Effect of Collaborative Learning Strategy on EFL Students' Skimming, Scanning and Questioning Abilities
Bibliographic record
Abstract
This paper examines how collaborative learning strategy (CLS) impacts Saudi University EFL students’ reading comprehension, especially their skimming, scanning and questioning abilities in order to determine the effect of CLS in improving reading strategies. This 14-week study employed a quasi-experimental method to collect data through the pre-test, the post-test, and the semi-structured interview. The participants were 30 students divided into the control group and the experimental group of 15 students each. The quantitative data were analysed using t-test as inferential statistic, and the qualitative data were analysed using thematic transcription. Findings from the t-test analysis revealed that the experimental group outperformed the control group in terms of reading skills under collaborative learning approach. The study will play a significant role in determining the impact of CLS on 30 EFL learners from monolingual backgrounds when they worked collaboratively in a classroom as an experimental group and a controlled group of 15 participants each. The research findings will be helpful in foregrounding the problems of reading strategy of monolingual EFL learners, facilitating thereby the designing of a more effective reading pedagogy to hone EFL, TOEFL, ELETLS reading skills.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".