning and communication in an educational environment using computer-medicated communication. Journal of Research on
Technology in Education, 34(4), 503–516.
Li, N., Kirkup, G. (2007). Gender and cultural differences in Internet use: a study of China and the UK. Computers Education, 48, 301–317.
Liao, S. H., Ho, Y. P. (2008). A knowledge-based architecture for implementing collaborative problem-solving methods in military e-training. Expert System with Application,
35(3), 976–990.
Lipponen, L. (2002). Exploring foundations for computer-supported collaborative learning. In G. Stahl (Ed.), Computer support for collaborative learning: Foundations for a CSCL
community. Proceedings of the computer-supported collaborative learning 2002 conference (pp. 72–81). Hillsdale, NJ: Erlbaum.
Lipponen, L., Hakkarainen, K., Paavola, S. (2004). Practices and orientations of CSCL. In J. Strijbos, P. Kirschner, R. Martens (Eds.), What we know about CSCL in higher
education (pp. 31–50). Boston, MA: Kluwer Academic Publishers.
Lo, H. (2009). Utilizing computer-mediated communication tools for problem-based learning. Journal of Educational Technology Society, 12(1), 205–213.
Loo, R., Thorpe, K. (2005). Relationships between attitudes toward women’s roles in society, and work and life values. The Social Science Journal, 42, 367–374.
Lou, Y. (2004). Learning to solve complex problems through between-group collaboration in project-based online courses. Distance Education, 25(1), 49–66.
Mayer, R. E. (1992). Thinking, problem solving, cognition (2nd ed.). New York: W. H. Freeman and Company.
Merrill, M. D., Gibert, C. G. (2008). Effective peer interaction in problem-centered instructional strategy. Distance Education, 29(2), 199–207.
Miller, J. (1956). The magic number seven, plus or minus two: some limit on our capacity to process information. Psychological Review, 63, 81–87.
Mitnik, R., Recabarren, M., Nussbaum, M., Soto, A. (2009). Collaborative robotic instruction: a graph teaching experience. Computers Education, 53(2), 330–342.
Murphy, K. L., Mahoney, S. E., Chen, C. Y., Mndoza-Diaz, N. V., Yang, X. (2005). A constructivist model of mentoring, coaching, and facilitating online discussion. Distance
Education, 26(3), 341–366.
Mustafa, S., Özgül, Y. T. (2009). Elementary students’ metacognition and epistemological beliefs considering science achievement, gender and socioeconomic status.
Elementary Education Online, 8(3), 676–693.
Neo, M. (2003). Developing a collaborative learning environment using a web-based design. Journal of Computer Assisted Learning, 19(4), 462–473.
Nunnaly, J. (1978). Psychometric theory. New York: McGraw-Hill.
Oloruntegbe, K. O., Ikpe, A., Kukuru, J. D. (2010). Factors in students’ ability to connect school science with community and real-world life. Educational Research and Reviews,
5(7), 372–379.
Onrubia, J., Engel, A. (2009). Strategies for collaborative writing and phases of knowledge construction in CSCL environments. Computers Education, 53(4), 1256–1265.
Paas, F. G. W. C. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: a cognitive load approach. Journal of Educational Psychology, 84, 429–434.
Paas, F., Renkle, A., Sweller, J. (2003). Cognitive load theory and instructional design: recent developments. Educational Psychologist, 38(1), 1–4.
Panjaburee, P., Hwang, G. J., Triampo, W., Shih, B. Y. (2010). A multi-expert approach for developing testing and diagnostic systems based on the concept effect model.
Computers Education, 55(2), 527–540.
Peterson, P. L., Janicki, T. C., Swing, S. R. (1981). Ability treatment interaction effects on children’s learning in large-group and small-group approaches.
Putnam, J. W. (1997). Cooperative learning in diverse classrooms. Upper Saddle River, N. J.: Merrill.
Rachel, H. L., Irit, B. N. (2002). Writing development of Arab and Jewish students using cooperative learning and computer-mediated communication. Computers
Education, 39(1), 19–36.
Renkl, A., Atkinson, R. K. (2003). Structuring the transition from example study to problem solving in cognitive skills acquisition: a cognitive load perspective. Educational
Psychologist, 38, 15–22.
Saleh, M., Lazonder, A. W., Jong, T. (2005). Effects of within-class ability grouping on social interaction, achievement, and motivation. Instructional Science, 33(2), 105–119.
Schellens, T., Valcke, M. (2005). Collaborative learning in asynchronous discussion groups: what about the impact on cognitive processing? Computers in Human Behavior,
21(6), 957–975.
Snyder, K. (2000). Asynchronous learning networks and cognitive apprenticeship: a model for teaching complex problem-solving skills in corporate environments.
Unpublished doctoral dissertation, New York.
Speeddie, S. M., Houtz, J. C., Ringenbach, S., Feldhusen, J. F. (1973). Abilities measured by the Purdue Elementary Problem Solving Inventory. Psychological Report, 33(3).
Sternberg, R. J. (1988). The nature of creativity: Contemporary psychological perspectives. New York: Cambridge University Press.
Su, A. Y. S., Yang, SJ. H., Hwang, W. Y., Zhang, J. (2010). A Web 2.0-based collaborative annotation system for enhancing knowledge sharing in collaborative learning
environments. Computers Education, 55(2), 752–766.
Sweller, J. (1994). Cognitive load theory, learning difficulty and instructional design. Learning and Instruction, 4, 295–312.
Sweller, J., van Merriënboer, J. J. G., Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–296.
Tsai, C. W., Shen, P. D. (2009). Applying web-enabled self-regulated learning and problem-based learning with initiation to involve low-achieving students in learning.
Computers in Human Behavior, 25(6), 1189–1194.
Tsai, P. S., Tsai, C. C., Hwang, G. J. (2011). The correlates of Taiwan teachers’ epistemological beliefs concerning Internet environments, online search strategies, and search
outcomes. The Internet and Higher Education, 14(1), 54–63.
Tseng, J. C. R., Hwang, G. J., Tsai, P. S., Tsai, C. C. (2009). Meta-analyzer: a web-based learning environment for analyzing student information searching behaviors.
International Journal of Innovative Computing, Information and Control, 5(3), 567–579.
Uribe, D. J., Klein, D. (2003). The effect of computer-mediated collaborative learning on solving ill-defined problems. Educational Technology Research Development, 51(1),
5–19.
Van Merriënboer, J., Sweller, J. (2005). Cognitive load theory and complex learning: recent developments and future directions. Educational Psychology Review, 17, 147–177.
Vygotsky, L. (1978). Mind in society: the development of higher psychological processes. Cambridge, MA: Harvard University Press.
Wang, S. L., Lin, SS. J. (2007). The effects of group composition of self-efficacy and collective efficacy on computer-supported collaborative learning. Computers in Human
Behavior, 23(5), 2256–2268.
Wright, P. S., Horn, S. P., Sanders, W. L. (1997). Teacher and classroom context effects on student achievement: implications for teacher evaluation. Journal of Personnel
Evaluation in Education, 11, 57–67.
Yiping, L., Philip, A. C., John, S. C. (2000). Effects of within-class grouping on students achievement: an exploratory model. Journal of Educational Research, 94(2), 101–112.
Yu, W. F., She, H. C., Lee, Y. M. (2010). The effects of Web-based/non-Web-based problem-solving instruction and high/low achievement on students’ problem-solving ability
and biology achievement. Innovations in Education and Teaching International, 47(2), 187–199.
Zheng, Z. (2007). Gender differences in mathematical problem solving patterns: a review of literature. International Education Journal, 8(2), 187–203.
F.-R. Kuo et al. / Computers Education 58 (2012) 351–364
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