Using Cooperative Learning Strategies to Develop Rural Primary Students' English Oral Performance
Bibliographic record
Abstract
This small-scale quasi-experimental research study aimed at investigating the effectiveness of cooperative learning (CL) strategies in the achievement of students’ oral performance at the A1 Common European Framework of Reference for Languages (CEFR) level. The study participants were twenty-four seventh graders from a small rural primary school located on the southern part of Cuenca city. The quantitative part was based on a descriptive statistic study. It was collected through the students’ speaking pre and post-test. The results were processed and analyzed through SPSS version 25. To compare the mean scores of the students in the pre and post- test, a T test for one sample was used. In addition, the qualitative part based on phenomenological research was gathered through direct classroom observations and group discussion. Findings indicated that: firstly, the study participants reached their A1 oral performance level in the evaluation criteria of comprehension, interaction, fluency, pronunciation. Secondly, students had positive attitudes toward CL strategies. Thirdly, through CL strategies students became more motivated and less reluctant during oral participation. In light of the findings, CL should be adopted in primary English learning as it helps improve learners’ EFL peaking skill.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".