Introducing Problem Schema with Hierarchical Exercise Graph for Knowledge Tracing release_hnplhi7e3fga5lqy356uf23why

by Hanshuang Tong, Yun Zhou, Zhen Wang, Ben Teng

Released as a article .

2021  

Abstract

Knowledge tracing (KT) which aims at predicting learner's knowledge mastery plays an important role in the computer-aided educational system. In recent years, many deep learning models have been applied to tackle the KT task, which has shown promising results. However, limitations still exist. Most existing methods simplify the exercising records as knowledge sequence, which fails to explore rich information existed in exercise texts. Besides, the latent hierarchical graph nature of exercises and knowledge remains unexplored. Thus, in this paper, we propose a hierarchical graph knowledge tracing model framework (HGKT) which can leverage the advantages of hierarchical exercise graph and of sequence model to enhance the ability of knowledge tracing. Besides, we introduce the concept of problem schema to better represent a group of similar exercises and propose a hierarchical graph neural network to learn representations of problem schemas. Moreover, in the sequence model, we employ two attention mechanisms to highlight important historical states of students. In the testing stage, we present a K\&S diagnosis matrix that could trace the transition of mastery of knowledge and problem schema, which can be more easily applied to different applications. Extensive experiments show the effectiveness and interpretability of our proposed models.
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Type  article
Stage   submitted
Date   2021-02-08
Version   v4
Language   en ?
arXiv  2006.16915v4
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