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Article: Applying partial least squares structural equation modeling (PLS-SEM) in an investigation of undergraduate students’ learning transfer of academic English

TitleApplying partial least squares structural equation modeling (PLS-SEM) in an investigation of undergraduate students’ learning transfer of academic English
Authors
KeywordsEAP
Learning transfer
Training transfer
PLS-SEM
Partial least squares
Issue Date2020
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/jeap
Citation
Journal of English for Academic Purposes, 2020, v. 46, p. article no. 100884 How to Cite?
AbstractPartial least squares structural equation modeling (PLS-SEM) is a statistical analysis technique that has been widely adopted in marketing and strategic management disciplines, yet there is a paucity of application in language education research. This study applies PLS-SEM in an investigation of undergraduate students’ learning transfer by analysing their perceptions of learned skills they find transferable in a general EAP course and the extent of transferability to interdisciplinary-organised Common Core courses. A theoretical model was proposed to examine the interrelationships of variables, namely content relevance, understanding of learning, transfer/applicability and transfer outcome. Two rounds of in-class e-survey were administered with 675 (63.2%) and 689 (64.3%) valid responses respectively. The results from the PLS-SEM reveal that there are significant relationships between the variables. This study not only can fill the research space of learning transfer in EAP in language learning, it can also model a new quantitative empirical research method which can be used to explain and predict learning transfer. Implications for future EAP research using PLS-SEM are discussed.
Persistent Identifierhttp://hdl.handle.net/10722/283778
ISSN
2019 Impact Factor: 1.893
2015 SCImago Journal Rankings: 1.164

 

DC FieldValueLanguage
dc.contributor.authorLaw, L-
dc.contributor.authorFong, N-
dc.date.accessioned2020-07-03T08:23:57Z-
dc.date.available2020-07-03T08:23:57Z-
dc.date.issued2020-
dc.identifier.citationJournal of English for Academic Purposes, 2020, v. 46, p. article no. 100884-
dc.identifier.issn1475-1585-
dc.identifier.urihttp://hdl.handle.net/10722/283778-
dc.description.abstractPartial least squares structural equation modeling (PLS-SEM) is a statistical analysis technique that has been widely adopted in marketing and strategic management disciplines, yet there is a paucity of application in language education research. This study applies PLS-SEM in an investigation of undergraduate students’ learning transfer by analysing their perceptions of learned skills they find transferable in a general EAP course and the extent of transferability to interdisciplinary-organised Common Core courses. A theoretical model was proposed to examine the interrelationships of variables, namely content relevance, understanding of learning, transfer/applicability and transfer outcome. Two rounds of in-class e-survey were administered with 675 (63.2%) and 689 (64.3%) valid responses respectively. The results from the PLS-SEM reveal that there are significant relationships between the variables. This study not only can fill the research space of learning transfer in EAP in language learning, it can also model a new quantitative empirical research method which can be used to explain and predict learning transfer. Implications for future EAP research using PLS-SEM are discussed.-
dc.languageeng-
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/jeap-
dc.relation.ispartofJournal of English for Academic Purposes-
dc.subjectEAP-
dc.subjectLearning transfer-
dc.subjectTraining transfer-
dc.subjectPLS-SEM-
dc.subjectPartial least squares-
dc.titleApplying partial least squares structural equation modeling (PLS-SEM) in an investigation of undergraduate students’ learning transfer of academic English-
dc.typeArticle-
dc.identifier.emailLaw, L: lockylaw@hku.hk-
dc.identifier.emailFong, N: fongsn@hku.hk-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.jeap.2020.100884-
dc.identifier.scopuseid_2-s2.0-85087115504-
dc.identifier.hkuros310781-
dc.identifier.volume46-
dc.identifier.spagearticle no. 100884-
dc.identifier.epagearticle no. 100884-
dc.publisher.placeUnited Kingdom-

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