Learning Path Recommendation Based on Reinforcement Learning.

In recent years, online education platforms have seen rapid growth, attracting an increasing number of students to digital learning environments. In online education, learners can choose learning content and plan their own learning path more freely. Although the online education platform gives learners a high degree of freedom, it reduces the learning guidance for learners, which leads to problems such as "information overload" and "knowledge loss". The main manifestation is that learners don't know how to plan their learning path, resulting in reduced learning efficiency and poor learning effects. To address these challenges, this paper proposes a learning path recommendation algorithm based on reinforcement learning called RLLP. The RLLP model takes into account the learner's learning goals, knowledge level, and the relationships between knowledge points. Simultaneously, it also considers the smoothness of the learning path and the learner's engagement, aiming to recommend efficient and sensible learning paths to learners. Extensive experimental results demonstrate the effectiveness of RLLP model. [ABSTRACT FROM AUTHOR]