Multidimensional IRT Models to Analyze Learning Outcomes of Italian Students at the End of Lower Secondary School

Abstract

In this paper, different multidimensional IRT models are compared in order to choose the best approach to explain response data on Italian student assessment at the end of lower secondary school. The results show that the additive model with three specific dimensions (reading comprehension, grammar, and mathematics abilities) and an overall ability is able to recover the test structure meaningfully. In this model, the overall ability compensates for the specific ability (or vice versa) in order to determine the probability of a correct response. Given the item characteristics, the overall ability is interpreted as a reasoning and thinking capability. Model estimation is conducted via Gibbs sampler within a Bayesian approach, which allows the use of Bayesian model comparison techniques such as posterior predictive model checking for model comparison and fit.