Evaluating Complexity of Digital Learning in a Multilingual Context: A Cross-Linguistic Study on WHO's Emergency Learning Platform.

COVID-19 Digital learning Natural language processing computational linguistics health emergencies linguistics

Journal

Studies in health technology and informatics
ISSN: 1879-8365
Titre abrégé: Stud Health Technol Inform
Pays: Netherlands
ID NLM: 9214582

Informations de publication

Date de publication:
27 May 2021
Historique:
entrez: 27 5 2021
pubmed: 28 5 2021
medline: 1 6 2021
Statut: ppublish

Résumé

Reproduction of knowledge, especially tacit knowledge can be expensive during a pandemic. One of the most common causes is the reduced information accessibility during the translation process. Having the ability to assess the linguistic complexity of any given contents could potentially improve knowledge reproduction. Authors conduct two cross-linguistic studies on the World Health Organization (WHO)'s emergency learning platform to assess the linguistic complexity of two online courses in 10 languages. Morpho-syntactically annotated treebanks, unannotated materials from Wikipedia and language-specific corpora are set as control groups. Preliminary findings reveal a clear reduced complexity of learning contents in the most candidate languages while retaining the maximum amount of information. Creating a baseline study on low-resourced languages on the learning genre could be potentially useful for measuring impact of normative products at country and local level.

Identifiants

pubmed: 34042628
pii: SHTI210222
doi: 10.3233/SHTI210222
doi:

Types de publication

Journal Article

Langues

eng

Pagination

516-517

Subventions

Organisme : World Health Organization
ID : 001
Pays : International

Auteurs

Yu Zhao (Y)

World Health Organization.

Giuseppe Samo (G)

Beijing Language and Culture University.

Heini Utunen (H)

World Health Organization.

Oliver Stucke (O)

World Health Organization.

Gaya Gamhewage (G)

World Health Organization.

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Classifications MeSH