Data-driven learning: Technology-enhanced learning of semantic prosody

Autor: Jarvis Looi, Alessandra Cacciato
Jazyk: francouzština
Rok vydání: 2024
Předmět:
Zdroj: ALSIC: Apprentissage des Langues et Systèmes d'Information et de Communication (2024)
Druh dokumentu: article
ISSN: 1286-4986
Popis: This exploratory study investigated the use of technology-enhanced activities to teach the semantic prosody of certain English expressions through a method known as Data-driven learning (DDL). The study involved 83 undergraduate social sciences students enrolled in a mandatory English course at a university. Various online tools, such as YouGlish and SKELL, were employed and found to enhance the learning experience in formal contexts. However, the Milanote platform did not fulfil its expected role in linking formal and non-formal learning contexts, as students were reluctant to continue their learning outside the classroom. Based on the results, this paper suggests ways to integrate formal and non-formal learning contexts by normalising DDL. By treating DDL as a technique for addressing real language problems and ensuring spontaneity, accessibility, efficiency, and effective scaffolding, DDL can bridge the gap between formal and non-formal learning contexts.
Databáze: Directory of Open Access Journals