Research

The modern information landscape offers an abundance of options to learn about science topics, but it is also ripe for the spread of mis- and disinformation and science denial. Science education can play a pivotal role in mitigating harm from untruthful information, strengthening trust in science, and fostering a more informed and critically engaged public. Across the articles in this special issue, 10 pedagogical strategies to address mis- and disinformation in the classroom were synthesized.

With a decade passing since the release of the Next Generation Science Standards (NGSS), it is timely to reflect and consider the extent to which the promise of science teaching and learning that values and centers learners’ varied epistemologies for scientific sensemaking has been realized. We argue that this potential, in part, lies in the hands of our science education research community becoming aware and intentional with how we situate learners’ language-related resources and practices in our work.

This is the project-created training course for new users to learn about the STEM-OP and how to use it.

This is the project-created training course for new users to learn about the STEM-OP and how to use it.

This is the project-created training course for new users to learn about the STEM-OP and how to use it.

Posterior Predictive Model Checking (PPMC) is frequently used for model fit evaluation in Bayesian Confirmatory Factor Analysis (BCFA). In standard PPMC procedures, model misfit is quantified by comparing the location of an ML-based point estimate to the predictive distribution of a statistic. When the point estimate is far from the center posterior predictive distribution, model fit is poor. Not included in this approach, however, is the variability of the Maximum Likelihood (ML)-based point estimates.

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).