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Deep Knowledge Tracing (DKT) models student learning behavior by usingRecurrent Neural Networks (RNNs) to predict future performance based onhistorical interaction data. However, the original implementation relied onstandard RNNs in the Lua-based Torch framework, which limited extensibility andreproducibility. In this work, we revisit the DKT model from two perspectives:architectural improvements and optimization efficiency. First, we enhance themodel using gated recurrent units, specifically Long Short-Term Memory (LSTM)networks and Gated Recurrent Units (GRU), which better capture long-termdependencies and help mitigate vanishing gradient issues. Second, were-implement DKT using the PyTorch framework, enabling a modular and accessibleinfrastructure compatible with modern deep learning workflows. We alsobenchmark several optimization algorithms SGD, RMSProp, Adagrad, Adam, andAdamW to evaluate their impact on convergence speed and predictive accuracy ineducational modeling tasks. Experiments on the Synthetic-5 and Khan Academydatasets show that GRUs and LSTMs achieve higher accuracy and improved trainingstability compared to basic RNNs, while adaptive optimizers such as Adam andAdamW consistently outperform SGD in both early-stage learning and final modelperformance. Our open-source PyTorch implementation provides a reproducible andextensible foundation for future research in neural knowledge tracing andpersonalized learning systems.

Improving Deep Knowledge Tracing via Gated Architectures and Adaptive Optimization