Vocabulary Learning Strategies and ELT Materials; A Study of the Extent to Which VLS Research Informs Local Coursebooks in Iran
Bibliographic record
Abstract
Lexical competence is now regarded to be at the heart of communicative competence. This is endorsed by psycholinguistic research and corpus linguistics which show more use of prefabricated chunks than rule-based constructions. The change has been embraced in ELT. But lexical needs are unique to the individuals, personally, professionally and academically. Research demonstrates that vocabulary learning strategies make learning more self-directed and transferrable to new situations but there is a need for training learners in the use of VLS. ELT coursebooks are agenda for classroom practices; hence a good place to incorporate learner training. This study analyzed local ELT materials to study to what extent insights from VLS research and learner training have informed the sampled coursebooks. The results show the new edition of Pre-University coursebook is a significant step in incorporating such insights however there is a long way before the treatment is adequate in the whole series.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".