Acquisition of collocations under different glossing modalities and the mediating role of learners' perceptual learning style.

  • Published In: IRAL: International Review of Applied Linguistics in Language Teaching, 2025, v. 63, n. 4. P. 2327 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Yuan, Xin; Tang, Xuan 3 of 3

Abstract

The current study investigated how different glossing modalities (textual and auditory) and learners' perceptual learning style (visual and auditory) influenced collocation learning. A total of 212 college students in China were first assigned to either a visual or auditory group based on their performance on a perceptual learning style questionnaire. Each style group was subsequently subdivided into three groups who were exposed to a series of reading texts containing 15 unknown collocations under one of the glossing conditions: textual glosses, auditory glosses or no glosses (control). Results of the study indicated that both textual, and that auditory glosses led to gains in productive and receptive collocation knowledge and auditory glosses were more effective than textual glosses. In addition, this study provided empirical evidence that perceptual learning style has a moderating effect on collocational learning. The auditory learners in the auditory glossing condition showed the highest rate of collocational learning among all treatment subgroups. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:IRAL: International Review of Applied Linguistics in Language Teaching. 2025/11, Vol. 63, Issue 4, p2327
  • Document Type:Article
  • Subject Area:Psychology
  • Publication Date:2025
  • ISSN:0019-042X
  • DOI:10.1515/iral-2023-0319
  • Accession Number:191009303
  • 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.)

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