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Systematic reviews in education: producing and acquiring knowledge in times of crisis and social change
The Covid-19 global pandemic required the production of systematic reviews and other forms of evidence synthesis at an unprecedented rate. Around the world, there was significant demand for rigorous evidence from decision makers in education, as governments, local authorities, and professional bodies moved quickly to commission reviews to support choices they had to make during the crisis. Policymakers’ concerns about the effects of lockdown restrictions, for example, led to reviews on the potential impact of school closures on rates of learning and learning loss (Education Endowment Foundation, 2020) and strategies for tackling the mental health impact of Covid-19 among children and college students (Elharake et al., 2022).
The pandemic not only re-emphasised the importance of methodologically rigorous evidence synthesis to inform decision-making but also further illustrated the problem that the process to produce a systematic review is often too long and resource intensive. Reviews can fall quickly out of date, sometimes even before they are published (Borah et al., 2017). In the current era of data abundance and advances in research complexity, the challenges of completing well-conducted reviews to (often short) timetables are growing.
The research community has responded by engaging in methodological development, exploring novel and underutilised ways to meet the growing demand for high quality, timely synthesised evidence to inform decision-making. Significantly, the rapid pace of technological and digital transformation in recent years has supported numerous innovations in systematic review methodology (Khalil et al., 2022; Marshall & Wallace, 2019). For instance, many researchers are now using machine learning and other tools aimed at (semi-) automating systematic review processes, including search term development (Stansfield et al., 2017), screening prioritisation (Tsou et al., 2020), and data extraction (Schmidt et al., 2021). Others have turned to bibliometrics analysis (e.g., Walsh & Rowe, 2022; Gessler et al., 2021), crowdsourcing (Noel-Storr et al., 2022) or living reviews (Iannizzi et al., 2022) to support ongoing efforts to improve the trustworthiness and efficiency of systematic reviews.
Articles are published open access and can be read freely online by anyone; please see the article list below.
References
Ahadi, A., Singh, A., Bower, M., & Garrett, M. (2022). Text mining in education—a bibliometrics-based systematic review. Education Sciences, 12(3), 210. https://doi.org/10.3390/educsci12030210.
Borah R, Brown AW, Capers PL, et al. (2017. Analysis of the time and workers needed to conduct systematic reviews of medical interventions using data from the PROSPERO registry. BMJ Open, 7:e012545. doi: 10.1136/bmjopen-2016-012545.
Education Endowment Foundation (2020). Impact of school closures on the attainment gap: Rapid Evidence Assessment. London: Education Endowment Foundation.
Elharake JA, Akbar F, Malik AA, Gilliam W, Omer SB. (2022). Mental health impact of COVID-19 among Children and college students: a systematic review. Child Psychiatry Hum Dev. 2022 Jan 11:1–13. doi: 10.1007/s10578-021-01297-1. Epub ahead of print. PMID: 35013847; PMCID: PMC8747859.
Gessler, M., Nägele, C., & Stalder, B. (2021). Scoping review on research at the boundary between learning and working: A bibliometric mapping analysis of the last decade. International Journal for Research in Vocational Education and Training, 8(4), 170-206.
Iannizzi, C., Dorando, E., Burns, J., Weibel, S., Dooley, C., Wakeford, H., Estcourt, L.J., Skoetz, N., & Piechotta, V. (2021). Methodological challenges for living systematic reviews conducted during the COVID-19 pandemic: A concept paper. Journal of Clinical Epidemiology, 141, 82–89.
Khalil, M., McGough, A. S., Pourmirza, Z., Pazhoohesh, M., & Walker, S. (2022). Machine Learning, Deep Learning and Statistical Analysis for forecasting building energy consumption—A systematic review. Engineering Applications of Artificial Intelligence, 115, 105287.
Marshall, I.J., Wallace, B.C. (2019). Toward systematic review automation: a practical guide to using machine learning tools in research synthesis. Systematic Reviews, 8, 163. https://doi.org/10.1186/s13643-019-1074-9
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097.
Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., Stewart, L. A., & PRISMA-P Group (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4(1), 1. https://doi.org/10.1186/2046-4053-4-1.
Noel-Storr, A, Gartlehner, G, Dooley, G, Persad, E, Nussbaumer-Streit, B. (2022). Crowdsourcing the identification of studies for COVID-19-related Cochrane Rapid Reviews. Research Synthesis Methods. 1-10. doi:10.1002/jrsm.1559.
Schmidt, L., Olorisade, B.K., McGuinness, L.A. et al. (2021). Data extraction methods for systematic review (semi)automation: A living systematic review [version 1; peer review: 3 approved]. F1000Research 2021, 10:401 (https://doi.org/10.12688/f1000research.51117.1)
Stansfield C, O'Mara-Eves A, Thomas J (2017) Text mining for search term development in systematic reviewing: a discussion of some methods and challenges, Research Synthesis Methods 8: 355-365
Tsou, A.Y., Treadwell, J.R., Erinoff, E. et al. Machine learning for screening prioritization in systematic reviews: comparative performance of Abstrackr and EPPI-Reviewer. Syst Rev 9, 73 (2020). https://doi.org/10.1186/s13643-020-01324-7
Walsh, I., & Rowe, F. (2022). BIBGT: combining bibliometrics and grounded theory to conduct a literature review. European Journal of Information Systems, 1-22.
Publication date: from 2nd October 2024
Editors
Dr Janice Tripney, Associate Professor of Social Policy, Social Research Institute, IOE - Faculty of Education and Society, UCL, UK
Dr Sabine Wollscheid, Research Professor, The Nordic Institute for Studies in Innovation, Research and Education (NIFU), Norway
Article list
Research article
Methodological issues in technology-mediated qualitative data collection: a mapping of research undertaken in schools during the Covid-19 pandemic
Leanete Thomas Dotta, André Freitas and Rita Tavares de Sousa
2024-10-09 Volume 22 • Issue 1 • 2024
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