JOURNAL ARTICLE

A genre-oriented analysis of TikTok instructional discourse.

  • Published In: Translation & Translanguaging in Multilingual Contexts (TTMC), 2024, v. 10, n. 1. P. 6 1 of 3

  • Database: Communication Source 2 of 3

  • Authored By: Tommaso, Laura 3 of 3

Abstract

This study intends to contribute to the analysis of digital instructional discourse in order to gain an insight into how the EFL classroom has in some sense shifted online in the hands of amateur experts (Tolson 2010; Bhatia 2018), who create an informal learning environment by drawing on their discursive competence, disciplinary knowledge and professional practice. By incorporating quantitative, qualitative, and case study data, this article considers the value of genre analysis in educational social media research. It focuses, particularly, on the rhetorical and lexico-grammatical features of a selection of TikTok video mini-lessons targeting Italian speakers of English as a foreign language. Analysis of the data reveals that digital language teaching discourse on TikTok is a structured event with recurrent rhetorical patterns and linguistic features for achieving both pedagogical and promotional communicative purposes. The research bears considerable relevance given the need for the analysis of transformation processes in instructional discourse amid the widespread use of information and communication technologies and the advance of online learning. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Translation & Translanguaging in Multilingual Contexts (TTMC). 2024/01, Vol. 10, Issue 1, p6
  • Document Type:Article
  • Subject Area:Literature and Writing
  • Publication Date:2024
  • ISSN:2352-1805
  • DOI:10.1075/ttmc.00124.tom
  • Accession Number:175368675
  • Copyright Statement:Copyright of Translation & Translanguaging in Multilingual Contexts (TTMC) 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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