A general dynamic learning model framework for cognitive diagnosis.

Abstract

Understanding students' learning trajectories is crucial for educators to effectively monitor and enhance progress. With the rise of computer-based testing, researchers now have access to rich datasets that provide deeper insights into student performance. This study introduces a general dynamic learning model framework that integrates response accuracy and response times to capture different test-taking behaviors and estimate learning trajectories related to polytomous attributes over time. A Bayesian estimation method is proposed to estimate model parameters. Rigorous validation through simulation studies confirms the effectiveness of the MCMC algorithm in parameter recovery and highlights the model's utility in understanding learning trajectories and detecting different test-taking behaviors in a learning environment. Applied to real data, the model demonstrates practical value in educational settings. Overall, this comprehensive and validated model offers educators and researchers nuanced insights into student learning progress and behavioral dynamics.