Game-Based Learning in Action: Exploring Students’ Engagement and Perceived Learning through SKUNK Probability

Authors

Nor Rashidah Paujah Ismail

Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perak Branch, Tapah Campus, 35400, Tapah Road, Perak (Malaysia)

Fadzilah Abdol Razak

Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perak Branch, Tapah Campus, 35400, Tapah Road, Perak (Malaysia)

Article Information

DOI: 10.47772/IJRISS.2026.1026EDU0602

Subject Category: Social science

Volume/Issue: 10/26 | Page No: 8170-8183

Publication Timeline

Submitted: 2026-09-14

Accepted: 2026-09-19

Published: 2026-10-01

Abstract

Probability remains one of the most challenging topics for students in mathematics and statistics, often perceived as abstract, formula-driven, and disconnected from real-life applications. This study introduces SKUNK Probability, an adaptation of the classic dice game SKUNK, as a game-based learning (GBL) activity designed to explore students’ engagement, motivation, and perceived understanding of probability concepts among diploma students. The research was conducted in introductory statistics courses at Universiti Teknologi MARA (UiTM) Tapah Campus, Perak, Malaysia. Students participated in the SKUNK Probability game, which integrates risk-taking, decision-making, and chance into an interactive learning experience. The learning activity was structured around Kolb’s experiential learning cycle, with gameplay (concrete experience), reflective discussions, and follow-up questions used to connect students’ gameplay experiences with formal probability concepts such as sample space, possible outcomes, probability of events, and experimental and theoretical probability. Data were collected using a post-activity feedback questionnaire and analysed using descriptive statistics. Findings showed highly positive student perceptions: 100% of students rated the activity as either enjoyable or very enjoyable, 97.6% agreed or strongly agreed that the activity helped them understand probability concepts better, and 95.2% agreed that the game helped them connect abstract concepts with real-life decision-making situations. Furthermore, 93.0% of students agreed or strongly agreed that they felt more motivated to learn probability following the SKUNK activity. The reflective discussions and reinforcement activities were incorporated to help students connect their gameplay experiences with formal probability concepts. Overall, the findings suggest that SKUNK Probability is a low-cost pedagogical activity with potential for wider classroom implementation. By bridging experiential and constructivist learning theories with engaging classroom practice, the activity offers educators a practical, student-centred strategy for teaching probability and providing students with an engaging and meaningful learning experience in probability education.

Keywords

Game-Based Learning (GBL), Experiential Learning, Student Engagement, Probability Education, Active Learning

Downloads

References

1. Arztmann, M., Hornstra, L., Jeuring, J., & Kester, L. (2023). Effects of games in STEM education: A meta-analysis on the moderating role of student background characteristics. Studies in Science Education, 59(1), 109–145. https://doi.org/10.1080/03057267.2022.2057732 [Google Scholar] [Crossref]

2. Andam, E. A., Awuah, F. K., & Obeng-Denteh, W. (2025). Probability concepts: A systematic literature review of students’ learning difficulties, errors and misconceptions. East African Journal of Arts and Social Sciences, 8(2), 114–128. [Google Scholar] [Crossref]

3. Bada, S. O., & Olusegun, S. (2015). Constructivism learning theory: A paradigm for teaching and learning. IOSR Journal of Research & Method in Education, 5(6), 66–70. https://doi.org/10.9790/7388-05616670 [Google Scholar] [Crossref]

4. Barz, N., Benick, M., Dörrenbächer-Ulrich, L., & Perels, F. (2024). The effect of digital game-based learning interventions on cognitive, metacognitive, and affective-motivational learning outcomes in school: A meta-analysis. Review of Educational Research, 94(2), 193–227. [Google Scholar] [Crossref]

5. https://doi.org/10.3102/00346543231167795 [Google Scholar] [Crossref]

6. Chen, C.-H., Shih, C.-C., & Law, V. (2020). The effects of competition in digital game-based learning (DGBL): A meta-analysis. Educational Technology Research and Development, 68(4), 1855–1873. https://doi.org/10.1007/s11423-020-09794-1 [Google Scholar] [Crossref]

7. Erşen, Z. B., & Ergül, E. (2022). Trends of game-based learning in mathematics education: A systematic review. International Journal of Contemporary Educational Research, 9(3), 603–623. [Google Scholar] [Crossref]

8. Kandeel, R. A. A. (2019). Students’ academic difficulties in learning a statistics and probability course: The instructors’ view. Journal of Education and Practice, 10(9), 43–52. https://doi.org/10.7176/JEP/10-9-05 [Google Scholar] [Crossref]

9. Khazanov, L., & Prado, L. (2010). Correcting students’ misconceptions about probability in an introductory college statistics course. Adults Learning Mathematics, 5(1), 23–35. [Google Scholar] [Crossref]

10. Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Prentice-Hall. [Google Scholar] [Crossref]

11. Lin, Y.-C., & Hou, H.-T. (2024). The evaluation of a scaffolding-based augmented reality educational board game with competition-oriented and collaboration-oriented mechanisms: Differences analysis of learning effectiveness, motivation, flow, and anxiety. Interactive Learning Environments, 32(2), 502–521 https://doi.org/10.1080/10494820.2022.2091606 [Google Scholar] [Crossref]

12. Morris, T. H. (2020). Experiential learning – A systematic review and revision of Kolb’s model. Interactive Learning Environments, 28(8), 1064–1077. https://doi.org/10.1080/10494820.2019.1570279 [Google Scholar] [Crossref]

13. Moon, J., & Ke, F. (2020). In-game actions to promote game-based math learning engagement. Journal of Educational Computing Research, 58(4), 863–885. https://doi.org/10.1177/0735633119878611 [Google Scholar] [Crossref]

14. Prince, M. (2004). Does active learning work? A review of the research. Journal of Engineering Education, 93(3), 223–231. https://doi.org/10.1002/j.2168-9830.2004.tb00809.x [Google Scholar] [Crossref]

15. Porter, S. R., Rumann, C., & Pontius, J. (2011). The validity of student engagement survey questions: Can we accurately measure academic challenge? New Directions for Institutional Research, 2011(150), 87–98. https://doi.org/10.1002/ir.391 [Google Scholar] [Crossref]

16. Sharma, S., Sharma, S., Doyle, P., Marcelo, L., & Kumar, D. (2021). Teaching and learning probability using games: A systematic review of research from 2010–2020. Waikato Journal of Education, 26(2), 51–64. https://doi.org/10.15663/wje.v26i2.881 [Google Scholar] [Crossref]

17. Sitzmann, T., Ely, K., Brown, K. G., & Bauer, K. N. (2010). Self-assessment of knowledge: A cognitive learning or affective measure? Academy of Management Learning & Education, 9(2), 169–191. [Google Scholar] [Crossref]

18. https://doi.org/10.5465/AMLE.2010.51428542 [Google Scholar] [Crossref]

19. Tsai, Y.-L., & Tsai, C.-C. (2020). A meta-analysis of research on digital game-based science learning. Journal of Computer Assisted Learning, 36(3), 280–294. [Google Scholar] [Crossref]

20. Triantafyllou, S. A. (2022). Constructivist learning environments. Proceedings of the 5th International Conference on Advanced Research in Teaching and Education. [Google Scholar] [Crossref]

21. https://doi.org/10.33422/5th.icate.2022.04.10 [Google Scholar] [Crossref]

22. Wang, L. H., Chen, B., Hwang, G. J., Guan, J. Q., & Wang, Y. Q. (2022). Effects of digital game-based STEM education on students’ learning achievement: A meta-analysis. International Journal of STEM Education, 9(1), 26. [Google Scholar] [Crossref]

23. Wijnen-Meijer, M., Brandhuber, T., Schneider, A., & Berberat, P. O. (2022). Implementing Kolb’s experiential learning cycle by linking real experience, case-based discussion and simulation. Journal of Medical Education and Curricular Development, 9, 1–5. https://doi.org/10.1177/2382120522109151 [Google Scholar] [Crossref]

24. Zajda, J. (2021). Constructivist learning theory and creating effective learning environments. In J. Zajda (Ed.), Globalisation and Education Reforms: Creating Effective Learning Environments (pp. 35–50). Springer. https://doi.org/10.1007/978-3-030-71575-5_3 [Google Scholar] [Crossref]

Metrics

Views & Downloads

Similar Articles