Incorporate Global Information from Entire Datasets for Knowledge Tracing via Mini-Batch Input
Hui Zhao, Tingyu Fu · 2025
Knowledge tracing uses students’ answer record data and the relationship between exercises to predict students’ future answering performance. However, in the deep learning model, the input manner of mini-batch may prevent the network from learning global information such as the difficulty of exercises. In this paper, we propose a method to add global information into mini-batches. The added global information is obtained from the entire dataset by using a specific algorithm. Experimental results show that on three classic datasets, the model performance is improved by about 3.9% (average) on the AUC scale. Additionally, the artificially designed global information enhances the interpretability of the model.