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The Korean Association for the Study of English Language and Linguistics
[ Article ]
Korea Journal of English Language and Linguistics - Vol. 24, No. 0, pp.1028-1050
ISSN: 1598-1398 (Print) 2586-7474 (Online)
Print publication date 31 Jan 2024
Received 29 Jul 2024 Revised 20 Aug 2024 Accepted 27 Aug 2024
DOI: https://doi.org/10.15738/kjell.24..202409.1028

Theorizing the Role of Machine Translation in L2 Reading Comprehension: Validating the Theory and Exploring the Learning Potential through MT Use

EunJou Oh ; Eun-Yong Kim
(1st and corresponding author) Associate Professor, Department of General Education Korean Bible University 01757 32, Dongil-ro 214-gil, Nowon-gu, Seoul, Korea, Tel: 02) 950-4328 eunjouoh@bible.ac.kr
(Co-author) Lecturer, Department of General Education Korean Bible University eunyongkim@bible.ac.kr


© 2024 KASELL All rights reserved
This is an open-access article distributed under the terms of the Creative Commons License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This study investigates the theoretical dimensions of how machine translation (MT) plays a role in relation to L1 reading competence in L2 reading comprehension. Leveraging Kintsch’s Construction-Integration (CI) model for L2 reading proposed by Oh (2014), our research posits that MT mediation enhances the textbase by alleviating constraints related to L2 proficiency. This, in turn, facilitates a more effective utilization of L1 reading resources, contributing to an enriched situation model. To explore this hypothesis, we conducted an empirical study with 89 college students enrolled in a general English course at a South Korean university. Participants engaged in reading activities under two conditions: a pre-MT (reading without MT) and a post-MT (reading with MT). The comprehensive model, incorporating a textbase indicated by L2 vocabulary, grammar, and sentence parsing, and a situation model represented by L1 reading competence, was examined through structural equation modelling across these conditions. Additionally, we analyzed learning potential scores (LPS)—reflecting the difference between the two conditions—using hierarchical multiple regression to identify significant predictors of enhanced comprehension with MT use. Our findings confirm the validity of the models, demonstrating the textbase as a key mechanism through which MT influences reading comprehension. The results also underscore the significance of L1 reading competence and L2 parsing ability as key predictors for LPS. These findings are discussed within the context of the changing multiliteracies landscape, contributing to the broader discourse on language education and technology integration.

Keywords:

machine translation, CI model, L2 reading, L1 reading, learning potential, translanguaging

Acknowledgments

This work was supported by the 2023 research fund of Korean Bible University.

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