A Comparative Study of Different Information Entropy Index in Personalized Exercise Recommendation
Qiulei Zheng, Huifan Gao, Fan Yang, Yinghui Pan, Yifeng Zeng · 2021
It is meaningful to recommend exercises to students in an online education system with a large amount of learning resource. Many recommendation methods usually rely on strategy in the recommendation system in order to predict an exercise score. In this paper, we compare different information entropy index in the exercise recommendation. These index consider how well exercise matches the student’s knowledge ability. In order to compare different methods, we introduce an interactive platform for dynamic exercise recommendation. We conduct a set of exercise recommendation experiments, compare the effect of different index and find the optimal index in the experiment.