Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/65346
Title: A comparative study on various vocabulary knowledge scales for predicting vocabulary pre-knowledge
Authors: Zou, D
Xie, H
Rao, Y
Wong, TL
Wang, FL
Wu, Q
Keywords: E-learning systems
Learner profile
Pre-knowledge prediction
User modeling
Vocabulary learning
Issue Date: 2017
Publisher: IGI Global
Source: International journal of distance education technologies, 2017, v. 15, no. 1, p. 69-81 How to cite?
Journal: International journal of distance education technologies 
Abstract: The 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 preknowledge 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.
URI: http://hdl.handle.net/10397/65346
ISSN: 1539-3100
EISSN: 1539-3119
DOI: 10.4018/IJDET.2017010105
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