The Effect of Vocabulary Self -Selection Strategy and Input Enhancement Strategy on the Vocabulary Knowledge of Iranian EFL Learners
Bibliographic record
Abstract
The present study was designed to investigate empirically the effect of Vocabulary Self -Selection strategy and Input Enhancement strategy on the vocabulary knowledge of Iranian EFL Learners. After taking a diagnostic pretest, both experimental groups enrolled in two classes. Learners who practiced Vocabulary Self-Selection were allowed to self-select each word from the text they wanted to, but the learners who practiced Input Enhancement strategy one session later than the other group, were just allowed to choose the words among the textually enhanced ones which were just limited to the finalized words of the Vocabulary Self-Selection group. After about three months of treatment, seen and unseen posttests were administered. The results revealed positive effects of both strategies on the vocabulary knowledge of the Iranian EFL learners. Thus it could be safely concluded that Vocabulary Self-Selection and Input Enhancement strategy were quite effective in the development of vocabulary knowledge. The performance of the two groups of Iranian EFL learners on the achievement posttest, as statistically shown, indicates that the Vocabulary Self-Selection group could outperform the Input Enhancement group on the vocabulary knowledge. The results revealed positive effects of both strategies on the vocabulary knowledge of the Iranian EFL learners. It was finally concluded that Vocabulary Self-Selection group outperformed those in Input Enhancement group. Thus it could be concluded that Iranian EFL learners who practiced Vocabulary Self-Selection strategy outperformed those who practiced Input Enhancement. Vocabulary Self-Selection strategy fostered vocabulary learning.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".