The Impact of Cooperative Learning in Comparison to Traditional Learning (Small Groups) on EFL Learners’ Outcomes When Learning English as a Foreign Language
Bibliographic record
Abstract
To identify the effect of cooperative learning (CL) on the achievement’s of EFL learners, a 12-week study was conducted in four secondary schools in Saudi Arabia. This study aims to examine the effectiveness of cooperative learning (CL) in developing English as a Foreign Language (EFL) students’ grammatical competence in a relatively under-researched context (i.e., Arabia). In particular, this this study aims to investigate the impact of cooperative learning in comparison to traditional instruction in learning English grammar on the achievement of students. This study contributed to the knowledge about how students learn English grammar as a foreign language when they work cooperatively together in cooperative learning groups in comparison to peers who work in traditional classrooms (small groups). The participants in this study were 139 tenth grade male students, aged 14-15 years, in four boys’ secondary schools in Al-Baha city.The results showed that there are statistically significant differences between the mean scores of the students who were taught English in the cooperative learning environment (the experimental group), and those who were taught the English by using the traditional small group method (the control group) in the post-test. This difference was in favour of the experimental group. However, the results showed that are not statistically significant differences between the students in the experimental classes and the students in the control groups in their English achievement test score at the pre-test.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".