Personalized learning is a promising educational approach that aims to provide high-quality
personalized services for each student with minimum demands for practice data. The key to …

Despite the increasing popularity, cognitive diagnosis models have been criticized for
limited utility for small samples. In this study, the authors proposed to use Bayes modal (BM) …

Missing data are hard to avoid, or even inevitable, in longitudinal learning diagnosis and
other longitudinal studies. Sample attrition is one of the most common missing patterns in …

The latent space item response model (LSIRM) is a newly-developed approach to analyzing
and visualizing conditional dependencies in item response data, manifested as the …

Objective The introduction of new clinical risk scores (eg European System for Cardiac
Operative Risk Evaluation (EuroSCORE) II) superseding original scores (eg EuroSCORE I) …

Timely diagnostic feedback is helpful for students and teachers, enabling them to adjust their
learning and teaching plans according to a current diagnosis. Motivated by the practical …

Diagnostic classification models (DCMs) are restricted latent class models with a set of cross-
class equality constraints and additional monotonicity constraints on their item parameters …

The Bayesian literature has shown that the Hamiltonian Monte Carlo (HMC) algorithm is
powerful and efficient for statistical model estimation, especially for complicated models …

Saturated diagnostic classification models (DCM) can flexibly accommodate various
relationships among attributes to diagnose individual attribute mastery, and include various …

In joint models for item response times (RTs) and response accuracy (RA), local item
dependence is composed of local RA dependence and local RT dependence. The two …