Predictive, scalable and interpretable knowledge tracing on structured
  domains
Hanqi Zhou Robert Bamler Charley M. Wu Álvaro Tejero-Cantero
Intelligent tutoring systems optimize the selection and timing of learning
materials to enhance understanding and long-term retention. This requires
estimates of both the learner's progress (''knowledge tracing''; KT), and the
prerequisite structure of the learning domain (''knowledge mapping''). While
recent deep learning models achieve high KT accuracy, they do so at the expense
of the interpretability of psychologically-inspired models. In this work, we
present a solution to this trade-off. PSI-KT is a hierarchical generative
approach that explicitly models how both individual cognitive traits and the
prerequisite structure of knowledge influence learning dynamics, thus achieving
interpretability by design. Moreover, by using scalable Bayesian inference,
PSI-KT targets the real-world need for efficient personalization even with a
growing body of learners and learning histories. Evaluated on three datasets
from online learning platforms, PSI-KT achieves superior multi-step predictive
accuracy and scalable inference in continual-learning settings, all while
providing interpretable representations of learner-specific traits and the
prerequisite structure of knowledge that causally supports learning. In sum,
predictive, scalable and interpretable knowledge tracing with solid knowledge
mapping lays a key foundation for effective personalized learning to make
education accessible to a broad, global audience.