MAKT: Multichannel Attention Networks based Knowledge Tracing with Representation Learning

Abstract

As an effective and emerging component of intelligent education, Knowledge Tracing(KT) achieves the combination of artificial intelligence and individualized learning, whose aim is to assess students’ mastery of knowledge concepts and assist in developing learning plans. Several existing KT models either use concepts sequence as input and evaluate students’ knowledge state or treat exercise as input to predict students’ future performance. In this paper, we introduce a constraint factor to extract concepts’ and exercises’ relation matrix, design three methods in representation learning, and propose a Multichannel Attention Networks based KT model(MAKT). Specifically, we restrict the co-occurrence relationship within a time window to extract the relation matrix and then train their representations via graph generative learning, graph contrastive learning, and matrix decomposition, respectively. In MAKT, a sliding window is implemented by multichannel where input sequence is sequentially lagged in turn by one position and attention mechanism is applied. We conduct experiments on several benchmark datasets and demonstrate that MAKT with concepts’ and exercises’ representation trained by matrix decomposition outperforms state-of-the-art models.