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This article examines how federal/state-level policy guidance and local context have influenced district and school leader responses to the COVID-19 pandemic, as well as how these external/internal factors might provide a window into K-12 crisis leadership and policy sensemaking more broadly.

Unlike many types of educational crises, the COVID-19 pandemic was a crisis leaders could not have prepared for. But research is only starting to examine how leaders’ responses and organisational context have played a role. This mixed-methods study accordingly examines how leaders have mitigated challenges presented by the COVID-19 pandemic, and what local factors are most salient.

Our conceptual framework for justice-centered STEM education engages all students in multiple STEM subjects, including data science and computer science, to explain and design solutions to pressing societal challenges and their disproportionate impact on minoritized groups. In the first part of this article, we extend our conceptual framework by articulating the affordances of justice-centered STEM education for one minoritized student group that has been traditionally denied meaningful STEM learning experiences: multilingual learners (MLs). In the second part of the article, we report on an initial inquiry into how 14 undergraduate pre-service teachers made sense of our conceptual framework after participating in lessons from our COVID-19 instructional unit.

Despite an increasing focus on integrating engineering design in K-12 settings, relatively few studies have investigated how to support students to engage in systematic processes to optimize the designs of their solutions. This study explored how middle school, high school, and pre-service students optimized the design of a home for energy efficiency, size, and cost using facets of fluency, flexibility, closeness, and quality.

Despite an increasing focus on integrating engineering design in K-12 settings, relatively few studies have investigated how to support students to engage in systematic processes to optimize the designs of their solutions. This study explored how middle school, high school, and pre-service students optimized the design of a home for energy efficiency, size, and cost using facets of fluency, flexibility, closeness, and quality.

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.

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.

Examining teachers’ knowledge on a large scale involves addressing substantial measurement and logistical issues; thus, existing teacher knowledge assessments have mainly consisted of selected-response items because of their ease of scoring. Although open-ended responses could capture a more complex understanding of and provide further insights into teachers’ thinking, scoring these responses is expensive and time consuming, which limits their use in large-scale studies. In this study, we investigated whether a novel statistical approach, topic modeling, could be used to score teachers’ open-ended responses and if so, whether these scores would capture nuances of teachers’ understanding.