This paper introduces an integrated AI framework to predict student performance and analyze how accurately students assess their own understanding in intelligent tutoring systems.

In plain terms

Intelligent tutoring systems aim to personalize learning, which requires predicting student performance and understanding how accurately students assess their own knowledge, known as **metacognitive calibration**. Traditionally, predicting performance and analyzing calibration were done separately, making it hard to interpret everything together. This paper introduces the UBP-CAP framework, an integrated system that uses **machine learning** to process student behavioral **telemetry**—data collected from their interactions within a learning system. First, it employs a **LightGBM classifier** (a type of machine learning model) to predict if a student will answer a question correctly based on their actions before attempting it. Second, it calculates formal metrics to measure how well students' self-assessments align with their actual performance, identifying errors in their metacognitive calibration. Finally, a **Generalized Linear Mixed-Effects Model (GLMM)** is used to break down the reasons for these calibration errors, distinguishing between factors specific to the student versus the task. The study found that students often systematically misjudge their own understanding, and this self-assessment ability is more dependent on the specific learning task than on a general student trait.