A Joint Diagnosis Model Using Response Time and Accuracy for Online Learning Assessment

Cognitive diagnosis models (CDMs) assess the proficiency of examinees in specific skills. Online education has increased the amount of data that is available on the response behaviour of examinees. Traditional CDMs determine the state of skills by modelling information on item response results and ignoring vital response time information. In this study, a CDM, named RT-CDM, which models the condition dependence between response time and response accuracy on the speed-accuracy exchange criterion, is proposed. The model’s continuous latent trait function and response time function, used for more precise cognitive analyses, makes it a tractable, interpretable skill diagnosis model. The Markov chain Monte Carlo algorithm is used to estimate the parameters of the RT-CDM. We evaluate RT-CDM through controlled simulations and three real datasets—PISA 2015 computer-based mathematics, EdNet-KT1, and MATH—against multiple baselines, including classical CDMs (e.g., DINA/IRT), RT-extended IRT and joint models (e.g., 4P-IRT, JRT-DINA), and neural CDMs (e.g., NCD, ICD, MFNCD). Across datasets, RT-CDM consistently achieves superior predictive performance, demonstrates stable parameter recovery in simulations, and delivers stronger diagnostic interpretability by leveraging RT alongside RA.