Article
Are They Learning
or Playing? Students’
Perception Traits
and Their Learning
Self-Efficacy in a
Game-Based Learning
Environment
Yu-Ling Lu1
Journal of Educational Computing
Research
2020, Vol. 57(8) 1879–1909
! The Author(s) 2019
Article reuse guidelines:
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DOI: 10.1177/0735633118820684
journals.sagepub.com/home/jec
and Chi-Jui Lien1
Abstract
As game-based learning continues to draw attention, students’ perceptions toward
classroom activities are vital in influencing the quality of learning. This study used the
social cognitive theory to show the perception traits of learning and playing in gamebased environments and for students to identify their self-efficacy toward
game-based learning by different trait groups. The game Formosa Hope was used in
an experiment with 362 fifth- and sixth-grade students at ages 11 to 12 years as
participants. Three perception traits were identified through a two-step cluster analysis: I—strong perceptions of learning and playing, II—moderate perceptions of learning and playing, and III—strong perception of playing but weak perception of learning.
This study showed that regardless of trait type, students demonstrated positive selfefficacy, with those with Trait I having significantly higher self-efficacy than those with
Traits II and III, indicating that students’ positive perceptions of learning and playing are
essential in prompting self-efficacy in game-based learning.
Keywords
game-based learning, learning and playing, perception trait, self-efficacy
1
Department of Science Education, National Taipei University of Education, Taiwan
Corresponding Author:
Yu-Ling Lu, Department of Science Education, National Taipei University of Education, No. 134, Section 2,
He-Ping East Road, Taipei City 10671, Taiwan.
Email: yllu@tea.ntue.edu.tw
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Introduction
Because of technological development, interaction with virtual environments
has been instilled in the culture of today’s youth. The young generation or the
digital natives differ from their parents in multiple ways, which can be seen in
how they are more thinking oriented and are more inclined to use parallel processing for different tasks, organize their ideas visually, identify themselves as
technophiles, and consider their work as play (Prensky, 2005). These perceptions
of playing and learning, as well as the cognitive skills provided by this technological development, require further psychological studies to examine and
support students’ learning (Greenfield, 2009).
However, regardless of the lack of instructional support (Callaghan, Long,
van Es, Reich, & Rutherford, 2018), the development of technology and the uses
of virtual interactive technologies in various settings did not wait for the development of respective needs. School subjects, including engineering, language,
mathematics, science, social studies, and many others, have adopted virtual
interactive technologies in instruction. Although there are some studies with
results that challenge the effectiveness of these technologies (Girard, Ecalle, &
Magnan, 2013), there remain successful cases involving different grade levels
and locations. Recently, meta-analysis studies have been conducted to evaluate
the overall effectiveness of virtual reality (VR)-based instruction, which is
an approach that incorporates technologies to produce a VR learning environment for students to explore and learn from (Merchant, Goetz, Cifuentes,
Keeney-Kennicutt, & Davis, 2014). Based on data from 69 studies, Merchant
et al. (2014) concluded that the VR approach is effective in improving students’
learning. Moreover, among games, simulations, and virtual worlds—the three
forms of VR-based instruction—game-based learning is considered the most
effective.
In another recent meta-analysis research, Clark, Tanner-Smith, and
Killingsworth (2016) maintained that game-based learning offers powerful affordances and has demonstrated its effectiveness in students’ learning in many
aspects including concept learning, processing skill acquisition, attitude formation, and others. Their meta-analysis results also confirmed and highlighted the
affordances of games for learning. Because of all these findings, the prevalence
and potential of using game-based instruction in schools are becoming increasingly notable, especially with the ongoing changes in the processes of teaching
and learning.
However, studies on learners’ psychology have received lesser attention than
studies that are related to applications development and effectiveness analyses of
game-based learning. Oswald, Prorock, and Murphy (2014) argued that as the
subjects of learning, learners had not been given sufficient attention; thus, questions that explore what they feel and how they learn in a game-based learning
environment need to be investigated.
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There are two major discrepancies in the comparison of game-based learning
environments with traditional classrooms: (a) stronger attributes of playfulness,
which is the inclination of people to engage in play (Pavlas, 2010), and (b)
students’ self-confidence in their ability to learn in a new, technology-based environment (Kennewell & Morgan, 2006). First, these environments, just like movies
or video games, are originally designed as recreational activities. In contrast,
traditional classrooms are related to thinking, understanding, studying, and engaging one’s mind with work. As a result, students consider learning in virtual
environments and traditional classrooms, respectively, as distinct from each
other in terms of playfulness. Second, learners in virtual learning environments
need to use technology to support their learning, which is not the case in traditional learning. Are students confident in coping with game-based learning? Do
they believe that they can learn from this new approach? Is there any relationship
between how students perceive playfulness and their self-efficacy in game-based
learning? Unfortunately, existing psychological studies have not provided sufficient answers to these questions, and this has led to an incomplete understanding
of learners’ psychological status, which is an essential factor in the development of
comprehensive and high-quality instruction in game-based learning environments.
The social cognitive theory and triadic reciprocal determinism (Bandura,
2001) emphasized the interaction among the learning environment, behavior
change, and personal factors. This theory can serve as a basis for exploring
students’ perception traits in terms of perceived playfulness and learning in
game-based learning environments and for identifying their self-efficacy
among different traits. For this study’s experiment, an educational game that
is designed to let students learn about science, technology, and society was
utilized. Through the said game, data of students’ self-efficacy and their perceptions of learning and playing in a game-based learning environment will be
collected and analyzed to deepen the understanding of students’ learning in
game-based learning environments.
Based on the aforementioned information, this study aimed to:
1. identify learners’ perception traits in terms of learning and playing over the
course of game-based learning and
2. reveal students’ self-efficacy toward game-based learning in different
perception trait groups.
Theoretical Background
Technology Acceptance Model and Model of Flow in
Game-Based Learning
To explore how and why students engage in meaningful learning in technologyenhanced and game-based learning environments, previous studies have
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developed two sets of theoretical models: (a) technology acceptance model
(TAM; Cheon, Chung, & Lee, 2015; Juarez Collazo, Wu, Elen, & Clarebout,
2014; Tarhini, Hone, Liu, & Tarhini, 2016; Yi & Hwang, 2003) and (b) model of
flow in game-based learning (MFGL; Kiili, 2005; Pavlas, 2010).
The TAM depicts factors that affect students’ acceptance of technology (Yi &
Hwang, 2003). It has been found that when a student has a higher acceptance of
a new learning technology (environment), a learning behavior is more likely to
form and be sustained. During the past decade, many TAMs have been studied
and empirically validated (Cheon et al., 2015; Juarez Collazo et al., 2014;
Tarhini et al., 2016). As one of the most cited studies, the TAM of Yi and
Hwang (2003) revised previous TAMs by adding three intrinsic motivation variables: learning goal orientation, enjoyment, and self-efficacy. In their study, the
construct of learning goal orientation was measured by five items (i.e., ‘‘I enjoy
challenging and difficult tasks where I’ll learn new skills,’’ ‘‘I prefer to work in
situations that require a high level of ability and talent,’’ and others). On the
other hand, the construct of enjoyment was measured by three items (i.e., ‘‘I
have fun using the . . . system,’’ ‘‘Using the . . . system is pleasant,’’ and ‘‘I find
using the . . . system to be enjoyable’’). These items focus on knowing the learning preferences that students have gained from their learning activities. Yi and
Hwang’s three-variable intrinsic motivation structure is considered adequate in
depicting the TAM; however, it should be noted that their items are not capable
of providing information for further illustrating how students’ perceptions
toward attending (not preferences gained from) the technology-enhanced learning affect their acceptance.
Second, the MFGL reveals factors that enhance students’ engagement and
learning. Csikszentmihalyi (1990) described the flow state as the optimal experience. When students experience this flow state, their senses of enjoyment and
concentration are enhanced, and higher learning outcomes are more likely to be
achieved (Hsieh, Lin, & Hou, 2016; Pavlas, 2010). Based on this, Pavlas (2010)
established a detailed MFGL from a study by Kiili (2005) with empirical results.
Pavlas’s MFGL was composed of the following four subconstructs:
1. Player traits: playfulness, self-efficacy, and intrinsic motivation
2. Player in-game states and behaviors: play, emotional experience, immersion,
flow state, and performance
3. Outcomes: enjoyment, learning, and intrinsic motivation
4. Game features: control, clear goals, challenge or skill balance, and feedback
Just like existing TAMs, the constructs relating to students, mainly the first
three items above, also focus on students’ experiences and the satisfaction gained
from game-based learning activities only.
Furthermore, both existing TAMs and MFGLs have stressed that
learning and playing are two important constructs for students’ acceptance of
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a new technology and the achievement of effective learning. These, however,
do not include items about how students identify their involvement in a technology-enhanced learning environment as either learning or playing;
therefore, further research on students’ perceptions toward game-based learning
is needed.
Triadic Reciprocal Determinism
When learners are in a learning environment, interactions take place back and
forth among three factors: environment, behavior, and personal factors.
According to triadic reciprocal determinism (Bandura, 1989, 2001), which is a
holistic model that is based on the social cognitive theory, human functioning is
a result of these interactions. This model was used as the framework of this study
to support design and experimental observations.
Environmental factors. A learning environment includes schools, classrooms,
equipment, teachers, peers, learning materials, and many more. To monitor
how learners interact with the educational environment, Fraser, McRobbie,
and Fisher (1996) developed the What Is Happening In This Class?
questionnaire, one of the most frequently used questionnaires in the field. This
questionnaire included seven dimensions, namely, student cohesiveness, teacher
support, involvement, investigation, task orientation, cooperation, and equity.
In technology-supported virtual learning, an environment includes other technology-oriented components such as hardware and software. Moreover, the
virtual world frequently provides goals (missions) that are designed for learners
to interact with their virtual surroundings by controlling the technological apparatuses (Vedder-Weiss & Fortus, 2013). Through the process, computers provide
feedback to learners and guide them in reaching their goals. An empirical study
carried out by Nebel, Schneider, Schledjewski, and Rey (2017) has proved that
goal setting in educational games could lower students’ cognitive load and
enhance their playfulness; thus, it helps create an effective learning environment.
For these emerging uses of technologies in classrooms, Dorman (2009) adopted
the tradition of Fraser et al. (1996) by adding three new dimensions to the
questionnaire: computer usage, differentiation, and young adult ethos, eventually coming up with the Technology-Rich Outcomes-Focused Learning
Environment Inventory (Pickett & Fraser, 2010), which is designed to conceptualize students’ learning in a technology-supported environment. The game-based
learning environment that was developed in this study adopted a goal-driven
approach and stressed the importance of creating a user-friendly interface,
respecting students’ learning needs and individual differences, and giving adequate instructional support, as Fraser et al. (1996) and Dorman (2009) have
emphasized, to let learners interact with the environment and induce a positive
learning behavior.
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Behavioral factors. Thummaphan, Yoelao, Suwanmonkha, and Damsuwan (2013)
adopted viewpoints from previous studies and maintained that learning behavior
is the actual behavior of students’ approach to learning ‘‘that describe[s] the way
in which children approach or react to learning situations’’ (p. 2). They divided
learning behaviors into the following: (a) preparation behavior (e.g., preparing
learning materials), (b) in-class learning behavior (e.g., participating in or practicing learning activities), and (c) postlearning behavior (e.g., finishing assignments and reviewing lessons). They define learning behavior as something that
can be seen. However, the value of true learning is to learn how to know and
think which cannot usually be seen in a literal sense, especially in game-based
learning, unless students show their processes or results verbally or in writing.
Some studies, such as that of Hwang and Hu (2013), have adopted this viewpoint and included reasoning thinking into the domain of learning behavior. As
such, their study maintains the position of viewing learning behavior from a
broader perspective, and it includes actions and reasoning thinking during the
entire learning process.
Many studies have investigated students’ learning behaviors in game-based
learning environments in terms of the development of different skills, including
those about students’ growth on hands-on skills (Linkenauger, Leyrer, Bülthoff,
& Mohler, 2013), subject knowledge (e.g., science; Ostrovsky, Poole, Sciortino,
Stanley, & Trunfio, 1991), life sciences (Cai et al., 2006), social studies (Mayer,
Mautone, & Prothero, 2002), and, in general, with a meta-analysis approach
(Merchant et al., 2014); as well as about life skills (Coles, Padgett, Strickland, &
Bellmoff, 2007), strategic thinking, reasoning, decision-making and problemsolving abilities (Bottino, Ferlino, Ott, & Tavella, 2007; Siddique, Ling,
Roberson, Xu, & Geng, 2013; Wanyama & Far, 2007; Yang, 2012), creative
thinking (Muirhead, 2007; Henderson, 2010), and collaboration (Chiong &
Jovanovic, 2012; Hämäläinen, Järvelä, Manninen, & Häkkinen, 2006). These
wide applications and their results have demonstrated that the virtual environment, specifically with the game-based learning approach, has a great impact on
learning and has made a positive impact on students’ actions and behavior.
Personal factors. According to Bandura’s social cognitive theory, person–behavior
interaction and person–environment interaction influence each other bidirectionally. Personal factors include psychological, affective, biological factors,
and more, and among these, affective and biological factors have gained the
attention of many researchers (Cheng, Su, Huang, & Chen, 2014; van
Reijmersdal, Jansz, Peters, & van Noort, 2013), whereas only minimal regard
was given to psychological factors in game-based learning. From a personality
theorist aspect, one’s perceptions of the environment can significantly influence
learning behaviors (Bandura, 1989). Students’ perceptions of game-based learning environments are important in providing key information so that instructional designers or teachers can develop an adequate learning scheme for
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students. Thus, this study focuses on students’ psychological factors, specifically
on their perceptions toward game-based learning environments.
Perception trait. As stated earlier, one of the most significant differences
between virtual learning environments and conventional classrooms is that students rarely feel that they are going to play in the latter. In contrast, students
rarely feel that they are not going to play in virtual interactive environments.
Some studies have pointed out that playfulness is an important topic in
human–computer interaction research (Webster, Trevino, & Ryan, 1993), and
the positive role of playing in learning and achievement has been supported by
many findings (Csikszentmihalyi, 1975; Glynn & Webster, 1992; Malouf, 1987;
Martocchio & Webster, 1992; Piaget, 1962); however, as indicated in a study by
Zhang (2015), the question of whether students just play leisurely or learn effectively in these game-based learning environments remains to be fully explored.
Furthermore, these studies and other research did not address the relative
strengths of perceptions regarding playfulness and learning in game-based learning environments and other virtual learning environments, which has led to an
incomplete understanding of the perception traits of young learners. As a result,
instructional designers and teachers can only guess their students’ perception
traits, as they do not have reliable research evidence. Taking this into consideration, this study aims to identify learners’ perception traits in terms of learning
and playing over the course of a game-based learning.
Self-efficacy. Another personal factor that this study will explore is students’
self-efficacy toward game-based learning environments. Studies on self-efficacy
have been conducted for many decades, and self-efficacy still exists as an important research area. A report by Redmond (2013) stated that the main principle
behind the self-efficacy theory is that ‘‘individuals are more likely to engage in
activities that they have a high self-efficacy for’’ (van der Bijl & ShortridgeBaggett, 2002; as cited in Redmond, 2013). That is, through learning environments that motivate students’ engagement, students may learn with a high selfefficacy and improve their learning results (Bonney, Cortina, Smith-Darden, &
Fiori, 2008). In studies regarding how students learn in game-based learning
environments, such as the TAM of Yi and Hwang (2003) and the MFGL of
Pavlas (2010), self-efficacy was seen as an important factor that affects how well
students accept and use new technology and how well they learn in a game-based
learning. Therefore, this study will also monitor students’ self-efficacy during
game-based learning, and it will attempt to reveal students’ self-efficacy toward
game-based learning by different perception trait groups. Noteworthily, selfefficacy and learning are dynamically and interactively influencing each other
(Pavlas, 2010; Yi & Hwang, 2003); moreover, the levels of self-efficacy could
change over time, as they are affected by new contexts (Gist & Mitchell, 1992).
Ideally, real-time measurement of students’ self-efficacy throughout the learning
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process would be best; however, this is not applicable because the most commonly used paper–pencil format questionnaire technique will severely impede
the learning process and, thus, interfere the measurement of students’ selfefficacy.
Plural Measurements Throughout the Experiment
Several measurements will be conducted during this study’s game-based learning
experiment, where the time dimension will be taken into consideration. Dörnyei
(2000) argued that the time dimension is important for the study of affection in
two aspects, namely, how motivation is generated and how it varies and develops over time. He emphasized that the process-oriented approach is vital in
understanding students’ motivation and the dynamic development of motivation
in prolonged learning processes. With such a viewpoint, motivation should not
be considered as a steady emotional or mental state as the portrayal of its
processes while they take place in time is more important (Dörnyei, 2000).
The context of games is generally organized with a storyline, and students’ perceptions of learning and playing develop dynamically. Hence, the time dimension should be considered relevant to the study of learners’ perceptions of
learning and playing in game-based learning environments. Based on the aforementioned information, the study of students’ perceptions of learning and playing should be considered as a holistic, process-oriented approach. Thus,
repeated measurements will be implemented throughout the experimental teaching period.
Research Methods
Participants
The expert evaluation approach, in which 17 science teachers participated, was
used to confirm that the context (environment) of Formosa Hope (FH) includes
both learning and playing factors. All the participating teachers were independent of the research group and belonged to different schools. To identify students’
perception traits, 193 fifth graders and 169 sixth graders at ages 11 to 12 years
from nine classes participated in this study, and the gender ratio of the participants was 52:48 (M ¼ 187, F ¼ 175).
Instruments
Instrument for game-based learning. The authors of this study led a research group
that developed Formosa Hope, a game that is designed to have fourth to seventh
graders learn scientific concepts and problem-solving skills, which are illustrated
in the national curriculum standard of Taiwan, from daily social contexts. FH
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has been tested in elementary schools, and its effectiveness in cultivating
students’ cultural identity has been verified and reported (Chen, Lien,
Annetta, & Lu, 2010). It consists mainly of the following parts: Stage 1: The
Scenario, Stage 2: The Village, and Stage 3: Touring the Island. The experimental
game-based learning lasted for two class sessions (with 40 minutes each
session, totaling 80 minutes). Deducting about 10 minutes for answering questionnaires, students used 5 minutes for Stage 1 and about 30 minutes each for
Stages 2 and 3.
Stage 1: The Scenario is a 5-minute video clip that presents the game’s scenario or storyline so that learners can adopt the mission of the game as their goal
based on the achievement goal theory. This process carries the belief that learners engage better in learning if they have perceived and accepted the goals that
the virtual environment has emphasized (Vedder-Weiss & Fortus, 2013). After
this, learners are ready to move on to the next stage to play as the avatar and to
learn by solving the problems that are presented to them.
The next stage, Stage 2: The Village, presents a role-playing game (RPG)
experience through its design that is focused on the situated learning theory
and the concept formation theory. First, the situated learning theory claims
that ‘‘knowledge is situated, being, in part, a product of the activity, context,
and culture in which it is developed and used’’ (Brown, Collins, & Duguid, 1989,
Abstract). The RPG presents an environment where the player is situated and
presented with problems that he or she needs to solve given the factors in the
game. Second, the RPG design presents an environment where a player is
required to connect ideas from the information that was given by the game,
which the player will understand through reading—an activity that is associated
with reflection and a key factor in developing critical thinking skills (Gadberry,
1980; Terenzini, Springer, Pascarella, & Nora, 1995; as cited in Greenfield, 2009)
following concept formation. In RPGs, the process of learning scientific concepts is arranged in connection with learners’ daily experiences. Stage 2: The
Village allows the player to freely explore the environment as seen in Figure 1.
During this exploration, players can learn from interacting with various kinds of
people in a virtual society. By doing so, students play, gain knowledge,
and make value judgments. For example, by learning about food additives
(Figure 1(a)), students learn the science in people’s daily lives. For this topic,
the question ‘‘Sausages, ham, and bacon are preserved with nitrites. Does that
mean we can’t eat these?’’ was given. Another example of making students
understand the impact of science in their daily lives is shown in Figure 1(b)
with the question ‘‘Why do grapes sink in water, while oranges float?’’ through
which students are taught to use physical quantity to solve similar problems they
might face.
In Stage 3: Touring the Island, any form of play carries messages, which
turn the form into an act of metacommunication (Bateson, 1955; as cited in
Salen and Zimmerman, 2006). This stage stresses the interactive sequence
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Figure 1. The RPG design allows players to explore and learn.
Figure 2. Screenshots of Stage 3: Touring the Island.
between humans and machines through its board game format that is interspersed with several time-limited minigames. Once the player fulfils certain
requirements of learning in the village, he or she will be able to use the dices
to move forward to visit the towns on the island to learn and play as seen in
Figure 2(a). For example, in an event wherein students see farmers cranking
heavy boxes of vegetables on visiting a town, the question ‘‘There are two spindles, but one axle is smaller than the others. Which is better for lifting a heavy
object?’’ was asked for students to relate scientific knowledge to its application in
the real world (Figure 2(b)).
This stage contains more than 100 events (e.g., learning science from food). It
also presents 11 time-limited minigames on different topics (e.g., learning foodrelated scientific concepts) to provide diverse learning and playing experiences.
One example of the minigames is displayed in Figure 3.
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Figure 3. Screenshot from a minigame.
Table 1. Key Features of the Three Stages of Formosa Hope.
Features
Stage 1:
The Scenario
Stage 2:
The Village
Format
Video clip
RPG
Objective
Establish emotional
attachment to
the mission
Teach basic
concepts
of STS
Stage 3: Touring the Island
A board game-type design with 11
time-limited minigames that appear
occasionally
Teach STS in daily life; the time-limited
minigames are designed for the
mastery of concepts
Note. RPG ¼ role-playing game; STS ¼ science, technology, and society.
The three stages vary in objective and format, and these differences are presented in Table 1.
Instrument for teachers’ evaluation of the educational game. To ensure that the quality
of the educational game is adequate for students’ learning, a game evaluation
instrument was modified from the study of Huang (2005) where previous studies
of courseware evaluation were synthesized from a literature review, and a Delphi
study involving 187 teachers was conducted to decide the evaluation of
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dimensions and items. The final evaluation instrument that was used in this
study included questions in three dimensions: (a) adequacy as teaching materials, (b) suitability for students, and (c) playfulness of the game. The first two
dimensions are related to the educational components of the courseware, while
the third dimension was used to monitor the game components of the courseware. In each dimension, there were 6 to 16 questions.
The teachers who participated in the courseware evaluation were asked
to evaluate FH based on this evaluation framework and give their professional judgment by scoring each item on the scale of 0 to 10. Then, the
mean in each dimension was calculated and used to indicate whether this educational game incorporated sufficient and adequate components for learning and
playing.
Instrument for measuring students’ perceptions of learning and playing. Previous studies
have already developed instruments for evaluating adults’ playfulness (Glynn &
Webster, 1992; Yu, Wu, Lin, & Yang, 2003). Unfortunately, these instruments
are not fit for young learners because of the complexity of their measurements.
Thus, this study established a self-developed instrument to collect data for identifying students’ perceptions. In previous studies, researchers used a large
number of items (close to 30) to measure the stable psychological status of
adults, and this vast number makes these scales unsuitable for this research
purpose. This study agreed with the viewpoint of Sim, MacFarlane, and Read
(2006) that children are capable of distinguishing between concepts of fun and
learning. With this belief, this study used a self-report approach to collect students’ subjective opinions about their perceptions of learning and playing.
Perception refers to the interpretation of what we perceive as a subject
through our senses (Heffner, 2014). It is a flux of impressions from our senses.
Accordingly, this study’s first objective was to identify and categorize students’
perception trait groups of learning and playing in game-based learning environments. Students’ perceptions of learning and playing are process-oriented, naturally occurring reactions from a holistic perspective; thus, they should be
portrayed in real time. Therefore, students were asked the following questions
several times throughout the experiment to understand their perceptions toward
game-based learning:
1. To what extent do you feel that you are learning now?
2. To what extent do you feel that you are playing now?
The scale was explained to students, and they were asked to record their
perceptions from a scale of 0 to 10 to represent the intensities of their perceptions of learning and playing, with 0 for doesn’t have any perception of playing or
learning and 10 for has a very strong perception of playing or learning. In this
study, there are four time points for students to report the answers to the
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questions earlier. Although researchers have questioned the validity and reliability of self-report methods, such methods are significantly valuable in assessing
students’ perceptions of various constructs (Bonney et al., 2008).
The first-time measurement (labeled as Stage 0: Before the Experiment) was
taken while students were in their computer classrooms and were told that they
would have a computer game class. Students were instructed to report to the
teacher and mark the scale each time they finish each of the game’s three stages,
which totals to four time points (Stages 0, 1, 2, and 3). Through this, their
perceptions of learning and playing throughout the experiment period were
recorded.
The reliability and validity of this instrument were established by the following procedures. Two professors from the science education field and three elementary school teachers who had at least 5 years of teaching experience reviewed
and revised the instrument for its appropriateness, correctness, and usefulness to
confirm its validity. Then, its reliability was obtained from 77 students’ test–
retest data that were collected during an interval of 3 weeks. The test–retest
reliabilities were .71 and .77 for learning and playing components, respectively,
which were indicative of medium–high stability.
This study’s authors believe that this instrument and procedure are feasible
ways of monitoring students’ perception traits and the fluctuation in the context
of an educational game.
Instrument for measuring students’ self-efficacy toward game-based learning. The impact
of most environmental influences on human motivation is weightily mediated
through a self-process (Bandura, 1993). Thus, students’ self-efficacy is an
important promoter of learning that is influenced by the environment.
For the second objective of this study, an eight-item questionnaire that is
related to the learning contexts was used to collect students’ self-efficacy of
learning. Perceived self-efficacy is associated with learners’ beliefs in their capabilities (Bandura, 1997). In Bandura’s guide for constructing self-efficacy scales,
he argued that efficacy items should be phrased in terms of can do instead of will
do. The items are judgments of the capability to execute given types of performances and make multiple judgments of their efficacy across the full range of task
domains (Bandura, 2006). Therefore, this study used the phrase be able to in the
items. Moreover, all items are domain specifications that are related to learners’
efficacy across the full range of the educational game. The eight items are as
follows:
1. I am able to know more about various food items in the educational game.
2. I am able to know more about food production, processing, and
transportation.
3. I am able to learn more about the use and preservation of resources.
4. I am able to know more about science concerning food.
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5.
6.
7.
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I am able to know more about our society.
I was able to think during the game.
I was able to make judgments during the game.
In general, I can learn with the help of this educational game.
Bandura’s (2006) Self-Efficacy Response Scale used single-unit intervals that
range from 0 to 10. This study assigns the full scores for each item from 0 to 10,
with 0 for cannot do and 10 for can do with high certainty. The mean scores of
these eight items were used to represent students’ self-efficacy of learning in the
educational game context. Cronbach’s a value of these items is .916, which is
regarded as having high reliability.
Procedure and Data Analysis
Instructional instrument evaluation. After the courseware of FH was ready for use,
17 teachers were invited as outside evaluators to assess whether FH incorporated
sufficient and adequate components of learning and playing. Moreover, they
evaluated whether this game-based learning activity and the environment are
suitable for elementary school students. The total evaluation session was 2
hours. For the first 45 minutes, the research group introduced the learning
experiences that this courseware will provide, and this was followed by a 15minute discussion. Then, these evaluators individually played FH until the end
of the session. After they had completed the evaluation questionnaire, the data
were collected, and the means were calculated.
Measurement of students’ perceptions of learning and playing and self-efficacy. To
observe students’ perceptions of learning and playing when they encounter different formats of educational contexts as addressed in FH, the study selected
nine classes in two local schools to participate. The experiment was conducted in
students’ computer science classes, and each student was given a computer that
had an installed FH. Schoolteachers taught the students how to use FH and how
to complete the questionnaire. Students in the same class played individually and
experienced FH simultaneously. A large sample of classes (nine) and students
(n ¼ 362) was used in this study. Data that were collected from the students’ four
measurements throughout the two class sessions (80 minutes) represent the students’ perception statuses in four stages: (a) Stage 0: Before the Experiment, (b)
after Stage 1: The Scenario, (c) after Stage 2: The Village, and (d) after Stage 3:
Touring the Island. Perceptions of scores of learning and playing were analyzed
with a one-way analysis of variance (ANOVA) to observe the differences in each
stage. If a significant difference was observed, a Tukey post hoc test was performed to find differences among stages. These data were also analyzed with a
two-step cluster analysis, which was selected for its capability to handle larger
samples, and the result was used for this study’s second objective. All statistical
Lu and Lien
1893
analyses were processed using SPSS 20.0 software (SPSS Inc., Chicago, IL,
USA).
Because there is a possibility for the multimeasurement of students’ self-efficacy to interfere with students’ learning, this study decided to measure students’
self-efficacy when they have the maximum engagement in game-based learning,
in an effort to gather the most reliable measurement. Thus, after the students
have completed the experiment and the four questions related to their perceptions of learning and playing, they were given the questionnaire of self-efficacy
concerning learning in FH. The scores of this questionnaire were used as the
dependent variable, and the means of different groups (clusters) were compared
with ANOVA. If a significant difference existed, a post hoc test was performed.
Results and Discussion
Teachers’ Evaluation of FH
FH was evaluated by 17 teachers with the evaluation questionnaire that was
previously described. The means in adequacy as teaching material, suitability for
students, and playfulness of the game were 7.4, 7.5, and 7.2, respectively, on a
10-point scale. This result indicates that the FH courseware that was developed
by this group has sufficient and suitable learning and playing components; thus,
it can be regarded as an educational game and be used in a virtual, game-based
learning environment for the elementary students in this study.
Students’ Perceptions of Learning and Playing in Game-Based
Learning Environments
The students’ responses to the perception scale of learning and playing in the
four stages of the experiment are listed in Table 2 and are plotted in Figure 4 to
show the fluctuation of the participants’ means of perceptions over time by
stage.
To examine the fluctuation of the participants’ perceptions over time,
repeated measures using a one-way ANOVA were conducted. Tables 3 and 4
show the ANOVA results of students’ perceptions of learning and playing. These
results showed no significant change in students’ perceptions of learning
(F ¼ 1.83, p > .05). However, in contrast, a significant tendency of increase in
students’ perceptions of playing has been observed (F ¼ 30.01, p < .05). The
results in Figure 4 and Table 4 also showed students’ high and increasingly
stronger perceptions of playing during a game-based learning activity. This supports the idea that incorporating learning contents into a virtual learning environment can attract students to play and increase their playfulness (means: 5.99–
7.73). It should be noted that the perception of learning is decreasing but not
significantly and is still maintained at a certain level throughout the whole
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Table 2. Students’ Perceptions Toward Game-Based Learning Over Time (by Stage).
Perception
Learning
Playing
Stage
0:
1:
2:
3:
0:
1:
2:
3:
Before the Experiment
The Scenario
The Village
Touring the Island
Before the Experiment
The Scenario
The Village
Touring the Island
M
SD
n
6.25
5.99
5.85
5.77
5.99
6.88
7.38
7.73
2.50
2.90
3.06
3.17
2.68
2.74
2.53
2.48
357
357
357
354
358
358
357
355
Note. SD ¼ standard deviation; M ¼ mean.
Figure 4. Fluctuation of students’ perceptions over time (by stage).
process (means: 5.77–6.25). The flat perception of learning, as seen in Figure 4,
and the nonsignificant difference among the four stages showed in Table 3 indicate that students sustain their perception of learning without being distracted
when the perception of playing increased. However, some concerns have been
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1895
Table 3. Analysis of Variance of Students’ Perceptions of Learning During the Four Stages
of the Experiment.
Resources
Between
Within
Sum
SS
df
MS
F
p
46.82
12,109.69
12,156.52
3
1,421
1,424
15.61
8.52
1.83
.14
Note. SS ¼ sum of squares; MS ¼ mean square.
Table 4. Analysis of Variance of Students’ Perceptions of Playing During the Four Stages of
the Experiment.
Resources
Between
Within
Sum
SS
df
MS
F
p
613.39
9,703.56
10,316.94
3
1,424
1,427
204.46
6.81
30.01
.00*
Note. SS ¼ sum of squares; MS ¼ mean square.
*p < .05.
raised about whether students would have a higher perception of learning. This
study believes that students’ perception of learning will increase when gamebased learning becomes a common learning approach in school and when students realize and are convinced that the main purpose of the game-based activity
is to let them learn better.
The Tukey post hoc test was conducted to compare students’ perceptions of
playing in each stage. The results of the said test are listed in Table 5.
These pairwise comparison results showed that participants reported significantly higher levels of feelings of playing at three time points in the learning
game (Stages 1, 2, and 3) when compared with that of before the experiment
(Stage 0; mean(1, 2, or 3) mean(0) ¼ 0.89, 1.39, and 1.74, p < .05). This finding
indicated that students’ playfulness significantly increased when they entered the
game-based learning environment.
In addition, although the students did not show a significant difference in
terms of playfulness between the board game-type, Stage 3: Touring the Island,
and the RPG-type, Stage 2: The Village (mean(3) mean(2) ¼ 0.35, p > .05), it
was noticed that Stage 3: Touring the Island is significantly helpful to students’
playfulness when compared with Stage 1: The Scenario (mean(3) mean(1)¼ 0.85, p < .05) but not when Stage 2: The Village is compared with Stage 1:
The Scenario (mean(2) mean(1) ¼ 0.50, p > .05). These results are possibly
derived from two cases. First, the RPG in Stage 2: The Village presents the
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Journal of Educational Computing Research 57(8)
Table 5. Post Hoc Pairwise Comparisons of Students’ Perceptions of Playing Among
Stages.
Mean(j)
Mean(i) Mean(j)
Mean(i)
Stage 0:
Stage 1:
Stage 2:
Stage 3:
Before the Experiment
The Scenario
The Village
Touring the Island
Stage 0:
Before the
Experiment
Stage 1:
The Scenario
Stage 2:
The Village
Stage 3:
Touring
the Island
—
0.89*
1.39*
1.74*
—
—
0.50
0.85*
—
—
—
0.35
—
—
—
—
*Note. p < .05
player with high flexibility to explore learning, thus offering a venue where students may lose their sense of direction in the game-based environment. In addition, the totally free and active exploration environment where the tempo of
positive stimuli could not be systematically controlled can slow down gameplay
and weaken playfulness. Second, as competition is a potent factor for enhancing
students’ playfulness and an FH-type educational game can strengthen their
immersion and self-identities (Chen et al., 2010), the game style and contents
in Stage 3: Touring the Island, where social culture and competition are abundant, might have significantly contributed to the phenomenon of students
having the highest level of playfulness on the said stage.
This can follow the idea that the more students play, the stronger their perception of playing is when an educational game is designed properly. Such a
result provides evidence to students having a higher level of enjoyment in educational games with social culture and competition characteristics. This also
supports the idea that incorporating learning content and learning activities in
a game environment can attract students to play and maintain—even increase—their playfulness.
Students’ Perception Traits in Game-Based Learning Environments
To identify and reveal students’ perception traits regarding learning and playing,
the collected data were analyzed through a two-step cluster analysis, and the
result of the analysis is presented in Table 6. The cluster analysis grouped the
students into three clusters; here, only 11 (3.0%) of the 362 students were not
classified, and hence, 97.0% of the students were successfully distinguished by
the classification system. It was found that each trait group represented a relatively significant portion of the students (from 18.2% to 41.6%), which supports
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1897
Table 6. Students’ Perceptions of Learning and Playing in Different Game-Based Learning
Stages (by Cluster).
Cluster 1
Stage
La
0
1
2
3
Studentsd
7.03
6.87
7.57
7.49
Cluster 2
Pb
(2.23)c 7.47
(2.36)
8.52
(2.12)
8.76
(2.41)
9.07
141 (40.2%)
(2.19)
(1.46)
(1.56)
(1.30)
L
6.29
6.79
6.01
6.04
Cluster 3
P
(2.16) 4.38 (2.17)
(2.39) 4.54 (2.37)
(2.48) 5.22 (2.12)
(2.49) 5.70 (2.34)
146 (41.6%)
4.33
2.34
1.64
1.39
L
P
(2.81) 6.31
(1.95) 8.47
(1.73) 9.20
(1.62) 9.44
64 (18.2%)
(2.73)
(1.74)
(1.17)
(1.08)
a
Learning.
Playing.
c
Mean (standard deviation); the mean range: 0 to 10.
d
n ¼ 362; grouped: 351 (97.0%); excluded: 11 (3.0%).
b
the premise that this classification scheme is acceptable. In Table 6, the means of
students’ self-reported perceptions of learning and playing was organized by
cluster and plotted in Figure 5.
Figure 5 shows that three clusters clearly appear. These clusters are named
and explained as follows:
1. Perception Trait I: Strong perceptions of learning and playing
2. Perception Trait II: Moderate perceptions of learning and playing
3. Perception Trait III: Strong perception of playing but weak perception of
learning (playing oriented)
A large proportion of students belongs to the Perception Trait I cluster
(n ¼ 141, 40.2%). Strong perceptions of learning and playing throughout the
period make up the characteristics of the intensive learning and playing of this
group of students. The tendency gets even stronger in both learning and playing
as gameplay progresses. Students with this perception trait are ideal candidates
for learning in game-based learning environments. This finding supports the flow
theory (Csikszentmihalyi, 1988) in explaining the positive interaction between
learning and playing, which has been addressed in several studies (Kiili, 2005;
Reid, 2004; Rieber, 1996).
Students in the Perception Trait II cluster (n ¼ 146, 41.6%) use a moderate
and steady mind to interact with the educational game. Perceptions of learning
and playing show a tendency of converging over time, and they were maintained
at a moderate level. It should be noted that during the process of the experiment,
the increase in perception of playing seems to accompany the tendency of the
weakening of the perception of learning.
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Journal of Educational Computing Research 57(8)
Figure 5. Students’ three perception traits in game-based learning.
A relatively smaller proportion of students belongs to the Perception Trait III
cluster (n ¼ 64, 18.2%). With a very high tendency of playing and an extremely
low tendency of learning, this group of students demonstrated a highly unique
trait, which poses concern over their actual learning. However, according to
Ebner and Holzinger (2007), students involved in game-based learning environments might feel that they are playing, but learning indeed occurs simultaneously. In the experiment, these students showed a stronger tendency to play
than to learn even before they entered the regular computer science class (Figure
5), which makes them different from their peers. Furthermore, after this study’s
follow-up interviews with the students’ teachers, it was found that most of the
students in this group have an outgoing personality. To enhance these playingoriented students’ learning in game-based learning environments, identifying
them based on this study’s findings and additional observations by teachers is
the priority. When students are identified as possibly belonging to the Perception
Trait III cluster, they should be given more attention and support to enhance
their perception of learning in game-based learning environments.
A total of 97.0% of the students were successfully grouped in the framework
of perception traits, with only 11 of the 362 considered unclassified. A visual
representation was created in Figure 6 to show students’ perception trait groups.
Each trait group is placed based on its average strengths of students’ perceptions
of learning and playing that were measured during the entire process. The average of the standard deviations of those means is used to represent the width and
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1899
Figure 6. The distribution of three perception trait groups of students in terms of their
perceptions of learning and playing.
the height of its rectangle. Figure 6 reveals that students are more likely to form
perceptions of playing than those of learning. According to the flow theory of
Csikszentmihalyi (2000), flow is a state of enjoyment in engaging in an activity.
Furthermore, it implied that when one works with a mental state of playing, this
will result in higher enjoyment and motivation than that with a mental state of
merely working (Nakamura & Csikszentmihalyi, 2014). In this study, when students engage in computer game-based learning, they tend to have higher levels of
perception of playing, which could lead to the flow state of learning and eventually enhance their learning efficacy. Thus, this study further explored the
learning efficacy of these three groups of students.
Self-Efficacy of Learning in Different Perception Trait Groups
The students’ responses to the questionnaire of self-efficacy at the end of the
session were used for this study’s second objective. The means and standard
deviations of the students’ scores for the three different perception trait
groups are listed in Table 7. These revealed that all three groups had, on average,
a positive self-efficacy (means > 5). Table 7 also shows that the group with strong
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Journal of Educational Computing Research 57(8)
Table 7. Students’ Self-Efficacy Toward Game-Based Learning
(by Perception Trait Group).
Perception trait group
I: Strong learning and playing
II: Moderate learning and playing
III: Playing oriented
Mean
M
SD
n
7.94
6.83
5.94
7.05
2.09
2.14
2.73
2.35
141
146
64
351
Note. M ¼ mean; SD ¼ standard deviation.
perceptions of learning and playing (Trait I) had the highest self-efficacy scores
with the smallest standard deviation (7.94 and 2.09). In contrast, the playingoriented group (Trait III), which has a strong perception of playing but weak
perception of learning, had the lowest self-efficacy scores with the largest standard deviation (5.94 and 2.73). This result implies that students in these groups
were different in terms of their self-efficacy toward learning in a game-based
learning environment.
To understand whether there were significant differences among these three
groups of students’ self-efficacy scores, ANOVA was used. The results of the
ANOVA are presented in Table 8, and they revealed that students’ self-efficacies
are significantly different in the three groups (F ¼ 18.814 and p < .001).
Follow-up pairwise comparisons of self-efficacy for the three different groups
indicate that students with Traits I and II reported significantly higher levels of
self-efficacy than students with Trait III (means ¼ 7.94, 6.83, and 5.94, respectively, p < .05, as listed in Tables 7 and 9). Furthermore, there was no significant
difference in the self-efficacy scores between the students with Trait I and those
with Trait II (means ¼ 7.94 and 6.83, respectively, p > .05, as listed in Tables 7
and 9). The results revealed that the strength of students’ perceptions of learning
and playing could be a factor for their positive self-efficacy in a game-based
learning environment, and their strong and positive perceptions of learning
and playing are important factors in prompting their positive self-efficacy in
game-based learning.
Overall, this study created a virtual, interactive learning environment through
an educational game. Within such a learning environment, it provided a set of
goals for learners. By doing so, students learned and constructed their knowledge of science and society in a goal-driven, virtual environment, which can help
boost their engagement in learning.
This study identified three perception traits that students possessed in the
virtual, game-based learning environment: Trait I—strong perceptions of learning and playing, Trait II—moderate perceptions of learning and playing, and
Trait III—strong perception of playing but weak perception of learning (playing
oriented).
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1901
Table 8. ANOVA of Students’ Self-Efficacy Toward Game-Based
Learning (by Perception Trait Group).
Resources
Between
Within
Sum
SS
df
MS
F
p
188.81
1,746.16
1,934.96
2
348
350
94.40
5.02
18.81
.00*
Note.SS ¼ sum of squares; MS ¼ mean square.
*p < .05.
Table 9. Post Hoc Pairwise Comparisons of Students’ Self-Efficacy Among Different
Perception Trait Groups.
Mean(j)
Perception pattern
Mean(i) Mean(j)
Mean(i)
Perception pattern
I: Strong learning
and playing
II: Moderate learning
and playing
III: Playing oriented
I:
Strong learning
and playing
II:
Moderate learning
and playing
III:
Playing
oriented
—
1.11*
—
2.00*
0.89
—
*Note. p < .05.
The mean data of the self-efficacies of these three perception trait groups
showed that students had, on average, a positive self-efficacy. As claimed by
Dickey (2007), an increasing intrinsic motivation is usually an emphasis on virtual learning design, and many studies have demonstrated that motivation is
helpful for learning behavior and self-efficacy (Bandura, 2006; Bonney et al.,
2008; Coffin & MacIntyre, 1999; David, Song, Hayes, & Fredin, 2007). These
provided possible explanations on why the students in this study demonstrated a
positive self-efficacy toward virtual, game-based learning and why students with
Trait I showed a significantly higher level of self-efficacy.
Furthermore, the results indicated that students’ perceptions of learning only
slightly decreased with time through game stages. This result is similar to previous research findings regarding hypermedia learning environments (Moos &
Azevedo, 2008). However, from a different perspective, the findings of this study
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Journal of Educational Computing Research 57(8)
give evidence to the idea that an educational game is capable of maintaining
players’ perceptions of learning during gameplay.
On the other hand, students’ perceptions of playing were stronger over time
through stages. In computer-assisted instruction, when a learning content was
integrated into a charming story, it can enhance the satisfaction of intrinsic
motivation (Ujita, Yokota, Tanikawa, & Mutoh, 1996). This viewpoint is supported by this study’s results. This study focused on students’ perception of
playing instead of their status of enjoyment. As Iten and Petko (2016) argued,
educational games should not only be fun; they should also encompass
emotional, behavioral, and cognitive engagement. Iten and Petko’s study also
showed that students’ willingness to learn through educational games depends
on their expectations of usefulness and ease of use more than on the level of fun.
In this study, the three learning stages were designed to shift from charming
story characteristics (i.e., scenario video and an RPG) to challenge and competition characteristics (i.e., board game-type format and time-limited minigames).
This implies that challenge and competition factors and a high frequency of
human–machine interaction may be more decisive of students’ playing perceptions than a charming story in terms of educational games. However, further
research that provides supporting evidence for this is needed.
This study also found that in terms of self-efficacy, students with Trait
I have the highest self-efficacy, while students with Trait III have the lowest.
The results also revealed that the strength of students’ strong perceptions
of learning and playing could be a factor for positive self-efficacy in a
game-based learning environment. Although the TAM of Yi and Hwang (2003)
indicated that self-efficacy is an important element in students’ acceptance of a
new learning technology, and the MFGL of Pavlas (2010) indicated that selfefficacy significantly contributes to students’ flow states and thus, their learning,
these two models did not properly address students’ perceptions in terms of
learning and playing. The lack of discussion on how students perceive gamebased learning environments in TAMs and MFGLs needs to be supplemented.
Conclusions
Grounded in the TAM, MFGL, and the social cognitive theory, this study discovered that students could be categorized into three perception trait groups in
terms of their feelings of learning and playing toward game-based, virtual learning environments. All these three perception trait groups demonstrated positive
self-efficacy toward game-based learning; however, students who have strong
perceptions of learning and playing (Trait I) or moderate perceptions of learning
and playing (Trait II) showed significantly higher self-efficacy than their peers
who are playing oriented (Trait III).
For educational practices, the findings of this study suggest that teachers can
identify their students’ orientation toward their game-based learning instruction
Lu and Lien
1903
by using the perception traits that were determined in this study. When designing game-based learning activities, instructional designers and teachers should
attempt to enhance students’ feelings of learning, playing, and self-efficacy so
that students who have a strong perception of playing but weak perception of
learning (Trait III) may shift their perceptions toward learning and playing to
the level of students with Traits I or II for more effective learning to take place.
The instructional practices for achieving this are currently beyond the scope of
this study, and further explorations should be carried out. This study identified
students’ perception trait groups in terms of learning and playing as important
factors in their game-based learning. The identification of the extent of the
role of students’ perceptions of learning and playing in these models should
be further explored.
This study found several important findings; however, a significant limitation
of this study is whether the educational game that was developed and used by its
authors can be regarded as a representative educational game. Although this
study used a courseware evaluation instrument and highly experienced teachers
to evaluate and verify the adequateness of this educational game, it still cannot
be claimed as a representative game. As previous studies (Bedwell, Pavlas,
Heyne, Lazzara, & Salas, 2012; De Lope & Medina-Medina, 2017) stated, the
lack of a consistent taxonomy of game attributes for educational games has
caused a huge difficulty for empirical studies on educational games. Until
now, no ready taxonomy and metrology can solve this problem. This study
attempted to describe the game-based, virtual learning environment to the
best of its ability. However, when interpreting this study’s findings, it should
be noted that there is no existing taxonomy and metrology for the authors to
characterize a game-based, virtual learning environment accurately.
Moreover, although this study successfully revealed that students’ perceptions
of learning and playing can be clustered and that students in one particular
cluster demonstrates better self-efficacy, which have substantial implications in
incorporating game-based learning in schools, teachers and professional practitioners need to be cautious that learning is a complex process. The authors of
this study maintain that either neglecting the understanding on students’ perceptions of learning and playing or oversimplifying game-based learning as
merely associating perceptions of learning and playing is not beneficial to successfully incorporate educational games into learning. The research findings aim
to deepen and empower readers’ understanding of the complex learning model
and process and not to suggest a simplistic approach of determining the value of
an educational game or game-based learning.
Acknowledgments
The authors thank four teachers, Chi-Ling Wu, Ying-Ya Liao, Hsiu-Chu Huang, and
Yi-Ying Lee, as well as Ms. Chien-Ju Li for helping with the experiments and data
collection.
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Journal of Educational Computing Research 57(8)
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research,
authorship, and/or publication of this article.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Ministry
of Science and Technology, Taiwan (96-2511-S-152-004-MY3 and 106-2511-S-152006-MY3).
ORCID iD
Yu-Ling Lu
http://orcid.org/0000-0003-3594-5541
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Author Biographies
Yu-Ling Lu is a professor of science education at the National Taipei University
of Education, Taiwan, with a bachelor’s degree in physics, a master’s degree in
chemistry, and a doctoral degree in science education. She has published articles
and instructional materials, such as books and K–12 science-specific courseware,
which explore science curriculum development, instructional design, and technology-enhanced learning. In 2018, she began to serve as the chairperson of the
Association of Science Education in Taiwan, a research group devoted to promoting the quality of science education research among many areas of the
academe.
Chi-Jui Lien is a former professor of the Department of Science Education of
National Taipei University of Education and currently an advisor of doctoral
students in the same department. He published articles in various journals that
tackle comparative education and technology-enhanced learning. His keen interest in science education led him to become the president of the East-Asian
Association for Science Education, from 2011 to 2013, which aims to provide
a platform for the advancement of, and increased collaboration in, science education research specifically in East Asia.