Exploring Teachers’ Conceptual Uses of Research as Part of the Development and Scale up of Research-Informed Practices
Bibliographic record
Abstract
This article examines the idea of research-informed teaching practice (RITP) and how such practice can best be facilitated to improve aspects of teaching and learning. After first exploring RITP as a concept, the paper then engages with Carol Weiss’ seminal typology of research-use, and makes the argument that Weiss’ notion of conceptual research-use is both more likely and more realistic than instrumental research-use. The paper then illustrates how the idea of conceptual research-use aided the design of a small-scale project that sought to help teachers engage with and employ research, such that this engagement might impact positively on teaching and learning. In-depth semi structured interviews were undertaken with 15 project participants to examine whether the approach employed by the project: 1) helped teachers engage with research; 2) helped teachers develop new strategies for teaching and learning; and 3) whether the strategies developed by teachers were thought to impact on practice and student outcomes. Analysis of the interview data indicates that the approach employed has enabled teachers to successfully engage with research and use research to improve teaching and learning. Furthermore, the analysis also provides clues regarding effective ways to replicate research-informed teaching strategies in new settings and contexts.
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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 | 0.232 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.009 | 0.056 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".