ENHANCING PROGRAMMING SKILLS THROUGH AN INNOVATIVE INTERACTIVE LEARNING FRAMEWORK FOR UNDERGRADUATE COMPUTER SCIENCE STUDENTS

This paper proposes the PRACTICE Framework as a structured approach to interactive programming education for undergraduate Computer Science students. By combining output prediction, micro-coding challenges, error analysis, peer explanations, progressive problem-solving, and reflective learning, the framework seeks to provide students with more opportunities to practice and understand programming concepts. The framework is designed to be affordable and adaptable to regular undergraduate teaching environments. A systematic classroom evaluation is necessary to determine whether it improves programming performance, debugging ability, student engagement, and independent problem-solving. Future research may examine its effectiveness across different institutions, programming languages, student backgrounds, and course durations.

1.                Calderon, I., Silva, W., & Feitosa, E. (2024). Active learning methodologies for teaching programming in undergraduate courses: A systematic mapping study. Informatics in Education, 23(2), 279–322. https://doi.org/10.15388/infedu.2024.11 2.                Bowman, N. A., Jarratt, L., Culver, K. C., & Segre, A. M. (2021). The impact of pair programming on college students' interest, perceptions, and achievement in computer science. ACM Transactions on Computing Education, 21(3), Article 3. https://doi.org/10.1145/3440759 3.                Sentance, S., Waite, J., & Kallia, M. (2019). Teachers' experiences of using PRIMM to teach programming in school. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (pp. 476–482). Association for Computing Machinery. https://doi.org/10.1145/3287324.3287477 4.                Sentance, S., Waite, J., & Kallia, M. (2019). Teaching computer programming with PRIMM: A sociocultural perspective. Computer Science Education, 29(2–3), 136–176. https://doi.org/10.1080/08993408.2019.1608781 5.                Yang, S., Baird, M., O'Rourke, E., Brennan, K., & Schneider, B. (2024). Decoding debugging instruction: A systematic literature review of debugging interventions. ACM Transactions on Computing Education, 24(4), Article 45. https://doi.org/10.1145/3690652 6.                Chen, C.-Y. (2025). Effects of worked examples with explanation types and learner motivation on cognitive load and programming problem-solving performance. ACM Transactions on Computing Education, 25(2), Article 23.