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dc.date.accessioned2024-03-03T18:13:13Z
dc.date.available2024-03-03T18:13:13Z
dc.date.created2024-02-08T17:22:21Z
dc.date.issued2024
dc.identifier.citationCampos, Diego Gonzalez Fütterer, Tim Gfrörer, Thomas Lavelle-Hill, Rosa Murayama, Kou König, Lars Hecht, Martin Zitzmann, Steffen Scherer, Ronny . Screening Smarter, Not Harder: A Comparative Analysis of Machine Learning Screening Algorithms and Heuristic Stopping Criteria for Systematic Reviews in Educational Research.. Educational Psychology Review. 2024
dc.identifier.urihttp://hdl.handle.net/10852/108955
dc.description.abstractAbstract Systematic reviews and meta-analyses are crucial for advancing research, yet they are time-consuming and resource-demanding. Although machine learning and natural language processing algorithms may reduce this time and these resources, their performance has not been tested in education and educational psychology, and there is a lack of clear information on when researchers should stop the reviewing process. In this study, we conducted a retrospective screening simulation using 27 systematic reviews in education and educational psychology. We evaluated the sensitivity, specificity, and estimated time savings of several learning algorithms and heuristic stopping criteria. The results showed, on average, a 58% ( SD = 19%) reduction in the screening workload of irrelevant records when using learning algorithms for abstract screening and an estimated time savings of 1.66 days ( SD = 1.80). The learning algorithm random forests with sentence bidirectional encoder representations from transformers outperformed other algorithms. This finding emphasizes the importance of incorporating semantic and contextual information during feature extraction and modeling in the screening process. Furthermore, we found that 95% of all relevant abstracts within a given dataset can be retrieved using heuristic stopping rules. Specifically, an approach that stops the screening process after classifying 20% of records and consecutively classifying 5% of irrelevant papers yielded the most significant gains in terms of specificity ( M = 42%, SD = 28%). However, the performance of the heuristic stopping criteria depended on the learning algorithm used and the length and proportion of relevant papers in an abstract collection. Our study provides empirical evidence on the performance of machine learning screening algorithms for abstract screening in systematic reviews in education and educational psychology.
dc.languageEN
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleScreening Smarter, Not Harder: A Comparative Analysis of Machine Learning Screening Algorithms and Heuristic Stopping Criteria for Systematic Reviews in Educational Research.
dc.title.alternativeENEngelskEnglishScreening Smarter, Not Harder: A Comparative Analysis of Machine Learning Screening Algorithms and Heuristic Stopping Criteria for Systematic Reviews in Educational Research.
dc.typeJournal article
dc.creator.authorCampos, Diego Gonzalez
dc.creator.authorFütterer, Tim
dc.creator.authorGfrörer, Thomas
dc.creator.authorLavelle-Hill, Rosa
dc.creator.authorMurayama, Kou
dc.creator.authorKönig, Lars
dc.creator.authorHecht, Martin
dc.creator.authorZitzmann, Steffen
dc.creator.authorScherer, Ronny
cristin.unitcode185,18,7,0
cristin.unitnameCentre for Educational Measurement
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.cristin2244430
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Educational Psychology Review&rft.volume=&rft.spage=&rft.date=2024
dc.identifier.jtitleEducational Psychology Review
dc.identifier.volume36
dc.identifier.issue1
dc.identifier.doihttps://doi.org/10.1007/s10648-024-09862-5
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.source.issn1040-726X
dc.type.versionPublishedVersion
cristin.articleid19


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