Learning loss and learning recovery
Harry Patrinos
DECISION: Official Journal of the Indian Institute of Management Calcutta, 2022, vol. 49, issue 2, No 4, 183-188
Abstract:
Abstract In 2020, most countries closed schools. Two years after the pandemic began, the evidence strongly indicates that school closures result in learning loss. A decrease in learning could decrease future employment prospects and lower future earnings. This means that schooling matters. One promising policy option for mitigating learning losses during closures as well for subsequent learning recovery and acceleration is tutoring. While tutoring is effective, the replicability was demonstrated during the COVID-19 school closures. These online experiments were very cost-effective, showing that it is possible to provide quality instruction across the cost spectrum in different contexts.
Keywords: Learning loss; COVID-19; School closures; Tutoring (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://link.springer.com/10.1007/s40622-022-00317-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:decisn:v:49:y:2022:i:2:d:10.1007_s40622-022-00317-w
Ordering information: This journal article can be ordered from
http://www.springer.com/journal/40622
DOI: 10.1007/s40622-022-00317-w
Access Statistics for this article
DECISION: Official Journal of the Indian Institute of Management Calcutta is currently edited by Rajesh Babu
More articles in DECISION: Official Journal of the Indian Institute of Management Calcutta from Springer, Indian Institute of Management Calcutta
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().