Please use this identifier to cite or link to this item: https://repository.cihe.edu.hk/jspui/handle/cihe/442
DC FieldValueLanguage
dc.contributor.authorXie, Haoran-
dc.contributor.authorWang, Philips Fu Lee-
dc.contributor.otherZou, D.-
dc.contributor.otherRao, Y.-
dc.contributor.otherWong, T.-L.-
dc.contributor.otherWu, Q.-
dc.date.accessioned2021-03-27T09:20:33Z-
dc.date.available2021-03-27T09:20:33Z-
dc.date.issued2017-
dc.identifier.urihttps://repository.cihe.edu.hk/jspui/handle/cihe/442-
dc.description.abstractThe world has encountered and witnessed the great popularity of various emerging e-learning resources such as massive open online courses (MOOCs), textbooks and videos with the development of the big data era. It is critical to understand the characteristics of users to assist them to find desired and relevant learning resources in such a large volume of resources. For example, understanding the pre-knowledge on vocabulary of learners is very prominent and useful for language learning systems. The language learning effectiveness can be significantly improved if the pre-knowledge levels of learners on vocabulary can be accurately predicted. In this research, the authors model the vocabulary of learners by extracting their history of learning documents and identify the suitable vocabulary knowledge scales (VKS) for pre-knowledge prediction. The experimental results on real participants verify that the optimal VKS and the proposed predicting model are powerful and effective.en_US
dc.language.isoenen_US
dc.publisherIGI Publishingen_US
dc.relation.ispartofInternational Journal of Distance Education Technologiesen_US
dc.titleA comparative study on various vocabulary knowledge scales for predicting vocabulary pre-knowledgeen_US
dc.typejournal articleen_US
dc.identifier.doi10.4018/IJDET.2017010105-
dc.contributor.affiliationSchool of Computing and Information Sciences-
dc.relation.issn1539-3119en_US
dc.description.volume15en_US
dc.description.issue1en_US
dc.description.startpage69en_US
dc.description.endpage81en_US
dc.cihe.affiliatedYes-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
item.openairetypejournal article-
item.grantfulltextnone-
crisitem.author.deptSchool of Computing and Information Sciences-
crisitem.author.deptSchool of Computing and Information Sciences-
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