Choosing the Right Tool for the Job: Screening Tools for Systematic Reviews in Education
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| Title: | Choosing the Right Tool for the Job: Screening Tools for Systematic Reviews in Education |
|---|---|
| Language: | English |
| Authors: | Qiyang Zhang (ORCID |
| Source: | Journal of Research on Educational Effectiveness. 2024 17(3):513-539. |
| Availability: | Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: | Y |
| Page Count: | 27 |
| Publication Date: | 2024 |
| Document Type: | Journal Articles Information Analyses |
| Descriptors: | Selection Tools, Educational Resources, Artificial Intelligence, Selection Criteria, Computer Software Selection, Computer Software Evaluation, Literature Reviews |
| DOI: | 10.1080/19345747.2023.2209079 |
| ISSN: | 1934-5747 1934-5739 |
| Abstract: | In recent years, the rapid development of artificial intelligence has enabled the launch of many new screening tools. This review aims to facilitate screening tool selection through a systematic narrative review and feature analysis. The current adoption rate of transparent tool reporting is low: by screening 191 studies published in the "Review of Educational Research" since 2015, we found that only eight studies reported screening tools. More research is needed to understand the reasons behind this phenomenon. After consulting various sources, 26 available screening tools in the market were found. Among them, we identified and evaluated 12 screening tools for educational reviewers and ranked them in descending order of feature score: Covidence (1), DistillerSR (2, tied), EPPI-Reviewer (2, tied), CADIMA (4), Swift-Active (5), Rayyan (6, tied), SysRev (6, tied), Abstrackr (8, tied), ReLiS (8, tied), RevMan (8, tied), ASReview (11), and Excel (12). In the discussion, we provide insights into the promise and bias in tools' machine learning algorithms. Our results encourage researchers to report their tool usage in publications and select tools based on suitability instead of convenience. |
| Abstractor: | As Provided |
| Entry Date: | 2024 |
| Accession Number: | EJ1431206 |
| Database: | ERIC |
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| Abstract: | In recent years, the rapid development of artificial intelligence has enabled the launch of many new screening tools. This review aims to facilitate screening tool selection through a systematic narrative review and feature analysis. The current adoption rate of transparent tool reporting is low: by screening 191 studies published in the "Review of Educational Research" since 2015, we found that only eight studies reported screening tools. More research is needed to understand the reasons behind this phenomenon. After consulting various sources, 26 available screening tools in the market were found. Among them, we identified and evaluated 12 screening tools for educational reviewers and ranked them in descending order of feature score: Covidence (1), DistillerSR (2, tied), EPPI-Reviewer (2, tied), CADIMA (4), Swift-Active (5), Rayyan (6, tied), SysRev (6, tied), Abstrackr (8, tied), ReLiS (8, tied), RevMan (8, tied), ASReview (11), and Excel (12). In the discussion, we provide insights into the promise and bias in tools' machine learning algorithms. Our results encourage researchers to report their tool usage in publications and select tools based on suitability instead of convenience. |
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| ISSN: | 1934-5747 1934-5739 |
| DOI: | 10.1080/19345747.2023.2209079 |
