JOURNAL ARTICLE

Young learners' bilingual status and cognitive development in foreign language aptitude testing.

  • Published In: ITL - International Journal of Applied Linguistics, 2023, v. 174, n. 2. P. 291 1 of 3

  • Database: Communication Source 2 of 3

  • Authored By: Suárez, Maria-del-Mar; Stansfield, Charles W. 3 of 3

Abstract

Young learners' L1s preference, cognitive development and bilingual status might influence their performance on language aptitude tests, particularly if these are language-dependent. The objective of this study was to test the validity, reliability and consistency across populations of two such tests: the Modern Language Aptitude Test-Elementary in Catalan (MLAT-EC) and in Spanish (MLAT-ES). 629 bilingual students from grades 3 to 7 took the MLAT-ES and the MLAT-EC for test comparison in a counterbalanced order. The results show that their performance on both tests presented hardly any significant differences considering students' L1 preference (Catalan, Spanish or no preference). In addition, these bilingual examinees outperformed the predominantly monolingual samples in the MLAT-ES norming study. The same score patterns related to young learner cognitive development stages were found across test versions. These results reinforce the confidence in the validity of the MLAT-E adaptations and support the hypothesis that bilingualism results in greater aptitude. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:ITL - International Journal of Applied Linguistics. 2023/07, Vol. 174, Issue 2, p291
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2023
  • ISSN:0019-0829
  • DOI:10.1075/itl.22017.sua
  • Accession Number:172413792
  • Copyright Statement:Copyright of ITL - International Journal of Applied 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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