A comparative study on various vocabulary knowledge scales for predicting vocabulary pre-knowledge

Di ZOU, Haoran XIE, Yanghui RAO, Tak-Lam WONG, Fu Lee WANG, Qingyuan WU

Research output: Journal PublicationsJournal Article (refereed)peer-review

9 Citations (Scopus)


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.

Original languageEnglish
Article number5
Pages (from-to)69-81
Number of pages13
JournalInternational Journal of Distance Education Technologies
Issue number1
Publication statusPublished - 2017
Externally publishedYes


  • E-Learning Systems
  • Learner Profile
  • Pre-Knowledge Prediction
  • User Modeling
  • Vocabulary Learning


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