Variational Temporal IRT: Fast, Accurate, and Explainable Inference of Dynamic Learner Proficiency
This work addresses the need for fast, real-time monitoring of learner proficiency in educational systems, though it appears incremental as it builds on existing dynamic IRT models.
The paper tackled the problem of slow inference in dynamic Item Response Theory models for tracking learner proficiency over time, proposing Variational Temporal IRT (VTIRT) which achieved orders of magnitude speedup while maintaining accurate predictions on 9 real student datasets.
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.