MARS: A Momentum-Aware Adaptive Rating System for Personalized Assessment
Adaptive assessment systems personalize learning by estimating learner proficiency and recommending suitable questions. However, many existing approaches rely on fixed difficulty labels, static rating updates, or black-box prediction models that provide limited control over rating stability, interpretability, and time-constrained routing. This paper proposes MARS, a Momentum-Aware Adaptive Rating System for interpretable adaptive assessment. MARS represents the question bank as a directed weighted knowledge graph and updates learner proficiency using dynamic rating sensitivity, response quality, and momentum-aware learner velocity. We also formulate a time-budgeted mark-maximization objec...
| Authors | Vikrant Sahu, Khethavath Sunil Naik, Gagan Raj Gupta |
|---|---|
| Primary category | cs.CY |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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