Concordance-based Data-Driven Learning (DDL) aims to help second language learners infer language usage rules from language usage regularities. A number of DDL pedagogical treatments have focussed on phraseological units such as collocations, widely recognised as a central component of second language learning. This study evaluates DDL effects from an emic perspective, reflecting the learners’ perceived usefulness of the approach, as opposed to etic perspectives, representing changes in language competence as a result of the approach. It compares a group of Chinese learners and a group of Belgian learners of Italian as a Second Language/Foreign Language (SL/FL). The findings indicate that the Belgian students seem to have gained familiarity with the approach faster than the Chinese, though the latter seems to perceive greater long-term benefits of the approach, and are more favourable to future mobile phone applications. The study aims to shed light on possible learner-related differences in DDL treatments and on the insightfulness of emic data in assessing DDL effects.

Learner attitudes towards data-driven learning: Investigating the effect of teaching contexts

Forti, Luciana
2019-01-01

Abstract

Concordance-based Data-Driven Learning (DDL) aims to help second language learners infer language usage rules from language usage regularities. A number of DDL pedagogical treatments have focussed on phraseological units such as collocations, widely recognised as a central component of second language learning. This study evaluates DDL effects from an emic perspective, reflecting the learners’ perceived usefulness of the approach, as opposed to etic perspectives, representing changes in language competence as a result of the approach. It compares a group of Chinese learners and a group of Belgian learners of Italian as a Second Language/Foreign Language (SL/FL). The findings indicate that the Belgian students seem to have gained familiarity with the approach faster than the Chinese, though the latter seems to perceive greater long-term benefits of the approach, and are more favourable to future mobile phone applications. The study aims to shed light on possible learner-related differences in DDL treatments and on the insightfulness of emic data in assessing DDL effects.
2019
978-2-490057-54-2
File in questo prodotto:
File Dimensione Formato  
Forti - 2019 - Learner attitudes towards data-driven learning in.pdf

accesso aperto

Descrizione: Articolo principale
Tipologia: Versione Editoriale (PDF)
Licenza: Creative commons
Dimensione 1.06 MB
Formato Adobe PDF
1.06 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12071/18089
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
social impact