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Advancing CALL research via data-mining techniques: Unearthing hidden groups of learners in a corpus-based L2 vocabulary learning experiment

Published online by Cambridge University Press:  11 September 2018

Hansol Lee Open the ORCID record for Hansol Lee [Opens in a new window] ,
Mark Warschauer Open the ORCID record for Mark Warschauer [Opens in a new window]  and
Jang Ho Lee Open the ORCID record for Jang Ho Lee [Opens in a new window]
Hansol Lee
Affiliation:
University of California, Irvine, United States Korea Military Academy, Republic of Korea (hansol3@uci.edu)
Mark Warschauer
Affiliation:
University of California, Irvine, United States (markw@uci.edu)
Jang Ho Lee
Affiliation:
Chung-Ang University, Republic of Korea (jangholee@cau.ac.kr)
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Abstract

In this study, we used a data-mining approach to identify hidden groups in a corpus-based second-language (L2) vocabulary experiment. After a vocabulary pre-test, a total of 132 participants performed three online reading tasks (in random orders) equipped with the following glossary types: (1) concordance lines and definitions of target lexical items, (2) concordance lines of target lexical items, and (3) no glossary information. Although the results of a previous study based on variable-centred analysis (i.e. multiple regression analysis) revealed that more glossary information could lead to better learning outcomes (Lee, Warschauer & Lee, 2017), using a model-based clustering technique in the present study allowed us to unearth learner types not identified in the previous analysis. Instead of the performance pattern found in the previous study (more glossary led to higher gains), we identified one learner group who exhibited their ability to make successful use of concordance lines (and thus are optimized for data-driven learning, or DDL; Johns, 1991), and another group who showed limited L2 vocabulary learning when exposed to concordance lines only. Further, our results revealed that L2 proficiency intersects with vocabulary gains of different learner types in complex ways. Therefore, using this technique in computer-assisted language learning (CALL) research to understand differential effects of accommodations can help us better identify hidden learner types and provide personalized CALL instruction.

Keywords

corpus usedata-driven learning (DDL)data-mining approachglossaryperson-centred analysis/approachsecond-language vocabulary learning
Type
Regular papers
Information
ReCALL , Volume 31 , Issue 2 , May 2019 , pp. 135 - 149
Copyright
© European Association for Computer Assisted Language Learning 2018 

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