Investigating the Effect of Cooperative Learning and Competitive Learning Strategies on the English Vocabulary Development of Iranian Intermediate EFL Learners
Bibliographic record
Abstract
<p>The current study investigated the effect of cooperative and competitive learning strategies on the acquisition of English vocabulary development by Iranian EFL intermediate learners. In addition, it explored what type of theses strategies was more effective. In such doing, utilizing an Oxford Placement Test (OPT), 45 out of 77 Iranian EFL intermediate learners from four language institutes in Tehran, Iran, were randomly selected. Then, the selected participants were equally divided into three groups, i.e. a control group and two experimental groups, (N=15). On experimental group was taught via cooperative learning, and the other experimental group was taught via competitive learning. The obtained results were analyzed via one-way ANOVA and independent sample t-test. The results revealed that both of these strategies were effective in English vocabulary development by Iranian EFL intermediate students. Furthermore, the findings indicated that the performance of the experimental group via cooperative strategy was better than their counterpart in the experimental group whom was taught via competitive strategy.</p>
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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 | 0.000 | 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".