The Development and Evaluation of a Computer-Simulated Science Inquiry Environment Using Gamified Elements

Abstract

This study developed a computer-simulated science inquiry environment, called the Science Detective Squad, to engage students in investigating an electricity problem that may happen in daily life. The environment combined the simulation of scientific instruments and a virtual environment, including gamified elements, such as points and a story for the scaffold and feedback mechanism that guides students to perform a scientific inquiry activity. This study conducted an experiment to understand the effect of this environment. The research findings revealed that students’ electricity knowledge was significantly increased through this activity, and most students had positive perceptions regarding the environment. This study also found that the participants’ inquiry performance gradually declined during the scientific inquiry activity, while performance of data analysis had the highest influence on their success in completing the activity. Thus, some design improvements for this virtual environment were derived from this study.

Keywords

simulation science inquiry gamification inquiry-based learning

Introduction

Helping students to learn and understand science through scientific inquiry activities has been promoted by numerous science education scholars and institutions for nearly half a century (American Association for the Advancement of Science, 1993; Kimball, 1967; Rutherford & Ahlgren, 1989). Recently, after the U.S. National Research Council (2000) listed scientific inquiry as the primary goal of science literacy in their National Science Education Standards, scientific inquiry has been recognized as a core objective for science education throughout the world.

As science is an inquiry process (Bybee, 1997), the main spirit of scientific inquiry is to help students learn science by participating in inquiry activities like a scientist, rather than passively receiving science knowledge delivered by teachers. Numerous empirical studies have verified that inquiry-based learning can improve students’ science learning (Furtak, Seidel, Iverson, & Briggs, 2012; Lazonder & Harmsen, 2016) and enhance their higher order thinking abilities (Hugerat & Kortam, 2014; Lawson, 1995). However, scientific inquiry activities may not actually be easy to implement, as it is often more time consuming than traditional instruction (Riga, Winterbottom, Harris, & Newby, 2017). In addition, students may learn unstructured science knowledge if using scientific inquiry activities without direct instruction by teachers (Kirschner, Sweller, & Clark, 2006). Moreover, as numerous real-world science problems cannot be implemented for scientific inquiry in a school environment, with the exception of using cookbook-based experiments, many science teachers do not clearly understand how to implement scientific inquiry activities in a traditional science classroom (Wallace & Louden, 2002).

To solve this problem, with the development of technology, an increasing number of researchers have promoted using computer simulation to support scientific inquiry activities. Generally, science simulation can be divided into the simulation of scientific instruments and virtual environments. The simulation of scientific instruments refers to having the experimental tools usually used in laboratories simulated for virtual experimentation using computers. The virtual environment allows the real world to be simulated using computer technology. Using computer simulation for scientific inquiry not only saves time spent on learning and the cost of experimental equipment but can also simulate an inquiry environment that cannot be realized in traditional classrooms. Safety concerns associated with traditional inquiry activities can also be minimized or eliminated (Hickey, Kindfield, Horwitz, & Christie, 2003). Numerous studies have empirically verified that computer simulation is helpful for students’ science learning (De Jong, Linn, & Zacharia, 2013; Finkelstein et al., 2005; Rutten, Van Joolingen, & Van der Veen, 2012).

In the field of technology-enhanced learning, digital game-based learning (DGBL) has become a popular research topic; hence, the number of scientific inquiry studies that integrate computer simulation with game mechanisms has been increasing. For example, various projects, including Quest Atlantis (Barab, 2006), River City (Ketelhut, Dede, Clarke, Nelson, & Bowman, 2008), and Crystal Island (Rowe et al., 2009), all use game mechanisms involving stories and gamified tasks to guide students in performing scientific inquiry in virtual environments such as virtual forests or rivers. As games can elicit intrinsic motivation (Garris, Ahlers, & Driskell, 2002) and enable people to enter the state of flow (Csikszentmihalyi, 1990), many studies have verified that the integration of computer simulation and game mechanisms can increase students’ motivation in science learning (Barab, Sadler, Heiselt, Hickey, & Zuiker, 2007; Hickey, Ingram-Goble, & Jameson, 2009; Jacobson, Taylor, & Richards, 2016).

Game elements are regarded as indispensable for facilitating learning in DGBL (Economou et al., 2015). Recently, as the educational effect of DGBL has become widely recognized, using game elements to stimulate participation motivation has been increasingly applied in various environments, such as the business field. This has also led to the creation of the term gamification, which refers to the use of game elements to change people’s behavior in nongame situations (e.g., education and business; Deterding, Dixon, Khaled, & Nacke, 2011). The crucial game elements frequently mentioned in studies on gamification include points, leaderboard, badges, levels, and story (Bunchball, 2010; Deterding et al., 2011; Hamari, Koivisto, & Sarsa, 2014). However, scientific inquiry studies that focused on the integration of computer simulation and game mechanisms, such as the aforementioned River City, Quest Atlantis, and Crystal Island, have mainly used the game elements of story or badges to stimulate learning (Filsecker & Hickey, 2014). In contrast, past scientific inquiry studies seldom applied the game element of points in a simulation-based inquiry environment.

Points provide the main element in game design (Elias, Garfield, Gutschera, & Whitley, 2012), as other crucial game elements, such as leaderboard and levels, must be based on points. The function of points is that they provide clear feedback linking a user’s effort and performance (Mekler, Brühlmann, Opwis, & Tuch, 2013). In other words, the better performance a player exhibits in a game, the more points the player obtains as feedback. Thus, points become rewards in games, and as people love rewards, points can increase motivation for participation. In addition, points can be used as status indicators. For example, the accumulation of points can indicate whether a user is able to unlock access to content (Bunchball, 2010), be listed on the leaderboard, or be leveled up. Numerous gamification studies have verified that points can generate positive effects; for example, using points can enhance motivation to engage in scientific activities (Prestopnik & Tang, 2015), increase speed to respond to mathematics assessments (Attali & Arieli-Attali, 2015), and increase the number of tasks being completed (Mekler et al., 2013). As scientific inquiry is a challenging and complex process (Van Joolingen, De Jong, & Dimitrakopoulou, 2007), students need adequate feedback and guidance in scientific inquiry activities (Osborne, Erduran, & Simon, 2004). Therefore, by integrating the game mechanism into computer-based scientific inquiry activities, points become a game element worth trying.

As discussed, computer simulation can solve the problem of how to implement scientific inquiry in traditional classrooms. While the mechanism of game points may be useful in scientific inquiry activities, up to the present, the use of points has rarely been applied to computer-simulated science inquiry. Accordingly, this study sought to develop a computer-simulated science inquiry environment that possessed game elements, named the Science Detective Squad (SDS), which combines the simulation of scientific instruments and a virtual environment, and includes gamified elements, such as points and story. By completing a detective task, students use a virtual environment to investigate an electricity price problem based on an actual situation in daily life. Gamified points and story-based tasks were adopted as the scaffold and feedback mechanism that guide students to perform scientific inquiry. Moreover, to examine the effect of the computer-simulated science inquiry environment, this study conducted an experiment designed to understand student learning outcomes and participation perceptions following participation in the activity. This study also assessed students’ scientific inquiry performance and investigated factors contributing their performance. Overall, the research objectives of this study were as follows:

To develop a computer-simulated science inquiry environment integrated with game elements.

To understand student learning outcomes of science knowledge in the self-developed scientific inquiry environment.

To understand student perceptions of participation in the self-developed scientific inquiry environment.

To understand student scientific inquiry performance in the self-developed scientific inquiry environment.

Science Detective Squad

SDS is a computer-simulated science inquiry activity based on the electricity unit of science education for ninth-grade students in Taiwan. Because science education scholars have suggested that science learning needs to be connected with learners’ daily-life experiences (Millar & Osborne, 1998) and that the best topics for scientific inquiry are based on real-world problems (Hsu et al., 2016), this activity is based on a real-world problem involving household electricity prices. The primary goal of this scientific inquiry is to have students investigate the main reason why residential electricity prices rise suddenly in a virtual simulated family. The inquiry is also designed to facilitate student knowledge of electric power, electric energy, electricity price calculations, energy savings, and the ability of scientific inquiry.

The computer interface of SDS is presented in Figure 1, which comprises the area of functions and messages and that of computer simulation. The function and message area provides buttons linked to crucial functions and gamified messages. Function buttons involve the activate button used to call the detective captain and provide access to the simulation tool. Gamified messages show the time left for completing the story-based tasks and the points earned so far. The computer simulation area displays various virtual scenes based on the storyline. For example, when students first start the detection process, the computer simulation area displays scenes in a household (e.g., living room, dining room, kitchen, bedroom, and balcony) to be investigated. This area also displays a screen describing various tasks or the experimental simulation tool according to the progress of the activity or when students activated the experimental tool.

Figure 1.

The interface of the SDS.

The SDS integrates a detective story into a scientific inquiry activity, and students follow the storyline to complete different story-based tasks. Initially, the detective captain (a virtual character) asks students to be a member of the SDS and help investigate a detective case commissioned by a family. Students follow the captain’s instructions to complete assigned tasks within a limited time and report their results to the captain immediately after finishing the tasks. To guide students in performing scientific inquiry step by step, this study defines the process of scientific inquiry according to relevant studies (Cuevas, Lee, Hart, & Deaktor, 2005; NRC, 2000), which outline the following steps: defining a question, forming a hypothesis, collecting data, analyzing data, and interpreting results. Thus, students attempt to solve two story-based tasks in the storyline. The first task requires students to enter a virtual household for preliminary investigation, where they can interact with virtual characters in the household, in order to understand the case and obtain information about this family’s behaviors of electricity use in daily life. They also can interact with the various electrical appliances appearing in the household to obtain information about the electrical appliances’ electric power and to make preliminary judgments about the case. The second task requires students to collect information about the appliances that is relevant to the case, and to use simulation tools to analyze the information and leave exact evidence, which is eventually used in presenting the investigation report. In other words, the first task was designed to guide students to follow steps in defining a question and forming a hypothesis for a scientific inquiry. The second task was to help students go through the steps of collecting data, analyzing data, and interpreting results.

To simulate the situation of students investigating science questions in the real world, students are able to use the scene-changing button (see Figure 1) to freely move around the various areas, such as the living room, dining room, and so forth. The SDS also simulates different members of the virtual family, including father, mother, and children. When the virtual characters appear in a virtual scene, the students can click on them to obtain information about the detective case. To investigate the reason why the residential electricity prices rise suddenly in this family, students can interact with the virtual characters by freely choosing dialog options from the preset dialog menu to receive information they want to know. For example, the students can obtain information, such as this family’s behaviors of purchasing new electrical appliances and using appliances in daily life. By interacting with the virtual characters, students also can collect other important information, such as the statistical figures regarding this family’s recent electricity charges. Various household electrical appliances are also simulated in this virtual environment for students to interact with. When students click on an appliance, information, such as the electric power and voltage of the appliance, will appear on the screen (see Figure 1). Students are also able to collect important information of the appliances and further analyze the collected information using the simulation tool in the period of second task.

SDS uses points as feedback and reward for students. For example, in the first task, students can earn points by clicking the electrical appliances and obtaining useful information during their interactions with the virtual characters. In the second task, they can earn points when collecting useful information and using the simulation tools for simulation. The points not only serve as a reward for students to motivate them to seriously participate this inquiry activity but they also serve as a status indicator for determining whether or not students are allowed to report each task result. In other words, although students can click the captain button at any time to check the assigned tasks or report investigation results, each task has a low-end point value for asking students must earn a certain amount of points before reporting their results, which aims to avoid students reporting task results without performing the scientific inquiry activities.

In SDS, students can activate the simulation tool at any time (see Figure 2), which provides relevant knowledge of electricity and enables students to check and simulate collected information. For example, students can see the appliance’s electric power and voltage information when they select one of the collected appliances, and can see the statistical figure of this family’s electricity charge when selecting the collected information gathered from the virtual characters. The students also can select one collected appliance and set a parameter of the simulation time to simulate the electricity expenditures, and further set the simulation results as the research evidence (see Figure 2). Therefore, various simulation data are able to be compared and used as evidence for the investigation report. In other words, except for using multiple-choice type questions for students to report their task results, students must interpret the entire investigation result using words and use the simulated data as evidence in the final reporting step.

Figure 2.

Screen of the experimental simulation tool.

Research Methods

To understand the effect of the computer-simulated science inquiry activity, this study conducted an experiment using the one-group pretest–posttest design. The experiment aimed to understand student learning outcomes and perceptions after using the SDS and to analyze their scientific inquiry performance. The research participants, procedures, and instruments for the experiment are introduced, as follows.

Research Participants

This study selected two classes of ninth-grade students in a junior high school as research participants. After excluding the students who were unable to participate in the entire experiment, 58 students, comprising 25 male and 33 female students, were the final research participants. The selected students had learned basic electricity-related knowledge before starting the experiment.

Research Procedures

One week before the experiment started, participants underwent the pretest of the electricity knowledge test developed in this study. When the experiment formally started, the teacher first introduced how to log in to the SDS and then asked the participants to conduct the computer-simulated science inquiry activity. After finishing the activity, the students were asked to perform the posttest of the electricity knowledge test and to complete the participation perception scale developed by this study. The time for the entire experiment was spread over two classes (one class was 45 min).

Research Instruments

Electricity Knowledge Test

This study developed an electricity knowledge test to measure the students’ change in knowledge of electricity after performing the computer-simulated science inquiry activity. The test questions centered on the electricity knowledge used in the SDS, such as the concept of electric power, the relationship between electric power and energy, and the calculation of electricity charges. A total of 20 multiple-choice questions were constructed (e.g., “Assume that 1 kWh costs NT$5, and an 800 watt oven was used for 2 h. How much does the electricity cost?” and “A microwave oven consumes 2,400 J of electrical energy in 30 s. What is the electric power of the microwave oven?”). The total score for the test was 100. Two junior high school teachers were invited to inspect the content validity of the items; the Kuder–Richardson reliability was 0.79.

Participation perception scale

This study developed a participation perception scale to understand students’ feeling of pleasures and learning perceptions. Therefore, the scale was divided into two subscales, namely the pleasure and learning perception scales. The pleasure perception subscale was developed according to the enjoyment scale constructed by Downs and Sundar (2011), which comprises six questions such as, “I felt the SDS was fun” and “I felt time passed very quickly when participating in the SDS activity.” The learning perception scale consisted of five questions such as, “Through participating in the SDS activity, I learned knowledge related to electric energy” and “Through participating in the SDS activity, I learned knowledge related to the calculation of electricity charges.” The entire scale adopted a 5-point scale ranging from strongly agree to strongly disagree on which students rated their feelings. In addition, to understand the students’ true feelings, an open-ended question was put at the end of the scale for students to write down their thoughts or suggestions. The Cronbach’s α for the entire questionnaire was .91, and those for the subscales of pleasure feeling and learning perception were .87 and .84, respectively.

Research Findings

Analysis of the Learning Outcomes

To understand whether the SDS had helped participants enhance their science knowledge, this study asked all participants to undertake the electricity knowledge test before and after the experiment. According to the research findings, the average score of the electricity knowledge pretest was 72.84 (N = 58, SD = 18.40), and that of the posttest was 77.67 (N = 58, SD = 16.76). Additionally, in the paired sample t-test, t (57) = 3.430, p < .05, and Cohen’s d = .27, indicating that the posttest score was significantly higher than the pretest score. This implies that the computer-simulated science inquiry activity developed in this study may be helpful for enhancing students’ knowledge of electricity.

Analysis of the Participation Perception

To understand students’ perceptions after using the SDS, participants were asked to complete the participation perception scale after finishing the scientific inquiry activity. Overall, the average scores for the questions were all higher than 3, and the average score for the entire scale was 3.74, indicating that most of the participants had a positive perception of the computer-simulated science inquiry activity. Regarding the results of the subscales, the average score for the pleasure perception subscale was 3.63, signifying that the participants responded positively to the feelings of pleasure they perceived during participation in the activity. However, this score was lower than that of the entire scale because the participants provided relatively low average scores for the questions of, “I felt time passed very quickly when participating in the SDS activity” (M = 3.24) and “I felt the SDS is fun” (M = 3.40), which affected the overall scores of the subscale. This implies that the use of gamified elements in the SDS to facilitate feelings of pleasure still requires improvement. However, this aspect may also have been influenced by the difficulty of the inquiry activity. Regarding the learning perception subscale, the average score was 3.85, signifying that most participants had a positive learning perception. For example, most of the participants responded positively to questions of, “Through participating in the SDS activity, I learned knowledge related to the calculation of electricity charges” (M = 4.16) and “Through participating in the SDS activity, I learned concepts related to energy savings” (M = 3.91).

For the open-ended question following the participation perception scale, most of the participants left positive comments. Examples are as follows: “I think the activity is interesting in that it required electricity knowledge for us to complete tasks. We can not only learn relevant knowledge but also enhance our reasoning ability. I think this activity is meaningful.” “This game helped us to understand the use of electricity in our home. I hope we can have this kind of activities next time.” “I am not usually good at science courses. However, through this experience, I had a better understanding of the calculation of electricity charges.” These comments all support the idea that the participants had positive perceptions of participation. However, some students expressed that the scientific inquiry activity was difficult. Examples are as follows:

When I was playing the game, to some extent I knew which electrical appliances consumed more electricity, but I did not know how to prove it and collect useful evidence. Nonetheless, this game helped me to have a better understanding of many electrical appliances used at home in daily life.

“I think the game was a little difficult, but I learned about the electricity charges for the electrical appliances used in daily life through the game, which motivated me to save electricity.” These comments implicate that the difficulty of the self-developed activity and its scaffold still require improvement, and may explain why the participants’ overall average score for the pleasure perception subscale was relatively low.

Analysis of the Scientific Inquiry Behavior

This study used the log data recorded in SDS to analyze students’ performance of scientific inquiry. First, this study evaluated the correctness in each scientific inquiry step by analyzing students’ records of their task responses and click logs. The first story-based task in SDS asked participants to make a preliminary investigation of the detective case and to report their results related to the primary problem and the possible reason for the investigation by answering multiple-choice questions. By examining the participants’ answers in the first task, this study evaluated whether or not students had adopted the correct behaviors of defining the question and forming a reasonable hypothesis in the process of scientific inquiry. Because the information including the statistical figure of the family’s electricity charge, the air conditioner, and the electric water heater in SDS was crucial to correctly solving the detective case, this study assessed whether the participants had correct behaviors of collecting data and analyzing data by examining the log data of whether they had ever collected, read, simulated, and analyzed the statistical figure of the family’s electricity charge and the information about the air conditioner and electric water heater. Moreover, this study examined whether the participants had the correct behaviors of interpreting results in the process of scientific inquiry by examining the results of the second story-based task; for example, whether students had recognized the main reason why the residential electricity prices rose in their text-based interpretation and whether they submitted the simulation data as evidence produced using the simulation tools. The criteria for determining the correctness of each scientific inquiry behavior are listed in Table 1.

Table 1.

Criteria for Determining the Correctness of the Scientific Inquiry Behaviors in the SDS.

Inquiry behaviors	Criteria
Defining the question	Whether the participants selected correct answers when reporting the first task results.
Forming the hypothesis	Whether the participants selected correct answers when reporting the first task results.
Collecting data	Whether the participants collected information from the statistical chart of electricity charges, air conditioner and electric water heater.
Analyzing data	Whether the participants used the computer simulation tool to read the statistical chart and calculated the electricity charges for the air conditioner and electric water heater.
Interpreting results	Whether the participants recognized the main factor for electricity consumption and provided necessary simulation data as evidence in the second task results.

SDS = Science Detective Squad.

According to the research findings, 83% of participants successfully defined the research question of the detective story and formed a reasonable hypothesis through preliminary investigation. In addition, 71% of the participants were able to collect critical data in SDS and thereby demonstrate correct behaviors for collecting data. However, only 50% of the participants had simulated the critical data in SDS, and only 24% of the participants were able to correctly solve the detective problem and provide reasonable evidence. Therefore, the participants’ performance in the stages of the scientific inquiry activity showed a gradually declining trend. Specifically, in the former stages of the activity, the majority of the participants were capable of understanding the research question, formulating the hypothesis, and collecting useful data. However, in the step of data analysis, approximately half the participants were unable to comprehend the key points of the collected data. Thus, eventually only 24% of the participants successfully interpreted the inquiry results and solved the inquiry activity.

Second, this study compared individual differences between students who successfully interpreted inquiry results and those who did not. The results are displayed in Tables 2 and 3. Table 2 shows differences of two groups in the number of the participants regarding gender and the correctness of scientific inquiry behaviors. Because the expected values in some cells of the table were smaller than 5, Fisher’s exact test was adopted for the chi-squared test. The result indicated no significant difference between the successful and unsuccessful groups in terms of the ratios of their gender. However, it revealed significant differences between the two groups for the ratios of their behaviors on data collection and analysis. This statistical data clearly shows that participants who successfully interpreted the inquiry results had significantly superior performance in data collection and analysis than those who did not, indicating that students who successfully solved the scientific inquiry activity had excellent data collection and analysis behaviors.

Table 2.

Group Comparison of Gender and Performance of Scientific Inquiry Behaviors.

	Group (whether successfully interpreted inquiry results)		χ ²
Variables	Successful group (N = 14)	Unsuccessful group (N = 44)	χ ²
Gender
Male Female	6 8	19 25	.000 .983
Defining question
Correct Incorrect	14 0	34 10	3.778 .098
Forming hypothesis
Correct Incorrect	14 0	34 10	3.778 .098
Collecting data
Correct Incorrect	14 0	27 17	7.520* .005
Analyzing data
Correct Incorrect	14 0	15 29	18.136* .000

p < .05.

Table 3.

Group Comparison of Scores Involved in the Inquiry Activity.

Variables		Group (whether they successfully interpreted inquiry results)		t
Variables	Overall (N = 58)	Successful group (N = 14)	Unsuccessful group (N = 44)	t
Electricity knowledge pretest	72.84	72.50	72.95	−.080
Electricity knowledge posttest	77.67	76.07	78.18	−.407
Pleasure perception	3.60	4.02	3.47	2.151*
Learning perception	3.89	4.16	3.80	1.468
Gamified points	12679	16293	11530	3.507*
Activity completion time	1450 (s)	1970 (s)	1284 (s)	3.233*

p < .05.

Table 3 shows the differences of two groups in their scores on the electricity knowledge pre- and posttest, pleasure perception, learning perception, gamified points earned in the inquiry activity, and activity completion time. The result indicates that the two groups of participants did not have significant difference in their electricity knowledge pre- and posttest scores and learning perception scores. However, significant difference existed in the scores for pleasure perception, gamified points, and activity completion time. In other words, the participants in the successful group had a higher score for pleasure perception, more points, and longer time for participation than those in the unsuccessful group. The students who successfully completed the scientific inquiry activity obtained stronger feelings of pleasure when participating in the activity. This also explains why the students overall had a relatively low average score regarding pleasure perception because most students who failed to solve the inquiry activity successfully did not hold a positive attitude regarding the feeling of pleasure. Moreover, the students who successfully completed the scientific inquiry tasks earned more points and committed more time to the activity, indicating that the students’ gamified points and activity completion time can reflect their scientific inquiry performance.

Finally, to understand which variables can have the greatest effect in predicting student performance in the scientific inquiry activity, this study adopted whether or not participants successfully interpreted the inquiry results as the dependent variable. Eleven participant characteristics (gender, the behaviors of defining a question, forming a hypothesis, collecting data and analyzing data, scores of electricity knowledge pre- and posttest, pleasure perception and learning perception, gamified points, and activity completion time) were selected as the independent variables for exploring the influence of the independent variables toward the dependent variable. Because the dependent variable is a binary variable (Y = 1, success; Y = 0, failure), this study adopted the forward stepwise logistic regression to determine the independent variables that had the highest predictability for predicting the dependent variable.

According to the regression analysis result, the independent variables selected into the regression model were the analyzing data behavior, activity completion time, and the forming hypothesis behavior. However, as shown in Table 4, eventually only two independent variables including the analyzing data behavior (β = 4.680, p < .05) and activity completion time (β = 0.002, p < .05) achieved significance in the regression model. This model can offer correct predictions for 87.93% of students regarding whether they can successfully solve the activity (χ²= 36.772, p < .05, Nagelkerke R²= 0.702) and the model had the goodness of fit (Hosmer-Lemeshow test = 2.305, p > .05). The research findings revealed that the behavior of analyzing data and the activity completion time in the scientific inquiry activity were the significant indicators for predicting the scientific inquiry performance of students. Especially, according to the odds ratio (Exp [B]) for the predictor of analyzing data behavior, it is indicated that student behavior in terms of correctly analyzing data collected in the scientific inquiry activity can effectively predict final performance. This finding also supports the aforementioned results in Tables 2 and 3.

Table 4.

Results of Logistic Regression Model.

Variables	B	S.E.	Wald	p	Exp(B)
Analyzing data behavior	4.680	1.861	6.325	.012*	107.801
Activity completion time	.002	.001	4.933	.026*	1.002
Forming hypothesis behavior	1.335	1.891	0.498	.480	3.799
Constant	−8.663	3.052	8.054	.005	.000

p < .05.

Discussion and Conclusion

This study developed computer-simulated science inquiry activity integrated with gamified elements and a virtual environment to engage students in investigating an electricity problem that may happen in daily life. This study also conducted an experiment to understand the effect of this virtual environment. The research results reveal that students’ knowledge of electricity increases significantly after participating in the computer-simulated science inquiry activity, indicating that the self-designed simulation environment may be helpful for facilitating students’s acquisition of science knowledge. This implies that the result is similar to the findings of previous studies arguing that inquiry-based learning can improve students’ learning (Furtak et al., 2012; Lazonder & Harmsen, 2016; Schroeder, Scott, Tolson, Huang, & Lee, 2007; Shymansky, Hedges, & Woodworth, 1990) and that computer simulation is beneficial to science learning (De Jong et al., 2013; Hickey et al., 2003). This result also signifies that the combination of computer simulation and game elements is advantageous to science learning, which also supports the results of previous studies that combined computer simulation with game mechanisms (Barab et al., 2007; Bowling, Klisch, Wang, & Beier, 2013; Jacobson et al., 2016; Lee, Mott, & Lester, 2010).

Another finding shows that students have positive perceptions toward the computer-simulated science inquiry activity. In particular, they have positive learning perceptions toward the activity and most students comment that they have learned electricity-related knowledge and concepts from the SDS. This result again supports that the computer-simulated environment established in this study could be beneficial to science learning. It is also consistent with the results of previous studies that have reported students’ positive learning perceptions in the gamified science inquiry environment (Clarke & Dede, 2005). Moreover, according to the scores of the pleasure perception subscale and the answers of the open-ended question, it is indicated that most students have a positive pleasure attitude toward the SDS. This indicates that incorporating game elements into a computer-simulated inquiry activity can enhance the fun of the activity and student motivation. This result is consistent with that of previous studies (Barab et al., 2007; Hickey et al., 2009). However, based on student reports on the perception scale and the analysis of students’ performance in the inquiry activity, the difficulty of the activity could be the reason why this study showed relatively low average scores on the pleasure perception subscale.

In this study, the participants’ performance in the computer-simulated science inquiry activity demonstrated a gradually declining trend. Although most of the students demonstrated satisfactory abilities in defining the question and collecting relevant data, approximately half of them were unable to grasp the key points from the analysis of data. Thus, only 24 percent of the participants correctly gave reasonable evidence and successfully deduced the investigation results. This is actually not surprising because scientific inquiry is itself a challenging and complex activity that requires a wide range of cognitive skills (Flick, 2006; McNeill, Lizotte, Krajcik, & Marx, 2006). Therefore, it appears the result that some students are unable to correctly finish the inquiry tasks may be reasonable; previous studies of scientific inquiry capabilities have obtained similar results. For example, Ebenezer, Kaya, and Ebenezer (2011) found that students had the optimal performance on defining a question and forming a hypothesis but had the worst performance with evidence explanation and reasoning. However, in this study, game story and points were used as the scaffold to assist and motivate students in performing scientific inquiry. Thus, the result implies that the difficulty of the inquiry activity and the function of the scaffold could be modified in the future.

This study compared differences between students and found that their behaviors of data collection and data analysis, the pleasure perception, the points earned in the activity, and activity completion time were key factors influencing their scientific inquiry performance. In other words, the students’ scientific inquiry performance had no significant relationship to their gender, abilities of defining questions and forming hypothesis, acquisition of scientific knowledge, and learning perception. This again supports the idea that scientific inquiry requires a wide range of cognitive skills; people with great science knowledge do not necessarily have excellent science inquiry performance. This result is also consistent with that of studies on gamified science inquiry, which reported that gender is unrelated to scientific inquiry performance (Dede, Ketelhut, & Nelson, 2004). In addition, the points mechanism designed in this study can reflect the students’ scientific inquiry performance because those who successfully completed the activity earned significantly higher points than those who did not. The correlation analysis also shows that the students’ performance is positively correlated with the points they earned (r = .424, p < .05).

This study conducted logistic regression and found that the data analysis ability and activity completion time were the most critical factors influencing students’ scientific inquiry performance. Their performance on data analysis in particular had the highest influence on their success in completing the activity. It indicates that if students spent more time on the inquiry and correctly analyzed crucial data, they would have a higher possibility of successfully finishing the activity. This implies that future modification of the SDS should provide more support for students at the step of data analysis or motivate students to spend more time on the inquiry activity.

In summary, the self-developed environment may facilitate student learning of science knowledge, and students had positive feeling regarding their participation. Moreover, this study found that gamified points can be used as a reference for the scientific inquiry performance as well as the criterion for the progress in the process of scientific inquiry. Therefore, points can be adopted to not only guide the scientific inquiry activity but also to identify students’ scientific inquiry performance. Thus, the mechanism of points is a highly potential gamified element to be used in the computer-simulated science inquiry environment. However, future research should be attempted to employ the mechanism of points in guiding students to achieve superior performance on data analysis as well as improving overall scientific inquiry performance and increasing their pleasure perception. Moreover, future researchers can use diverse gamified elements such as leaderboard and levels and examine whether these elements can cause students to respond positively regarding their feelings of pleasure and therefore be willing to spend more time on the inquiry activity. In the future, the limitation of an experimental method without a control group, as conducted in this study, requires improvement, and more rigorous research designs should be adopted to verify the findings of this study.

Footnotes

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research received financial support from the Ministry of Science and Technology of Taiwan under contract numbers MOST 104-2511-S-415-010-MY2.

Author Biography

Fu-Hsing Tsai is currently an associate professor in the Teacher Education Center at National Chiayi University, Chiayi County, Taiwan. His research interests include digital game-based learning and e-learning in science education and technology education.

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