UKT

Uncertainty-aware knowledge tracing that represents student states as distributions instead of fixed deterministic embeddings.

UKT models a learner’s interaction history as a Gaussian distribution, using its mean and covariance to represent estimated knowledge and uncertainty.

The implementation combines Wasserstein self-attention with an uncertainty-aware contrastive objective, allowing the model to track changes in both knowledge state and confidence across learning interactions.