Research

This study examines the demographics, qualifications, and turnover of STEM teachers in Kansas and Missouri—two contiguous, predominantly rural states in the Midwestern region of the United States. The existing literature lacks detailed insights regarding U.S. STEM teachers, especially with recent economic and social changes over the COVID-19 pandemic, and there is particularly limited evidence regarding STEM teachers in the U.S. Midwest. Utilizing large-scale administrative longitudinal data, we filled part of this gap by documenting the characteristics and turnover patterns of STEM teachers in Kansas and Missouri over a 13-year period, from 2010 through 2023.

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.

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.

This report takes stock of what we currently know as well as what we need to know to make classroom assessment maximally beneficial for the teaching and learning of STEM subject matter in K–12 classrooms.

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. We propose a new method of PPMC based on comparing posterior predictive distributions of a hypothesized and saturated BCFA model.

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.