Challenges Faced by Classroom Teachers in Distance Learning for Students with Attention Deficit Hyperactivity During COVID-19 Pandemic
Bibliographic record
Abstract
Remote and virtual classrooms could negatively affect the academics of students, especially in case of a child suffering from attention deficit hyperactivity disorder (ADHD). The aim of the present study was to understand the obstacles and differences between the teachers regarding their use of teaching methods, tasks and assignments, tests and evaluation methods and communication between teacher and parents of students with ADHD, during distance learning in COVID-19 pandemic. Data were collected through a questionnaire which was reviewed by some experts in the field of this study from several KSA universities. The result showed that there were statistically significant differences among responses of classroom teachers regarding all the dimensions. Furthermore, there were statistically significant differences between participants in their opinion toward all dimensions according to their specialization and years of experience which was not in the case of age and academic qualifications of the participants. Thus, it was concluded that teachers were willing to co-operate with the students having ADHD during online classes, but they face difficulties in handling them as ADHD students lacks concentration and can be distracted easily.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".