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Charting the landscape of data-driven learning using a bibliometric analysis

Published online by Cambridge University Press:  22 November 2022

Jihua Dong Open the ORCID record for Jihua Dong [Opens in a new window] ,
Yanan Zhao Open the ORCID record for Yanan Zhao [Opens in a new window]  and
Louisa Buckingham Open the ORCID record for Louisa Buckingham [Opens in a new window]
Jihua Dong
Affiliation:
Shandong University, China (dongjihua@sdu.edu.cn)
Yanan Zhao
Affiliation:
Shandong University, China (202220253@mail.sdu.edu.cn)
Louisa Buckingham
Affiliation:
The University of Auckland, New Zealand (l.buckingham@auckland.ac.nz)
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Abstract

This study employs a bibliometric approach to analyse common research themes, high-impact publications and research venues, identify the most recent transformative research, and map the developmental stages of data-driven learning (DDL) since its genesis. A dataset of 126 articles and 3,297 cited references (1994–2021) retrieved from the Web of Science was analysed using CiteSpace 6.1.R2. The analysis uncovered the principal research themes and high-impact publications, and the most recent transformative research in the DDL field. The following evolutionary stages of DDL were determined based on Shneider’s (2009) scientific model and the timeline generated by CiteSpace, namely, the conceptualising stage (1980s–1998), the maturing stage (1998–2011), and the expansion stage (2011–now), with Stage 4 just emerging. Finally, the analysis discerned potential future research directions, including the implementation of DDL in larger-scale classroom practice and the role of variables in DDL.

Keywords

data-driven learningco-citation analysisstructural variation analysisbibliometric analysis
Type
Research Article
Information
ReCALL , Volume 35 , Issue 3 , September 2023 , pp. 339 - 355
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Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of EUROCALL, the European Association for Computer-Assisted Language Learning

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