Impact of COVID-19 on Virtual Schooling: Insights from Elementary School Teachers in Aspirational Districts of the North-East Region of India
Bibliographic record
Abstract
The study investigated the social, economic, and technical impacts of COVID-19 on elementary school education, with a focus on the challenges and barriers to virtual schooling in the North-East Region of India. Approved and funded by the Government of India through the Ministry of Education, it utilized primary data collected from 224 elementary school teachers across 20 randomly selected schools in aspirational districts during the 2022–23 academic session across all states of the North-East Region. Findings showed that 87% of teachers had actively participated in online teaching, though inadequate infrastructure, limited resources, and insufficient training had posed significant challenges. Teachers had adapted to online teaching through platforms such as WhatsApp, YouTube, Zoom, and DIKSHA, while advanced tools like virtual labs and QR-code-enabled textbooks had been underutilized. Support materials had been only partially available, and systemic issues—such as the lack of digitization, online evaluation tools, and funding—had constrained virtual learning. Programs such as NISHTHA had provided training to 70% of teachers, though participation in MOOCs and SWAYAM had been notably low. Teachers had reported significant psycho-social challenges among children, including isolation, anxiety, reduced physical activity, and disengagement—particularly among disadvantaged children and those experiencing personal losses. Strategies such as counselling, home visits, and remedial teaching had been employed to address these challenges. The study underscored teachers' resilience during the pandemic while highlighting the need for enhanced digital infrastructure, training, and psycho-social support to ensure inclusive online education. It had proposed recommendations addressing infrastructure development, teacher training, governance and policy support, psycho-social assistance, alternative learning methods, enhanced collaboration, and specialized learning materials, aiming to bridge existing gaps and ensure that virtual schooling became effective, inclusive, and aligned with NEP-2020's goals to foster equitable and quality learning opportunities for all.
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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 | 0.002 |
| 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 | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".