Teachers’ Perception of the Use of Microsoft Teams for Remote Learning in Southwestern Nigerian Schools
Bibliographic record
Abstract
The outbreak of COVID-19 pandemic has required schools in Nigeria to embrace remote learning using technology solutions, in this case, Microsoft Teams, to effectively engage students. This study, therefore, aimed to reveal teachers’ perception of the use of Microsoft Teams for remote learning. The descriptive survey research design was adopted. The participants in the study were 51 teachers who were randomly selected using convenient sampling technique. E-questionnaire was used in the collection of data. Descriptive statistics of frequency counts, simple percentages, mean and standard deviations were used to analyze the data. Results revealed that teachers’ perception of effectiveness of Microsoft Teams for assignment and grading, for teacher and student interaction, and for classroom organisation was very good. The result obtained revealed that Microsoft Teams was effective in addressing some of the major challenges encountered by teachers during remote learning which includes students being often on other websites and poor student engagement. It was concluded that Microsoft Teams was effective for smooth interaction between teacher and students. Its use enhanced classroom organization and consequently facilitated teaching and learning process. The study encourages wider adoption of the application by schools.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".