The Effectiveness of Cooperative Learning Activities in Enhancing EFL Learners’ Fluency
Bibliographic record
Abstract
This research-paper aims at examining the effectiveness of cooperative learning activities in enhancing EFL learners’ fluency. The researcher has used the descriptive approach, recorded interviews for testing fluency as tools of data collection and the software program SPSS as a tool for the statistical treatment of data. Research sample consists of (48) first year-students, studying English language in the Faculty of Education at Omdurman Islamic University-Sudan. The students were divided into experimental and control groups for the requirement of the research-paper. The program of the experimental group lasted for a whole month in which much practice was conducted through the Cooperative Learning activities for enhancing the experimental group’s fluency. The most important result indicates a statistically significant correlation between the Cooperative Learning activities and the improvement of EFL learners’ oral fluency of speaking. The most important recommendation addresses the concerned authorities to train EFL teachers in the use of Cooperative Learning activities in the teaching/learning process for the purpose of furnishing to generalize their use in the various institutions where English language is studied.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".