A learning resource recommendation algorithm based on online learning sequential behavior

作者：Wang, Xuebin; Zhu, Zhengzhou^*; Yu, Jiaqi; Zhu, Ruofei; Li, DeQi; Guo, Qun

来源：International Journal of Wavelets, Multiresolution and Information Processing, 2019, 17(2): 1940001.

DOI：10.1142/S0219691319400010

• Learning resource recommendation
• sequential behavior sequence
• H-K clustering algorithm
• collaborative filtering

The accuracy of learning resource recommendation is crucial to realizing precise teaching and personalized learning. We propose a novel collaborative filtering recommendation algorithm based on the student's online learning sequential behavior to improve the accuracy of learning resources recommendation. First, we extract the student's learning events from his/her online learning process. Then each student's learning events are selected as the basic analysis unit to extract the feature sequential behavior sequence that represents the student's learning behavioral characteristics. Then the extracted feature sequential behavior sequence generates the student's feature vector. Moreover, we improve the H-K clustering algorithm that clusters the students who have similar learning behavior. Finally, we recommend learning resources to the students combine similarity user clusters with the traditional collaborative filtering algorithm based on user. The experiment shows that the proposed algorithm improved the accuracy rate by 110% and recall rate by 40% compared with the traditional user-based collaborative filtering algorithm.

• 出版日期2019-3
• 单位北京大学