Virtual Reality-Based Programming Learning System Using Random Forest for Assessing and Enhancing Programming Skills Among Computer Science Students
Keywords:
virtual reality, programming education, random forest, programming skills, student performance assessmentAbstract
Programming is one of the skills that Computer Science students should acquire, although there are students who have challenges comprehending programming knowledge and solving problems by applying it. There may also be limitations in conventional programming lessons in terms of providing chances for the students to program interactively. One technology that is capable of offering immersive and interactive learning opportunities is virtual reality (VR), which gives chances to the learner to participate in learning activities within the simulation environment. Prior research studies how that immersive virtual reality helps in learning, especially if the activities are interactive (Hamilton et al., 2020). This study proposes a Virtual Reality-Based Programming Learning System Using Random Forest for Assessing and
Enhancing Programming Skills Among Computer Science Students. This system will offer programming lessons, exercises, coding exercises, and assessments in a virtual environment. The performance parameters chosen for analysis using the Random Forest classification technique include the student’s assessment score, coding efficiency, errors, time taken, and task completion. Classification of the students into groups based on the level of their programming skills will be carried out using the identified performance parameters. From the assessment, the students will receive feedback and learning activities that will improve their skills. The purpose of this system is to provide a system of interactive learning and performance evaluation based on students assessment.
References
Agbo, F. J., Oyelere, S. S., Suhonen, J., & Tukiainen, M. (2023). Design, development, and evaluation of a virtual reality game-based application to support computational thinking. Educational Technology Research and Development, 71(2), 505–537. https://doi.org/10.1007/s11423-023-10204-7
Almodiel, M. C., Cañas-Llamas, A. M. E. F., Hernandez, M. S., Duria, A. M. C., & Villanueva, Z. E. M. (2024). Expanding campus engagement: A study on the user acceptance of a virtual reality-enhanced campus tour in an open university. International Journal in Information Technology in Governance, Education and Business, 6(2).
Ante, M. G. L., Balbero, R. V., & Cordesar, E. L. (2022). Virtual reality in science classes of junior high school students. Paidadogos, 10(1).
Arada, B. P. (2025). Arduino programming education using Tinkercad: A mixed-method study on usability and student engagement during the COVID-19 pandemic. International Journal in Information Technology in Governance, Education and Business, 7(1), 135–150.
Cabrera-Duffaut, A., Pinto-Llorente, A. M., & Iglesias-Rodríguez, A. (2024). Immersive learning platforms: Analyzing virtual reality contribution to competence development in higher education—A systematic literature review. Frontiers in Education, 9, Article 1391560. https://doi.org/10.3389/feduc.2024.1391560
Cheah, C. S. (2020). Factors contributing to the difficulties in teaching and learning of computer programming: A literature review. Contemporary Educational Technology, 12(2), Article ep272. https://doi.org/10.30935/cedtech/8247
Coban, M., Bolat, Y. I., & Goksu, I. (2022). The potential of immersive virtual reality to enhance learning: A meta-analysis. Educational Research Review, 36, Article 100452. https://doi.org/10.1016/j.edurev.2022.100452
Conrad, M., Kablitz, D., & Schumann, S. (2024). Learning effectiveness of immersive virtual reality in education and training: A systematic review of findings. Computers & Education: X Reality, 4, Article 100053. https://doi.org/10.1016/j.cexr.2024.100053
Dewi, I. P., Ambiyar, A., Effendi, H., Giatman, M., Hanafi, H. F., & Ali, S. K. (2024). The impact of virtual reality on programming algorithm courses on student learning outcomes. International Journal of Learning, Teaching and Educational Research, 23(10), 45–61. https://doi.org/10.26803/ijlter.23.10.3
Di Natale, A. F., Repetto, C., Riva, G., & Villani, D. (2020). Immersive virtual reality in K-12 and higher education: A 10-year systematic review of empirical research. British Journal of Educational Technology, 51(6), 2006–2033. https://doi.org/10.1111/bjet.13030
Efan, K., Krismadinata, Jama, J., & Mulya, R. (2023). A systematic literature review of teaching and learning on object-oriented programming course. International Journal of Information and Education Technology, 13(2), 302–312. https://doi.org/10.18178/ijiet.2023.13.2.1804
Hamilton, D., McKechnie, J., Edgerton, E., & Wilson, C. (2021). Immersive virtual reality as a pedagogical tool in education: A systematic literature review of quantitative learning outcomes and experimental design. Journal of Computers in Education, 8(1), 1–32. https://doi.org/10.1007/s40692-020-00169-2
Janani, A. S., Yang, Y., Lu, X., Liang, H.-N., & Baghaei, N. (2026). Virtual reality applications in computer science education: Scoping review. Computers & Education: X Reality, 8, Article 100159. https://doi.org/10.1016/j.cexr.2026.100159
Jin, Z., Bai, Y., Song, W., Yu, Q., Yue, X., & Jia, X. (2024). DVRT: Design and evaluation of a virtual reality drone programming teaching system. Computers & Graphics, 125, Article 104114. https://doi.org/10.1016/j.cag.2024.104114
Kalelioğlu, F., & Keskinkılıç, F. (2026). Difficulties of learning programming in higher education: A systematic literature review of recent research. Ankara University Journal of Faculty of Educational Sciences, 59(2).
Lampropoulos, G., & Kinshuk. (2024). Virtual reality and gamification in education: A systematic review. Educational Technology Research and Development, 72(4), 1691–1785. https://doi.org/10.1007/s11423-024-10351-3
Morais, C. G. B., Mendes Neto, F. M., & Osório, A. J. M. (2020). Difficulties and challenges in the learning process of algorithms and programming in higher education: A systematic literature review. Research, Society and Development, 9(10), Article e3389109287. https://doi.org/10.33448/rsd-v9i10.9287
Nachouki, M., & Abou Naaj, M. (2022). Predicting student performance to improve academic advising using the Random Forest algorithm. International Journal of Distance Education Technologies, 20(1), 1–17. https://doi.org/10.4018/IJDET.296702
Nachouki, M., Mohamed, E. A., Mehdi, R., & Abou Naaj, M. (2023). Student course grade prediction using the random forest algorithm: Analysis of predictors' importance. Trends in Neuroscience and Education, 33, Article 100214. https://doi.org/10.1016/j.tine.2023.100214
Pirker, J., Dengel, A., Holly, M., & Safikhani, S. (2020). Virtual reality in computer science education: A systematic review. In Proceedings of the 26th ACM Symposium on Virtual Reality Software and Technology (Article 27, pp. 1–8). Association for Computing Machinery. https://doi.org/10.1145/3384382.3384533
Prather, J., Reeves, B. N., Leinonen, J., MacNeil, S., Randrianasolo, A. S., Becker, B. A., Kimmel, B., Wright, J., & Briggs, B. (2024). The widening gap: The benefits and harms of generative AI for novice programmers. In Proceedings of the 2024 ACM Conference on International Computing Education Research (Vol. 1, pp. 469–486). Association for Computing Machinery. https://doi.org/10.1145/3632620.3671100
Qian, Y., Yan, S., & Pan, X. (2021). Teaching and learning to program: Umbrella review of introductory programming in higher education. Mathematics, 9(15), Article 1737. https://doi.org/10.3390/math9151737
Reddy, E. M., Venkateshwarlu, B., Raju, N. Ch., & Sreenivasulu, M. (2024). A systematic literature review on teaching and learning introductory programming in higher education. International Journal of Emerging Technologies in Learning, 9(1).
Srimadhaven, T., Chris Junni, A. V., Naga Harshith, S., Jessenth Ebenezer, S., & Shabari Girish, S. (2020). Learning analytics: Virtual reality for programming course in higher education. Procedia Computer Science, 172, 433–437. https://doi.org/10.1016/j.procs.2020.05.097
Su, Y.-S., Lin, Y.-D., & Liu, T.-Q. (2022). Applying machine learning technologies to explore students’ learning features and performance prediction. Frontiers in Neuroscience, 16, Article 1018005. https://doi.org/10.3389/fnins.2022.1018005
Tolentino, A. N., & Varela, B. V. (2025). Learning biology through virtual reality instructional approach: Effects on conceptual understanding, performance, and perceived usefulness. International Journal of Research and Innovation in Social Science.
Wee, C., Yap, K. M., & Lim, W. N. (2022). iProgVR: Design of a virtual reality environment to improve introductory programming learning. IEEE Access, 10, 100054–100078. https://doi.org/10.1109/ACCESS.2022.3204392
Won, M., Ungu, D. A. K., Matovu, H., Tsai, C.-C., & Treagust, D. F. (2023). Diverse approaches to learning with immersive virtual reality identified from a systematic review. Computers & Education, 195, Article 104701. https://doi.org/10.1016/j.compedu.2022.104701
Yang, C., Zhang, J., Hu, Y., Yang, X., Chen, M., Shan, M., & Li, L. (2024). The impact of virtual reality on practical skills for students in science and engineering education: A meta-analysis. International Journal of STEM Education, 11(1), Article 28. https://doi.org/10.1186/s40594-024-00487-2
Yu, E. R., Pineda, E. D., Tano, I. M., Lagman, A. C., Victoriano, J. M., Mababa, J. C., & Pulumbarit, J. P. (2025). Predicting programming anxiety among computer studies students using automated machine learning. International Journal of Latest Technology in Engineering, Management & Applied Science, 14(5), 314–324.
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Copyright (c) 2026 Pauleen David (Author)
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