1. A Comparative Study on Various Vocabulary Knowledge Scales for Predicting Vocabulary Pre-Knowledge
- Author
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Fu Lee Wang, Qingyuan Wu, Di Zou, Tak-Lam Wong, Yanghui Rao, and Haoran Xie
- Subjects
Vocabulary ,Computer Networks and Communications ,Computer science ,Online instruction ,media_common.quotation_subject ,Big data ,Prior learning ,02 engineering and technology ,computer.software_genre ,Education ,Rating scale ,0202 electrical engineering, electronic engineering, information engineering ,media_common ,business.industry ,Knowledge level ,User modeling ,05 social sciences ,050301 education ,Vocabulary development ,Computer Science Applications ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,0503 education ,computer ,Natural language processing - 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 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.
- Published
- 2017