Current Status, Existing Problems and Future Challenges of Research on Personalized Learning Path Recommendation in Online Learning Model

With the support of artificial intelligence, big data, mobile Internet and other information technologies, the teaching mode has undergone profound changes, making online learning methods an important way to cultivate innovative talents. However, the problems of information overload and information disorientation cause learners in online learning environment do not get good learning effect and learning experience. Personalized learning path recommendation, as a key method to overcome the above problems, has received more and more attention from researchers. To this end, this paper provides a systematic overview of current research on personalized learning path recommendation, with a detailed overview of five aspects: what is the content of learning path recommendation, what are the target strategies used, what are the characteristic parameters considered, what are the core recommendation algorithms used, and how to evaluate the learning path. Finally, the following shortcomings of existing research and future challenges are proposed in these five aspects. Through this paper, we hope to provide useful reference and reference for the subsequent related research.