Learning path recommendation based on knowledge tracing and reinforcement learning

被引:0
|
作者
Wan, Han [1 ]
Che, Baoliang [1 ]
Luo, Hongzhen [1 ]
Luo, Xiaoyan [2 ]
机构
[1] Beihang Univ, Sch Comp Sci & Engn, State Key Lab Virtual Real Technol & Syst, Beijing, Peoples R China
[2] Beihang Univ, Image Proc Ctr, Sch Astronaut, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
learning path recommendation; knowledge tracing model; reinforcement learning; e-learning system;
D O I
10.1109/ICALT58122.2023.00021
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Adaptive and intelligent web-based educational systems arc made to automate the adaptation of the system to the learners' behaviors and needs. Personalized e-learning platforms should make adaptive adjustments according to the individual students' interactions and their knowledge states (KS). This study proposes a more effective personalized learning path recommendation algorithm to promote the individualized development of students. First, the Dynamic Key-Value Memory Network (DKVMN) is enhanced by integrating a learning behavior module, which is used to trace student knowledge states. Then, the proposed knowledge tracing model is used to simulate virtual students and train recommendation policy based on reinforcement learning (RL). The experimental results show that our personalized learning path recommendation algorithm increases the average knowledge state of students by 12.11% and 5.38% on two different data sets, respectively.
引用
收藏
页码:55 / 57
页数:3
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