JOURNAL ARTICLE

Shortcomings and challenges in the intersection of L2 pedagogy and applied cognitive linguistics: A case study of the Spanish simple pasts.

  • Published In: Review of Cognitive Linguistics, 2024, v. 22, n. 2. P. 426 1 of 3

  • Database: Communication Source 2 of 3

  • Authored By: Llopis-García, Reyes; Alonso-Aparicio, Irene 3 of 3

Abstract

This paper aims to explore the shortcomings and challenges of Applied Cognitive Linguistics (ACL) in L2 classrooms and research. It examines the main issues and presents a case study with three quasi-experimental classroom studies. These experiments taught the Spanish past simple tenses using approaches informed by Cognitive Linguistics and Communicative Language Teaching, plus a control group, through a pretest/posttest design. Results favored cognitive-pedagogical instruction, but only in one production task (Alonso-Aparicio & Llopis-García, 2019). Subsequent studies replicated and extended the first, with further changes in instruction and assessment design, but found no significant differences between experimental groups in the posttest. The discussion highlights the steps taken to ensure study success, pointing out shortcomings in traditional assessment tests that favor notional-functional instruction. We suggest alternative testing tasks that consider cognitive-based approaches and new avenues for future research, aligning with Martín-Gascón et al. (2023). [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Review of Cognitive Linguistics. 2024/07, Vol. 22, Issue 2, p426
  • Document Type:Article
  • Subject Area:Language and Linguistics
  • Publication Date:2024
  • ISSN:1877-9751
  • DOI:10.1075/rcl.00204.llo
  • Accession Number:180922107
  • Copyright Statement:Copyright of Review of Cognitive Linguistics 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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