Impact of Explicit Vocabulary Instruction on Writing Achievement of Upper-Intermediate EFL Learners
Bibliographic record
Abstract
Studying explicit vocabulary instruction effects on improving L2 learners’ writing skill and their short and long-term retention is the purpose of the present study. To achieve the mentioned goal, a fill-in-the blank test including 36 single words and 60 lexical phrases were administrated to 30 female upper-intermediate EFL learners. The EFL participants were asked to write a composition titled 'A Cruel Sport' after a reading activity on 'Bull Fighting'. Comparing this writing to the one written after target vocabulary instruction, it caused a significant increase in the number of vocabularies used productively in learners’ writing. The statistical analysis revealed that in delayed writing, the participant retained the newly-learned vocabularies even sometimes after the instruction. Based on the obtained results, this research offers below suggestions for L2 instructors: 1) productive use of words is not guaranteed by word comprehension per se, 2) learners are not only able to increase the active vocabulary under their control but also use the words they just learned, 3) in a writing task which was immediately fulfilled through explicit vocabulary instruction, vocabulary recognition is converted into a productive one, improving retention and leading to productive use of newly learned vocabulary at the same time. This productiveness, however, is loss prone and more practice is needed in producing newly learned vocabulary.
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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 | 0.000 | 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 | 0.000 |
| 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".