COVID-19 led to school closures and the necessity to use remote learning in 2020 and 2021 around the globe. This article provides results for a three-level random-effects meta-analysis examining the average effect of the COVID-19-related school closures with respect to several moderator variables. The results showed a robust average effect of $$d = - 0.175( SE = 0.063$$, $$p = 0.013,95\% {\rm{CI}}[ { - 0.308, - 0.041} ] )$$. The moderator analysis was largely insignificant; however, the results tentatively point out that younger students in schools were more negatively affected compared to older students, and that the negative effect reduced with subsequent lockdowns in autumn and winter 2020/2021. The results are discussed with respect to potential explanations.

Given the lack of active nurses in industrialized countries throughout the world, in combination with demographic changes, it is of utmost importance to protect nurses’ well-being and to prevent psychological distress, because of their strong association with premature occupational leave. The aim of this study was to investigate the effects of quality of leadership and social support at work on well-being and psychological distress of nurses and to determine whether nurses’ overcommitment mediates the relationship between the abovementioned determinants and the outcomes. A cross-sectional survey design was used to gather our data. This study utilized part of the database of the Nurses’ Early Exit Study. A total of 34,771 nurses (covering all nurse qualifications) working in hospitals, nursing homes, and home-care institutions in 8 European countries filled out a questionnaire (response rate = 51.4%). For all model variables (job satisfaction, satisfaction with salary, positive affectivity, personal burnout, negative affectivity, quality of leadership, social support from immediate supervisor, social support from near colleagues, and overcommitment), psychometrically sound, that is, valid and reliable measures were used. Outcomes from testing a structural equation mediation model indicated that, respectively, positive and negative influences of leadership quality and social support from supervisor and colleagues on nurses’ well-being and psychological distress are partially mediated, that is, reduced, by nurses’ overcommitment. Social work environment is highly important in relation to nurses’ well-being and psychological distress.

The ability of a scientific discipline to build cumulative knowledge depends on its predominant method of data analysis. A steady accumulation of knowledge requires approaches which allow researchers to consider results from comparable prior research. Bayesian statistics is especially relevant for establishing a cumulative scientific discipline, because the incorporation of background (or prior) knowledge is fundamentally anchored in its basic principles. The aim of the current systematic review is to provide insights into the current state of methodological affairs in educational research, with a focus on Bayesian statistics and the use of prior information. An analysis of publication histories of the 224 educational journals currently listed in the Thomson Reuters Journal Citation Report 2015 indicates that Bayesian statistics is primarily used to solve methodological problems, rather than used to build cumulative knowledge based on a combination of study results with comparable prior research. The utilisation of Bayesian statistics is motivated by its flexibility: models are estimated which would not be estimable with frequentist approaches, thus expanding the methodological repertoire of educational researchers and producing knowledge which otherwise would not have been available. Lastly, the predominant use of noninformative prior distributions indicates that one of the biggest advantages of Bayesian statistics, namely the combination of study results with comparable prior research, remains underutilised in educational research. Practical implications of these findings for educational research are illustrated and discussed.

The COVID-19 pandemic led to numerous governments deciding to close schools forseveral weeks in spring 2020. Empirical evidence on the impact of COVID-19-relatedschool closures on academic achievement is only just emerging. The present workaimed to provide a first systematic overview of evidence-based studies on generaland differential effects of COVID-19-related school closures in spring 2020 on studentachievement in primary and secondary education. Results indicate a negative effect ofschool closures on student achievement, specifically in younger students and studentsfrom families with low socioeconomic status. Moreover, certain measures can beidentified that might mitigate these negative effects. The findings are discussed in thecontext of their possible consequences for national educational policies when facingfuture school closures.

Large‐scale assessments such as the Programme for International Student Assessment (PISA) have field trials where new survey features are tested for utility in the main survey. Because of resource constraints, there is a trade‐off between how much of the sample can be used to test new survey features and how much can be used for the initial item response theory (IRT) scaling. Utilizing real assessment data of the PISA 2015 Science assessment, this article demonstrates that using fixed item parameter calibration (FIPC) in the field trial yields stable item parameter estimates in the initial IRT scaling for samples as small as n = 250 per country. Moreover, the results indicate that for the recovery of the county‐specific latent trait distributions, the estimates of the trend items (i.e., the information introduced into the calibration) are crucial. Thus, concerning the country‐level sample size of n = 1,950 currently used in the PISA field trial, FIPC is useful for increasing the number of survey features that can be examined during the field trial without the need to increase the total sample size. This enables international large‐scale assessments such as PISA to keep up with state‐of‐the‐art developments regarding assessment frameworks, psychometric models, and delivery platform capabilities.