Deep Knowledge Tracing

Deep Knowledge Tracing uses Recurrent Neural Networks to model student learning trajectories and predict future performance with high precision.

Piech and colleagues introduced Deep Knowledge Tracing (DKT) in 2015 to solve the limitations of traditional Bayesian Knowledge Tracing. By leveraging Long Short-Term Memory (LSTM) networks, DKT maps a student's history of interactions (correct and incorrect responses) onto a high-dimensional latent space. This allows the model to capture complex dependencies between distinct skills without manual tagging. In benchmarks using the Assistments 2009 dataset, DKT achieved an AUC of 0.86, significantly outperforming classical Markov models. It remains a foundational architecture for adaptive learning platforms that require real-time, data-driven mastery estimation.

https://arxiv.org/abs/1506.05901