From Receptive to Productive: Improving ESL Learners' Use of Vocabulary in a Postreading Composition Task
Bibliographic record
Abstract
Limited research on ESL learners' use of vocabulary in writing prompted our investigation of vocabulary use in composition by secondary school multi-L1 intermediate ESL learners in Greater Vancouver (n = 48). This study showed that though intermediate learners' use of 1,000–2,000-word-level vocabulary tended to remain constant, their productive use of higher level target vocabulary improved in postreading composition and was largely maintained in delayed writing. It also showed how, in so doing, their lexical frequency profile (LFP) improved. We attribute this improvement to the teacher's use of interactive elicitation of vocabulary and a writing frame, and specific instruction to learners to use target vocabulary. Though the exact factor or factors of vocabulary acquisition in this study is unclear, it is obvious that teacher elicitation, explicit explanation, discussion and negotiation, and multimode exposure to target vocabulary are all means of scaffolding and manipulating vocabulary that increased learners' use of target vocabulary. All these strategies in turn improve LFP in writing. The results suggest that this approach also makes vocabulary learning durable. Increased productive vocabulary acquisition also implies a much larger increase in recognition vocabulary, improving overall classroom language performance. Hinkel (2006, p. 109) calls for integrated and contextualized teaching of multiple language skills, in this case, reading, writing, and vocabulary instruction.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".