 Structural Equation Modelling of EFL Learners’ Perceived Preferences for Data-driven Learning and Learners’ AgencyJinfang Liang and Ying Tan World Journal of English Language, 2023, vol. 13, issue 3, 90 Abstract: Data-driven learning (DDL) has drawn researchers’ eyes on corpus linguistics and language learning successfully, particularly on English writing. However, the structural relation between the students’ preferences for data-driven learning and the EFL students’ learning agency has not been well examined yet. This study examined the hypothetical model of measurement for EFL learners’ perceived preferences for DDL and their learning agency. Two questionnaires were used for collecting the data. Structural equation modeling (SEM) was assessed using AMOS. The results revealed that the developed model enjoyed an acceptable level of goodness of fit. The results also showed that the students’ perceived preferences for DDL strongly affect their learning agency. Therefore, it could be concluded that exposure to DDL fosters language learners’ self-efficacy and the ability to self-regulate their learning activities. All in all, the results have implications (theoretical and practical) for language teachers, learners and those interested in corpus linguistics. Date: 2023 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:jfr:wjel11:v:13:y:2023:i:3:p:90 Access Statistics for this article World Journal of English Language is currently edited by Joe Nelson More articles in World Journal of English Language from Sciedu Press |
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