Digital Game Approaches for Cultivating Computational Thinking Skills in College Students †
1
School of Big Data, Fuzhou University of International Studies and Trade, Fuzhou 350202, China
2
Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu City 30010, Taiwan
3
School of Art & Design, Fuzhou University of International Studies and Trade, Fuzhou 350202, China
*
Author to whom correspondence should be addressed.
†
Presented at the IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, Tainan, Taiwan, 2–4 June 2023.
Eng. Proc. 2023, 55(1), 62; https://doi.org/10.3390/engproc2023055062
Published: 6 December 2023
(This article belongs to the Proceedings of 2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability)
Abstract
:Computational thinking (CT) has become one of the critical goals of teaching CS programming courses. Computational skills consist of skills taken in a computational form in learning programming and dealing with daily life. More research adopted games to teach CT skills. This paper investigated two games, Little Alchemy 2 and Dr. Sudoku, to promote CS students’ CT skills and applied international Bebras tests to measure their CT skills. The results showed that CT skills in problem decomposition and pattern recognition could be improved via digital games. Thus, this study contributes to computing education using available digital games to promote CS college students’ CT abilities.
1. Introduction
Computational thinking is an algorithmic skill used by computer and mathematical scientists and is one of the basic daily life skills that everyone needs [1,2]. Computational thinking applications are ubiquitous in life, and anyone can use computational thinking skills to solve problems [2]. CT skills include problem decomposition, pattern recognition, abstraction, and algorithm design as the concept of computational thinking [1,2]. For example, computer programmers who are programming games often break the code into small chunks to control a character’s movement and sound effects. In a game-based learning environment, like Little Alchemy 2, students learn that mist can be decomposed into air and water (Figure 1). Problem decomposition is a useful problem-solving strategy. It can help us write a complex computer program, plan a holiday, or make a model plane. Using computational thinking skills can help people simplify complex things in different subject areas [3].
The skills of CT enable students to decompose problems, find the rules, and reconstruct them into familiar problems when they encounter new difficulties [4]. In programming education, students imitate codes and learn algorithms to strengthen their mathematical modeling and calculation abilities. However, nearly half of college students have no experience with programming, and it is not accessible to “code” [5]. Teaching rule discovery and pattern recognition in programming courses is challenging [6]. As a result, there are considerable problems with CT training in the curriculum.
Many researchers move from programming to non-programming environments to cultivate computational thinking. Recent programming education has emphasized how computational thinking can be fostered using more game-based learning in programming education [5,7,8]. The digital game environment provides students with repetitive actions to find rules, plan resource arrangements, and optimal paths, and solve encountered problems at the current level [7,8,9]. Planning is like computational thinking in a game environment, and following directions is like programming. The playing process reflects students’ logic and ability to find rules and solve problems. Therefore, this study applied two digital games (Little Alchemy 2 and Dr. Sudoku) to explore the possibility of enhancing students’ computational thinking skills.
2. Related Works
Computational thinking is valued in science, technology, engineering, and mathematics (STEM) education [10]. In the 21st century, people have more opportunities to apply information technology to integrate knowledge, skills, values, and attitudes. Computational thinking and programming are problem-solving methods used at different learning stages and disciplines. Whether programming education or courses that cultivate computational thinking skills, it enables students to discover helpful information, find rules, and use existing skills to solve problems in their daily lives [11,12]. Computational thinking and digital literacy are components of programming skills [5]. An attractive computer science education, such as building block programming and game-based learning environments, keeps students engaged in learning [1]. People have the opportunities to explore and make mistakes in a digital game-based learning environment, synthesize information, discover rules, and formulate strategies [11]. Game-based learning environments also stimulate students’ imagination, creativity, and motivation to learn, thereby helping to improve CT ability. Therefore, digital games have been promoted as effective systems for teaching rules and knowledge [13,14]. Block-based game environments, like Scratch and Webduino Blockly, have been adopted as CT development tools [15]. Commercial or online web games enable students to engage in more natural playing behaviors that are not constrained by specific learning subjects. Sudoku can be used to teach collaboration and problem-solving abilities, which can be transferred to other learning and living conditions [16]. Little Alchemy 2 lets players combine icons representing concepts, objects, substances, and elements to discover more items. Effective combinations form a new project waiting to be combined with other things [17]. Sudoku has been identified as a tool for helping players to teach reasoning skills and logical thinking [11]. Using Dr. Sudoku and Little Alchemy 2, we explored whether games can improve students’ problem decomposition and pattern recognition skills.
A proper evaluation of CT competencies can review the effectiveness of training activities. Bebras is an international information science challenge designed to increase learners’ interest in informatics and information and communication technology as a basic science by solving problems on computer science topics. Questions about computers and various logical thinking skills (e.g., pattern recognition, problem decomposition, algorithm, abstraction) are used to increase interest and creativity in the computer science field. This study applied the Bebras challenge to investigate whether the game activity was effective in improving students’ CT skills.
3. Methods
3.1. Participants and Procedures
In June 2021, two weeks before the end of the semester, we conducted a quasi-experimental study with 76 juniors studying at an applied university in Fuzhou, China. These students have rudimentary C language and network programming concepts. Two classes were divided into playing Little Alchemy 2 (32 students) and Dr. Sudoku (44 students). To reduce potential distractions, we did not tell the students the research purpose of playing the game, nor did we give them any other information about group conditions. Bebras’ CT tests were taken before and after 20-minute game training individually for 30 min. Finally, the students had an open discussion about the gameplay process and filled out a gaming questionnaire. The experimental process is shown in Figure 2.
3.2. Training Environments
This study explored whether games enhance learners’ problem-solving and pattern-recognition CT skills. Little Alchemy 2 was adopted to teach students problem decomposition skills by disassembling and synthesizing elements. According to the characteristics of two or more elements, such as air, fire, water, and earth, the game combines them into 720 items. Observing the crafted items, the player can break them into related constituent elements. Figure 3 shows that steam can be decomposed into water and lava. Then, a worksheet was provided for students to fill out. Another game, Dr. Sudoku, required players to fill in the missing 1 to 9 in the 9 × 9 cells. In this game, there are three functions (slips of different colors, display of all candidate numbers, and display of some candidate numbers) for students to solve the puzzle. Players use their logical reasoning abilities during the gameplay. For example, red boxes display that two numbers of 2 already exist in the two rows of green arrows, and students use the horizontal line division method to infer that the unfilled number in the middle block is 2 (Figure 4).
Bebras Challenge Test was taken to understand students’ problem decomposition and pattern recognition skills before and after the CT game training. Considering that the subjects are college students with a fundamental programming foundation, and the difficulty of the competition is only up to 12-grade levels, eight questions, including four simple questions, three medium questions, and one difficult question, were chosen. The Bebra challenge questions from 2013 to 2016 were selected as pre-test and post-test to evaluate the two CT skills. After adding up the dimensions of each question and normalizing the aggregated scores to one hundred, we obtained the ability scores for the two dimensions (i.e., problem decomposition and pattern recognition) and the total computational thinking score. For example, the pre-test dimension contains six questions for problem decomposition and pattern recognition. The students correctly answered the first and second questions in the pre-test (q1: pattern recognition and problem decomposition, q3: pattern recognition). The total computational thinking skill score was 25, the problem decomposition skill score was 16.7 points, and the pattern recognition skill score was 33.3 points.
4. Results and Discussion
The independent sample t-test showed no significant difference in pre-test scores between the two groups (t(56.92) = 1.64, p = 0.106, d = 0.396). The students’ starting CT skills of the two groups showed no difference (Table 1).
The paired sample t-test for paired data showed that the post-test total score of Little Alchemy 2 was significantly larger than the total pre-test score (t(31) = −4.12, p = 0.000, d = 0.858), and the total post-test score of Dr. Sudoku was significantly larger than total pre-test score (t(43) = −4.48, p = 0.000, d = 0.832). The average post-test scores of Little Alchemy 2 and Dr. Sudoku had improved, and the average improvement score (i.e., the post-test score minus the pre-test score) of Little Alchemy 2 was greater than that of Dr. Sudoku (78.85 > 57.43) (Table 2).
Next, we try to analyze whether these two games have an improved effect on problem decomposition ability and pattern recognition ability. Paired sample t-test results showed that pre-test and post-test scores of problem decomposition (t(31) = −4.78, p = 0.000, d = 1.033) and pre-test and post-test scores of pattern recognition (t(31) = −2.85, p = 0.008, d = 0.572) were all the statistically significant difference. After playing the Little Alchemy 2 game, the problem decomposition score progressed from 50.00 to 76.04, and the pattern recognition score improved from 60.93 to 75.00 (Table 2). We analyzed the participants’ pre- and post-test performance and found that more than half of the students progressed their problem decomposition and pattern recognition scores after playing Little Alchemy 2 (Figure 5). Due to the lack of recognizable cues in this game-based learning environment, students were strongly driven to create new objects based on their exploration. This result shows that they use their world knowledge to drive their exploration.
In the Dr. Sudoku game, problem decomposition pre-test and post-test scores (t(43) = −4.65, p = 0.000, d = 0.847) were statistically significant differences. Problem decomposition scores had progressed from 63.25 to 81.81 (Table 2). However, the pattern recognition score had no statistically significant difference, and more than half of students’ pattern recognition scores decreased (Figure 6). This result suggested that filling in numbers from Dr. Sudoku to find the pattern was already familiar to college students, so there was no noticeable improvement.
Although CS students already have basic programming concepts of C and Java, the problem decomposition and pattern recognition abilities of CT skills can be effectively enhanced via the digital game (Little Alchemy 2) with self-control and self-exploration. Therefore, non-programming digital games should be added to the CS course to teach students CT abilities.
5. Conclusions and Future Work
This paper investigated how the problem decomposition and pattern recognition of CT skills can be trained through specific digital games. This study found that utilizing non-programming digital games can promote computational thinking skills in college CS students. In addition, a digital game (Little Alchemy 2) with the function of free decomposition and integration can effectively improve the problem decomposition and pattern recognition ability. Since the experiment participants are only CS students, data can be collected from the School of Biology and Technology, the School of Foreign Language, and the School of Art and Design to analyze the benefits in the future.
Author Contributions
Conceptualization, L.-X.C. and C.-H.L.; methodology, L.-X.C. and S.-M.Y.; validation, L.-X.C.; S.-W.S. and M.-J.H.; formal analysis, L.-X.C. and S.-W.S.; writing—original draft preparation, L.-X.C. and S.-W.S.; writing—review and editing, L.-X.C. and C.-H.L.; visualization, L.-X.C. and M.-J.H.; project administration, S.-M.Y. All authors have read and agreed to the published version of the manuscript.
Funding
This work was partially funded by National Science and Technology Council Taiwan (grant number: 108-2511-H-009-009-MY3) and the High-level Talent Research Project at Fuzhou University of International Studies and Trade (grant no. FWKQJ201909).
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data presented in this study are available upon request from the corresponding author.
Acknowledgments
The authors wish to thank the blind reviewers for their insightful and constructive comments.
Conflicts of Interest
The authors declare no conflict of interest.
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Figure 1.
Problem decomposition in the programming and game environment.
Figure 2.
Game education process for training CT.
Figure 3.
Little Alchemy 2 screen shot.
Figure 4.
Dr. Sudoku screen shot.
Figure 5.
Skill performance after playing the Little Alchemy 2 game.
Figure 6.
Skill performance after playing Dr. Sudoku game.
Table 1.
Independent sample t-test of pre-test in two groups.
| Mean (S.D.) | df | t | Effect Size (d) | ||
|---|---|---|---|---|---|
| Little Alchemy 2 (N = 44) | Dr. Sudoku (N = 32) | ||||
| score | 257.27 (76.72) | 223.23 (97.43) | 56.92 | 1.64 | 0.9 |
Table 2.
Paired sample t-test for paired data of Little Alchemy 2 and Dr. Sudoku.
| Mean (S.D.) | df | t | Effect Size (d) | ||
|---|---|---|---|---|---|
| Pre-Test | Post-Test | ||||
| Little Alchemy 2 (N = 32) | |||||
| Total score | 223.22 (97.43) | 302.08 (85.97) | 31 | −4.12 *** | 0.858 |
| Problem decomposition | 50.00 (23.18) | 76.04 (27.08) | −4.78 *** | 1.033 | |
| Pattern recognition | 60.93 (27.30) | 75.00 (21.55) | −2.85 ** | 0.572 | |
| Dr. Sudoku (N = 44) | |||||
| Total score | 257.27 (76.72) | 314.69 (60.33) | 43 | −4.48 *** | 0.832 |
| Problem decomposition | 63.25 (21.73) | 81.81 (22.10) | −4.65 *** | 0.847 | |
| Pattern recognition | 69.31 (20.94) | 72.27 (22.50) | −0.77 | 0.136 | |
** p < 0.01, *** p < 0.001.
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MDPI and ACS Style
Chen, L.-X.; Su, S.-W.; Liao, C.-H.; Hsiao, M.-J.; Yuan, S.-M. Digital Game Approaches for Cultivating Computational Thinking Skills in College Students. Eng. Proc. 2023, 55, 62. https://doi.org/10.3390/engproc2023055062
AMA Style
Chen L-X, Su S-W, Liao C-H, Hsiao M-J, Yuan S-M. Digital Game Approaches for Cultivating Computational Thinking Skills in College Students. Engineering Proceedings. 2023; 55(1):62. https://doi.org/10.3390/engproc2023055062
Chicago/Turabian StyleChen, Li-Xian, Shih-Wen Su, Chia-Hung Liao, Mei-Jin Hsiao, and Shyan-Ming Yuan. 2023. "Digital Game Approaches for Cultivating Computational Thinking Skills in College Students" Engineering Proceedings 55, no. 1: 62. https://doi.org/10.3390/engproc2023055062
