A Latent Variable Model with Change-Points and Its Application to Time Pressure Effects in Educational Assessment

TL;DR

This paper introduces a latent variable model with change-points to detect and adjust for behavioral shifts in test responses caused by factors like time pressure, improving the accuracy of ability estimates in educational assessments.

Contribution

It extends traditional IRT models by incorporating person-specific change-points, enabling simultaneous estimation of item parameters, individual traits, and behavioral change locations.

Findings

The model accurately recovers item parameters and change-points in simulations.

Accounting for change-points reduces bias in ability estimates.

Real data analysis reveals different change-point patterns between test types.

Abstract

Educational assessments are valuable tools for measuring student knowledge and skills, but their validity can be compromised when test takers exhibit changes in response behavior due to factors such as time pressure. To address this issue, we introduce a novel latent factor model with change-points for item response data, designed to detect and account for individual-level shifts in response patterns during testing. This model extends traditional Item Response Theory (IRT) by incorporating person-specific change-points, which enables simultaneous estimation of item parameters, person latent traits, and the location of behavioral changes. We evaluate the proposed model through extensive simulation studies, which demonstrate its ability to accurately recover item parameters, change-point locations, and individual ability estimates under various conditions. Our findings show that accounting for change-points significantly reduces bias in ability estimates, particularly for respondents affected by time pressure. Application of the model to two real-world educational testing datasets reveals distinct patterns of change-point occurrence between high-stakes and lower-stakes tests, providing insights into how test-taking behavior evolves during the tests. This approach offers a more nuanced understanding of test-taking dynamics, with important implications for test design, scoring, and interpretation.