State-of-the-art Survey on Personalized Learning Path Recommendation

Recently, with the rapid development of information technology, emerging technologies represented by artificial intelligence are widely applied in education, triggering profound changes in the concept and mode of learning. In addition, online learning transcends the limitations of time and space, providing more possibilities for learners to learn anytime and anywhere. Nevertheless, the separation of time and space of teachers and students in online learning makes teachers could not handle students' learning process, limits the quality of teaching and learning. Diversified learning targets and massive learning resources generate some new problems, such as how to quickly accomplish learning targets, reduce learning costs, and reasonably allocate learning resources. These problems have become the limitations of the development of individuals and the society. However, traditional one size fits all educational model can no longer fit human's needs, thus, one more efficient and scientific personalized education model is needed to help learners maximize their learning targets with minimal learning costs. Based on these considerations, new adaptive learning system is needed which could automatically and efficiently identify learner's personalized characteristics, efficiently organize and allocate learning resources, and plan a global personalized learning path. This study systematically reviews and analyzes the current researches on personalized learning path recommendation and the different research vision from multidisciplinary perspective. Then, the most applied algorithm in current research is summarized. Finally, the main shortcomings of the current research, which should be paid more attention to, are highlighted. © 2022 Chinese Academy of Sciences. All rights reserved.