Adaptive learning path planning based on reinforcement learning
Journal: Region - Educational Research and Reviews DOI: 10.32629/rerr.v7i5.4082
Abstract
This article proposed the concept of "fit", focusing on the matching between learners and the learning environment, and digitally expressing the matching relationship from three levels: education, group, and technology. This study explores a reinforcement learning-based method for generating learning paths, aiming to realize the adaptive generation of learning paths. Experimental results showed this method feasible and effective, with the proposed adaptive BP neural network algorithm having the shortest path selection time (78 seconds) and higher optimization rate (38.75%) among the compared algorithms.
Keywords
English digital education; adaptive learning path; reinforcement learning; fit relationship
Funding
This paper was part of the project of Research on Classroom Management Strategies for Foreign Language Teachers in Universities in the Age of Artificial Intelligence，(Grant No. 24WY0415).
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[3] El-Sabagh H A. 2021. Adaptive e-learning environment based on learning styles and its impact on development students' engagement. International Journal of Educational Technology in Higher Education, 18(1): 1-24.
[4] Shemshack A, Kinshuk, Spector J M. 2021. A comprehensive analysis of personalized learning components. Journal of Computers in Education, 8(4): 485-503.
[5] Wang S, Christensen C, Cui W. 2023. When adaptive learning is effective learning: comparison of an adaptive learning system to teacher-led instruction. Interactive Learning Environments, 31(2): 793-803.
[6] Padakandla S. 2021. A survey of reinforcement learning algorithms for dynamically varying environments. ACM Computing Surveys (CSUR), 54(6): 1-25.
[7] Rolf B, Jackson I, Müller M. 2023. A review on reinforcement learning algorithms and applications in supply chain management. International Journal of Production Research, 61(20): 7151-7179.
[8] Huang S, Dossa RFJ, Ye C. 2022. Cleanrl: High-quality single-file implementations of deep reinforcement learning algorithms. Journal of Machine Learning Research, 23(274): 1-18.
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