Impact of Cooperative Learning on the Achievement of EFL Tertiary Level Learners: A Case-Study of a Mainstream University in a Middle Eastern Country
Bibliographic record
Abstract
Academic achievement of most of the Saudi EFL learners is generally poor. A lot of research has been done to probe problems of EFL learners but very little attention has been paid to overcome these problems via better classroom environment and teaching strategies. This quasi-experimental study aimed at investigating the impact of cooperative learning on academic achievement of EFL tertiary learners at a mainstream public sector university in a Middle Eastern country. The sample of the study included 50 EFL non-English major male students enrolled at the preparatory year program in the first semester of 1434-35 corresponding to 2014 A.D. Pretest posttest experimental group research design was used for the study. Scores of the pretest and posttest for the two course-based assessments were analyzed using MS Excel 2013 and SPSS Version 20. The results of the study revealed that the experimental group showed better performance in the posttest compared to that of control group, showing that cooperative learning has positive impact on academic achievement of Saudi EFL tertiary level learners. The low achievers and the medium achievers in the EG showed statistically significant improvement after the CL treatment whereas high achievers performed equally well in both CL and traditional setting. Low achievement of Saudi EFL tertiary level learners should be given a serious consideration and proper remedial measures should be implemented. CL can be instrumental in this regard.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".