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Scientific argumentation skills are a key component of science learning practices needed by students in the 21st century. Where the essence of scientific argumentation is to support the argument with evidence and reasoning and then refute the claims and evidence of the opponent's argument. Supporting arguments with evidence and reasoning can be facilitated through inquiry activities. Meanwhile, refuting claims and evidence from opposing arguments can be facilitated through debate activities. Inquiry and debate can be a solution to improving students' scientific argumentation skills. This study aims to prove the truth of this statement through a systematic literature review by applying the PRISMA 2020 criteria which are expanded by bibliometric analysis with the help of the VOSviewer software. The results of the study show that research related to inquiry, debate, and their influence on students' scientific argumentation skills has a pretty good trend. In Indonesia the research climate is still quite good, research topics are still in great demand, and it is very possible to carry out research innovations on related topics. Another finding is that the integration of inquiry and debate in science learning can be a potential strategy to improve students' scientific argumentation skills.

The present study contributes to the development of effective measures to evaluate classroom climate, especially in elementary education where these remain limited. In addition, it addresses a typical "flaw" of several studies by approaching classroom climate as a group-level construct rather than an individual characteristic. Following a multilevel approach, we attempted to establish validity evidence for an adapted version of the Classroom Behavioral Climate (CBC) questionnaire and examine its equivalence across two contexts. The participants included 2963 students from 176 fourth and fifth grade classes. Multilevel Confirmatory Factor Analyses resulted in a shorter version of the CBC with three correlated factors in both countries. Two-level models performed better than the single-level models. Configural, metric, and partial scalar invariance was also achieved. The study highlights the need for researchers and practitioners to use appropriate methods and instruments to evaluate the classroom environment based on their research aims and intended use.

Some patients benefit from a treatment while others may do so less or do not benefit at all. We have previously developed a two-stage network meta-regression prediction model that synthesized randomized trials and evaluates how treatment effects vary across patient characteristics. In this article, we extended this model to combine different sources of types in different formats: aggregate data (AD) and individual participant data (IPD) from randomized and non-randomized evidence. In the first stage, a prognostic model is developed to predict the baseline risk of the outcome using a large cohort study. In the second stage, we recalibrated this prognostic model to improve our predictions for patients enrolled in randomized trials. In the third stage, we used the baseline risk as effect modifier in a network meta-regression model combining AD, IPD randomized clinical trial to estimate heterogeneous treatment effects. We illustrated the approach in the re-analysis of a network of studies comparing three drugs for relapsing--remitting multiple sclerosis. Several patient characteristics influence the baseline risk of relapse, which in turn modifies the effect of the drugs. The proposed model makes personalized predictions for health outcomes under several treatment options and encompasses all relevant randomized and non-randomized evidence.

Data extraction is a crucial, yet labor-intensive and error-prone part of evidence synthesis. To date, efforts to harness machine learning for enhancing efficiency of the data extraction process have fallen short of achieving sufficient accuracy and usability. With the release of large language models (LLMs), new possibilities have emerged to increase efficiency and accuracy of data extraction for evidence synthesis. The objective of this proof-of-concept study was to assess the performance of an LLM (Claude 2) in extracting data elements from published studies, compared with human data extraction as employed in systematic reviews. Our analysis utilized a convenience sample of 10 English-language, open-access publications of randomized controlled trials included in a single systematic review. We selected 16 distinct types of data, posing varying degrees of difficulty (160 data elements across 10 studies). We used the browser version of Claude 2 to upload the portable document format of each publication and then prompted the model for each data element. Across 160 data elements, Claude 2 demonstrated an overall accuracy of 96.3% with a high test-retest reliability (replication 1: 96.9%; replication 2: 95.0% accuracy). Overall, Claude 2 made 6 errors on 160 data items. The most common errors (n = 4) were missed data items. Importantly, Claude 2's ease of use was high; it required no technical expertise or labeled training data for effective operation (i.e., zero-shot learning). Based on findings of our proof-of-concept study, leveraging LLMs has the potential to substantially enhance the efficiency and accuracy of data extraction for evidence syntheses.

Qualitative comparative analysis (QCA) is a hybrid method designed to bridge the gap between qualitative and quantitative research in a case-sensitive approach that considers each case holistically as a complex configuration of conditions and outcomes. QCA allows for multiple conjunctural causation, implying that it is often a combination of conditions that produces an outcome, that multiple pathways may lead to the same outcome, and that in different contexts, the same condition may have a different impact on the outcome. This approach to complexity allows QCA to provide a practical understanding for complex, real-world situations, and the context of implementing interventions. There are guides for conducting QCA in primary research and quantitative systematic reviews yet, to our knowledge, no guidance for conducting QCA in systematic mixed studies reviews (SMSRs). Thus, the specific objectives of this paper are to (1) describe a step-by-step approach for novice researchers for using QCA to integrate qualitative and quantitative evidence, including guidance on how to use software; (2) highlight specific challenges; (3) propose potential solutions from a worked example; and (4) provide recommendations for reporting.

This scoping review aims to identify and systematically review published mapping reviews to assess their commonality and heterogeneity and determine whether additional efforts should be made to standardise methodology and reporting. The following databases were searched; Ovid MEDLINE, Embase, CINAHL, PsycINFO, Campbell collaboration database, Social Science Abstracts, Library and Information Science Abstracts (LISA). Following a pilot-test on a random sample of 20 citations included within title and abstracts, two team members independently completed all screening. Ten articles were piloted at full-text screening, and then each citation was reviewed independently by two team members. Discrepancies at both stages were resolved through discussion. Following a pilot-test on a random sample of five relevant full-text articles, one team member abstracted all the relevant data. Uncertainties in the data abstraction were resolved by another team member. A total of 335 articles were eligible for this scoping review and subsequently included. There was an increasing growth in the number of published mapping reviews over the years from 5 in 2010 to 73 in 2021. Moreover, there was a significant variability in reporting the included mapping reviews including their research question, priori protocol, methodology, data synthesis and reporting. This work has further highlighted the gaps in evidence synthesis methodologies. Further guidance developed by evidence synthesis organisations, such as JBI and Campbell, has the potential to clarify challenges experienced by researchers, given the magnitude of mapping reviews published every year.

Prediction-based accounts of language acquisition have the potential to explain several different effects in child language acquisition and adult language processing. However, evidence regarding the developmental predictions of such accounts is mixed. Here, we consider several predictions of these accounts in two large-scale developmental studies of syntactic priming of the English dative alternation. Study 1 was a cross-sectional study (N = 140) of children aged 3-9 years, in which we found strong evidence of abstract priming and the lexical boost, but little evidence that either effect was moderated by age. We found weak evidence for a prime surprisal effect; however, exploratory analyses revealed a protracted developmental trajectory for verb-structure biases, providing an explanation as for why prime surprisal effects are more elusive in developmental populations. In a longitudinal study (N = 102) of children in tightly controlled age bands at 42, 48, and 54 months, we found priming effects emerged on trials with verb overlap early but did not observe clear evidence of priming on trials without verb overlap until 54 months. There was no evidence of a prime surprisal effect at any time point and none of the effects were moderated by age. The results relating to the emergence of the abstract priming and lexical boost effects are consistent with prediction-based models, while the absence of age-related effects appears to reflect the structure-specific challenges the dative presents to English-acquiring children. Overall, our complex pattern of findings demonstrates the value of developmental data sets in testing psycholinguistic theory.

This study provides a comprehensive overview of methodological aspects when using heart rate variability (HRV) measures in educational research. Following PRISMA 2020 guidelines, we searched four databases for relevant studies published until March 5, 2024. From the 48 studies reviewed, we extracted data across three analytical categories: (1) area of study interest and participant populations, (2) data collection and analysis methodologies, and (3) the concurrent and predictive validity of HRV measurement for educational research. Study quality was evaluated using QualSyst assessment criteria. Most studies measured stress and enlisted undergraduate students as participants. Data were predominately collected using wearable devices, measuring HRV for durations of less than 30 min, and in varied contexts, including during exams, while learning, and in experiments. The parameters analyzed varied within both time and frequency domains. HRV data had a moderate level of concurrent validity as a measure of stress in an educational context. The concurrent validity of HRV data for measuring attention remains uncertain with insufficient evidence. Limited correlations appeared between stress and performance. The findings, potentials, and limitations of HRV measures are discussed, and synthesized recommendations for educational research using HRV data are provided.

This paper explored the link between information literacy (IL) and other factors that enable or inhibit the utilisation of research evidence in policymaking in Zimbabwe. The study assumes that if policymakers possess appropriate IL skills to access, assess, synthesise, and apply research evidence, they will naturally use the evidence to inform their policy decisions. Face-to-face interviews with 26 policymakers -- technocrats selected from the Parliament of Zimbabwe and two ministries, Industry and Commerce, and Youth, Sport, and Recreation -- produced evidence to inform the findings and conclusions. Data synthesis using thematic content analysis confirmed the findings. The results show that while IL skills are critical in enabling policymakers' use of research evidence, multiple other factors also influence the use of research evidence in policymaking due to the complexity of the process. The political and socioeconomic context plays a profound role because of the intricate and nonlinear nature of the policymaking process. Therefore, enhancing evidence use in policymaking revolves around strengthening IL skills at the individual level, including institutional and the broader policy ecosystem, by acknowledging and leveraging personal and institutional relationships. This insight illuminates the need to reorient IL programmes to link them to these other factors.

McKinney (2018) has argued that for the scholarship of teaching and learning (SoTL) to advance within a discipline, the integration of SoTL must be closely examined and opportunities for growth in SoTL must be recognized and discussed. To that end, this paper reflects on the degree to which SoTL is integrated into communication sciences and disorders (CSD) by examining a variety of topics: perspectives and theories historically valued by our discipline, existing supports for SoTL at various levels (i.e., individual teacher-scholars, departments, institutions, and the CSD discipline as a whole), the application of SoTL findings in teaching and learning. Four specific recommendations are made because of this examination and reflection.

Research has previously examined the role of the epistemological background of students in accepting or rejecting the theory of evolution. In this study, the participants were 11 undergraduate biology major students in a private university in Beirut, Lebanon. We collected data through semi-structured interviews, and we extracted the patterns regarding how they perceived tentativeness, what constituted acceptable evidence for them to accept the theory of evolution and the role of religion in their lives. Our findings indicated that, unlike their professor, students uncertain about the theory or rejecting it did so due to the tentative nature of science. They also rejected historical evidence as valid evidence in science, struggled to form a cooperative relationship between evolution and religion and refused common descent because they considered humans as superior organisms. We discuss the implications of those results for curriculum and instruction.

In current educational research communications, especially in English and increasingly also in German publications, the term "evidence" refers to international homogenizing gold standards and is often linked to European evidence policies, large scale assessments and justified and proven knowledge. Against the background of international communication and the related circulation of terms and concepts, this paper analyses German and English OECD publications that recommend the development of educational research in 1970s and 90s. In England, the OECD and thus the external perception of their own education system hardly played a significant role due to years of awareness of the need for reform. Rather, Hargreaves's lecture (1996) about the disappointing effects of educational research when compared with the achievements of evidence-based medicine was decisive for the evidence movement. In addition, non-university institutions have gained legitimacy through acting as "evidence" providers for the school system. Based on analyses of German educational research literature, we show that "evidence" appeared in neurological, medical, technological, and economic texts in the 90s and early 2000s. Usage of "evidence" increased after the PISA shock in 2000 and is now linked to the expression of disciplinary development into empirical educational research. However, based on relatively stable patterns of communication and interpretation in two academic cultures, it is shown that the connectivity of an epistemological term like "evidence" does not necessarily reduce misunderstanding.

This article provides a conceptual framework for considering both the theoretical and methodological factors that underpin the successful validation of a competency framework. Drawing on educational assessment literature, this article argues that a valid competency framework relates to an interpretive judgement of the credibility of the claims made. To establish a credible approach to competency framework validation, there is a requirement to align the purposes of the competency framework, the claims developers make concerning the uses of the framework, and evidence collection methods to substantiate or challenge these claims. This article concludes with a template of questions for competency framework developers to consider in determining the range of potential claims to be made concerning their framework, and in understanding competency framework users and contexts.

Background: Local governments increasingly rely on sales taxes to raise revenue, often justifying the need for a local sales tax increase with a specific programmatic goal, such as better education or transportation. In Washington State, the legislature explained that a local sales tax increase was necessary to support criminal justice because criminal justice requires more police, courts, and jails. Objective: Informed by decades of literature questioning the efficacy of fighting crime through police, courts, and jails, the objective of this study seeks to use empirical evidence to explain whether the social fabric offers indicators that can better define criminal justice and thus inform local tax policy so that local sales tax revenue may be used to prevent crime rather than fight it. Method: The study compiled a 29-year history of 36 social variables across all 39 Washington counties to determine whether crime is predictable and what is most predictive of crime. Pearson coefficients of determination were calculated to identify cross-sectional associations between social variables and crime variables. An ARIMAX predictive model was then constructed to test the predictive power of the multivariate time series. Results: The study finds that crime is predictable and social observations specific to how a child grows up are consistently predictive explanatory variables of crime. There is reason to believe that the most effective action state and local governments can take to promote criminal justice and prevent crime is to leverage their taxing power to ensure that every child (a) has access to food and basic necessities, (b) is raised in a safe and stable home, and (c) graduates from college.

This paper describes a student-centered approach to teaching and learning physics in a high school classroom. The teacher designs and uses a 5E learning cycle to teach series and parallel electric circuits. This article provides an example of the implementation of 5E model for a physics lesson to address three-dimensional learning-science and engineering practices, disciplinary core ideas, and crosscutting concepts. In this lesson, students could make predictions, exchange ideas, and collect and analyze data to construct evidence-based explanations about electric circuits, Ohm's law. This type of lesson design is effective to enhance students' engagement in scientific practices and develop conceptual understanding.

Stereotypes can exert a powerful influence on our interactions with others, potentially leading to prejudice when factual evidence is ignored. Here, we identify neuroanatomical and developmental factors that influence the real-time integration of stereotypes and factual evidence during live social interactions. The study uses precisely quantified communicative exchanges in a longitudinal cohort of seventeen-year-olds followed since infancy, testing their ability to moderate stereotype tendencies toward children as contrary evidence accumulates. Our results indicate that the impact of stereotypes on communicative behavior is linked to individual variations in gray matter density and cortical thickness in the right anterior cingulate gyrus. In contrast, the ability to moderate stereotype tendencies is influenced by developmental exposure to social interactions during the initial years of life, beyond the effects of familial environment and later experiences. These findings pinpoint a key brain structure underlying stereotype tendencies and suggest that early-life social experiences have lasting consequences on how individuals integrate factual evidence in interpersonal communication.

The growing use of artificial intelligence in education extends and intensifies technologies of governing, including datafication, performativity and accountability. In this article, we outline how the use of AI and data science has the disruptive potential to create new norms in education policy and governance. We report on an ethnographic investigation into an education department initiative that involved data scientists using machine learning to identify causal links between different school-based and non-school based factors, and educational outcomes. The article proposes that machine learning is being introduced into this education department as a set of minor experiments in education governance. We focus on issues of changing expertise and evidence and differences in method between new data science approaches and established statistical expertise in this education department, in order to highlight minor but important new governance practices. We conclude that the increasing use of AI in education, as part of new cognitive infrastructures, is quietly shifting the ways in which education systems operate.

We explore ways in which statistics can be used to understand disease spread and support decision-making by governments. "Past performance does not guarantee future results"--we hope. We discuss and show examples from the National Science Foundation (NSF)-funded COVID-Inspired Data Science Education through Epidemiology (CIDSEE) project. Throughout, the emphasis is on the relationships between evidence, modeling and theorizing, and appropriate action. Statistics should be an essential element in all these aspects. We point to some "big statistical ideas" that underpin the whole process of modeling, which can be illustrated vividly in the context of pandemics. We argue that statistics education should emphasize the application of statistics in practical situations, and that many curricula do not equip students to use their understandings of statistics outside the classroom. We offer a framework for curriculum analysis and point to some rich teaching resources.

Previous research has shown that linguistic cues such as mental and modal verbs can influence young children's judgments about the reliability of informants. Further, certain languages include grammatical morphemes (i.e. evidential markers), which clarify the source of information coming from testimony (e.g., Bulgarian, Japanese, Turkish). Accordingly, the first aim of the current study is to examine whether Turkish-speaking children's reliability judgments change based on evidential markers (i.e. the past-tense "direct" evidential marker, -"DI", and the past-tense "indirect" evidential marker, -"mIs"). The literature has also investigated whether selective trust abilities are related to understanding the epistemic states of others (i.e., Theory of Mind). Therefore, the second aim is to examine the relation between selective trust based on evidential markers and ToM abilities by including a comprehensive ToM battery. Eighty-six Turkish-speaking preschool and elementary school children between the ages of 4 and 7, residing in a metropolitan city in Turkey, participated in a selective trust task based on evidential markers, a general language task, and a ToM battery. The results of the current study showed that after the age of 6, Turkish-speaking children start to selectively trust the informant using the past-tense direct evidential marker, -"DI", over the past-tense indirect evidential marker, -"mIs". Selective trust performance was related to receptive vocabulary but not to ToM abilities after controlling for participant gender and age. Overall, the results contribute to current discussions about children's selective trust ability based on linguistic cues and its relation with ToM.

The spread of misinformation has underscored the importance of cultivating citizens' competency to critically evaluate popular accounts of scientific evidence. Extending the prevailing emphasis on evidence in the natural sciences, we argue for fostering students' understanding of psychological evidence and its communication in the media. In this study, we illustrate how this goal can be advanced in undergraduate psychology instruction by actively engaging students in evidence evaluation and design. We employed the Grasp of Evidence framework to document students' evidence evaluation ideals and processes and how these changed over a course in which students engaged in a series of collaborative evidence evaluation and design tasks. Prior to instruction, students exhibited a mechanistic understanding of scientific methods, coupled with substantial reliance on personal experience. Following instruction, students demonstrated three key shifts in grasp of evidence: a shift in perception of the sources of psychological knowledge, a shift in views of scientific objectivity, and a shift in definitions of psychological phenomena. Analysis of students' collaborative discourse highlighted three design principles that supported increasingly complex understandings of psychological evidence: collaborative critique and redesign of flawed studies, engagement with diverse sources of popular evidence, and confronting elusive conceptual constructs.

The Interpersonal Behaviors Questionnaire (IBQ) in Sport assesses athlete perceptions of coach interpersonal behaviors that support or thwart athletes' basic psychological needs. While different sources of validity evidence for responses to the IBQ have been provided for adults, only validity evidence for relations to other variables has been provided with youth. Moreover, potential problems with internal structure for youths' responses to the IBQ have recently been noted and motivated the current study. Specifically, a three-phase mixed-methods design was employed to provide validity evidence for modification of the IBQ for youth. Phase I empirically examined the internal structure for youth athletes' responses to the IBQ. Phase II used cognitive think-aloud interviews to identify and then modify problematic IBQ items to create the Youth Interpersonal Behaviors Questionnaire in Sport (Y-IBQ). Phase III examined the psychometric properties of the Y-IBQ to provide initial validity evidence for this instrument in a youth athlete sample.

The purpose of this study was to explore the validity of evidence for self-efficacy to regulate physical activity scale (SERPA) measurement using an exploratory latent variable approach. The objectives were to explore the dimensionality, temporal invariance, and external validity of scores produced by the SERPA, a modified version of the barriers self-efficacy scale. Data (N[subscript baseline]= 461 and N[subscript 30 days post-baseline] = 427) from the Well-Being and Physical Activity Study (ClinicalTrials.gov, identifier: NCT03194854), which deployed the Fun For Wellness intervention, were analyzed. A two-dimensional factor structure explained responses to the SERPA at baseline. There was evidence for at least partial strict temporal measurement invariance for this two-dimensional structure. There was evidence for external validity of the SERPA measurement as evidenced by group assignment (i.e. the intervention) exerting a direct effect on the proposed two-dimensional structure of latent self-efficacy to regulate physical activity in adults with obesity at 30 days post-baseline.

Purpose: Educators need to engage in continuous learning to ensure that their knowledge and practice responds to the changing needs of society and students. Collaborative approaches, in which social capital resource is exchanged, can serve as an effective way of facilitating such learning. With this systematic review, the authors identify the opportunities and benefits inter-school social capital networks might bring by exploring: (1) what inter-school networks are available internationally for primary and secondary schools, (2) the features and activities present within inter-school networks and (3) evidence of impact of inter-school network activities. Design/methodology/approach: For this study, the authors employed a systematic review methodology. The review comprised the five stages of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses: PRISMA, 2021) protocol. The study findings derived from 111 research outputs (from a total of 1,221 originally identified). Findings: The review highlights a number of different inter-school networks and their diverse purposes as well as key network features, such as the actors present in networks and the activities network participants engage with. At the same time, however, the authors only identify limited reliable evidence of the impact of inter-school networks. Originality/value: The study fills a knowledge gap by exploring, for the first time, the presence, features of inter-school social capital networks available to school leaders as well as investigating the impact of these networks.

Anxiety and school fears are relatively frequent in childhood. Psychology and education professionals need to have assessment instruments for screening for school anxiety in schools. This study aimed to develop, adapt, and examine the reliability and validity evidence of the School Anxiety Inventory for Primary Education (SAI-PE) scores. Using random cluster sampling, a sample of 843 Spanish students (51.7% girls), ages ranging from 8 to 12 years (M = 10.03, SD = 1.25), was selected from public and concerted schools. The content validity of the SAI-PE scores was analyzed through experts' judgment and students' assessment of their comprehension of the instrument's items. Exploratory and confirmatory factorial analyses support the validity of the SAI-PE scores. The results revealed a multifactorial structure for the scores of anxiety reactions (cognitive, psychophysiological, and behavioral) and school situations scores (school punishment, victimization, social, and academic evaluation). The study conclusions reinforce the importance of having instruments that assess the complexity of emotional responses such as school anxiety, favoring its early detection and evaluating the effectiveness of preventive and/or therapeutic programs in schools.

Educational assessments, from kindergarden to 12th grade (K-12) to licensure, have a long, well-documented history of oppression and marginalization. In this paper, we (the authors) ask the field of educational assessment/measurement to actively disrupt the White supremacist and racist logics that fuel this marginalization and re-orient itself toward assessment justice. We describe how a justice-oriented, antiracist validity (JAV) approach to validation processes can support assessment justice efforts, specifically with respect to language assessment. Relying on antiracist principles and critical quantitative methodologies, a JAV approach proposes a set of critical questions to consider when gathering validity evidence, with potential utility for language testers.

Meta-analyses have become the gold standard for synthesizing evidence from multiple clinical trials, and they are especially useful when outcomes are rare or adverse since individual trials often lack sufficient power to detect a treatment effect. However, when zero events are observed in one or both treatment arms in a trial, commonly used meta-analysis methods can perform poorly. Continuity corrections (CCs), and numerical adjustments to the data to make computations feasible, have been proposed to ameliorate this issue. While the impact of various CCs on meta-analyses with rare events has been explored, how this impact varies based on the choice of pooling method and heterogeneity variance estimator is not widely understood. We compare several correction methods via a simulation study with a variety of commonly used meta-analysis methods. We consider how these method combinations impact important meta-analysis results, such as the estimated overall treatment effect, 95% confidence interval coverage, and Type I error rate. We also provide a website application of these results to aid researchers in selecting meta-analysis methods for rare-event data sets. Overall, no one-method combination can be consistently recommended, but some general trends are evident. For example, when there is no heterogeneity variance, we find that all pooling methods can perform well when paired with a specific correction method. Additionally, removing studies with zero events can work very well when there is no heterogeneity variance, while excluding single-zero studies results in poorer method performance when there is non-negligible heterogeneity variance and is not recommended.

Students' evidence-based reasoning was examined across two studies. In Study 1, students were asked to evaluate newspaper excerpts including anecdotal, descriptive, correlational, and causal evidence provided in support of causal claims as well as to justify their quality ratings for two of these excerpts. In Study 2, students' justifications for quality ratings were further probed and students were asked to select the criterion that they considered to be most important to consider in evaluating information. Key findings included that while students were fairly effective at discounting anecdotal evidence relative to evidence that was descriptive, correlational, and causal, students did not seem to distinguish among these three latter evidence types, when these were provided in support of causal claims. While students cited a variety of justification criteria for their quality evaluations, they were found to rate methods-related factors as more important to consider when evaluating quantitative, rather than anecdotal, evidence.

In educational research, there is the general trade-off that empirical evidence should be generalizable to be applicable across contexts; at the same time, empirical evidence should be as specific as possible to be localizable in subject-specific educational interventions to successfully transfer the empirical evidence to educational practice. This trade-off is further increased by the fact that the diverse instructional contexts, such as school or student characteristics constrain the applicability of empirical evidence. Several approaches have been proposed to address this issue, however, emphasized the different problems (i.e., localization, generalization, transferability) rather in an isolated manner. To this end, in this article, we introduce a synergistic approach, the LoGeT (localize, generalize, transfer) model, which systematically integrates co-design (localization strategies) and ManyClasses principles (generalization strategies) with co-constructive transfer activities, to generate empirical evidence that may be applicable in educational practice. To illustrate the LoGeT model, we present three long-term projects, covering different granularities and durations of educational interventions across different fields of education (teacher education, adaptive teaching, non-interactive teaching) that successfully applied the LoGeT approach. Finally, we outline further directions for future iterations of the LoGeT model. We hope that the LoGeT approach may be a stimulus to guide researchers as well as practitioners alike to design generalizable and evidence-based educational interventions that are rooted in localized instructional contexts.

Today's students live within a world filled with complexity, uncertainty, and misinformation; thus, educators need to help all learners, including students with learning disabilities, how to comprehend complex information about the natural world and make credible evidence-based claims. Our study is a first step in making this possible. In this investigation, our goal was for students to make a claim, use evidence and content-specific vocabulary, and then use reasoning to link claims with evidence in an argument. These outcomes were assessed using a pre-post design with a scenario-based assessment administered twice before and once after instruction, which used multimodal STEM text sets to fuse science and literacy learning. Data indicate that (a) both students with disabilities and those without disabilities made significant gains in argumentation and (b) the effects of instruction were similar for both groups of students. Gains for students with disabilities suggest the multimodal STEM text sets provided important scaffolding that enabled this group to learn important content in the general education classroom.

Global climate change (GCC) is one of the greatest challenges of our age and a highly significant socio-scientific issue (SSI). Developing secondary students' understanding about the Earth's climate and GCC is critical for empowering future citizens and a key focus of the "Next Generation Science Standards" (NGSS Lead States, 2013). In this cross-sectional study, we investigated secondary students' evidence-based reasoning about GCC grounded in a curricular intervention involving the use of a data-driven, computer-based global climate model--EzGCM--over 3 years with four teachers who adapted the module in their own courses--two secondary science teachers during the Spring of 2018 (year 1), and all four teachers during the Fall of 2018 (year 2) and the Fall of 2019 (year 3). Our research questions were: "(i) to what extent has the EzGCM-based curriculum supported students' conceptual understanding about Earth's climate and GCC over time? and (ii) in what ways do EzGCM features enhance students' evidence-based reasoning about Earth's climate system and GCC?" We evaluated students' evidence-based learning using: (i) pre/post concept inventory and (ii) students' interviews. Results from the quantitative analyses of a pre- and post-module assessment indicated that student learning gains increased mainly in the third year. Results from the qualitative analysis of student interviews showed that EzGCM helped students use the different graphic outcomes to develop a more robust understanding of the temporal and spatial changes in surface air temperature and CO[subscript 2], the trends in and relationships between different climate variables, and temperature anomalies. Overall, these results highlight affordances of the EzGCM-based curriculum to support students' reasoning about Earth's climate and GCC.

In Bayesian random-effects meta-analysis, the use of weakly informative prior distributions is of particular benefit in cases where only a few studies are included, a situation often encountered in health technology assessment (HTA). Suggestions for empirical prior distributions are available in the literature but it is unknown whether these are adequate in the context of HTA. Therefore, a database of all relevant meta-analyses conducted by the Institute for Quality and Efficiency in Health Care (IQWiG, Germany) was constructed to derive empirical prior distributions for the heterogeneity parameter suitable for HTA. Previously, an extension to the normal-normal hierarchical model had been suggested for this purpose. For different effect measures, this extended model was applied on the database to conservatively derive a prior distribution for the heterogeneity parameter. Comparison of a Bayesian approach using the derived priors with IQWiG's current standard approach for evidence synthesis shows favorable properties. Therefore, these prior distributions are recommended for future meta-analyses in HTA settings and could be embedded into the IQWiG evidence synthesis approach in the case of very few studies.

Predatory journals are a blemish on scholarly publishing and academia and the studies published within them are more likely to contain data that is false. The inclusion of studies from predatory journals in evidence syntheses is potentially problematic due to this propensity for false data to be included. To date, there has been little exploration of the opinions and experiences of evidence synthesisers when dealing with predatory journals in the conduct of their evidence synthesis. In this paper, the thoughts, opinions, and attitudes of evidence synthesisers towards predatory journals and the inclusion of studies published within these journals in evidence syntheses were sought. Focus groups were held with participants who were experienced evidence synthesisers from JBI (previously the Joanna Briggs Institute) collaboration. Utilising qualitative content analysis, two generic categories were identified: predatory journals within evidence synthesis, and predatory journals within academia. Our findings suggest that evidence synthesisers believe predatory journals are hard to identify and that there is no current consensus on the management of these studies if they have been included in an evidence synthesis. There is a critical need for further research, education, guidance, and development of clear processes to assist evidence synthesisers in the management of studies from predatory journals.