DAGKT: Difficulty and Attempts Boosted Graph-based Knowledge Tracing

• URL: http://arxiv.org/abs/2210.15470v1
• Date: Tue, 18 Oct 2022 14:39:50 GMT
• Title: DAGKT: Difficulty and Attempts Boosted Graph-based Knowledge Tracing
• Authors: Rui Luo, Fei Liu, Wenhao Liang, Yuhong Zhang, Chenyang Bu and Xuegang Hu
• Abstract summary: We propose a difficulty and attempts boosted graph-based knowledge tracing (DAGKT) using rich information from students' records. In this paper, a novel method is designed to establish the question similarity relationship inspired by the F1 score.
• Score: 11.147659368393263
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the field of intelligent education, knowledge tracing (KT) has attracted increasing attention, which estimates and traces students' mastery of knowledge concepts to provide high-quality education. In KT, there are natural graph structures among questions and knowledge concepts so some studies explored the application of graph neural networks (GNNs) to improve the performance of the KT models which have not used graph structure. However, most of them ignored both the questions' difficulties and students' attempts at questions. Actually, questions with the same knowledge concepts have different difficulties, and students' different attempts also represent different knowledge mastery. In this paper, we propose a difficulty and attempts boosted graph-based KT (DAGKT), using rich information from students' records. Moreover, a novel method is designed to establish the question similarity relationship inspired by the F1 score. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed DAGKT.

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