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- W4387193227 abstract "Adaptive and intelligent web-based educational systems are 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." @default.
- W4387193227 created "2023-09-30" @default.
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- W4387193227 date "2023-07-01" @default.
- W4387193227 modified "2023-09-30" @default.
- W4387193227 title "Learning Path Recommendation Based on Knowledge Tracing and Reinforcement Learning" @default.
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- W4387193227 doi "https://doi.org/10.1109/icalt58122.2023.00021" @default.
- W4387193227 hasPublicationYear "2023" @default.
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