Variational Temporal IRT: Fast, Accurate, and Explainable Inference of Dynamic Learner Proficiency

• URL: http://arxiv.org/abs/2311.08594v1
• Date: Tue, 14 Nov 2023 23:36:39 GMT
• Title: Variational Temporal IRT: Fast, Accurate, and Explainable Inference of Dynamic Learner Proficiency
• Authors: Yunsung Kim, Sreechan Sankaranarayanan, Chris Piech, Candace Thille
• Abstract summary: We propose Variational Temporal IRT (VTIRT) for fast and accurate inference of learner proficiency. VTIRT offers orders of magnitude speedup in inference runtime while still providing accurate inference. When applied to 9 real student datasets, VTIRT consistently yields improvements in predicting future learner performance.
• Score: 5.715502630272047
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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