Laying Down Pale Memories:Learners Reflecting on Language, Self, and Other in the Middle-School Drama-Languages Classroom
Bibliographic record
Abstract
This article explores one teacher/researcher’s development of a drama–language unit and the learners’ responses to it. The work is underpinned by a model of intercultural language learning which also acknowledges the pluricultural and plurilingual contexts in which foreign languages are taught in Australia. As part of a participatory-action-research doctoral study, process drama became the basis for planned language-learning experiences undertaken with a class of 12-year-old beginner students of German. Challenges still remain when transferring theories of intercultural language learning to classroom practice, particularly in the face of learner disengagement with school language learning. Therefore, the article enhances understandings of the potential benefits of process drama for middle-school languages pedagogy, particularly in relation to the challenging reciprocal aspect of intercultural work. The article is developed around a Bakhtinian framework of language as referential social practice and demonstrates how the drama–languages model can extend traditional cognitive approaches to pedagogy into physical and affective worlds. The effects of this shift are explored from the perspectives of unit planning, learner engagement with language, the development of cultural referents, and awareness of self and other.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".