An Integrated Machine Learning and Hierarchical Variance Decomposition Pipeline for Student Performance Prediction and Metacognitive Calibration on Multi-Signal Telemetry
arXiv:2606.28881v1 Announce Type: new Abstract: Predicting student performance and characterizing metacognitive calibration are essential for personalization in intelligent tutoring systems. Prior research treats performance prediction, calibration error calculation, and variance decomposition as separate pipelines, preventing unified interpretation. I propose the Unified Behavioral Prediction and Calibration Analysis Pipeline (UBP-CAP), an integrated framework processing student pre-execution behavioral telemetry through three linked modules: (1) a LightGBM classifier with SHAP for binary cor
The continuous evolution of AI in education, coupled with increased data availability from intelligent tutoring systems, drives the need for more integrated analytical frameworks.
This development allows for more personalized and effective intelligent tutoring systems by unifying student performance prediction and metacognitive calibration.
The ability to interpret student behavior and cognitive states in a unified manner could lead to more adaptive and impactful educational technologies.
- · EdTech companies
- · Students
- · Educational researchers
- · AI developers
- · Legacy educational assessment methods
- · One-size-fits-all learning platforms
More sophisticated and adaptive intelligent tutoring systems will emerge, leading to improved student outcomes.
The widespread adoption of these systems could fundamentally alter traditional pedagogical approaches and curriculum design.
Enhanced understanding of individual learning processes might lead to new paradigms in cognitive science and personalized development.
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