From science teacher to ‘teacher scientist’: exploring the experiences of research-active science teachers in the UK
Bibliographic record
Abstract
We explore the professional identities of UK-based secondary science teachers who actively participated in science research for at least six months. The study uses thematic analysis to analyse semi-structured interviews with 17 participants across England and Scotland, from a variety of educational/socio-economic contexts. We found that through participation in research projects, teachers develop a multi-faceted sense of professional identity that includes the roles of teacher, scientist/researcher, mentor and coach. Teachers who are research-active develop complex professional networks that have a positive impact upon their sense of professional worth and self-belief. Through participation in research, teachers identified as both science teachers and scientists and this has been encapsulated in this research as a transition in professional identity to ‘teacher scientist’. The key enabling factor in identification as a ‘teacher scientist’ is a teacher’s positive interaction with scientists/researchers. Teachers are motivated to participate in research projects in response to the enthusiasm of their students and a desire for students to contribute to research that could provide solutions to real-world challenges. This understanding of the capacity of science teachers to become ‘teacher scientists’, and recognising teachers' altruistic motivations, could contribute to teacher retention and recruitment strategies that are less focused on financial incentives.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".