Longitudinal Telemetry-Driven Cognitive Profiling and Mathematical Mastery Indexing for High-Stakes Standardized Medical Entrance Examinations

This paper presents NeetTracker, a production-grade cognitive telemetry and mastery profiling engine for NEET-UG exam preparation. We formalize algorithms for mastery indexing, pacing bottleneck isolation, retention decay correction, concept dependency mapping, and NEET score and rank trajectory projection. The system employs longitudinal telemetry-driven analysis to build a Neuro-Cognitive Mastery Index (NCMI) for each student, integrating Ebbinghaus retention decay modelling, knowledge tracing, and educational data mining techniques. The framework is validated on live student cohorts and addresses the challenge of high-stakes standardized medical entrance examinations in India, providing actionable, personalized learning interventions grounded in empirical cognitive profiling data.