Big data in SLA: advances in methodology and analysis

Theodora Alexopoulou, Detmar Meurers, Akira Murakami

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Abstract

With computers being used in all areas of life, vast amounts of digital data are generated continuously, much of it language data. Such big data is attractive to researchers for its volume, variety, and velocity, holding the promise of overcoming limitations of smaller-scale analyses. At the same time, extracting reliable and meaningful information from such data remains a constant methodological challenge because of the unknown quality of the data (veracity) and the conceptual challenges, computational techniques, and statistical methods required to interpret it and support scientific insight. For SLA, especially the online learning and assessment platforms increasingly used for foreign language teaching and learning offer unprecedented opportunities for big data exploration of SLA research questions, while also making it possible to turn SLA interventions into real-life applications. In this chapter, we review the key methodological challenges arising for big data SLA research and identify natural language processing and statistical methods that can help overcome challenges. We review empirical case studies that demonstrate the value of big data for SLA research and highlight areas where big data is likely to benefit future research and practice.
Original languageEnglish
Title of host publicationThe Routledge handbook of second language acquisition and technology
EditorsNicole Ziegler, Marta González-Lloret
PublisherRoutledge
Chapter7
Pages92-106
Number of pages15
Edition1st
ISBN (Electronic)9781351117586
ISBN (Print)9780815360773
DOIs
Publication statusE-pub ahead of print - 1 Feb 2022

Publication series

NameThe Routledge Handbooks in Second Language Acquisition
PublisherRoutledge

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