Transitioning to remote teaching platforms: Examining the impact of COVID-19
Bibliographic record
Abstract
Amidst the global spread of COVID-19, prevention measures forced school closures causing a shift to virtual learning. Educators worldwide were forced to adapt to new teaching methodologies without sufficient guidance, training, or resources. Overall, this pandemic has highlighted the essential role of teachers; however, the lack of research done on their experiences with remote education has left little guidance for educators worldwide. Eight high school instructors, four in Montreal, Canada, and four in Zarqa, Jordan, were interviewed in this study to explore their experience with the transition to remote education during the COVID-19 pandemic. More specifically, teachers were asked to discuss the limitations and challenges they encountered, how they adapted their teaching practices to online methods, as well as recommendations they have to improve online education. The data collected from the semi-structured interviews and lesson plan analysis revealed commonalities between the limitations and challenges experienced by the participants in Jordan and Canada (i.e., lack of instruction, lack of resources, lack ICT training, increased workload, increased learning difficulties, and mental health issues). This paper highlights a gap in the research on online teaching in the context of the COVID-19 pandemic and informs educators and researchers about solutions and techniques in creating lessons leading to quality education through online platforms. The goal is not to adapt, but to reshape the purpose and methods of education via online schooling
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".