Are They Learning or Playing? Students’ Perception Traits and Their Learning Self-Efficacy in a Game-Based Learning Environment

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 game-based 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 self-efficacy, 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

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.

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:

identify learners’ perception traits in terms of learning and playing over the course of game-based learning and

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 technology-enhanced and game-based learning environments, previous studies have 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:

Player traits: playfulness, self-efficacy, and intrinsic motivation

Player in-game states and behaviors: play, emotional experience, immersion, flow state, and performance

Outcomes: enjoyment, learning, and intrinsic motivation

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 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.

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 problem-solving 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 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 & Shortridge-Baggett, 2002; as cited in Redmond, 2013). That is, through learning environments that motivate students’ engagement, students may learn with a high self-efficacy 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, self-efficacy 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 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’ self-efficacy.

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 = 187, F = 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 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.

Figure 1.

The RPG design allows players to explore and learn.

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 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)).

Figure 2.

Screenshots of Stage 3: Touring the Island.

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 food-related scientific concepts) to provide diverse learning and playing experiences. One example of the minigames is displayed in Figure 3.

Figure 3.

Screenshot from a minigame.

The three stages vary in objective and format, and these differences are presented in Table 1.

Table 1.

Key Features of the Three Stages of Formosa Hope.

Features	Stage 1: The Scenario	Stage 2: The Village	Stage 3: Touring the Island
Format	Video clip	RPG	A board game-type design with 11 time-limited minigames that appear occasionally
Objective	Establish emotional attachment to the mission	Teach basic concepts of STS	Teach STS in daily life; the time-limited minigames are designed for the mastery of concepts

Note. RPG = role-playing game; STS = science, technology, and society.

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 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:

To what extent do you feel that you are learning now?

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 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:

I am able to know more about various food items in the educational game.

I am able to know more about food production, processing, and transportation.

I am able to learn more about the use and preservation of resources.

I am able to know more about science concerning food.

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 α 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 15-minute 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 = 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 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.

Figure 4.

Fluctuation of students’ perceptions over time (by stage).

Table 2.

Students’ Perceptions Toward Game-Based Learning Over Time (by Stage).

Perception	Stage	M	SD	n
Learning	0: Before the Experiment	6.25	2.50	357
	1: The Scenario	5.99	2.90	357
	2: The Village	5.85	3.06	357
	3: Touring the Island	5.77	3.17	354
Playing	0: Before the Experiment	5.99	2.68	358
	1: The Scenario	6.88	2.74	358
	2: The Village	7.38	2.53	357
	3: Touring the Island	7.73	2.48	355

Note. SD = standard deviation; M = mean.

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 = 1.83, p > .05). However, in contrast, a significant tendency of increase in students’ perceptions of playing has been observed (F = 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 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 raised about whether students would have a higher perception of learning. This study believes that students’ perception of learning will increase when game-based 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.

Table 3.

Analysis of Variance of Students’ Perceptions of Learning During the Four Stages of the Experiment.

Resources	SS	df	MS	F	p
Between	46.82	3	15.61	1.83	.14
Within	12,109.69	1,421	8.52
Sum	12,156.52	1,424

Note. SS = sum of squares; MS = mean square.

Table 4.

Analysis of Variance of Students’ Perceptions of Playing During the Four Stages of the Experiment.

Resources	SS	df	MS	F	p
Between	613.39	3	204.46	30.01	.00*
Within	9,703.56	1,424	6.81
Sum	10,316.94	1,427

Note. SS = sum of squares; MS = mean square.

*p < .05.

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.

Table 5.

Post Hoc Pairwise Comparisons of Students’ Perceptions of Playing Among Stages.

Mean₍ _i ₎ − Mean₍ _j ₎	Mean₍ _j ₎
Mean₍ _i ₎ − Mean₍ _j ₎	Stage 0: Before the Experiment	Stage 1: The Scenario	Stage 2: The Village	Stage 3: Touring the Island
Mean₍ _i ₎
Stage 0: Before the Experiment	—	—	—	—
Stage 1: The Scenario	0.89*	—	—	—
Stage 2: The Village	1.39*	0.50	—	—
Stage 3: Touring the Island	1.74*	0.85*	0.35	—

*Note. p < .05

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 ₎ = 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₎ = 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₎ = 0.85, p < .05) but not when Stage 2: The Village is compared with Stage 1: The Scenario (mean₍₂₎ − mean₍₁₎ = 0.50, p > .05). These results are possibly derived from two cases. First, the RPG in Stage 2: The Village presents the 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 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.

Students’ three perception traits in game-based learning.

Table 6.

Students’ Perceptions of Learning and Playing in Different Game-Based Learning Stages (by Cluster).

Stage	Cluster 1		Cluster 2		Cluster 3
Stage	L^a	P^b	L	P	L	P
0	7.03 (2.23)^c	7.47 (2.19)	6.29 (2.16)	4.38 (2.17)	4.33 (2.81)	6.31 (2.73)
1	6.87 (2.36)	8.52 (1.46)	6.79 (2.39)	4.54 (2.37)	2.34 (1.95)	8.47 (1.74)
2	7.57 (2.12)	8.76 (1.56)	6.01 (2.48)	5.22 (2.12)	1.64 (1.73)	9.20 (1.17)
3	7.49 (2.41)	9.07 (1.30)	6.04 (2.49)	5.70 (2.34)	1.39 (1.62)	9.44 (1.08)
Students^d	141 (40.2%)		146 (41.6%)		64 (18.2%)

Learning.

Playing.

Mean (standard deviation); the mean range: 0 to 10.

n = 362; grouped: 351 (97.0%); excluded: 11 (3.0%).

Figure 5 shows that three clusters clearly appear. These clusters are named and explained as follows:

Perception Trait I: Strong perceptions of learning and playing

Perception Trait II: Moderate perceptions of learning and playing

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 = 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 = 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.

A relatively smaller proportion of students belongs to the Perception Trait III cluster (n = 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 playing-oriented 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 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.

Figure 6.

The distribution of three perception trait groups of students in terms of their perceptions of learning and playing.

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 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 playing-oriented 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.

Table 7.

Students’ Self-Efficacy Toward Game-Based Learning (by Perception Trait Group).

Perception trait group	M	SD	n
I: Strong learning and playing	7.94	2.09	141
II: Moderate learning and playing	6.83	2.14	146
III: Playing oriented	5.94	2.73	64
Mean	7.05	2.35	351

Note. M = mean; SD = standard deviation.

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 = 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 = 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 = 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.

Table 8.

ANOVA of Students’ Self-Efficacy Toward Game-Based Learning (by Perception Trait Group).

Resources	SS	df	MS	F	p
Between	188.81	2	94.40	18.81	.00*
Within	1,746.16	348	5.02
Sum	1,934.96	350

Note.SS = sum of squares; MS = mean square.

p < .05.

Table 9.

Post Hoc Pairwise Comparisons of Students’ Self-Efficacy Among Different Perception Trait Groups.

Mean₍ _i ₎ − Mean₍ _j ₎	Mean₍ _j ₎
	Perception pattern
	I: Strong learning and playing	II: Moderate learning and playing	III: Playing oriented
Mean₍ _i ₎
Perception pattern
I: Strong learning and playing	—
II: Moderate learning and playing	−1.11*	—
III: Playing oriented	−2.00*	−0.89	—

Note. p < .05.

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).

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 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 self-efficacy 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 game-based 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 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.

Footnotes

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.

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-152-006-MY3).

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.

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