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

Research output: Chapter in Book/Report/Conference proceedingChapters

4 Citations (Scopus)

Abstract

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. Copyright © 2017 Springer International Publishing AG.
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
EditorsVincent Tien-Chi HUANG, Rynson LAU, Yueh-Min HUANG, Marc SPANIOL, Chun-Hung YUEN
Place of PublicationCham
PublisherSpringer
Pages495-499
ISBN (Electronic)9783319710846
ISBN (Print)9783319710839
DOIs
Publication statusPublished - 2017

Citation

Zou, D., Xie, H., Wong, T.-L., Wang, F. L., Kwan, R., & Chan, W. H. (2017). An explicit learner profiling model for personalized word learning recommendation. In V. T.-C. Huang, R. Lau, Y.-M. Huang, M. Spaniol, & C.-H. Yuen (Eds.), Emerging 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 (pp. 495-499). Cham: Springer.

Keywords

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

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