Facilitating Research-Informed Educational Practice for Inclusion. Survey Findings From 147 Teachers and School Leaders in England
Bibliographic record
Abstract
This paper considers the engagement by teachers and school leaders in England in educational practices that are both ‘research-informed’ and supportive of inclusive education. We do so by seeking to understand the benefits, costs, and signifying factors these educators associate with research-use. In undertaking the study, we first worked to develop and refine a survey instrument (the ‘Research-Use BCS survey’) that could be used to uniquely and simultaneously measure these concepts. Our survey development involved a comprehensive process that comprised: (1) a review of recent literature; (2) item pre-testing; and (3) cognitive interviews. We then administered this questionnaire to a representative sample of English educators. Although response rates were somewhat impacted by the recent COVID-19 pandemic, we achieved a sufficient number of responses (147 in total) to allow us to engage in descriptive analyses, as well as the production of classification trees. Our analysis resulted in several key findings, including that: (1) if respondents see the benefits of research, they are likely to use it (with the converse also true); (2) if educators have the needed support of their colleagues, they are more likely to use research; and (3) perceiving research-use as an activity that successful teachers and schools engage in is also associated with individual-level research use. We conclude the paper by pointing to potential interventions and strategies that might serve (at least, in the English context) to enhance research-use, so increasing the likelihood of the development and use of effective inclusive practices in schools.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".