CodeQuest: A Game-Based Learning System to Enhance C++ Programming Education

Learning to program in C++ poses significant challenges for students, particularly beginners, due to the abstract nature of concepts and lack of engaging, adaptive feedback in traditional educational settings. This paper introduces CodeQuest, a game-based learning system designed to enhance C++ programming education at the Faculty of Computer and Mathematical Sciences (FSKM), Universiti Teknologi MARA (UiTM). The system was developed using the Rapid Application Development (RAD) approach, with requirements gathered from both educators and students. The system’s primary features include user management, learning materials, interactive exercises with immediate feedback, and progress monitoring. This system incorporates gamification elements such as badges and achievements, leaderboards, and points and rewards systems. CodeQuest aims to increase student engagement and provide educators with a clear view of student progress, enabling them to offer timely support

game-based learning, C++ programming

1. Alghamdi, M. (2025). Dealing with Coding Challenges Through Digital Platforms: Assessing Their Effectiveness in Skill Development. CLEI Electronic Journal, 28(1), 9- 1. [Google Scholar] [Crossref]

2. Cheah, C. S. (2020). Factors contributing to the difficulties in teaching and learning of computer programming: A literature review. Contemporary Educational Technology, 12(2), 1–14. https://doi.org/10.30935/cedtech/8247 [Google Scholar] [Crossref]

3. Cyganek, B. (2022). Modern C++ in the era of new technologies and challenges – why and how to teach modern C++? Proceedings of the 17th Conference on Computer Science and Intelligence Systems, FedCSIS 2022, 35–40. 116 https://doi.org/10.15439/2022F308 [Google Scholar] [Crossref]

4. Gananjaya, I., Chandra, J. O. T., Christanto, J. F. A., Widianto, M. H., & Audrey, J. (2022). “a Lone Burglar” Stealth Game Development Using Rapid Application Development. 2022 4th International Conference on Cybernetics and Intelligent System, ICORIS 2022. https://doi.org/10.1109/ICORIS56080.2022.10031499 [Google Scholar] [Crossref]

5. Islam, N., Shafi Sheikh, G., Fatima, R., & Alvi, F. (2019). A Study of Difficulties of Students in Learning Programming. Journal of Education & Social Sciences, 7(2), 38–46. https://doi.org/10.20547/jess0721907203 [Google Scholar] [Crossref]

6. Lovrenčić, S., & Sekovanić, V. (n.d.). How Well Students Perceive Their Understanding of Logic Programming Course Content? [Google Scholar] [Crossref]

7. Marwan, S., Akram, B., Barnes, T., & Price, T. W. (2022). Adaptive Immediate Feedback for Block-Based Programming: Design and Evaluation. IEEE Transactions on Learning Technologies, 15(3), 406–420. https://doi.org/10.1109/TLT.2022.3180984 [Google Scholar] [Crossref]

8. Sobral, S. R. (2021). Teaching and learning to program: Umbrella review of introductory programming in higher education. Mathematics, 9(15). https://doi.org/10.3390/math9151737 [Google Scholar] [Crossref]

9. Suhaimi A., Kapi, A., Y., Hasmy, H., Jabal, M., F., A., (2024). SPARK: Simplified Practices, Analogies, and Resources for Knowing C++ Functions. International Jasin Multimedia & Computer Science Invention and Innovation Exhibition. https://ir.uitm.edu.my/id/eprint/94395/1/94395.pdf [Google Scholar] [Crossref]

• What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
• Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
• Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
• Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
• Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet