  Recently, knowledge tracing models have been applied in educational data
mining such as the Self-attention knowledge tracing model(SAKT), which models
the relationship between exercises and Knowledge concepts(Kcs). However,
relation modeling in traditional Knowledge tracing models only considers the
static question-knowledge relationship and knowledge-knowledge relationship and
treats these relationships with equal importance. This kind of relation
modeling is difficult to avoid the influence of subjective labeling and
considers the relationship between exercises and KCs, or KCs and KCs
separately. In this work, a novel knowledge tracing model, named Knowledge
Relation Rank Enhanced Heterogeneous Learning Interaction Modeling for Neural
Graph Forgetting Knowledge Tracing(NGFKT), is proposed to reduce the impact of
the subjective labeling by calibrating the skill relation matrix and the
Q-matrix and apply the Graph Convolutional Network(GCN) to model the
heterogeneous interactions between students, exercises, and skills.
Specifically, the skill relation matrix and Q-matrix are generated by the
Knowledge Relation Importance Rank Calibration method(KRIRC). Then the
calibrated skill relation matrix, Q-matrix, and the heterogeneous interactions
are treated as the input of the GCN to generate the exercise embedding and
skill embedding. Next, the exercise embedding, skill embedding, item
difficulty, and contingency table are incorporated to generate an exercise
relation matrix as the inputs of the Position-Relation-Forgetting attention
mechanism. Finally, the Position-Relation-Forgetting attention mechanism is
applied to make the predictions. Experiments are conducted on the two public
educational datasets and results indicate that the NGFKT model outperforms all
baseline models in terms of AUC, ACC, and Performance Stability(PS).
