Effectiveness of Microsoft Teams in Student Teachers' Achievement in an EFL Teaching Methods Course
Bibliographic record
Abstract
The effectiveness of the Microsoft Teams platform in various subject areas was evident in literature, particularly as much as reflected by students’ and instructors’ perceptions and as rarely as proven by students’ achievement on tests. It also appeared that achievement in EFL teaching methodology courses through assessing Microsoft Teams' effectiveness in students' achievement appeared not to have been addressed. Therefore, this study aimed at assessing the effectiveness of the platform in student teachers' achievement of an EFL Teaching Methods I course offered at the Faculty of Education, Suez Canal University, Egypt, as well as the students’ perceptions of the platform. The study adopted a quasi-experimental approach comprising an experimental group teaching via the platform and another in the traditional face-to-face method. Thirty-two third-year student participants training to be future teachers were involved in the study, forming an equal student number in both groups (n. 16). The study designed and administered an achievement pretest-posttest and a student perception form. Results indicated that the platform was effective in the achievement of the EFL Teaching Methods I course at the experimental group level. It proved even more effective than the traditional method at the experimental-control levels. The participants reported they were generally in favor of the platform. Reasons included the platform utilization of interactive tasks, such as chat rooms, content-sharing, webinars, files, calls, email communication, class notebook, calendars, assignments, and emojis featured by the platform. The study recommends that knowledgeable instructors who are skillful at using the platform capabilities should make a difference in students’ learning reflected by their students’ significant achievement and perceptions.
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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 | 0.003 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".