Dynamic Bayesian Item Response Model with Decomposition (D-BIRD): Modeling Cohort and Individual Learning Over Time

TL;DR

D-BIRD is a Bayesian model that decomposes student ability into cohort and individual components to better understand learning progress over time from sparse data.

Contribution

The paper introduces D-BIRD, a novel Bayesian dynamic item response model that explicitly separates cohort and individual learning trajectories for improved interpretability.

Findings

Accurately recovers parameters in simulation studies.

Effectively models real-world learning data.

Provides interpretable insights into student learning patterns.

Abstract

We present D-BIRD, a Bayesian dynamic item response model for estimating student ability from sparse, longitudinal assessments. By decomposing ability into a cohort trend and individual trajectory, D-BIRD supports interpretable modeling of learning over time. We evaluate parameter recovery in simulation and demonstrate the model using real-world personalized learning data.