Modelling second language learners for learning task recommendation

Haoran XIE, Di ZOU, Tak Lam WONG, Fu Lee WANG

Research output: Contribution to journalArticlespeer-review

2 Citations (Scopus)


How to recommend appropriate and effective learning tasks based on the characteristics of a second language learner is a vital question in the field of second language acquisition. In this research, we investigate the issue by dividing it into two sub-questions: how to model the characteristics of language learners as different learners may have varied expertise on and subjective preferences of many topics; and how to select learning tasks according to the constructed learner model. Research on the second sub-question has been widely conducted in domains such as recommender systems, and we focus on the first sub-question in this study from the perspective of how to model the preferred learning contexts of a learner in a non-intrusive manner. We conducted an experiment among eighty-two students, and the results showed that our proposed framework outperformed other systems as it provides significantly more effective and enjoyable word learning experience. Copyright © 2018 Inderscience Enterprises Ltd.
Original languageEnglish
Pages (from-to)76-92
JournalInternational Journal of Innovation and Learning
Issue number1
Early online dateDec 2017
Publication statusPublished - 2018


Xie, H., Zou, D., Wong, T.-L., & Wang, F. L. (2018). Modelling second language learners for learning task recommendation. International Journal of Innovation and Learning, 23(1), 76-92. doi: 10.1504/IJIL.2018.10009632


  • Learner modelling
  • Context familiarity
  • Task recommendation
  • Word learning
  • e-learning


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