Research on Personalized Recommendations for Students’ Learning Paths Based on Big Data
Ziyu Liu, Liye Dong, Changli Wu
Abstract
With the development of the Internet, the use of hybrid learning is spreading in colleges and universities across the country. The urgent problem now is how to improve the quality of hybrid learning; specifically, how to improve the learning effect of students under an online learning mode. In this paper, we build an online learning path model by exploring the big data of students' online learning processes. The model can be used to find excellent learning paths. Based on students’ learning habits, we recommend personalized and excellent learning paths with a high degree of similarity for general students. By comparison, experimental results indicate that our proposed methods not only provide sound recommendations regarding appropriate learning paths with significantly improved learning results in terms of accuracy and efficiency, but our methods also provide support that helps to improve teaching quality, promote personalized learning and target teaching.