Variational Temporal IRT: Fast, Accurate, and Explainable Inference of
  Dynamic Learner Proficiency
Yunsung Kim Sreechan Sankaranarayanan Chris Piech Candace Thille
Dynamic Item Response Models extend the standard Item Response Theory (IRT)
to capture temporal dynamics in learner ability. While these models have the
potential to allow instructional systems to actively monitor the evolution of
learner proficiency in real time, existing dynamic item response models rely on
expensive inference algorithms that scale poorly to massive datasets. In this
work, we propose Variational Temporal IRT (VTIRT) for fast and accurate
inference of dynamic learner proficiency. VTIRT offers orders of magnitude
speedup in inference runtime while still providing accurate inference.
Moreover, the proposed algorithm is intrinsically interpretable by virtue of
its modular design. When applied to 9 real student datasets, VTIRT consistently
yields improvements in predicting future learner performance over other learner
proficiency models.