A multidimensional Bayesian IRT method for discovering misconceptions from concept test data.

ABSTRACT: We present an exploratory method for discovering likely misconceptions from multiple-choice concept test data, as well as preliminary evidence that this method recovers known misconceptions from real student responses. Our procedure is based on a Bayesian implementation of the Multidimensional Nominal Categories IRT model (MNCM) combined with standard factor-analytic rotation methods; by analyzing student responses at the level of individual distractors rather than at the level of entire questions, this approach is able to highlight multiple likely misconceptions for subsequent investigation without requiring any manual labeling of test content. We explore the performance of the Bayesian MNCM on synthetic data and find that it is able to recover multidimensional item parameters consistentl

SUBMITTER: Segado M 

PROVIDER: S-EPMC11841472 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature