JOURNAL ARTICLE

A task-based framework for oral language instruction in support of young language learners in French immersion.

  • Published In: TASK: Journal on Task-Based Language Teaching & Learning, 2023, v. 3, n. 1. P. 74 1 of 3

  • Database: Education Source Ultimate 2 of 3

  • Authored By: Bourgoin, Renée; Bouthillier, Josée Le 3 of 3

Abstract

In Canada, French immersion (FI) is a popular language program. In addition to needing strong pedagogies for language and content instruction, primary FI teachers need to attend to students' age, cognitive level, and lack of exposure to the target language. We turned our attention to the potential of task-based instruction to conduct a two-year classroom-based study exploring communicative tasks designed specifically for young language students in FI. The study led to the development of an instructional sequence for task-based instruction for children ages 5–6, grounded in the Gradual Release of Responsibility model and scaffolded instruction. In addition to presenting this pedagogical framework, we discuss identify promising task-based pedagogical principles at play and highlight pathways for classroom application. We also discuss communicative tasks proposed to young students and offer the use of symbolic play as a type of task that shows promise for young learners in second language learning contexts. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:TASK: Journal on Task-Based Language Teaching & Learning. 2023/01, Vol. 3, Issue 1, p74
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2023
  • ISSN:26661748
  • DOI:10.1075/task.22001.bou
  • Accession Number:172783889
  • Copyright Statement:Copyright of TASK: Journal on Task-Based Language Teaching & Learning is the property of John Benjamins Publishing Co. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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