Student teachers’ collaborative construction of grammar awareness : the case of a highly competent learner
Bibliographic record
Abstract
The paper investigates how a student on a master’s-level teacher education course for English language teachers goes about constructing her grammar knowledge. The learner is a novice teacher with English as a foreign language. Learner diary, interview, and group interaction data were analysed thematically, revealing that she made relatively few, usually brief, verbal contributions to the group work but was nonetheless a very active and competent participant. One of her main strategies was ‘listening to others’. At the same time, important learning skills allowed her to identify, create, and make use of learning opportunities. She is contrasted with another learner who speaks considerably more and is a risk-taker. The central argument to emerge is that the learners are each enacting their individual identities. How learners approach the construction of knowledge is therefore unique to each learner; there are a number of ways of being a ‘competent learner’.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".