Predicting pre-knowledge on vocabulary from e-learning assignments for language learners

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

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

4 Citations (Scopus)


In the current big data era, we have witnessed the prosperity of emerging massive open online courses, user-generated data and ubiquitous techniques. These evolving technologies and applications have significantly changed the ways for people to learn new knowledge and access information. To find users’ desired data in an effective and efficient way, it is critical to understand/model users in applications involving in such a large volume of learning resources. For instance, word learning systems can be promoted significantly in terms of learning effectiveness if the preknowledge on vocabulary of learners can be predicted accurately. In this research, we focus on the issue of how to model a specific group of users, i.e., language learners, in the context of e-learning systems. Specifically, we try to predict the pre-knowledge on vocabulary of learners from their previous learning documents such as writing assignments and reading essays. The experimental study on real participants shows that the proposed predicting model is very effective and can be exploited for various applications in the future.

Original languageEnglish
Title of host publicationCurrent Developments in Web Based Learning - ICWL 2015 International Workshops, KMEL, IWUM, LA, Revised Selected Papers
EditorsDi ZOU, Zhiguo GONG, Dickson K.W. CHIU
PublisherSpringer-Verlag GmbH and Co. KG
Number of pages7
ISBN (Print)9783319328645
Publication statusPublished - 2016
Externally publishedYes
EventThe 14th International Conference on Web-based Learning - Guangzhou, China
Duration: 5 Nov 20158 Nov 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9584 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceThe 14th International Conference on Web-based Learning
Abbreviated titleICWL 2015
Internet address

Bibliographical note

The work described in this paper was fully supported by a grant from Research Grants Council of Hong Kong Special Administrative Region, China (UGC/FDS11/E06/14), a grant from the National Natural Science Foundation of China (Grant No. 61502545), a grant from the Soft Science Research Project of Guangdong Province (Grant No. 2014A030304013), and “the Fundamental Research Funds for the Central Universities” (Grant No. 46000-31610009).


  • Learner profile
  • Vocabulary pre-knowledge
  • Word learning


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