Indian Academy of Pediatrics Guidelines on School Reopening, Remote Learning and Curriculum in and After the COVID-19

Swati Ghate1, Bakul Jayant Parekh2, Rajeev Kumar Thapar3

  • 1Babylon's Newton Child Development and Support Centre, Jaipur, India.

Indian Pediatrics
|October 12, 2020
PubMed

Insights

The Indian Academy of Pediatrics recommends reopening schools only when local COVID-19 conditions are favorable and stakeholders are prepared. Remote learning and curriculum revision are crucial for uninterrupted education and psychosocial development.

Area of Science:

  • Pediatrics
  • Education
  • Public Health

Background:

  • COVID-19 pandemic led to widespread school closures, causing educational, psychosocial, and physical challenges for children.
  • Urgent need for scientific guidance to support children's educational development during the pandemic.

Purpose of the Study:

  • Formulate guidelines for school reopening in the Indian context.
  • Provide recommendations for effective remote learning strategies.
  • Identify essential curriculum content for restructuring.

Main Methods:

  • A task force comprising pediatricians, educationists, and technology experts was formed by the Indian Academy of Pediatrics.
  • Information was gathered and exchanged through virtual platforms.
  • Guidelines were drafted by a writing committee and approved by all task force members.

Main Results:

  • School reopening contingent on favorable local epidemiology, adequate infrastructure, and stakeholder preparedness.
  • Emphasis on ensuring remote learning reaches all students for educational continuity.
  • Curriculum revision focusing on core content and promoting informal learning for psychosocial skills.

Conclusions:

  • School reopening requires careful consideration of epidemiological factors and readiness.
  • Remote learning must be accessible to all students to prevent educational disruption.
  • Curriculum should be adapted, prioritizing essential knowledge and psychosocial skill development over formal learning.
Abstract

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