English Language Teacher Education and the Multiliteracies Pedagogy: Constructing Complex Professional Knowledge and Identities
Bibliographic record
Abstract
Contemporary literacy practices need to be addressed in school settings. That requires awareness by teachers and students of the cultural and linguistic diversity present in our cosmopolitan societies. In the field of English language teaching (ELT), one way of responding to such demand is engaging teachers with multiliteracies pedagogies throughout their professional preparation. Based on that assumption, this paper reports on a component experience of the Brazilian Government Program for Initial Teacher Education, where, in 2017, three teacher candidates planned and taught three English lessons using the multiliteracies pedagogy framework. It stands as a case study that seeks to identify the impacts of using multiliteracies pedagogy in a teacher education context, in terms of knowledge building and identity work. Teacher candidates engaged in a designing process of multimodal teaching materials and documented their experience in journals. Those items were used as data to investigate the impact of the pedagogy on teachers’ development, focusing on the following elements of design: reference, dialogue, structure, situations, and intention. Research findings suggest the positive impact of that experience, both as a source of professional knowledge and as a fruitful opportunity for teachers to change preconceptions about ELT.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".