EFL Teachers' beliefs and Challenges towards Technology-Integrated Online Classrooms during the Covid-19 Pandemic at Ahram Canadian University
Bibliographic record
Abstract
AbstractThe Covid-19 pandemic has offered us a challenging opportunity to pave the way for introducing digital learning. The unexpected shift of teaching the English language from face-to-face classroom to online learning activities through digital platforms has raised significant challenges for English teachers and students. Thus, the current study aims to investigate the EFL teachers' beliefs towards the effectiveness of technology-integrated online classrooms and the challenges they experienced during the Covid-19 pandemic. The study adopts an exploratory sequential design whereby online questionnaire and interviews were used to come up with a comprehensive report on the impact of technology-integrated online classrooms during the Covid-19 pandemic and the EFL teachers' challenges faced in online teaching. For data collection, 48 instructors at the English language department, Ahram Canadian University, participated in the study. Due to the pandemic lockdown, the study took place in the spring semester of the academic year 2020. The current study focused on addressing the following questions: Is technology-integrated online classroom beneficial during the Covid-19 pandemic?
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".