The Impact of Textual Input Enhancement on Iranian Elementary EFL learners’ Vocabulary Intake
Bibliographic record
Abstract
Nowadays, there has been a lot of emphasis on L2 vocabulary learning in the language teaching curriculum. Dueto the emergence and prevalence of growing methods in the area of second language teaching, lots of researchershave tried to take advantage of these methods in enhancing L2 learning vocabulary. Thus, the present studyinvestigated the effect of textual input enhancement as a focus on form method on Iranian EFL learners’vocabulary intake from reading. Ninety one elementary EFL learners in Tabriz Azad University participated in astudy for eight sessions. A quasi-experimental design with a randomized control and an experimental group wasused. Both groups were given five reading texts and comprehension questions to complete. While theparticipants in experimental group read the textually enhanced input through bolding, the participants in thecontrol group read the same texts without input manipulation. Multiple-choice recognition tests were used tomeasure the intake of vocabulary. The results showed a significant difference between control and experimentalgroup. The study concluded with some pedagogical implications.
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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 | 0.004 |
| 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) | 0.004 | 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".