To evaluate multidimensional factor structure, a popular method that combines features of confirmatory and exploratory factor analysis is Bayesian structural equation modeling with small-variance normal priors (BSEM-N). This simulation study evaluated BSEM-N as a variable selection and parameter estimation tool in factor analysis with sparse…

Structural equation models comprise a large class of popular statistical models, including factor analysis models, certain mixed models, and extensions thereof. Model estimation is complicated by the fact that we typically have multiple interdependent response variables and multiple latent variables (which may also be called random effects or…

New knowledge tracing models are continuously being proposed, even at a pace where state-of-the-art models cannot be compared with each other at the time of publication. This leads to a situation where ranking models is hard, and the underlying reasons of the models' performance -- be it architectural choices, hyperparameter tuning, performance…

Data from student learning provide learning curves that, ideally, demonstrate improvement in student performance over time. Existing data mining methods can leverage these data to characterize and improve the domain models that support a learning environment, and these methods have been validated both with already-collected data, and in…

When item scores are ordered categorical, categorical omega can be computed based on the parameter estimates from a factor analysis model using frequentist estimators such as diagonally weighted least squares. When the sample size is relatively small and thresholds are different across items, using diagonally weighted least squares can yield a…

This article reviews the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) in model selection and the appraisal of psychological theory. The focus is on latent variable models, given their growing use in theory testing and construction. Theoretical statistical results in regression are discussed, and more important…

Identifying collaboration between learners in a course is an important challenge in education for two reasons: First, depending on the courses rules, collaboration can be considered a form of cheating. Second, it helps one to more accurately evaluate each learners competence. While such collaboration identification is already challenging in…

In this paper four classes of procedures for measuring the instructional sensitivity of reading comprehension test items are reviewed. True experimental designs are not recommended because some of the most important reading comprehension variables do not lend themselves to experimental manipulation. "Ex post facto" factorial designs are…