Evaluating an in-service corpus literacy training programme for EFL practitioners
Bibliographic record
Abstract
Despite calls by corpus linguists for incorporating corpora into the language learning classroom, language researchers have reported slow or low uptake of corpora in mainstream teaching practise. We argue that this is due to insufficient pre-service corpus-training and the subsequent inflexibility of working conditions. We propose remedying this situation by focussing on in-service corpus-literacy training for EFL teachers, using a 3-step training programme which utilises learner needs analysis, exploratory practice and reflection. Learner needs analysis is the basis for small corpus-based activities that can be incorporated into existing syllabi. Our evaluation is based on reflective journals completed during the training programme and interviews conducted with participants one week after training was completed. Our findings suggest that teachers felt the first two steps of the training programme were helpful when learning to use corpora; however, there are mixed views on whether reflection helped in this regard. With a majority of teachers reporting that collecting learner needs was the most effective step in the training programme, we suggest that future corpus literacy training programmes incorporate this step where possible.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".