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    Building Adaptive Tutoring Model Using Artificial Neural Networks and Reinforcement Learning

    Access Status
    Fulltext not available
    Authors
    Fenza, G.
    Orciuoli, F.
    Sampson, Demetrios
    Date
    2017
    Type
    Conference Paper
    
    Metadata
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    Citation
    Fenza, G. and Orciuoli, F. and Sampson, D. 2017. Building Adaptive Tutoring Model Using Artificial Neural Networks and Reinforcement Learning, in Proceedings of the 17th International Conference on Advanced Learning Technologies (ICALT), Jul 3-7 2017, pp. 460-462. Timisoara, Romania: IEEE.
    Source Title
    Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017
    DOI
    10.1109/ICALT.2017.124
    ISBN
    9781538638705
    School
    School of Education
    URI
    http://hdl.handle.net/20.500.11937/59172
    Collection
    • Curtin Research Publications
    Abstract

    With the emergence of new technology-supported learning environments (e.g., MOOCs, mobile edu games), efficient and effective tutoring mechanisms remain relevant beyond traditional intelligent tutoring systems. This paper provides an approach to build and adapt a tutoring model by using both artificial neural networks and reinforcement learning. The underlying idea is that tutoring rules can be, firstly, learned by observing human tutors' behavior and, then, adapted, at run-time, by observing how each learner reacts within a learning environment at different states of the learning process. The Zone of Proximal Development has been adopted as the underlying theory to evaluate efficacy and efficiency of the learning experience.

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