Identifying Latent Classes and Differential Item Functioning in a Cohort of E-Learning Students

Abstract

A differential item functioning analysis is performed on a cohort of E-Learning students undertaking a unit in computational finance. The motivation for this analysis is to identify differential item functioning based on attributes of the student cohort that are unobserved. The authors find evidence that a model containing two distinct latent classes of students is preferred, and identify those examination items that display the greatest level of differential item functioning. On reviewing the attributes of the students in each of the latent classes, and the items and categories that mostly distinguish those classes, the authors conclude that the bias associated with the differential item functioning is related to the a priori background knowledge that students bring to the unit. Based on this analysis, they recommend changes in unit instruction and examination design so as to remove this bias.