The Effect of Form versus Meaning-Focused Tasks on the Development of Collocations among Iranian Intermediate EFL Learners
Bibliographic record
Abstract
This study attempts comprehensively to investigate the effect of form versus meaning-focused tasks on the development of collocations among Iranian Intermediate EFL learners. To this end, 65 students of Mashhad High schools in Iran were selected as the participants. A general language proficiency test of Nelson (book 2, Intermediate 200A) was used to measure their general language ability. Moreover, a teacher-made collocation test was implemented to examine the participants’ collocation knowledge. Participants were divided into: form-focused instruction group (FFI), meaning-focused instruction (MFI) group, and a control group. The FFI group performed dictogloss task (DT) which focused on both target items and meaning. The MFI group assigned communicative task (pair /group discussion task) which did not required attention to the target items. The control group is designated as the Conventional Group, simply to reflect the fact that they did not receive focus-on-form instruction but rather received combination of explaining collocation or new vocabulary and reading a text silently to mention its main idea or answer to comprehension questions. The results revealed the fact that FFI group (dictogloss task) significantly outperformed the other two groups on the collocation test.
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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 | 0.005 |
| 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".