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
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
516-517Subventions
Organisme : World Health Organization
ID : 001
Pays : International