Knowledge tracing is the task of understanding student’s knowledge acquisition processes by estimating whether to solve the next question correctly or not. Most deep learning-based methods tackle this problem by identifying hidden representations of knowledge states from learning histories. However, due to the sparse interactions between students and questions, the hidden representations can be easily over-fitted and often fail to capture student’s knowledge states accurately. This paper introduces a contrastive learning framework for knowledge tracing that reveals semantically similar or dissimilar examples of a learning history and stimulates to learn their relationships. To deal with the complexity of knowledge acquisition during learning, we carefully design the components of contrastive learning, such as architectures, data augmentation methods, and hard negatives, taking into account pedagogical rationales. Our extensive experiments on six benchmarks show statistically significant improvements from the previous methods. Further analysis shows how our methods contribute to improving knowledge tracing performances.

Predicting the clinical outcome of patients from the historical electronic health records (EHRs) is a fundamental research area in medical informatics. Although EHRs contain various records associated with each patient, the existing work mainly dealt with the diagnosis codes by employing recurrent neural networks (RNNs) with a simple attention mechanism. This type of sequence modeling often ignores the heterogeneity of EHRs. In other words, it only considers historical diagnoses and does not incorporate patient demographics, which correspond to clinically essential context, into the sequence modeling. To address the issue, we aim at investigating the use of an attention mechanism that is tailored to medical context to predict a future diagnosis. We propose a medical context attention (MCA)-based RNN that is composed of an attention-based RNN and a conditional deep generative model. The novel attention mechanism utilizes the derived individual patient information from conditional variational autoencoders (CVAEs). The CVAE models a conditional distribution of patient embeddings and his/her demographics to provide the measurement of patient's phenotypic difference due to illness. Experimental results showed the effectiveness of the proposed model.