Modelling second language learners for learning task recommendation Online publication date: Tue, 19-Dec-2017
by Haoran Xie; Di Zou; Tak-Lam Wong; Fu Lee Wang
International Journal of Innovation and Learning (IJIL), Vol. 23, No. 1, 2018
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.
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