Christopher Glen Thompson

This study synthesizes empirical studies on the digital competencies of K‐12 in‐service teachers, examining the definition of digital competence as well as the research purpose, methodologies, outcomes and limitations of a total of 33 studies. This paper aims to systematically assess published studies using the Mixed Methods Appraisal Tool (MMAT), which enables the evaluation of qualitative, quantitative and mixed‐methods research to ensure a thorough and balanced appraisal of their methodological rigour. The rationale for this review stems from the need to address inconsistencies and gaps in the existing literature regarding digital competence among K‐12 in‐service teachers. While previous studies have provided useful insights into digital competence for university and pre‐service teachers, many rely on generalized definitions that overlook the complexities of diverse educational environments. This review seeks to clarify the concept definition of digital competence by suggesting an inductive approach to digital competence, acknowledging that the required digital skills may differ significantly across contexts, such as geographical locations, available resources and subject‐specific demands. Thus, this review not only contributes to a more nuanced understanding of digital competence but also guides future research, ensuring that studies are aligned with the specific needs of K‐12 in‐service teachers in diverse educational settings. This will help avoid both oversimplification and underspecification, enabling more precise and context‐responsive applications of digital competence in K‐12 in‐service teacher education. Context and implications: Rationale for this study: This systematic literature review addresses the gap in defining and applying digital competence among K‐12 in‐service teachers, emphasizing contextual variations. It synthesizes empirical studies to examine key research purposes, methods, outcomes and limitations, providing deeper insights and informing future research towards a more nuanced understanding of in‐service teachers' digital competence. Why the new findings matter: Our findings contribute to advancing understanding by critically examining how digital competence is conceptualized and operationalized for K‐12 in‐service teachers. By identifying the key competency areas, influencing factors, and methodological gaps in existing research, the review offers actionable insights to support the development of in‐service teachers' digital competence. Implications for teacher educators, policymakers, and researchers: Teacher educators should incorporate digital competence development into continuous learning programmes, incorporating formative and performance assessments to track teachers' pedagogical and technological skills. Policymakers should implement structured, longitudinal professional development programmes that systematically monitor teachers' digital competence through competency assessments at key career stages. Beyond self‐reports, policies should integrate performance‐based evaluations, classroom observations, and peer or collegial feedback. Researchers should conduct long‐term investigations that monitor teachers' digital competence in real time through mixed methods, ensuring reliable and valid instruments that address both fundamental (e.g., digital literacy, ethical use) and subskills (e.g., cybersecurity awareness) essential for effective technology integration.

Most meta-analytic methods examine effects across a collection of primary studies. We introduce an application of meta-analytic techniques to estimate effects and homogeneity within a single, primary study consisting of multiple, pretest-intervention-posttest units. This novel assessment was used to validate the recently created “Common Cause” (CC) design. In each case, we established the CC design by eliminating control groups from randomized studies, thereby deconstructing each experiment. This deconstruction enabled us to compare difference-in-difference results in randomized designs with a control group to pretest-posttest differences in a CC design without a control group. Meta-analysis results of multiple OXO effects from the CC designs were compared to meta-analytic effects of multiple randomized studies. This within-study-comparison logic and associated analyses produced consistent similarity between CC and validating-study results when directions of findings and patterns of statistical significance were considered. We provide plausible explanations for varying CC effect-size estimates, describe strengths and limitations, and address future research directions.

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A meta-analytic review of studies that experimentally elicited awe and compared the emotion to other conditions (84; 487 effects; 17,801 participants) examined the degree to which experimentally elicited awe (1) affects outcomes relative to other positive emotions (2) affects experience, judgment, behaviour, and physiology, and (3) differs in its effects if the awe state was elicited through positive or threatening contexts. The efficacy of methods that have been used to experimentally elicit awe and the possibility of assessing changes in the state of the self with experimental awe elicitations were also examined. Meta-analyses with robust variance estimation revealed that awe affected outcomes compared to other positive emotions and control conditions; affected experience, judgment, and behaviour; and had similar effects if elicited through positive or threatening contexts. The ability to compare awe to negative emotion states and its effects on physiology was limited by a small number of available effects. Images, videos, autobiographical recall, and naturalistic exposure were effective in eliciting awe. Exploratory analyses suggested that some processes involved in changes in the self can be related to experimental awe elicitations. These findings suggest awe is a discrete emotion and identifies areas for future investigation.

The National Reading Panel identified phonemic awareness (PA) as one of the five components of reading and found explicit instruction effective in developing PA skills in students. In the current meta‐analysis, we explored the extent to which PA instruction was effective for developing PA skills in preschool through first‐grade students and examined moderators related to malleable instructional characteristics relevant to the effective implementation of PA instruction in schools (e.g., type of instructor, specifically including computer programs and parents aside from teachers as instructors) and their possible interactions. We collected data from 46 experimental and quasi‐experimental primary studies, with 59 independent samples providing 119 effect sizes (treatment groups, N = 1892; control groups, N = 1747). Using robust variance estimation, PA instruction was moderately effective (g = 0.63, p < .001) at improving PA outcomes. No statistically significant difference was found for type of instructor. Other moderator analyses revealed no significant differences for group size, duration, PA skills taught, use of letters, grade level, at‐risk for reading difficulties status, or English language learner status. Finally, examination of the interaction between type of instructor and at‐risk status suggested teachers, computer programs, and parents are effective instructors for both at‐risk and low‐risk students. As schools plan for effective PA instruction, results indicate computer programs and parents can be useful resources to build PA skills for students to supplement teacher instruction.

Driven by the movement of evidence-based practices, Bayesian statistical methods have become increasingly popular. This paper introduces a Bayesian approach to meta-regression, focusing on the use and implementation in prevention science research. We first compare Bayesian meta-analysis and meta-regression to a frequentist approach. Thereafter, we illustrate Bayesian methods in meta-regression, highlighting advantages, providing detailed interpretation, and presenting results. The example is completed using several R packages. We also provide annotated R code for readers as a foundation for their own research.

Bayesian methods are an important set of tools for performing meta-analyses. They avoid some potentially unrealistic assumptions that are required by conventional frequentist methods. More importantly, meta-analysts can incorporate prior information from many sources, including experts’ opinions and prior meta-analyses. Nevertheless, Bayesian methods are used less frequently than conventional frequentist methods, primarily because of the need for nontrivial statistical coding, while frequentist approaches can be implemented via many user-friendly software packages. This article aims at providing a practical review of implementations for Bayesian meta-analyses with various prior distributions. We present Bayesian methods for meta-analyses with the focus on odds ratio for binary outcomes. We summarize various commonly used prior distribution choices for the between-studies heterogeneity variance, a critical parameter in meta-analyses. They include the inverse-gamma, uniform, and half-normal distributions, as well as evidence-based informative log-normal priors. Five real-world examples are presented to illustrate their performance. We provide all of the statistical code for future use by practitioners. Under certain circumstances, Bayesian methods can produce markedly different results from those by frequentist methods, including a change in decision on statistical significance. When data information is limited, the choice of priors may have a large impact on meta-analytic results, in which case sensitivity analyses are recommended. Moreover, the algorithm for implementing Bayesian analyses may not converge for extremely sparse data; caution is needed in interpreting respective results. As such, convergence should be routinely examined. When select statistical assumptions that are made by conventional frequentist methods are violated, Bayesian methods provide a reliable alternative to perform a meta-analysis.