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Deep learning based knowledge tracing model has been shown to outperformtraditional knowledge tracing model without the need for human-engineeredfeatures, yet its parameters and representations have long been criticized fornot being explainable. In this paper, we propose Deep-IRT which is a synthesisof the item response theory (IRT) model and a knowledge tracing model that isbased on the deep neural network architecture called dynamic key-value memorynetwork (DKVMN) to make deep learning based knowledge tracing explainable.Specifically, we use the DKVMN model to process the student's learningtrajectory and estimate the student ability level and the item difficulty levelover time. Then, we use the IRT model to estimate the probability that astudent will answer an item correctly using the estimated student ability andthe item difficulty. Experiments show that the Deep-IRT model retains theperformance of the DKVMN model, while it provides a direct psychologicalinterpretation of both students and items.

Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory