The Effects of Using Microsoft Teams on Improving EFL Learners' Speaking Abilities at Unaizah High School Students
Bibliographic record
Abstract
Advanced technology has affected all aspects of life, including education. Technology in education has proven effective in several topics, including learning ESL/EFL. By reviewing previous research, the study aimed to explore the effects and challenges of using Microsoft Teams as an online learning tool on EFL learners at Unaizah, Saudi Arabia, from the students’ point of view. To improve their speaking skills more efficiently than in traditional classrooms, a questionnaire as a descriptive approach was used to collect data. The sample consisted of 351 female students. The results indicated that learners find using Microsoft Teams has advantages in improving English speaking skills. Thus it had a positive impact. Furthermore, the study found that learners face challenges in using Microsoft Teams. Moreover, there are no statistically significant differences at the level of the significance (α ≤ 0.05). The study recommends that educational institutions apply Microsoft Teams for its advantages.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".