Research on Personalized Recommendations for Students’ Learning Paths Based on Big Data

Authors

  • Ziyu Liu Hebei University of Science and Technology
  • Liye Dong Hebei University of Science and Technology
  • Changli Wu Hebei University of Science and Technology

DOI:

https://doi.org/10.3991/ijet.v15i08.12245

Keywords:

Big Data, Learning path, similarity, personalized recommendation

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.

Author Biographies

Ziyu Liu, Hebei University of Science and Technology

School of Economics and Management

Liye Dong, Hebei University of Science and Technology

School of Economics and Management

Changli Wu, Hebei University of Science and Technology

School of Economics and Management

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Published

2020-04-24

How to Cite

Liu, Z., Dong, L., & Wu, C. (2020). Research on Personalized Recommendations for Students’ Learning Paths Based on Big Data. International Journal of Emerging Technologies in Learning (iJET), 15(08), pp. 40–56. https://doi.org/10.3991/ijet.v15i08.12245

Issue

Section

Papers