Incorporate Global Information from Entire Datasets for Knowledge Tracing via Mini-Batch Input

Abstract

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.