Incorporating Item Response Theory into Knowledge Tracing

The popularity of artificial neural networks has brought high predictive power to many difficult machine learning problems. Knowledge tracing (KT), the task of tracking students' understanding of various concepts over time, is included in this category. But the deep learning methods which have performed best in knowledge tracing are hard to explain in a statistical sense.In this work, we leverage the psychological theory from Item Response Theory (IRT) to build interpretable neural networks for knowledge tracing which are competitive with other deep learning methods. This presents a trade-off between a small loss in predictive power and an increase in interpretability. The advantage of IRT-inspired knowledge tracing is that it transforms the high-dimensional student ability representation from deep learning models into an explainable IRT representation at each timestep. Further, the item parameters from IRT models can be directly recovered from the trained neural network weights.