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Vocabulary learning in a foreign language: multimedia input, sentence-writing task, and their combination

  • Mark Feng Teng

    Mark Feng Teng is Associate Professor in applied linguistics at Beijing Normal University. His main research interests include L2 vocabulary acquisition and L2 writing. His latest publications appeared in TESOL Quarterly, Applied Linguistics, Language Teaching Research, Computer Assisted Language Learning, Computers & Education, Foreign Language Annals, and other international flagship journals. His recent monographs were published by Springer, Routledge, and Bloomsbury. He has edited special issues for Asian EFL Journal, TESOL Journal, and Journal of Writing Research. He is the winner of best paper award by Hong Kong Association for Applied Linguistics (2017).

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    and Danyang Zhang

    Danyang Zhang is an Assistant Professor at College of International Studies, Shenzhen University. Her research interests include second language vocabulary acquisition, technology-enhanced language learning and teacher development. Danyang has published her work in many SSCI journals including Applied Linguistics, Computer Assisted Language Learning and Language Teaching Research. She currently serves as a journal reviewer of ReCALL, Language Learning & Technology, Virtual Reality and Interactive Learning Environments. She presented her project in many international conferences including BERA, CALL and EUROCALL. She is the winner of the BJET Best EdTech Paper Award and the awardee of mLearn 2019 Conference Best Paper.

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Published/Copyright: February 20, 2023

Abstract

This article examines the effects of task conditions (i.e., with and without a sentence-writing task), multimedia input (definition only, definition + information, definition + information + videos), and combinations of these two variables on the learning gains of new words. This study involved a 2 × 3 research design. In total, 235 Chinese EFL learners were allocated to the six conditions. Vocabulary learning outcomes were measured by pre- and post-tests on 24 target items. The results showed that the definition + information + videos group scored significantly higher than the definition + information and the definition-only groups. Additionally, the sentence-writing task increased the effectiveness of vocabulary learning versus the condition without this task. The combination of the definition + information + videos condition and sentence-writing task was identified as the most effective technique for learning lexical items. This study highlighted the effectiveness of combining multimedia input with a sentence-writing task to learn new words. Relevant teaching and theoretical implications were also discussed.


Corresponding author: Danyang Zhang, College of International Studies, Shenzhen University, 3688 Nanhai Ave, Nanshan, Shenzhen, Guangdong, 518060, China, E-mail:

About the authors

Mark Feng Teng

Mark Feng Teng is Associate Professor in applied linguistics at Beijing Normal University. His main research interests include L2 vocabulary acquisition and L2 writing. His latest publications appeared in TESOL Quarterly, Applied Linguistics, Language Teaching Research, Computer Assisted Language Learning, Computers & Education, Foreign Language Annals, and other international flagship journals. His recent monographs were published by Springer, Routledge, and Bloomsbury. He has edited special issues for Asian EFL Journal, TESOL Journal, and Journal of Writing Research. He is the winner of best paper award by Hong Kong Association for Applied Linguistics (2017).

Danyang Zhang

Danyang Zhang is an Assistant Professor at College of International Studies, Shenzhen University. Her research interests include second language vocabulary acquisition, technology-enhanced language learning and teacher development. Danyang has published her work in many SSCI journals including Applied Linguistics, Computer Assisted Language Learning and Language Teaching Research. She currently serves as a journal reviewer of ReCALL, Language Learning & Technology, Virtual Reality and Interactive Learning Environments. She presented her project in many international conferences including BERA, CALL and EUROCALL. She is the winner of the BJET Best EdTech Paper Award and the awardee of mLearn 2019 Conference Best Paper.

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Received: 2022-10-27
Accepted: 2023-01-14
Published Online: 2023-02-20
Published in Print: 2024-09-25

© 2023 Walter de Gruyter GmbH, Berlin/Boston

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