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

Optimization and Control