Output Tasks, Noticing, and Learning: Teaching English Past Tense to Iranian EFL Students
Bibliographic record
Abstract
EFL Learners often have problems using the past tense accurately. In an attempt to solve their problem, this study was carried out to examine the effects of using two different types of output tasks on noticing and learning the English past tense. Sixty female school-age EFL learners were divided into groups of 18, 19, and 23 participants. A pretest was administered at the outset of the study, the scores of which proved that all the participants equally lacked the required accuracy in using the target structure. Therefore, five treatment sessions followed, during which the first two groups were given picture-cued writing tasks and reconstruction tasks respectively. The comparison group, however, did comprehension check-up tasks. Finally, a posttest was given. The results of the statistical analyses revealed that only the reconstruction group improved in their noticing of the target feature. However, both experimental groups equally promoted their learning of the form.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".