Online Learning via Microsoft TEAMS During the Covid-19 Pandemic as Perceived by Kuwaiti EFL Learners
Bibliographic record
Abstract
Covid-19 pandemic made a sudden shift of all ages to online learning and distance learning instructions. However, there is a paucity of research to address the possible impacts of the pedagogical shift integrated into new online platforms on learning, interaction, and assessment, especially in higher education settings from the vantage point of EFL students. Framed in a descriptive quantitative study, the main objective of this study includes two folds: a) to identify the possible effects of online learning via Microsoft TEAMS platform during the COVID-19 pandemic on assessment, interaction, and learning English as a foreign language from EFL students’ perception and b) to reveal the possible significant correlation between learning, interaction and online assessment via Microsoft TEAMs. Data were collected using a developed questionnaire consisting of 30 items focusing on three dimensions: interaction, learning, and assessment among 440 EFL students whose major was English at the College of Basic Education in Kuwait. At the significance level of 0.01, the results revealed the effect of online learning via Microsoft TEAMS during the COVID-19 pandemic on learning of English skills, students’ interaction and achievement assessment as perceived by the EFL students in the English Language Department in the CBE was rather high, moderate and moderate and moderate respectively. There is a strong/high statistically significant correlation between Interaction and Assessing (r = 0.538), interaction and Learning (r = 0.747). There is a statistically significant moderate correlation between Assessing and Learning (r = 0.467). This study is of some pedagogical and assessment ramifications for EFL contexts in the pandemic era.
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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 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".