Data-driven learning for languages other than English: The cases of French, German, Italian, and Spanish
Issue Date
2020-12-13Author
Jablonkai, Reka
Forti, Luciana
Castelló, Magdalena Abad
Iguenane, Isabelle Salengros
Schaeffer-Lacroix, Eva
Vyatkina, Nina
Publisher
EuroCALL
Type
Article
Article Version
Scholarly/refereed, publisher version
Rights
© 2020 Reka Jablonkai, Luciana Forti, Magdalena Abad Castelló, Isabelle Salengros Iguenane, Eva Schaeffer-Lacroix, Nina Vyatkina. This work is licensed under a Creative Commons Attribution 4.0 International License.
Metadata
Show full item recordAbstract
This paper summarises the contributions to EuroCALL’s CorpusCALL SIG Symposium for the year 2020. In line with this year’s EuroCALL conference theme, ‘CALL for widening participation’, the Symposium centred around the theme of Data-driven learning for languages other than English. This paper gives a brief overview of developments and challenges when using Data-Driven Learning (DDL) to teach French, German, Italian, and Spanish. As research suggests, a DDL approach has been effectively utilised to teach these languages. However, there are differences in available DDL resources and corpora for the respective languages that are appropriate for language teaching. The main challenges for future developments are also discussed.
Description
This article is published under the Attribution (CC BY) licence. The CC BY licence lets others distribute, remix, tweak, and build upon authors’ work, even commercially, as long as they credit authors for the original creation.
Collections
Citation
Jablonkai, Reka; Forti, Luciana; Abad Castelló, Magdalena; Salengros Iguenane, Isabelle; Schaeffer-Lacroix, Eva; Vyatkina, Nina. (2020). Data-driven learning for languages other than English: the cases of French, German, Italian, and Spanish. In Frederiksen, Karen-Margrete; Larsen, Sanne; Bradley, Linda; Thouësny, Sylvie (Eds), CALL for widening participation: short papers from EUROCALL 2020 (pp. 132-137). Research-publishing.net. https://doi.org/10.14705/rpnet.2020.48.1177
Items in KU ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
We want to hear from you! Please share your stories about how Open Access to this item benefits YOU.