British Education Research and Its Quality: An Analysis of Research Excellence Framework Submissions
Saved in:
| Title: | British Education Research and Its Quality: An Analysis of Research Excellence Framework Submissions |
|---|---|
| Language: | English |
| Authors: | Matthew Inglis (ORCID |
| Source: | British Educational Research Journal. 2024 50(5):2495-2518. |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 24 |
| Publication Date: | 2024 |
| Document Type: | Journal Articles Information Analyses |
| Descriptors: | Foreign Countries, Educational Research, Evaluation Methods, Evaluation Research, Peer Evaluation, Evaluation Criteria, Interviews, Writing for Publication, Qualitative Research |
| Geographic Terms: | United Kingdom (England) |
| DOI: | 10.1002/berj.4040 |
| ISSN: | 0141-1926 1469-3518 |
| Abstract: | We analysed the full text of all journal articles returned to the education subpanel of the 2021 Research Excellence Framework (REF2021). Using a latent Dirichlet allocation topic model, we identified 35 topics that collectively summarise the journal articles that research units, typically schools of education, selected for submission. We found that the topics which units wrote about in their submitted articles collectively explained a large proportion (84.1%) of the variance in the quality assessments they received from the REF's expert peer review process. Further, with the important caveat that we cannot attribute causality, we found that there were strong associations between what the subpanel perceived to be excellent research and the adoption of particular methods or approaches. Most notably, units that returned more interview-based work typically received lower scores, and those which returned more analyses of large-scale data and meta-analyses typically received higher scores. Finally, we applied our 2021 model to articles submitted to the previous exercise, REF2014. We found that education research seems to have become less qualitative and more quantitative over time, and that our 2021 model could successfully predict the scores assigned by the REF2014 subpanel, suggesting a reasonable degree of between-exercise consistency. |
| Abstractor: | As Provided |
| Entry Date: | 2024 |
| Accession Number: | EJ1442213 |
| Database: | ERIC |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | We analysed the full text of all journal articles returned to the education subpanel of the 2021 Research Excellence Framework (REF2021). Using a latent Dirichlet allocation topic model, we identified 35 topics that collectively summarise the journal articles that research units, typically schools of education, selected for submission. We found that the topics which units wrote about in their submitted articles collectively explained a large proportion (84.1%) of the variance in the quality assessments they received from the REF's expert peer review process. Further, with the important caveat that we cannot attribute causality, we found that there were strong associations between what the subpanel perceived to be excellent research and the adoption of particular methods or approaches. Most notably, units that returned more interview-based work typically received lower scores, and those which returned more analyses of large-scale data and meta-analyses typically received higher scores. Finally, we applied our 2021 model to articles submitted to the previous exercise, REF2014. We found that education research seems to have become less qualitative and more quantitative over time, and that our 2021 model could successfully predict the scores assigned by the REF2014 subpanel, suggesting a reasonable degree of between-exercise consistency. |
|---|---|
| ISSN: | 0141-1926 1469-3518 |
| DOI: | 10.1002/berj.4040 |
