International Journal of Research Studies in Management
CollabWritive Special Issue
2025 Volume 13 Issue 3
Available Online: 25 April 2025
Author/s:
Wang, Hai
Nanning University, China
Abstract:
With the development of information technology, computer programming courses have become increasingly important in higher education. These courses are characterized by fragmented knowledge, strong logicality, high practical requirements, and diverse learning styles, foundations, and goals among students, posing challenges to teaching. This study introduces personalized recommendation algorithms to provide students with customized learning resources and paths, enhancing learning efficiency and quality. It categorizes personalized recommendation algorithms, constructs an effectiveness evaluation framework tailored for this course, and designs experiments to collect student data. Experimental results show that the hybrid recommendation algorithm group (collaborative filtering + knowledge graph embedding) significantly outperforms the traditional fixed-path recommendation algorithm in terms of accuracy, coverage, learning path completion rate, code quality, and user satisfaction. This demonstrates the effectiveness of personalized recommendation algorithms in improving learning efficiency and quality in programming courses. Additionally, the study proposes directions for algorithm optimization and practical suggestions, while summarizing research limitations and future directions.
Keywords: personalized recommendation algorithms, computer programming courses, practical effectiveness, learning efficiency, learning quality, algorithm optimization
DOI: https://doi.org/10.5861/ijrsm.2025.25042
Cite this article:
Wang, H. (2025). A study on the practical effectiveness of personalized recommendation algorithms in computer programming courses. International Journal of Research Studies in Management, 13(3), 67-71. https://doi.org/10.5861/ijrsm.2025.25042