Deep Bayesian Knowledge Tracing

Deep Bayesian Knowledge Tracing integrates the interpretability of Bayesian models with the high-dimensional predictive power of recurrent neural networks to track student mastery.

Deep Bayesian Knowledge Tracing (DBKT) solves the black-box problem of standard Deep Knowledge Tracing by injecting structured Bayesian priors into deep learning architectures. This hybrid approach uses Long Short-Term Memory (LSTM) networks to model complex learning curves while maintaining the clear performance parameters found in traditional Bayesian Knowledge Tracing (BKT). By mapping hidden neural states to explicit mastery probabilities, DBKT provides educators with actionable insights and 15 percent higher predictive accuracy on datasets like ASSISTments2009. It effectively balances the need for precise student modeling with the transparency required for pedagogical intervention.

https://arxiv.org/abs/1805.01104