Exploration of Personalized Teaching Mode for Business Administration Courses Based on Learning Behavior Analysis
DOI:
https://doi.org/10.62177/jetp.v2i4.836Keywords:
Learning Behavior Analysis, Personalized Teaching, Business Administration Course, Teaching ModeAbstract
The traditional teaching of business administration courses has problems such as theoretical and practical disconnection, single teaching forms, uneven student participation, and one-sided evaluation systems. This paper constructs a personalized teaching mode through learning behavior analysis, and promotes curriculum reform from four aspects: Drawing student learning portraits, layered push of learning resources, designing personalized teaching processes, and establishing multi-dimensional evaluation systems. Learning behavior analysis technology can accurately identify students' diverse learning needs, facilitating teachers to optimize teaching arrangements and resource allocation. Drawing student learning portraits helps teachers provide targeted guidance and facilitate students' independent selection of learning content; Layered push of learning resources according to students' abilities can stimulate their enthusiasm for learning; Designing personalized teaching processes enhances classroom interaction and practice; Establishing multi-dimensional evaluation systems reflects learning outcomes. This teaching mode can improve teaching efficiency and learning effectiveness, providing feasible solutions for personalized teaching reform in business administration courses.
Downloads
References
Li, Y. K., & Shi, J. (2025). Multimodal deep learning for art behavior analysis and personalized teaching path generation. Discover Artificial Intelligence, 5, 215.
Wang, S. J., Cheng, Z. J., Sun, H. Z., & Luo, P. C. (2018). Mooc learning behavior analysis of the military academy cadets based on hierarchical clustering method. Education Teaching Forum, 2, 116–117.
Wang, M. (2024). Design of a college student management platform based on embedded neural network algorithm for network security technology. Computer Fraud and Security, 10, 91–101.
Dai, J. X., & Li, Q. Y. (2022). Improving random forest algorithm for university academic affairs management system platform construction. Advances in Multimedia, (4), 1–9.
Lu, H. (2024). Personalized music teaching service recommendation based on sensor and information retrieval technology. Measurement: Sensors, 33, 101207.
Guo, W. (2025). Research on artificial intelligence-driven mathematical knowledge mapping construction and personalized teaching path optimization. Academic Journal of Management and Social Sciences, 10(2), 230–236.
Chen, Q. Q. (2018). The trade-off between teacher-student commonality and individuality in teaching—reflection based on personalized teaching. Modern Education Science, (10), 123–128.
Jia, S. (2025). A personalized recommendation approach for blended English teaching database services based on content retrieval. International Journal of High Speed Electronics & Systems, 34(1), 2540181.
Zhong, J., & Zhang, W. J. (2025). Optimizing personalized recommender systems for teachers' digital learning models using deep learning algorithms. IEEE Access, 13, 78461–78470.
Hartley, K., Hayak, M., & Ko, U. H. (2024). Artificial intelligence supporting independent student learning: An evaluative case study of ChatGPT and learning to code. Education Sciences, 14(2), 12.
Downloads
How to Cite
Issue
Section
License
Copyright (c) 2025 Man Liu

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.











