Back

Exploring aural vocabulary knowledge for TOEIC as a language exit requirement in higher education in Taiwan.

  • Published In: IRAL: International Review of Applied Linguistics in Language Teaching, 2024, v. 62, n. 4. P. 1853 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Li, Chen-Hong 3 of 3

Abstract

The Test of English for International Communication (TOEIC) is a high-stakes test for students in higher education in Taiwan to fulfill the English-language graduation requirement. However, little is known regarding the vocabulary threshold for the test or the effects of the lexical coverage and profiles of test items on the adequate comprehension of the test. This study used a validated Listening Vocabulary Levels Test (LVLT) and the TOEIC listening subtest to estimate learners' aural vocabulary knowledge required for an exit TOEIC listening score. The findings showed: (1) aural lexical knowledge accounted for more than half of the variance in comprehension performance; (2) a minimum level of 3,000 word families for a lexical coverage of 98 % considerably affected the comprehension of spoken texts; and (3) lexical profiles varied in the individual parts of the listening subtest, with a range of 3,000–5,000 word families required for achieving a lexical coverage of 98 %. The crucial role of lexical knowledge/coverage in comprehension performance on the exit test was discussed. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:IRAL: International Review of Applied Linguistics in Language Teaching. 2024/11, Vol. 62, Issue 4, p1853
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2024
  • ISSN:0019-042X
  • DOI:10.1515/iral-2023-0021
  • Accession Number:180835692
  • Copyright Statement:Copyright of IRAL: International Review of Applied Linguistics in Language Teaching is the property of De Gruyter 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.