  Knowledge tracing (KT) is a fundamental task in educational data mining that
mainly focuses on students' dynamic cognitive states of skills. The
question-answering process of students can be regarded as a thinking process
that considers the following two problems. One problem is which skills are
needed to answer the question, and the other is how to use these skills in
order. If a student wants to answer a question correctly, the student should
not only master the set of skills involved in the question but also think and
obtain the associative path on the skills graph. The nodes in the associative
path refer to the skills needed and the path shows the order of using them. The
associative path is referred to as the skill mode. Thus, obtaining the skill
modes is the key to answering questions successfully. However, most existing KT
models only focus on a set of skills, without considering the skill modes. We
propose a KT model, called APGKT, that exploits skill modes. Specifically, we
extract the subgraph topology of the skills involved in the question and
combine the difficulty level of the skills to obtain the skill modes via
encoding; then, through multi-layer recurrent neural networks, we obtain a
student's higher-order cognitive states of skills, which is used to predict the
student's future answering performance. Experiments on five benchmark datasets
validate the effectiveness of the proposed model.
