Research

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, both of which are needed to ensure the meaning of classes and model parameters. In this paper, we develop an efficient, Gibbs sampling-based Bayesian Markov chain Monte Carlo estimation method for general DCMs with monotonicity constraints. A simulation study was conducted to evaluate parameter recovery of the algorithm which showed accurate estimation of model parameters.

The purpose of this study was to examine the effects of different data conditions on item parameter recovery and classification accuracy of three dichotomous mixture item response theory (IRT) models: the Mix1PL, Mix2PL, and Mix3PL. Manipulated factors in the simulation included the sample size (11 different sample sizes from 100 to 5000), test length (10, 30, and 50), number of classes (2 and 3), the degree of latent class separation (normal/no separation, small, medium, and large), and class sizes (equal vs. nonequal).

Artificial neural networks with a specific autoencoding structure are capable of estimating parameters for the multidimensional logistic 2-parameter (ML2P) model in item response theory (Curi et al. in International joint conference on neural networks (IJCNN), 2019), but with limitations, such as uncorrelated latent traits. In this work, we extend variational auto encoders (VAE) to estimate item parameters and correlated latent abilities, and directly compare the ML2P-VAE method to more traditional parameter estimation methods, such as Monte Carlo expectation-maximization.

Diagnostic classification models (DCMs) are psychometric models for evaluating a student’s mastery of the essential skills in a content domain based upon their responses to a set of test items. Currently, diagnostic model and/or Q-matrix misspecification is a known problem with limited avenues for remediation. To address this problem, this paper defines a one-sided score statistic that is a computationally efficient method for detecting under-specification at the item level of both the Q-matrix and the model parameters of the particular DCM chosen in an analysis.

A growing number of studies have shown the benefits of K-12 integrated science and engineering education. With this study, we add to the literature by documenting the relationship between STEM learning and engagement, and the demographic characteristics that impact achievement in STEM. This longitudinal study followed a diverse group of 245 middle school students from sixth grade to eighth grade. Students in two cohorts, cohort I and cohort II, participated in three different integrated STEM units during middle school, one in each grade level.

This book is about scientific inquiry. Designed for early and mid-career researchers, it is a practical manual for conducting and communicating high-quality research in (mathematics) education. Based on the authors’ extensive experience as researchers, as mentors, and as members of the editorial team for the Journal for Research in Mathematics Education (JRME), this book directly speaks to researchers and their communities about each phase of the process for conceptualizing, conducting, and communicating high-quality research in (mathematics) education.

These narratives explore what it might entail to begin school–university partnerships towards the goal of transformative social changes through the voices of two women scholars of color. Using two school–university partnerships as focal cases, we unpack the complexity, tensions, and possibilities that arise through collaborations driven by the objective to promote new and more just forms of science learning within public schools.