DDL is in the details... and in the big themes

Alex Boulton (1, 2)

1 CRAPEL - Centre de Recherches et d'Applications Pédagogiques en Langues
2 ATILF - Analyse et Traitement Informatique de la Langue Française

Data-driven learning (DDL) relies on learners being able to perceive patterns in raw data, typically in the form of concordances. As with all areas of DDL, empirical support for this is scarce, especially in major areas of grammar. This paper describes an experiment where 100 French engineering students were provided with either a) traditional grammar rules or b) concordances for will and going to. Tests before and after the experiment allow a direct comparison of traditional rule-based learning the pattern-detection “discovery-learning” involved in DDL.

Mots clés: will; going to; data-driven learning; pattern-detection; empirical research

Domaines: Linguistique

Alex Boulton. DDL is in the details... and in the big themes. Corpus Linguistics Conference: CL2007, 2007, Birmingham, United Kingdom. pp.XX. ⟨hal-00326994⟩