Collaborative Embedding for Knowledge Tracing

Knowledge tracing predicts students’ future performance based on their past performance. Most of the existing models take skills as input, which neglects question information and further limits the model performance. Inspired by item-item collaborative filtering in recommender systems, we propose a question-question Collaborative embedding method for Knowledge Tracing (CoKT) to introduce question information. To be specific, we incorporate student-question interactions and question-skill relations to capture question similarity. Based on the similarity, we further learn question embeddings, which are then integrated into a neural network to make predictions. Experiments demonstrate that CoKT significantly outperforms baselines on three benchmark datasets. Moreover, visualization illustrates that CoKT can learn interpretable question embeddings and achieve more obvious improvement on AUC when the interaction data is more sparse.