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Vocabulary suitability of science TED talks for English for science teaching and learning.

  • Published In: Spanish Journal of Applied Linguistics / Revista Española de Lingüística Aplicada (John Benjamins Publishing Co.), 2023, v. 36, n. 1. P. 307 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Vuković-Stamatović, Milica 3 of 3

Abstract

With a view to advancing their students' listening comprehension and vocabulary, English for Science teachers are frequently in search of new and interesting audio and video materials. TED Talks on science topics are a good option for this purpose. But just how suitable, vocabulary-wise, are they for English for Science learners? This study explores the lexical profile of TED Talks on science and compares it against non-science TED Talks and science academic lectures. We use a 5-million-word TED Talk corpus, with a 1.3-million-word science subsection. Of the two categories of TED Talks, it is the science ones that are lexically more similar to science academic lectures. Science TED Talks also feature significantly more technical vocabulary than non-science TED Talks. Reasonable listening comprehension is achieved at 4,000 words for science TED Talks, while ideal comprehension is achieved at 8,000 words for both categories. These results recommend science TED Talks for English for Science listening. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Spanish Journal of Applied Linguistics / Revista Española de Lingüística Aplicada (John Benjamins Publishing Co.). 2023/01, Vol. 36, Issue 1, p307
  • Document Type:Article
  • Subject Area:Language and Linguistics
  • Publication Date:2023
  • ISSN:0213-2028
  • DOI:10.1075/resla.20062.vuk
  • Accession Number:163546395
  • Copyright Statement:Copyright of Spanish Journal of Applied Linguistics / Revista Española de Lingüística Aplicada (John Benjamins Publishing Co.) 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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