[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120204-en":3,"doc-seo-120204-105":29,"detail-sidebar-cat-0-en-105":82},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},120204,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Using machine learning for continuous updating of meta-analysis in educational context","Machine learning and learning analytics help researchers measure and improve learning and also aggregate evidence on effective educational practices. This study develops and applies machine learning algorithms for semi-automatic abstract selection in meta-analyses focused on simulation-based learning in higher education. Models were trained, validated, and tested on 3187 pre–April 2018 studies, then used for a follow-up meta-analysis of 2373 studies. Screening workload dropped from 2373 to 711 while maintaining high precision for full-text inclusion.","Computers in Human Behavior 156 (2024) 108215  \nContents lists available at ScienceDirect  \nComputers in Human Behavior  \njournal [homepage: www.elsevier.com/locate/comphumbeh](homepage: www.elsevier.com/locate/comphumbeh)  \n| Using machine learning for continuous updating of meta-analysis in educational context |  |  |  |\n| --- | --- | --- | --- |\n| Olga Chernikovaa, *, Matthias Stadler b, Ivan Melev c, Frank Fischer a\u003Cbr>a Ludwig-Maximilians-Universit¨at in Munich, Germany\u003Cbr>b Institute of Medical Education, University Hospital, Ludwig-Maximilians-Universit¨at in Munich, Germany c Technical University of Munich, Germany |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Handling editor: Nicolae Nistor |  | Machine learning and learning analytics are powerful tools that not only support researchers in the detailed measurement and enhancement of learning processes in various learning environments, but also enable the aggregation and synthesis of evidence regarding effective educational practices. This paper describes the development and application of machine learning algorithms aimed at semi-automatic selection of abstracts for a meta-analysis on the effects of simulation-based learning in higher education. The goal was to reduce the workload while also maintaining the transparency and objectivity of the selection process. The algorithms were trained, validated, and tested on a set of 3187 studies on simulation-based learning found in medical and educational databases collected before April 2018. Subsequently, they were utilized to classify abstracts for a follow-up meta-analysis consisting of 2373 studies (published between 2018 and 2020). The aim of training the algorithms was to predict studies’ abstract eligibility based on words and combinations of words used in these abstracts. The application of the algorithms reduced the number of studies that had to be manually screened from 2373 to 711. A total of 458 studies from automatically selected abstracts were included in the full-text screening, indicating the high precision of the algorithms (also compared to the performance of human raters). We conclude that machine learning algorithms can be trained and used to classify abstracts for their eligibility, significantly reducing the workload for the researchers without diminishing objectivity and quality when updating systematic literature reviews with or without a meta-analysis. |  |\n| Keywords:\u003Cbr>Machine learning Abstract screening Systematic literature review Meta-analysis |  |  |  |\n\nFunding  \nThis research was funded by a grant from the German Research Community (Deutsche Forschungsgemeinschaft (DFG FOR2385; FI792/ 1)).  \n1. Problem statement  \nSystematic literature reviews with and without a meta-analysis that aggregate and systematize evidence from empirical research are essential for advancing research as well as for theory development and informing practical decisions. This is crucial in the field of education and educational psychology to keep up with rapid developments in educational technologies and to facilitate fair, inclusive, and high-quality education worldwide.  \nOne of the biggest challenges in performing systematic literature  \nreviews, with or without a meta-analysis across different contexts, is the time and resources needed to ensure quality standards. A group of researchers (Borah et al., 2017) estimated that the average amount of time needed to complete a meta-analysis is 67.3 weeks, and a large part of that time is spent on the manual selection of eligible studies, which in turn might be associated with errors, biases, and increased costs. Furthermore, the increased quantity of publications in recent years (e.g., Ware & Mabe, 2015) creates a range of complications for systematizing research, including research on education. Among these issues are appropriate location and selection of studies addressing the specific research question.  \nThere is a range of different tools available to s","cbCainG1JFF2r4Js","https://ap.wps.com/l/cbCainG1JFF2r4Js","pdf",483734,1,"English","en",105,"# Problem statement\n## Challenges in conducting systematic reviews and meta-analyses\n# Theoretical background\n## Synthesizing research: systematic literature reviews with or without a meta-analysis\n# Machine learning approach (abstract selection)\n## Training, validation, and testing datasets\n## Continuous updating for follow-up meta-analysis\n# Results and evaluation\n## Reduction in manual screening workload\n## Precision compared with human raters\n# Conclusion\n## Maintaining objectivity and quality during updates","[{\"question\":\"Do the authors claim the method preserves objectivity and quality during continuous updates?\",\"answer\":\"Yes. The study concludes that machine learning algorithms can classify abstract eligibility while significantly reducing workload without diminishing objectivity and quality during updates of systematic literature reviews with or without meta-analysis.\"}]","Using machine learning for continuous updating of meta-analysis in educational context | PDF",1785728712,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":77,"head_meta":79,"extra_data":81,"updated_unix":27},"using-machine-learning-for-continuous-updating-of-meta-analysis-in-educational-context","",{"@graph":35,"@context":76},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/using-machine-learning-for-continuous-updating-of-meta-analysis-in-educational-context/120204/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70],{"name":71,"@type":72,"acceptedAnswer":73},"Do the authors claim the method preserves objectivity and quality during continuous updates?","Question",{"text":74,"@type":75},"Yes. 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