How has drama education training strengthened our teaching skills? Perspectives from preservice teachers and a university professor
Bibliographic record
Abstract
In this paper, we have reflected on how drama training and drama experiences have helped us in our own journeys as educators and how the artform has invited learners into new communities and collaborative problem-solving. The very spaces of schools are isolating, with classroom doors opening and shutting in long corridors. The curriculum is often siloed and distant from other disciplines and young people and adults spend hours in zombie-like trances, seemingly addicted to hand-held devices, yearning for affirmations of their existence. Drama invites us to connect with one another and come face to face with human beings. Drama invites us to ‘try on’ a lifestyle and language that may be unfamiliar. In addition, study after study has proven that literacy skills are strengthened and enhanced when the crafts of drama—expressive speaking, risk-taking, creativity, imaginative and cooperative thinking and doing—are infused into teaching and learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".