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Title: An explicit learner profiling model for personalized word learning recommendation
Author(s): Xie, Haoran 
Wang, Philips Fu Lee 
Kwan, Reggie Ching Ping 
Author(s): Zou, D.
Wong, T.-L.
Chan, W. H.
Issue Date: 2017
Publisher: Springer
Related Publication(s): Emerging Technologies for Education (Second International Symposium, SETE 2017) Revised Selected Papers
Start page: 495
End page: 499
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.
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