Some Student Teachers’ Conceptions of Creativity in Secondary School English
Bibliographic record
Abstract
This article explores a group of trainee teachers’ conceptions of Creativity in Secondary School English. Data was collected by means of questionnaires and interviews. Whilst there are many promising notions of creativity, the results also reveal some evidence of narrow conceptions, inconsistent thinking and some misconceptions. This suggests that there may be significant implications for teacher trainers in universities and schools if we are to equip our students with the knowledge, understanding and skills to teach, support and facilitate creativity in their new careers. Romantic notions of original and innate genius, and a progressive emphasis on boundless, directionless play are two possible sources of misconceived ideas for training teachers of English. Creativity can be supported and developed within pedagogical frameworks and settings. This article, therefore, offers a consideration of how Sternberg’s 21 suggested strategies for “Developing creativity as a decision” might be adapted and implemented in the Secondary English classroom. Practical teaching methods and competencies are presented which could be developed and incorporated into graduate trainee teacher programmes.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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; 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".