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Knowledge tracing (KT) is a fundamental task in educational data mining thatmainly focuses on students' dynamic cognitive states of skills. Thequestion-answering process of students can be regarded as a thinking processthat considers the following two problems. One problem is which skills areneeded to answer the question, and the other is how to use these skills inorder. If a student wants to answer a question correctly, the student shouldnot only master the set of skills involved in the question but also think andobtain the associative path on the skills graph. The nodes in the associativepath refer to the skills needed and the path shows the order of using them. Theassociative path is referred to as the skill mode. Thus, obtaining the skillmodes is the key to answering questions successfully. However, most existing KTmodels only focus on a set of skills, without considering the skill modes. Wepropose a KT model, called APGKT, that exploits skill modes. Specifically, weextract the subgraph topology of the skills involved in the question andcombine the difficulty level of the skills to obtain the skill modes viaencoding; then, through multi-layer recurrent neural networks, we obtain astudent's higher-order cognitive states of skills, which is used to predict thestudent's future answering performance. Experiments on five benchmark datasetsvalidate the effectiveness of the proposed model.

APGKT: Exploiting Associative Path on Skills Graph for Knowledge Tracing