2018/10/12 by Xiao Li, Hanchen Xu, Li, Xiao +6
Computer Science · Mathematics · #Computers and Society (cs.CY) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Online Learning and Analytics #cs.CY #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1810.05347
arxiv created 2018/10/12 · openalex publication_date 2018/10/12 · arxiv updated 2018/10/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
E-learning systems are capable of providing more adaptive and efficient learning experiences for students than the traditional classroom setting. A key component of such systems is the learning strategy, the algorithm that designs the learning paths for students based on information such as the students' current progresses, their skills, learning materials, and etc. In this paper, we address the problem of finding the optimal learning strategy for an E-learning system. To this end, we first develop a model for students' hierarchical skills in the E-learning system. Based on the hierarchical skill model and the classical cognitive diagnosis model, we further develop a framework to model various proficiency levels of hierarchical skills. The optimal learning strategy on top of the hierarchical structure is found by applying a model-free reinforcement learning method, which does not require information on students' learning transition process. The effectiveness of the proposed framework is demonstrated via numerical experiments.