Think-Pair-Share: An Active Learning Strategy to Enhance EFL Learners’ Oral Communication Skills
Bibliographic record
Abstract
Mastering communication skills has become a requirement to integrate with global societies that have adopted English as a lingua franca for communication. However, given the obstacles Omani students face, including a limited vocabulary, a mass of grammatical errors, and an inability to construct correct sentences, teaching oral communication skills cannot be considered a facile task. Therefore, the primary purpose of conducting this study was to investigate the effect of the Think-Pair-Share strategy (TPS), an active learning strategy, on Omani EFL high school learners’ oral communication skills. Moreover, it aimed to survey the target sample’s opinions on the usefulness of the TPS strategy. The current study targeted two groups of 10th-grade students enrolled in Al-Rubaie’ Al-Najaria for Girls School (9–12). The 60 students participating in this study were divided into two groups with an equal number of students: 30 students in the experimental group and 30 in the control group. An oral communication test, a questionnaire, and a TPS teaching manual were used to obtain data. Additionally, the TPS treatment was carried out in 12 sessions held over a month-and-a-half. As for data analysis, independent samples t-tests, paired-samples t-tests, and one-way multivariate analysis of variance tests were used. The findings showed that the strategy did not contribute to achieving a significant difference between the means of the experimental and control groups, except in one sub-skill, which was pronunciation. Although the results were not completely in favour of the experimental group, the participating students expressed positive opinions about the benefit they gained from the strategy in developing their oral communication 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".