Modelling second language learners for learning task recommendation

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

*Corresponding author for this work

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

Original languageEnglish
Pages (from-to)76-92
Number of pages17
JournalInternational Journal of Innovation and Learning
Issue number1
Early online date11 Dec 2017
Publication statusPublished - 2018
Externally publishedYes


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


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