An Interpretable Statistical Ability Estimation in Web-based Learning Environment

Abstract

With growing interest in estimating true ability in contemporary learning, the demand for personalized learning and Web-based learning environments has become increasingly important. This paper develops a statistical and interpretable method of estimating ability. This method captures the succession of learning over time and provides an explainable interpretation of a statistical measurement, based on Item Response Theory and the quantiles of acquisition distributions. The results from the simulation and empirical study demonstrate that the estimated abilities can successfully recognize the actual abilities of students. The correlation values between the estimated abilities and the post-test score, which incorporate this testing history, are higher than values that only consider test responses at the time of testing. Furthermore, the pre-test and post-test administered to the experimental group show significant student improvement. These results suggest that this method serves as a successful alternative ability estimation and provides a better understanding of student competence.