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Modelling second language learners for learning task recommendation

Author

Listed:
  • Haoran Xie
  • Di Zou
  • Tak-Lam Wong
  • Fu Lee Wang

Abstract

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.

Suggested Citation

  • Haoran Xie & Di Zou & Tak-Lam Wong & Fu Lee Wang, 2018. "Modelling second language learners for learning task recommendation," International Journal of Innovation and Learning, Inderscience Enterprises Ltd, vol. 23(1), pages 76-92.
    • Handle: RePEc:ids:ijilea:v:23:y:2018:i:1:p:76-92
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