Context: Computing Education Research (CER) is critical to help the computing education community and policy makers support the increasing population of students who need to learn computing skills for future careers. For a community to systematically advance knowledge about a topic, the members must be able to understand published work thoroughly…

We propose cross-disciplinary learning as a construct that can guide instruction and assessment in programs that feature sequential learning across multiple science disciplines. Crossdisciplinary learning combines insights from interdisciplinary learning, transfer, and resources frameworks and highlights the processes of resource activation,…

Purpose: A literature research conducted in education and agricultural education journals published during a period of 10 years revealed that 98% of the studies used parametric analyses. In general, model assumptions were not tested, and statistical criteria were not followed to apply the parametric approach. The objective of this paper is to…

Given the inadequacy of assessed outcomes (e.g., final exam) and the importance of evaluating the learning process in STEM education, we use deep learning to develop the STEM learning behavior analysis system (SLBAS) to assess the behavior of learners in STEM education. We map learner behavior to the ICAP (interactive, constructive, active,…

Mathematics teacher educators play a key role in supporting secondary mathematics teachers' development of effective, research-based formative assessment (FA) practices. We used qualitative research synthesis as a tool to identify actionable recommendations for mathematics teacher educators as they work with teachers on FA practices in secondary…

When numerous treatments exist for a disease (Treatments 1, 2, 3, etc), network meta-regression (NMR) examines whether each relative treatment effect (eg, mean difference for 2 vs 1, 3 vs 1, and 3 vs 2) differs according to a covariate (eg, disease severity). Two consistency assumptions underlie NMR: consistency of the treatment effects at the…

The effect sizes of studies included in a meta-analysis do often not share a common true effect size due to differences in for instance the design of the studies. Estimates of this so-called between-study variance are usually imprecise. Hence, reporting a confidence interval together with a point estimate of the amount of between-study variance…

A systematic review and meta-analysis is an important step in evidence synthesis. The current paradigm for meta-analyses requires a presentation of the means under a random-effects model; however, a mean with a confidence interval provides an incomplete summary of the underlying heterogeneity in meta-analysis. Prediction intervals show the range…

Physics education researchers (PER) commonly use complete-case analysis to address missing data. For complete-case analysis, researchers discard all data from any student who is missing any data. Despite its frequent use, no PER article we reviewed that used complete-case analysis provided evidence that the data met the assumption of missing…

A common goal in discipline-based education research (DBER) is to determine how to improve student outcomes. Linear regression is a common technique used to test hypotheses about the effects of interventions on continuous outcomes (such as exam score) as well as control for student nonequivalence in quasirandom experimental designs. (In…

Detection of differential item functioning (DIF) is needed in the development of tests to obtain useful items. The Mantel-Haenszel method and standardization are tools for DIF detection based on classical theory assumptions. The study was conducted to highlight the sensitivity and accuracy between the Mantel-Haenszel method and the standardization…

School leaders today are making important decisions regarding education innovations based on published average effect sizes, even though few understand exactly how effect sizes are calculated or what they mean. This article explains how average effect sizes are determined in meta-analyses and the importance of including measures of variability…

Utilizing the perspective of finite mixture modeling, this note considers whether a finding of a plausible one-parameter logistic model could be spurious for a population with substantial unobserved heterogeneity. A theoretically and empirically important setting is discussed involving the mixture of two latent classes, with the less restrictive…

Purpose: Null hypothesis significance testing is commonly used in audiology research to determine the presence of an effect. Knowledge of study outcomes, including nonsignificant findings, is important for evidence-based practice. Nonsignificant "p" values obtained from null hypothesis significance testing cannot differentiate between…

This note confronts the common use of a single coefficient alpha as an index informing about reliability of a multicomponent measurement instrument in a heterogeneous population. Two or more alpha coefficients could instead be meaningfully associated with a given instrument in finite mixture settings, and this may be increasingly more likely the…