Abstract

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.

Bio

Álvaro’s career spans the fields of theoretical physics, computational neuroscience, and machine learning. Initially focusing on the interference of entangled material particles, and later on models of memory sequences through theoretical neural-network models and data analysis, his work has been deeply interdisciplinary. He has worked at the German Aerospace Agency, the European Southern Observatory, the University of Oxford and several startups. His recent work involves applying machine learning to mechanistic models for solving inverse problems through simulation-based inference methods.

At the ml ⇌ science colab, Álvaro and his team leverage machine learning methods for scientific discovery in fields as diverse as archaeology, paleo-climatology and high-energy physics.