Parents helping a child with disability learn at home during <scp>COVID</scp>‐19: Experiences from Slovenia and Canada
Bibliographic record
Abstract
Abstract The COVID‐19 pandemic caused much disruption in many global sectors, including education where schools were closed in most countries and children had to learn remotely from home. This was particularly challenging for children with special learning needs and disabilities, often already somewhat marginalized, as they were more likely to be left behind and less able to adapt easily to remote online learning. This study inquired into the experiences of 67 parents from Slovenia and 15 parents from Canada who helped their children with special learning needs or disabilities learn remotely. Parents in both countries identified several specific advantages and disadvantages to learning remotely from home. The Slovenian children spent more hours per day at their lessons and attended more lessons than the Canadian children. Both samples of parents received some support from their schools, although the Slovenia parents perceived these as more satisfactory. This study provides a unique opportunity to study the effects of remote learning during a prolonged crisis situation, and it provides valuable lessons for how both families and school personnel can work to improve the potential educational experiences of students who are required to learn remotely. A 12‐point framework for planning for future emergencies is provided.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".