Microsoft teams’ acceptance for the e-learning purposes during Covid-19 outbreak: A case study of UAE
Bibliographic record
Abstract
The brisk spread of Covid-19 led educational policymakers and organizations to opt for better alternatives to resume the students' educational journey. In this context, Microsoft Teams remained one of the most devoted and credible online platforms that greatly facilitated the educational process across the globe. Current research also analyzed Microsoft Teams acceptance using the self-proposed conceptual model supported by the Technology Acceptance Model by Davis. We employed the survey method and examined the gathered using the applied Structural Equation Modelling (SEM). Results indicated that there is a significant impact of Covid-19 on the Perceived Ease of Use (p> 0.000) and Perceived Usefulness (p> 0.000). Besides, the relationship between Perceived Ease of Use and Perceived Usefulness also remained significant (p> 0.000). Moreover, the proposed relationship between Attitude, Perceived Ease of Use, and Perceived Usefulness also remained substantial (p> 0.009 and p> 0.000). However, the relationship between Attitude and Behavioral Intention remained insignificant (p> 0.556). Finally, the relationship between Behavioral Intention and Microsoft Teams Acceptance remained significant (p> 0.088). Thus, we concluded that Microsoft Teams is an effective study tool that unites students and instructors in the United Arab Emirates. It governs eLearning experiences and, therefore, provides a virtual environment to students and directly influences our perceptions and behavior.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".