A Comparative Study on the Effects of Core and Peripheral Teaching on Iranian EFL Learners’ Writing Skill in Conventional and Cyber Environments
Bibliographic record
Abstract
This study aimed to find outthe effects of core and peripheral teaching on Iranian EFL learners’ writing skills in conventional and cyber environments. After administrating a Nelson (Fowler and Coe, 1976) test, a group of 160 homogeneous students at language institute were selected from a total population of 200 at the intermediate level in Dehdasht, Kohgiloyeh & Boyer Ahmad province, Iran. Then, they were randomly assigned to control and experimental groups and each into subgroups. While experimental sub-group A was assigned to physical (conventional) environment and received instructions regarding core and peripheral teaching, experimental sub-group B was assigned to cyber environment and received instructions regarding core and peripheral teaching. One sub-control group was assigned to physical environment and received no instruction, while the other control subgroup was assigned cyber environment. A T-test was conducted to compare the subjects’ means and to determine the effect of core and peripheral teaching. The results depicted that peripheral teaching in both physical and cyber environments had a significant effect on improving Iranian EFL learners’ writing skills, (P<.05). Key words : Core teaching; Peripheral teaching; Conventional environment; Cyber environments
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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) | 0.002 | 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".