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We propose a new model to assess the mastery level of a given skillefficiently. The model, called Bayesian Adaptive Mastery Assessment (BAMA),uses information on the accuracy and the response time of the answers given andinfers the mastery at every step of the assessment. BAMA balances the length ofthe assessment and the certainty of the mastery inference by employing aBayesian decision-theoretic framework adapted to each student. All theseproperties contribute to a novel approach in assessment models for intelligentlearning systems. The purpose of this research is to explore the properties ofBAMA and evaluate its performance concerning the number of questionsadministered and the accuracy of the final mastery estimates across differentstudents. We simulate student performances and establish that the modelconverges with low variance and high efficiency leading to shorter assessmentduration for all students. Considering the experimental results, we expect ourapproach to avoid the issue of over-practicing and under-practicing andfacilitate the development of Learning Analytics tools to support the tutors inthe evaluation of learning effects and instructional decision making.

Personalized Stopping Rules in Bayesian Adaptive Mastery Assessment