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HOW DO DIFFERENT FORMS OF GLOSSING CONTRIBUTE TO L2 VOCABULARY LEARNING FROM READING?

A META-REGRESSION ANALYSIS

Published online by Cambridge University Press:  14 January 2020

Akifumi Yanagisawa Open the ORCID record for Akifumi Yanagisawa [Opens in a new window] ,
Stuart Webb  and
Takumi Uchihara
Akifumi Yanagisawa
Affiliation:
University of Western Ontario
Stuart Webb
Affiliation:
University of Western Ontario
Takumi Uchihara
Affiliation:
University of Western Ontario
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Abstract

This meta-analysis investigated the overall effects of glossing on L2 vocabulary learning from reading and the influence of potential moderator variables: gloss format (type, language, mode) and text and learner characteristics. A total of 359 effect sizes from 42 studies (N = 3802) meeting the inclusion criteria were meta-analyzed. The results indicated that glossed reading led to significantly greater learning of words (45.3% and 33.4% on immediate and delayed posttests, respectively) than nonglossed reading (26.6% and 19.8%). Multiple-choice glosses were the most effective, and in-text glosses and glossaries were the least effective gloss types. L1 glosses yielded greater learning than L2 glosses. We found no interaction between language (L1, L2) and proficiency (beginner, intermediate, advanced), and no significant difference among modes of glossing (textual, pictorial, auditory). Learning gains were moderated by test formats (recall, recognition, other), comprehension of text, and proficiency.

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Research Article
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Studies in Second Language Acquisition , Volume 42 , Issue 2 , May 2020 , pp. 411 - 438
Open Practices
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Copyright © Cambridge University Press 2020 

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Footnotes

We would like to thank Dr. James E. Pustejovsky for his input about statistical analysis. We are also grateful to Susan Gass, Luke Plonsky, and the anonymous SSLA reviewers for their constructive feedback on earlier versions of the manuscript.

The experiment in this article earned an Open Data badge for transparent practices. The materials are available at https://osf.io/zr6t2/.

References

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