Virtual Classrooms: An Inclusive Approach to Educate the Children with Autism
Bibliographic record
Abstract
ASD children often struggle with social interactions, leading to difficulties in interpersonal relationships and academic achievements. Inclusive education is crucial for their success, providing them with the environment they need while giving nonASD children an equal chance. Virtual classrooms, utilizing technology like Zoom and Microsoft Teams, facilitate meaningful interactions and convenient learning processes, offering flexibility and reducing power disturbances. Teacher training and support are essential for the success of virtual learning. This article examines the impact of virtual classrooms on inclusive education for autistic learners, comparing their interaction and academic achievement in virtual settings to regular classrooms. The study uses a phenomenology design to analyze the experiences of primary school students with disabilities in virtual education post-COVID-19. Virtual classrooms are suitable for accommodating individual needs, increasing accessibility, and providing a secure environment. However, cost and accessibility remain major obstacles for families. The consequences of virtual learning on children with autonomy and responsible technology use remain unanswered. The article suggests that improving the accessibility and inclusivity of virtual classrooms could significantly enhance their efficacy. Advancements in technology and educational regulations have made virtual classrooms beneficial for children with Autism Spectrum Disorder (ASD). They cater to individual needs, increase accessibility, and provide a secure environment. However, challenges remain, and AI technologies could improve inclusive education.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".