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This study explores the correlation between technology utilization and language acquisition while analyzing the impact of moderating variables on this relation. Our meta-analysis approach analyzes data from 43 extracts out of 19 primary studies published between 2012 and 2021. Our data analysis employs a random-effect model utilizing a significance level of [Alpha] = 0.05. Additionally, the authors examine four moderating variables: level of education, location of research, proficiency in language, and year of publication. Technology-based language acquisition outperforms traditional methods, indicating a significant and moderate impact on the learning process. This study enhances comprehension of the efficacy of technology in language acquisition by identifying various factors, such as the geographical location of research, methods of assessing language proficiency, and technology type employed. However, there is insufficient evidence to support the notion that educational level or sample size significantly impact technology-based language acquisition. This meta-analysis highlights the importance of considering nuanced factors when integrating technology into language learning. The findings emphasize the possibility of technology to transform methods of acquiring language and urge additional investigation into customized strategies that optimize its advantages.

This systematic literature review (SLR) aimed to investigate the potential of digital online game-based learning (DOGBL) to enhance motivation in English as a foreign language (EFL). Online gaming has grown in popularity among students, opening up the possibility of using games as powerful instructional resources. Academic achievement depends on motivation, and this study, led by self-determination theory (SDT), explored how external rules, like rewards and recognition, could increase motivation in EFL utilizing DOGBL. The study used the SLR method, examining databases and choosing articles based on predetermined criteria. The chosen publications were examined in-depth, and a preferred reporting items for systematic reviews and meta-analyses (PRISMA) diagram was employed for analysis. For results, DOGBL could enhance teaching EFL by providing flexible and interesting learning environments. Key elements in motivating in DOGBL included game design, personalization, social engagement, curricular integration, and instructor assistance. As a promising method to improve EFL instruction, game-based learning, especially DOGBL, saw considerable developments between 2018 and 2023. Thus, these groundbreaking techniques transformed the way people learn English vocabulary and provided a fun and engaging way to learn the language. For educators and students, the potential for DOGBL to change EFL education is still exciting as technology develops.

This study aims to ascertain the impact of the Active learning method on high-order thinking skills (HOTS) within the realm of Islamic education. Our primary contribution is the comprehensive meta-analysis of various preliminary and university educational studies. Using RStudio, we analyzed the differences in effect sizes, variations, and groups among studies. The findings indicate that the Active learning method significantly enhances HOTS, with the mixed learning method showing the most pronounced effect. We adopted a systematic approach, examining diverse learning methods like genre-based learning and blended learning model. Our research design entailed a meticulous meta-analysis of pre-existing literature rather than primary experimental or survey-based methods. However, we identified high heterogeneity in effect sizes as a limitation, suggesting the need for more nuanced studies in varied contexts. This research underscores educators' need to adapt learning strategies suited for specific educational levels, pointing towards a brighter horizon for tailored pedagogical interventions.

The aim of this study is to reveal the effect of cooperative learning method in science education on academic achievement with mixed-meta method. For this purpose, various national and international databases were searched, and among 81 articles and 37 theses covering the period between 2001 and 2022, 46 articles and 31 theses that included pre-test-post-test data and included the statistical information required for experimental or quasi-experimental analysis were preferred for meta-analysis. The effect size of 77 studies included in the analysis was found to be g = 1.070 and it was seen that the cooperative learning method in science education had a positive and large effect on academic achievement. On the other hand, as a result of the meta-thematic analysis, it was determined that the cooperative learning method increased academic achievement due to the meaningful and permanent realisation of learning. In addition, thanks to the activities in which the student plays a central role, it is understood that it develops positive attitudes towards the science course depending on the results such as making the course enjoyable and fun, making it popular and interesting; it is effective in gaining 21st century skills such as creativity, communication and cooperation, productivity and entrepreneurship. It is thought that examining the effects of cooperative learning method in science education with mixed-meta method will contribute to the literature by providing comprehensive results on the subject.

This article presents a systematic review of the literature with the aim of providing an updated framework for the scientific production developed in the field of digital competence and inclusive education in Higher Education, as indexed in the Web of Science (WoS) and SCOPUS databases. Twenty-one scientific articles published between 2016-2023 were included and analyzed, with no temporal cohort established. The search matches yielded a first document of the topic of study in 2016 in the Social Sciences Citation Index. The results obtained on the conceptual structure of the analyzed documents were carried out through a multiple correspondence analysis (MCA) observing the existence of two dimensions composed of three clusters. Cluster one was composed of studies on digital competence, virtual learning environments, digital divide, and functional diversity, among others. Cluster two comprised studies carried out on digital tools and competencies, inclusive education, educational processes, and digital strategies; while cluster three was made up of educational inclusion related to interculturality and digital competence. Among its limitations, there were aspects related to the heterogeneity of the studies, which make it difficult to compare the data, and the sample and size of the study, which makes the results obtained and the generated data have a lower percentage of generalization compare to studies that use larger simples. This study has implications for researchers and Higher Education institutions interested in research on digital competence for inclusive education, with the possibilities of digital competence for inclusive education being established from the studied variables, allowing teachers to adapt and personalize learning to meet individual student needs. All of this is in line with the goals of the 2030 Agenda, concerning the empowerment of citizens and the digitization of public services to ensure the population's access to such services via the internet.

High-Variability Phonetic Training (HVPT) has demonstrated effectiveness in second language (L2) acquisition. This study utilizes a meta-analysis to explore the influence of high variability on learning L2 pronunciation and identifies the factors that moderate this impact. The studies were collected using a keyword search in the SCOPUS database. In total, our meta-analysis incorporated 18 primary studies that presented results obtained from experimental and control group designs, encompassing a total of 22 effect sizes. The results of our meta-analysis revealed that the overall effect size of HVPT on L2 pronunciation was medium (g = 0.77). Specifically, the effect size was large for consonant sounds and lexical tones, particularly pronounced when utilized by advanced learners. For beginners, the effect size was medium. The context in which the training was conducted also played a role, with a medium effect size observed when sentences were used. Additionally, the number of talkers involved in the study influenced the effect size, with a medium effect size found when 5 to 8 talkers were included. In other cases, the reported effect sizes were either small or non-significant.

The purpose of the present study was to examine the effect of individual and school average achievement on students' self-concept in science. The data of the last four Trends in International Mathematics and Science Study (TIMSS) cycles were used, and a two-stage individual data meta-analysis was conducted to examine the relationship between achievement and self-concept in science. In the first stage, multilevel structural equation modeling was performed for each dataset. Furthermore, the effect sizes were synthesized via meta-analysis with a three-level random-effect model. In addition, moderation analysis was conducted for the year, grade, and degree of stratification in countries. The results suggested a positive and significant effect of individual science achievement on the self-concept of students in science. On the other hand, the relation of the average school achievement was negative. The results were discussed by considering the big-fish-little-pond effect.

The use of mobile technology in physics education has become more prevalent, but more data about its effect on student academic performance needs to be collected. This meta-analysis examines the effects of mobile learning on student achievement in physics and any moderating factors. The study collected 36 primary studies from various scientific databases (Scopus, ERIC, DOAJ, Google Scholar) that met the inclusion criteria. The findings indicate that using mobile technology has a significant effect on student performance in physics compared to without mobile learning. Moderator analysis revealed differences in the effects of mobile learning on physics learning outcomes based on sample size, academic level, gender composition, learning media type, learning model type, learning outcome type, and measurement instrument type. However, no effect difference was observed in country status, publication year, sampling technique, and physics content. No publication bias was found in this study. Overall, the study suggests that mobile learning has a strong positive effect on student achievement in physics.

The ethnomathematics approach is considered effective on students' mathematics learning achievement. Integrating culture and mathematics can provide meaningful mathematics learning so that it makes it easier for students to understand mathematical concepts. However, previous research investigating the effectiveness of applying an ethnomathematics approach to mathematics learning achievement showed ambiguous results. Therefore, this study will test the effectiveness of the ethnomathematics approach to student achievement compared to conventional learning with a meta-analysis approach. This study analyzed 14 effect sizes from 11 primary studies published in national and international journals that met the inclusion criteria. Data collection uses the online database Google Scholar, Education Resources Information Center (ERIC), Directory of Open Access (DOAJ), Springer publishing, AIP Proceedings, IOP Sciences, and Elsevier. The results of the analysis using the random-effect approach obtained a combined effect size of (d=1.35; p<0.05). This value is included in the very large effect category. Therefore, it can be concluded that the use of the ethnomathematics approach has a significant effect on students' mathematics achievement when compared to conventional learning. The findings of this meta-analysis show the consistency of the publication of research results regarding the effectiveness of using the ethnomathematics approach to student achievement. This research can also be used as a basis for decision making by educators in improving the quality of learning mathematics in the classroom.

The paper has three major objectives. The first objective of the paper is to synthesize and define common categories of meta-analysis. The second objective is to propose a way to comprehend these common categories of meta-analysis through learning from their respective generic conceptual frameworks. The third objective is to point out which R packages to use to conduct a meta-analysis for each category presented in this paper. In practice, research novices may not know where to begin to learn a topic of meta-analysis for their research. Furthermore, they may not know how the topic of meta-analysis progresses from basics to advanced. This makes it almost next to impossible to use meta-analysis in their research. Certainly, this is problematic. Thus, this paper intends to help research novices overcome this problem. To achieve this, the three objectives mentioned at the beginning were implemented. To propose a synthesis of common categories of meta-analysis along with their respective definitions, a literature review was used as a method to synthesize and define the common categories of meta-analysis presented in this paper. Here, synthesizing meta-analysis categories in one figure would make it much easier for research novices to see the breadth of the subject in question. To propose an easy way to understand the concepts of meta-analysis through learning from their respective generic conceptual frameworks, personal experience was used as a method to make that offering. Here, it is encouraged that research novices should go beyond the definitions of categories of meta-analysis. They can try to frame their research by using example conceptual frameworks presented in the paper as a starting point. To point out which R packages to use with what categories of meta-analysis, a literature review was used as a method to identify those appropriate R packages available. Here, it would be useful for research novices to know which R packages they may need for their meta-analysis research. In summary, the contributions of the paper here would make research novices see and understand the common categories of meta-analysis from the bottom to the top of the pyramid, also offers an easy way to understand the common categories of meta-analysis by illustrating how to develop conceptual frameworks for the common categories of meta-analysis, and finally recommending the R packages to be used with the categories of meta-analysis. Ultimately, we hope that research novices will be able to use meta-analysis successfully in their research.

This study analyzes Turkish studies on web-based instruction in the field of science education using the meta-synthesis method with the aim of determining the general trends of the analyzed studies. A total of 20 studies published between 2006 and 2022, including 7 doctoral dissertations, 11 master theses, and 2 journal articles, were analyzed with a qualitative research design. These studies were identified using the databases of the YÖK National Thesis Center, EBSCO, Google Scholar, and ProQuest. The studies were analyzed using a coding form that included ten categories: year of publication, type of publication, purpose of the research, method, data collection tools, data analysis method, grade level, sample size, learning domain, and outcome of the research. The obtained data were interpreted according to the specified categories. It was concluded that a significant portion of the analyzed studies were conducted with the aim of examining web-based instruction and Web 2.0 tools, mixed research designs and semi-structured interview forms were most often used, most studies were conducted with students in the 6th and 7th grades, and web-based instruction was generally found to be more effective than traditional instruction. This study provides an overview of research on webbased instruction in the field of science education, serves as a resource for future research, and offers new ideas and suggestions for researchers in this field.

The purpose of this meta-analysis was to examine prior research that compared the differences between the academic performance of students who undertook online homework as against those that undertook traditional pen and pencil homework. Eighteen experimental and quasi-experimental studies met inclusion criteria for the meta-analysis. Eleven found online homework to produce better academic performance and seven found traditional pen and pencil homework to produce better academic performance. The Random-Effects model was used for this meta-analysis and the overall effect size was found to be 0.06 [-0.19, 0.32]. The comparison between academic performance from online homework as against traditional pen and pencil homework was not statistically significant from the published studies examined. [Note: The page range (1-20) shown on the PDF is incorrect. The correct page range is 8-20.]

This meta-synthesis reviewed and synthesized the findings of 53 studies on supervisory post-conferences and feedback with a total of 807 participants published between 2004-2023. The aim of this meta-synthesis was to illustrate the trends in the reviewed studies and synthesize the results of the studies on supervisory discourse and models of supervision in the field of English language teaching. The criteria for eligibility for selection were being empirical, peer-reviewed, and published in English. Articles were scanned through the Web of Science, ERIC, SCOPUS, and Google Scholar till July, 2023. The studies lacking a report of detailed and clear-cut data collection and analysis phases were removed in the appraisal phase to avoid a risk of bias. The results indicated that supervisors implemented directive supervisory styles as well as collaborative approaches exploiting power dynamics such as expert power. The findings also showed that the use of conversational techniques such as mediation, mitigators, and elicitation in supervisory talk play a central role in supervising English language teachers. The results suggested that supervised teachers demonstrated confronting, autonomous, and fluid identities when faced with a directive style of supervision. The limitations of evidence for this study related the search strategy, participants and variations in educational settings.

This research aims to analyze the results of studies conducted in Turkey on reasoning skills in mathematics teaching and to reveal what kind of trend there is in this field. Within the scope of this study, databases were searched with the keywords "reasoning"(muhakeme, akil yürütme) and "reasoning skill" (Muhakeme becerisi, akil yürütme becerisi), and the results were examined in accordance with the inclusion criteria regarding mathematics teaching. One hundred sixty-three studies were included. Each of the studies included in the meta-synthesis study was analyzed descriptively according to type, year, method, sample type and size, data collection tools, statistical analysis, learning field, keywords, reasoning type and purpose. In addition, the studies' results were content analyzed and tabulated by coding the differences and similarities between them with a holistic approach. It has been determined that studies on the learning fields in the mathematics curriculum are mostly carried out in the field of learning numbers and operations in the secondary school mathematics curriculum. When evaluated in terms of reasoning types, almost half of the studies were conducted on mathematical reasoning. It has been observed that after mathematical reasoning, the most focus is on proportional reasoning. When the aims of the studies included in the research were examined, it was determined that the most focused ones were "examining the factors affecting reasoning skills", "measurement of reasoning skills" and "the effect of teaching practices on reasoning skills". In the studies examined, it was seen that there were 33 teaching practices whose effects on reasoning skills were examined.

Using R to conduct univariate meta-analyses is becoming common for publication. However, R can also conduct multivariate meta-analysis (MMA). However, newcomers to both R and MMA may find using R to conduct MMA daunting. Given that, R may not be easy for those unfamiliar with coding. Likewise, MMA is a topic of advanced statistics. Thus, it may be very challenging for most newcomers to conduct MMA using R. If this holds, this can be viewed as a practice gap. In other words, the practice gap is that researchers are not capable of using R to conduct MMA in practice. This is problematic. This paper alleviates this practice gap by illustrating how to use R (the metaSEM package) to conduct MMA on educational psychology data. Here, the metaSEM package is used to obtain the required MMA text outputs. However, the metaSEM package is not capable of producing the other required graphical outputs. As a result, the metafor package is also used as a complimentary to generate the required graphical outputs. Ultimately, we hope that our audience will be able to apply what they learn from this method paper to conduct MMA using R in their teaching, research, and publication.

Research in languages and literacies in science education (LLSE) has developed substantial theoretical and pedagogical insights into how students learn science through language, discourse, and multimodal representations. At the same time, language is central to the functioning of generative artificial intelligence (GenAI). On this common basis concerning the role of language, this paper explores how foundational ideas from LLSE studies can inform the use of GenAI in science education. A bibliometric analysis of 412 journal articles from Web of Science provided the initial step to identify major themes and relationships in the LLSE literature. The analysis revealed four clusters of research in LLSE: reading and writing scientific text, science discourse and interaction, multilingual science classroom, and multimodality and representations. Each cluster was further analyzed through close reading of selected articles to identify and connect key constructs to the potential use of GenAI. These constructs include the interactive-constructive reading model, text genre, reading-writing integration, dialogic interaction, critical questioning, argumentation, translanguaging, hybridity, thematic pattern, modal affordance, and transduction. From these ideas and connections, the paper recommends several pedagogical principles for science educators to guide the use of GenAI. It concludes that LLSE research offers valuable insights for researchers and teachers to investigate and design the use of GenAI in science education. In turn, the impending use of GenAI also calls for a rethinking of literacy that will shape future research in LLSE.

Accurate monitoring of performance in problem-solving tasks is an important prerequisite for students' future academic success. A wide variety of interventions aiming to enhance students' monitoring accuracy have been developed, but their effectiveness is not apparent from the individual studies in which they have been examined. This meta-analysis classified these interventions in terms of how they target students' monitoring and investigated their relative effectiveness to improve monitoring accuracy in problem-solving tasks. Findings across the 35 included studies indicated that all interventions combined have a small positive effect on students' monitoring accuracy (g = 0.25). Intervention type moderated the findings. Interventions on the whole task, metacognitive knowledge, and external standards improved monitoring accuracy. On the other hand, interventions targeting the timing of metacognitive judgment negatively impacted monitoring accuracy and significantly differed from all other interventions. Exploratory moderator analyses of study features indicated that secondary school students benefited least from the interventions compared to primary school students and adults, laboratory studies showed larger effects than classroom studies, and interventions were more effective for retrospective confidence judgments than for judgments of learning. For educational practice, interventions targeting the whole task, metacognitive knowledge, and external standards are recommended, while reconsideration and possibly discontinuation of timing interventions is needed.

The impact of a major historical event on child and youth development has been of great interest in the study of the life course. This study is focused on assessing the causal effect of the Great Recession on youth disconnection from school and work. Building on the insights offered by the age-period-cohort research, econometric methods, and developmental psychology, we innovatively develop a causal inference strategy that takes advantage of the multiple successive birth cohorts in the National Longitudinal Study of Youth 1997. The causal effect of the Great Recession is defined in terms of counterfactual developmental trajectories and can be identified under the assumption of short-term stable differences between the birth cohorts in the absence of the Great Recession. A meta-analysis aggregates the estimated effects over six between-cohort comparisons. Furthermore, we conduct a sensitivity analysis to assess the potential consequences if the identification assumption is violated. The findings contribute new evidence on how precipitous and pervasive economic hardship may disrupt youth development by gender and class of origin.

We conducted a multi-pronged investigation of different types of reporting bias in autism early childhood intervention research. First, we investigated the prevalence of reporting failures of completed trials registered on clinicaltrials.gov, and found that only 7% of registered trials were updated with results on the registration platform and only 64% had associated published reports. Next, we investigated the extent to which inadequate reporting prevents inclusion in meta-analytic summary estimates by identifying reports of studies that were eligible for inclusion in a prior meta-analysis, and found that 25% were excluded due to inadequate reporting. Finally, we investigated selective reporting practices by analyzing the protocols of the studies included in the meta-analysis which had been registered on any trial registry and coding their timing, completeness, and consistency. We found that 23% of studies were pre-registered, 71% were late-registered, and 5% were registered at an unclear date. Only 8% of registrations specified all of the necessary components. Evidence of selective reporting was common; 36% failed to report a registered outcome, 61% reported unregistered outcomes, 23% switched primary and secondary outcomes, and 43% had assessment timepoints that differed from registration specification. Given the inadequacy of registration and reporting practices, we offer practical recommendations to facilitate improvement for the field of autism research.

This study used a meta-analytic structural equation modeling approach to build extended versions of the simple view of reading (SVR) model in second and foreign language (SFL) learning contexts (i.e., SVR-SFL). Based on the correlation coefficients derived from primary studies, we replicated and integrated two previous extended meta-analytic SVR models, that is, Quinn and Wagner's (2018) model with an English-speaking population with a cognitive factor and Peng et al.'s (2021) model with Chinese-speaking readers with metalinguistic skills. A total of 180 independent samples (N = 36,235) obtained from 152 empirical studies in SFL contexts were included in our meta-analytic structural equation model. The results revealed that the collected data successfully replicated H. Lee et al.'s (2022) SVR model in SL contexts (SV2R) in terms of overall model fit and moderation effects; in addition, the results confirmed that the data fit well with the extended SVR-SFL models.

This study reports two independent meta-analyses on the relationship between: (a) classroom climate and academic achievement; and (b) school climate and academic achievement. The analyses were based on extant correlational research studies at the K-12 level that were published between 2000 and 2020. The relationships were analyzed in terms of various moderator variables. The first dataset included 53 research studies focusing on classroom climate and academic achievement, and the second dataset included 37 research studies on school climate and academic achievement. The meta-analyses were carried out with a random effects model. Fisher's effect size was calculated for each study. Publication bias was tested with Egger's linear regression method and Duval and Tweedie's trim and fill method. We used the Q-test for moderation analyses and I2 test for heterogeneity analyses. The results revealed that the mean effect size for the relationship between classroom climate and academic achievement was small, while it was medium for the relationship between school climate and academic achievement. The effect sizes regarding classroom climate differed significantly only in terms of the academic area moderator variable, and the effect sizes regarding school climate differed in terms of school area, report type, and unit of analysis. This meta-analysis study confirmed that school climate and classroom climate are significant correlates of academic achievement, and the dataset in this study revealed a greater magnitude of relationship in favor of school climate (although a comparison is not meant in this study). This study suggests that policymakers and practitioners should invest their efforts in promoting sound school climates while maintaining an emphasis on classroom climate components, as the two types of climates are intertwined.

We conducted a descriptive analysis and meta-analysis of single-case research design (SCRD) studies investigating the effectiveness of the graduated guidance procedure. Once we identified studies through electronic databases and reference lists, we used What Works Clearinghouse (WWC) Standards to evaluate each study. Then, we described studies in terms of various descriptive variables, calculated effect sizes through three non-parametric effect size methods, and analyzed results across studies. Results showed 11 of the 27 studies met WWC Standards or met standards with reservation. Of the 11, seven studies resulted in a large effect. We found the graduated guidance procedure to be an evidence-based practice when evaluating the findings against contemporary evidence standards. However, this review also showed that the majority of the reviewed studies (n = 20) had no effects and only one third of the studies had moderate or strong effects. Implications for researchers and practitioners are discussed.

A best-evidence synthesis and meta-analysis were conducted to evaluate interventions designed to decrease noncompliance in early childhood settings. Studies were examined for quality, intervention components, and outcomes for young children (ages 2-8 years) across settings. Results indicated most designs were high-quality and produced desired outcomes. However, participant inclusion criteria were commonly underreported, and social and ecological validity data were absent. Thus, for whom noncompliance interventions are effective, appropriate, feasible, or preferred is largely unknown. Contrary to prior evidence from the broader challenging behavior literature, function-based assessments had no impact on intervention outcomes. Reasons for this finding are explored, including the narrow application of function-based assessment findings to compliance interventions. Future implications for research and practice are presented.

The main purpose of this study is to investigate the best approaches to teaching a foreign language to older people to help them achieve the desired results and explore their benefits. This review strictly follows the PRISMA methodology for systematic reviews and meta-analyses to identify the core experimental studies that deal with the topic of foreign language learning approaches among the older generations. Altogether eight studies detected were included in the systematic review. The available sources were found in Web of Science and Scopus. The findings indicate that foreign language learning can promote seniors' welfare and successful aging despite their learning outcomes, which means that the key benefit for third-age foreign language learners while learning a foreign language is not the achieved proficiency level, but the feeling of subjective satisfaction. This can be a good incentive to achieve better learning outcomes, provided that learners have been offered a pleasant and safe learning environment, using suitable learning approaches during which they can build on their acquired knowledge and experience, as well as discuss the topics they are interested in. Thus, foreign language teachers play an important part in seniors' educational process because their teaching methods and materials when adapted to the elderly's educational needs can have a positive impact on the maintenance and possibly, enhancement of the older people's cognitive functions and on the improvement of their mental activity, which consequently maintains their healthy aging.

Although physical activity (PA) has a profound impact on health, many college and university students are still physically inactive. There is some evidence to suggest that social support (SS) could impact the PA levels of students, but the internal relationship and specific effects are not very clear. The purpose of this review was to determine the strength of the relationship between SS and PA and examine whether any potential associations differed in terms of age, gender, and region among college and university students. Studies were identified using the following electronic databases: PubMed, SPORTDiscus, Web of Science, and Sociological Abstracts. Moderator analyses investigating the effects of students' age, gender, and region (nation) were performed. This review included 19 articles. The results showed total SS was significantly associated with PA (r = 0.30, 95% confidence interval [CI]: [0.22, 0.37], p < 0.001). With respect to different types of support, friend support was more strongly associated with PA than family support. Gender factors had a significant moderating effect on the correlation between SS and PA (QM = 17.433, p < 0.001). Separate analyses examining the moderating effects of gender (percentage of females) found that the association between SS and PA was stronger with the increase in female percentage. In conclusion, SS is an important factor associated with PA levels and should foster SS within intervention programs according to types of SS and gender differences to increase PA levels among college and university students.

There is extensive literature focusing on students' misconceptions in various subject domains. Several conceptual change approaches have been trying to understand how conceptual change occurs to help learners handle these misconceptions. This meta-analysis aims to integrate studies investigating the effectiveness of three types of conceptual change strategy: cognitive conflict, cognitive bridging, and ontological category shift in science learning. We conducted a random-effects meta-analysis to calculate an overall effect size in Hedges' g with a sample of 218 primary studies, including 18,051 students. Our analyses resulted in a large overall effect size (g = 1.10, 95% CI [1.01, 1.19], k = 218, p < 0.001). We also performed a robust Bayesian meta-analysis to calculate an adjusted effect size, which specified a large effect (adjusted g = 0.93, 95% CI [0.68, 1.07], k = 218). Results are also consistent across the conceptual change strategies of cognitive conflict (g = 1.10, 95% CI [0.99, 1.21], k = 150, p < 0.001), cognitive bridging (g = 1.06, 95% CI [0.84, 1.28], k = 30, p < 0.001), and ontological category shift (g = 0.88, 95% CI [0.50, 1.26], k = 9, p < 0.001). However, a wide-ranging prediction interval [0.19, 2.38] points out a high level of heterogeneity in the distribution of effect sizes. Thus, we investigated the moderating effects of several variables using simple and multiple meta-regression. The final meta-regression model we created explained 35% of overall heterogeneity. This meta-analysis provides robust evidence that conceptual change strategies significantly enhance students' learning in science.

Scale errors are intriguing phenomena in which a child tries to perform an object-specific action on a tiny object. Several viewpoints explaining the developmental mechanisms underlying scale errors exist; however, there is no unified account of how different factors interact and affect scale errors, and the statistical approaches used in the previous research do not adequately capture the structure of the data. By conducting a secondary analysis of aggregated datasets across nine different studies (n = 528) and using more appropriate statistical methods, this study provides a more accurate description of the development of scale errors. We implemented the zero-inflated Poisson (ZIP) regression that could directly handle the count data with a stack of zero observations and regarded developmental indices as continuous variables. The results suggested that the developmental trend of scale errors was well documented by an inverted U-shaped curve rather than a simple linear function, although nonlinearity captured different aspects of the scale errors between the laboratory and classroom data. We also found that repeated experiences with scale error tasks reduced the number of scale errors, whereas girls made more scale errors than boys. Furthermore, a model comparison approach revealed that predicate vocabulary size (e.g., adjectives or verbs), predicted developmental changes in scale errors better than noun vocabulary size, particularly in terms of the presence or absence of scale errors. The application of the ZIP model enables researchers to discern how different factors affect scale error production, thereby providing new insights into demystifying the mechanisms underlying these phenomena.

Computational thinking (CT), an essential 21st century skill, incorporates key computer science concepts such as abstraction, algorithms, and debugging. Debugging is particularly underrepresented in the CT training literature. This multi-level meta-analysis focused on debugging as a core CT skill, and investigated the effects of various debugging interventions. Moderator analyses revealed which interventions were effective, in which situations, and for what kind of learner. A significant overall mean effect of debugging interventions ([g-bar] = 0.64, CI = (0.32, 0.96), p < 0.001), was found based on 62 effect sizes from 18 source articles. Significant between-studies variation indicated that true effects could range from -0.54 to 1.82. In addition, sensitivity analyses and checks on confounding provided further understandings of intervention features and their impacts. Interventions using enhanced debuggers and systematic instruction were particularly effective in fostering debugging skills. Debugging intervention effects varied by participant population and potentially by publication type. Moreover, debugging interventions had impact regardless of how debugging skills were measured, programming medium used, control-group type, and whether the study was randomized. Future studies should investigate the best practices for improving debugging abilities for whom and under what circumstances.

Though the relationship between mobile learning and academic achievement has increasingly gained attention by some researchers, the meta-analysis studies have yet to definitively conclude the overall impact of mobile learning on academic achievement. In order to synthesize the findings of 22 meta-analyses that are directly pertinent to the research question, encompassing papers that have been published, we examine to what extent publication year, subject category, and grade level moderate the influences of mobile learning on academic achievement as well as the moderating effect of methodological quality of first-order meta-analyses. There is the positive overall effect with significance (g = 0.665 p < 0.001, 95% CI = 0.443-0.887) of mobile learning on students' academic achievement compared to traditional learning. Moreover, the results confirmed that meta-analyses published in 2019-2020 had a significantly larger effect size than the ones published in 2014-2018, and the results were stable regardless of subject category, grade level, and methodological quality of first-order meta-analyses. Finally, insight and limitations were discussed.

While numerous studies of robot-assisted language learning (RALL) for English-as-a-foreign-language (EFL) learners' language skill development have been done, a comprehensive and theoretically-driven meta-analysis on its effects is still in paucity. To fill the gap, drawing on Activity Theory (AT), this study reported a meta-analysis from 47 independent studies out of 29 literature samples involving 1791 EFL learners on RALL for language skill development published during 2004-2023. The results indicated that the overall effect size was g = 0.69, 95% CI [0.49, 0.90], suggesting that RALL outperforms non-RALL conditions. In addition, educational levels and intervention durations were found to be significant moderators. Based on the results, implications for practice were discussed.

Most students with reading difficulties struggle to read words. We examined intervention effects for students with "significant" word reading difficulties (SWRD; standard score of 80 on at least one pretest measure of word reading), which includes individuals with or at risk for dyslexia. We investigated: (a) What are the effects of reading interventions for students in Grades 3-12 with SWRD? and (b) What intervention features (i.e., instructional components and elements of dosage) are related to improved reading outcomes for the target population? A meta-analysis of 22 studies and 208 effect sizes revealed a statistically significant, positive, mean effect (g = 0.14, standard error [SE] = 0.04, p = 0.01, 95% confidence interval [CI]: [0.04, 0.23]) of interventions for the target population. Subset analyses revealed positive, statistically significant intervention effects on measures of pseudoword reading (g = 0.38, SE = 0.07, p = 0.0003, 95% CI [0.21, 0.54]) and pseudoword reading fluency (g = 0.29, SE = 0.09, p = 0.010, 95% CI [0.09, 0.49]). Moderator analyses yielded statistically significant, positive effects associated with increased total hours of intervention, [beta] = 0.003, SE = 0.0009, t(8.31) = 3.58, p = 0.007. Overall, findings indicate a need for interventions that improve generalized real-world reading for the target population.

Reading proficiency is important because it has life-long consequences and influences success in other academic areas. Many students with behavior problems are poor readers and many students with learning disabilities have more behavior problems than their typical peers. We conducted a correlational meta-analysis to examine the association between reading and externalizing behavior in students ages 5-12. We identified 33 studies that reported 88 effect sizes. Using a random-effects linear regression model with robust variance estimation, we found a significant, negative correlation (r= -0.1698, SE = 0.01, p < 0.0001) between reading and externalizing behavior. We tested several moderators related to measurement and sample characteristics. We found that rater type, behavior dimension (e.g., aggression), time between longitudinal measurement points, age of the sample, and percentage male of the sample moderated the relation between reading and behavior. Whether the reading assessment measured comprehension or word reading and socioeconomic status of the sample did not moderate the relation. Understanding the association between reading and externalizing behavior has implications for disability identification and intervention practices for children in elementary school. Future research should examine shared cognitive factors and environmental influences that explain the relation between the constructs.

Understanding the magnitudes represented by numerals is a core component of early mathematical development and is often assessed by accuracy in situating numerals and fractions on a number line. Performance on these measures is consistently related to performance in other mathematics domains, but the strength of these relations may be overestimated because general cognitive ability has not been fully controlled in prior studies. The first of two meta-analyses (162 studies, 33,101 participants) confirmed a relation between performance on whole number (r = 0.33) and fractions number (r = 0.41) lines and overall mathematics performance. These relations were generally consistent across content domains (e.g., algebra and computation) and other moderators. The second (71 studies, 14,543 participants) used meta-analytic structural equation modeling to confirm these relations while controlling general cognitive ability (defined by IQ and working memory measures) and, in one analysis, general mathematics competence. The relation between number line performance and general mathematics competence remained significant but reduced ([beta] = 0.13). Controlling general cognitive ability, whole number line performance consistently predicted competence with fractions but not performance on numeracy or computations measures. The results suggest an understanding of the magnitudes represented by whole numbers might be particularly important for students' fractions learning.

As a significant tool to integrate information technology and education, dynamic mathematical software (DMS) has been widely concerned in recent years. However, how to better apply it to instruction practice deserves further exploration. Thus, we adopted the meta-analysis method to analyze the DMS-based experiments published between 2000 and 2020. A three-level meta-analysis of data from 107 studies involving 10,507 participants and 138 effect sizes revealed a moderate effect size (d = 0.632, 95% CI = [0.550, 0.713]). Moreover, moderator analyses showed that: (1) cultural background had significant moderating effects; (2) students performed better on near-transfer tests than far-transfer tests; (3) DMS used by students independently had better effects; (4) intervention duration had significant moderating effects; (5) some of the above significant moderating effects were unique after controlling for others. Overall, our findings suggest that DMS has positive effects on students' performance and teachers should be meticulous in designing their teaching plans.

We aimed to explore, in a sample of systematic reviews (SRs) with meta-analyses of the association between food/diet and health-related outcomes, whether systematic reviewers selectively included study effect estimates in meta-analyses when multiple effect estimates were available. We randomly selected SRs of food/diet and health-related outcomes published between January 2018 and June 2019. We selected the first presented meta-analysis in each review (index meta-analysis), and extracted from study reports all study effect estimates that were eligible for inclusion in the meta-analysis. We calculated the Potential Bias Index (PBI) to quantify and test for evidence of selective inclusion. The PBI ranges from 0 to 1; values above or below 0.5 suggest selective inclusion of effect estimates more or less favourable to the intervention, respectively. We also compared the index meta-analytic estimate to the median of a randomly constructed distribution of meta-analytic estimates (i.e., the estimate expected when there is no selective inclusion). Thirty-nine SRs with 312 studies were included. The estimated PBI was 0.49 (95% CI 0.42-0.55), suggesting that the selection of study effect estimates from those reported was consistent with a process of random selection. In addition, the index meta-analytic effect estimates were similar, on average, to what we would expect to see in meta-analyses generated when there was no selective inclusion. Despite this, we recommend that systematic reviewers report the methods used to select effect estimates to include in meta-analyses, which can help readers understand the risk of selective inclusion bias in the SRs.

Meta-analysis is a useful tool in clinical research, as it combines the results of multiple clinical studies to improve precision when answering a particular scientific question. While there has been a substantial increase in publications using meta-analysis in various clinical research topics, the number of published meta-analyses in metabolomics is significantly lower compared to other omics disciplines. Metabolomics is the study of small chemical compounds in living organisms, which provides important insights into an organism's phenotype. However, the wide variety of compounds and the different experimental methods used in metabolomics make it challenging to perform a thorough meta-analysis. Additionally, there is a lack of consensus on reporting statistical estimates, and the high number of compound naming synonyms further complicates the process. Easy-Amanida is a new tool that combines two R packages, "amanida" and "webchem", to enable meta-analysis of aggregate statistical data, like p-value and fold-change, while ensuring the compounds naming harmonization. The Easy-Amanida app is implemented in Shiny, an R package add-on for interactive web apps, and provides a workflow to optimize the naming combination. This article describes all the steps to perform the meta-analysis using Easy-Amanida, including an illustrative example for interpreting the results. The use of aggregate statistics metrics extends the use of Easy-Amanida beyond the metabolomics field.

The "LFK" index has been promoted as an improved method to detect bias in meta-analysis. Putatively, its performance does not depend on the number of studies in the meta-analysis. We conducted a simulation study, comparing the "LFK" index test to three standard tests for funnel plot asymmetry in settings with smaller or larger group sample sizes. In general, false positive rates of the "LFK" index test markedly depended on the number and size of studies as well as the between-study heterogeneity with values between 0% and almost 30%. Egger's test adhered well to the pre-specified significance level of 5% under homogeneity, but was too liberal (smaller groups) or conservative (larger groups) under heterogeneity. The rank test was too conservative for most simulation scenarios. The Thompson-Sharp test was too conservative under homogeneity, but adhered well to the significance level in case of heterogeneity. The true positive rate of the "LFK" index test was only larger compared with classic tests if the false positive rate was inflated. The power of classic tests was similar or larger than the "LFK" index test if the false positive rate of the "LFK" index test was used as significance level for the classic tests. Under ideal conditions, the false positive rate of the "LFK" index test markedly and unpredictably depends on the number and sample size of studies as well as the extent of between-study heterogeneity. The "LFK" index test in its current implementation should not be used to assess funnel plot asymmetry in meta-analysis.

Using a sample of 70,399 published p-values from 192 meta-analyses, we empirically estimate the counterfactual distribution of p-values in the absence of any biases. Comparing observed p-values with counterfactually expected p-values allows us to estimate how many p-values are published as being statistically significant when they should have been published as non-significant. We estimate the extent of selectively reported p-values to range between 57.7% and 71.9% of the significant p-values. The counterfactual p-value distribution also allows us to assess shifts of p-values along the entire distribution of published p-values, revealing that particularly very small p-values (p < 0.001) are unexpectedly abundant in the published literature. Subsample analysis suggests that the extent of selective reporting is reduced in research fields that use experimental designs, analyze microeconomics research questions, and have at least some adequately powered studies.

The research aims to analyse the most current (2015-2023) literature on STEM education to gain a deeper understanding of current trends, priorities and developments in the field of STEM education. Scientific studies determined within the scope of the study were examined using bibliometric and content analysis methods, which are among the systematic compilation methods. First, access to the WoS database was gained, and a subject-based search was conducted using the keywords "STEM education" and "Meta-analysis." In this context, 38 scientific studies containing these relevant keywords were found in the search. The collected data were initially analyzed on the basis of descriptive attributes in the WoS. Subsequently, the listed publications were analyzed using the bibliometric analysis tool VOSviewer for bibliographic attributes, including citation counts, co-citation, co-authorship, cooccurrence, and bibliographic coupling analysis types, based on authors, institutions, and countries. Finally, 18 articles from these scientific publications were evaluated through content analysis in terms of various characteristics of following the PRISMA flowchart. The most prominent keywords associated with the examined concepts included technology-related terms like "computer-based learning," "digital game-based learning," "academic achievement," "active learning," and "problem-centered instruction." Furthermore, the general focus of scientific studies appears to be on determining the impact of a STEM education method on student performance.

The paradoxical effects of contextual interference (CI) assume that high CI practices hinder performances during the acquisition phase of learning, while providing more permanent enhancement during the retention phase. This meta-analysis evaluates the possible generalizability of the CI phenomenon in physical education (PE) and sports contexts, with regard to the acute and relatively permanent gains in performance outcomes. A total of 933 records from five electronic databases were screened using the PICOS criteria, of which 36 studies were selected. Outcomes evaluating the performance changes ([dellta]) from pre-post, post-retention, and pre-retention tests were included. Out of 183 overall pooled outcomes, [delta] in only 37 performance outcomes (20%) agreed with the paradoxical CI effects on the acquisition or the relatively permanent gains. No statistically significant overall difference was detected for "[delta] pre-post" between low (blocked) (28.9 ± 59.5%) and high (random/serial) (27.9 ± 52.8%) CI (effect size (ES) = 0.1, p = 0.35). An overall significant difference (p = 0.001) in favor of high CI practice was detected in "[delta] post-retention." However, this difference was not large enough (ES = -0.35) to produce an overall greater long-term gain following high (24.56 ± 4.4%), compared to low (21.9 ± 9.8%) CI (ES = -0.13, p = 0.18). Out of 10 tested variables, only the age significantly moderated both CI effects (p < 0.0001 for both [delta] pre-post and [delta] pre-retention) and the female proportion significantly moderated only the first CI effect (p = 0.009 for [delta] pre-post). These findings found very limited evidence supporting the recommendation to employ high CI practices to gain a longer-term performance advantage, calling into question the generalization of the CI model to PE and sports practices. High-quality follow-up research evaluating alternative motor-learning models are therefore needed.

Subject relative clauses (RCs) have been shown to be acquired earlier, comprehended more accurately, and produced more easily than object RCs by children. While this subject preference is often claimed to be a universal tendency, it has largely been investigated piecemeal and with low-powered experiments. To address these issues, this meta-analysis follows an established and rigorous scientific method to test the generalizability of the subject preference in RC acquisition by evaluating the collective evidence. While the results show a significant crosslinguistic subject preference, there is a large amount of heterogeneity in the data. The manifestation of this subject preference may not be uniform across languages, depending on typological properties such as language headedness, RC headedness, and main clause similarity. The true impact of these features, however, requires research on more typologically diverse languages.

This study synthesized the effect of performance-based funding (hereafter, PBF) through multi-stages of a systematic literature review based on 36 articles. In addition, this study analyzes how policy design relates to the effectiveness of PBF in individual states. Similar to other meta-analyses, this study found that PBF policy is not effective in improving completion and equity of higher education institutions in the US. Further, this study assumed that there might be some positive effects of PBF on institutional performance in the states that maintained it for more than seven consecutive years. Specifically, this study assumed that some policy details such as institutional types, funding method (baseline or bonus), share of funding linked to institutional performance, and funding metrics were related to policy effects. However, this systematic review found that a majority of evaluative studies arrived at inconclusive results across different types of policy designs. One notable finding is that 2-year institutions show more positive results than 4-year institutions. In addition, this study found that statistically significant effects are both positive and negative. Based on the findings of the selected evaluative studies, this review concluded that PBF policy is not very successful in improving institutional performance across different types of policy designs in each state.

This systematic review and meta-analysis examined interventions targeting play skills of children with autism spectrum disorder (ASD) involving typically developing (TD) peers. The objectives of this work are to (a) identify and describe the characteristics and components of interventions aimed at improving play skills in children with ASD and their TD peers, (b) examine the role of peers in interventions, (c) evaluate intervention effects, and (d) identify potential moderating variables that may influence intervention outcomes. Twenty single-case experimental design (SCED) studies published between 2000 and 2020 were included and summarized. The majority of interventions produced significant effects, further supporting the inclusion of TD peers in interventions targeting play skills for children with ASD. Recommendations for future research and practice are discussed.

This paper shares the results of a meta-analysis on the parental-relational component of parental-involvement and its association with the academic and behavioral outcomes of urban students. This meta-analysis includes 76 quantitative studies. The results indicated that statistically significant effects emerged across students of different backgrounds, including differences by age groups, race, gender, and nationality. The results were also substantial in the highest quality studies. The significance of these results is discussed.

We conducted a meta-analysis to investigate reading-writing relations. Beyond the overall relation, we systematically investigated moderation of the relation as a function of linguistic grain size (word reading and spelling versus reading comprehension and written composition), measurement of reading comprehension (e.g., multiple choice, open-ended, cloze), and written composition (e.g., writing quality, writing productivity, writing fluency, writing syntax), and developmental phase of reading and writing (grade levels as a proxy). A total of 395 studies (k = 2,265, N = 120,669) met inclusion criteria. Overall, reading and writing were strongly related (r = 0.72). However, the relation differed depending on the subskills of reading and writing such that word reading and spelling were strongly related (r = 0.82) whereas reading comprehension and written composition were moderately related (r = 0.44). In addition, the word reading-spelling relation was stronger for primary-grade students (r = 0.82) than for university students/adults (r = 0.69). The relation of reading comprehension with written composition differed depending on measurement of reading comprehension and written composition--reading comprehension measured by multiple choice and open-ended tasks had a stronger relation with writing quality than reading comprehension measured by oral retell tasks; and reading comprehension had moderate relations with writing quality, writing vocabulary, writing syntax, and writing conventions but had weak relations with writing productivity and writing fluency. Relations tended to be stronger when reliability was higher, and the relation between word reading and spelling was stronger for alphabetic languages (r = 0.83) than for Chinese (r = 0.71). These results add important nuances about the nature of relations between reading and writing.

Accurately monitoring one's learning processes during self-regulated learning depends on using the right cues, one of which could be perceived mental effort. A meta-analysis by Baars et al. (2020) found a negative association between mental effort and monitoring judgments (r = -0.35), suggesting that the amount of mental effort experienced during a learning task is usually negatively correlated with learners' perception of learning. However, it is unclear how monitoring judgments and perceptions of mental effort relate to learning outcomes. To examine if perceived mental effort is a diagnostic cue for learning outcomes, and whether monitoring judgments mediate this relationship, we employed a meta-analytic structural equation model. Results indicated a negative, moderate association between perceived mental effort and monitoring judgments ([beta] = -0.19), a positive, large association between monitoring judgments and learning outcomes ([beta] = 0.29), and a negative, moderate indirect association between perceived mental effort and learning outcomes ([beta] = -0.05), which was mediated by monitoring judgments. Our subgroup analysis did not reveal any significant differences across moderators potentially due to the limited number of studies included per moderator category. Findings suggest that when learners perceive higher levels of mental effort, they exhibit lower learning (confidence) judgments, which relates to lower actual learning outcomes. Thus, learners seem to use perceived mental effort as a cue to judge their learning while perceived mental effort only indirectly relates to actual learning outcomes.

Teacher wellbeing has received widespread and increasing global attention over the last decade due to high teacher turnover, growing teacher shortages, and the goal of improving the quality of teaching and student performance. No review has yet sought to undertake a cumulative quantitative assessment of the literature pertaining to teacher wellbeing. Using meta-analysis, we address this gap by systematically examining the relative strength of key antecedents, consequences, and correlates of teacher wellbeing, using the Job Demands-Resources theory as a guide to positioning factors in the nomological network. Following PRISMA guidelines, our systematic search yielded 173 eligible studies for inclusion (N = 89,876). Results showed that hope, autonomous motivation, psychological capital and job competencies were the top four strongest positive predictors of overall wellbeing, whereas neuroticism and disengagement coping were the top two strongest negative predictors. Occupational commitment was the strongest positive consequence of overall wellbeing, and turnover intentions were the strongest negative consequence. Burnout and work engagement were the strongest correlates of overall wellbeing. We also found that some effects were moderated by factors such as whether teachers were in-service or pre-service, and the educational setting (e.g., K-12, initial teacher education). Our review provides a useful empirical resource that may help guide practice in terms of how teachers, school leaders, and policy makers can support teacher wellbeing.

Artificial intelligence (AI) has considerably advanced the methods for individualizing language learning opportunities, such as assessing learning progress and recommending effective individual instruction. In the present study, we conducted a meta-analysis to synthesize recent empirical findings pertaining to the utilization of AI-guided language learning and collected 61 samples (N = 8,282) from 17 research projects (e.g., Assessment to Instruction [A2i], Duolingo, and Project LISTEN). The results of our meta-analysis confirmed that AI-guided individualized language learning was effective for learners' language development (d = 1.18, based on 26 within-group samples, N = 2,262) and had an overall positive treatment effect compared to business-as-usual conditions (d = 0.39, based on 35 between-group samples, N = 6,020). Moreover, the results of our moderator analyses for the treatment effect revealed that AI-guided language learning with machine learning and hybrid systems were more impactful than those with rule-based systems, which may be more helpful (compared to the former) in understanding how predictions are made from a pedagogical perspective. Evidence-based implications are provided based on the results of this meta-analysis.