An explicit learner profiling model for personalized word learning recommendation

Di ZOU, Haoran XIE*, Tak-lam WONG, Fu Lee WANG, Reggie KWAN, Wai Hong CHAN

*Corresponding author for this work

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

5 Citations (Scopus)


Word knowledge is the foundation of language acquisition for second language learners. Due to the diversity of background knowledge and language proficiency levels of different learners, it is essential to understand and cater for various needs of users in an e-learning system. A personalized learning system which meets this requirement is therefore necessary. Users may also be concerned about the possible risk of revealing their private information and prefer controls on the personalization of a system. To leverage these two factors: personalization and control, we propose an explicit learner profiling model for word learning task recommendation in this paper. This proposed profiling model can be fully accessed and controlled by users. Moreover, the proposed system can recommend learning tasks based on explicit user profiles. The experimental results of a preliminary study further verify the effectiveness of the proposed model.
Original languageEnglish
Title of host publicationEmerging Technologies for Education : Second International Symposium, SETE 2017, held in conjunction with ICWL 2017, Cape Town, South Africa, September 20-22, 2017, revised selected papers
EditorsTien-Chi HUANG, Rynson LAU, Yueh-Min HUANG, Marc SPANIOL, Chun-Hung YUEN
PublisherSpringer Nature Switzerland AG
Number of pages5
ISBN (Electronic)9783319710846
ISBN (Print)9783319710839
Publication statusPublished - 2017
Externally publishedYes
Event2nd International Symposium on Emerging Technologies for Education - Cape Town, South Africa
Duration: 20 Sept 201722 Sept 2017

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2nd International Symposium on Emerging Technologies for Education
Abbreviated titleSETE 2017
Country/TerritorySouth Africa
CityCape Town

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), the Internal Research Grant (RG 66/2016-2017) and the Start-Up Research Grant (RG 37/2016-2017R) of The Education University of Hong Kong.


  • Language acquisition
  • Word learning
  • User modeling
  • Task recommendation


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