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dc.contributor.authorFenza, G.
dc.contributor.authorOrciuoli, F.
dc.contributor.authorSampson, Demetrios
dc.date.accessioned2017-12-10T12:39:05Z
dc.date.available2017-12-10T12:39:05Z
dc.date.created2017-12-10T12:20:13Z
dc.date.issued2017
dc.identifier.citationFenza, 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.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/59172
dc.identifier.doi10.1109/ICALT.2017.124
dc.description.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.

dc.titleBuilding Adaptive Tutoring Model Using Artificial Neural Networks and Reinforcement Learning
dc.typeConference Paper
dcterms.source.startPage460
dcterms.source.endPage462
dcterms.source.titleProceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017
dcterms.source.seriesProceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017
dcterms.source.isbn9781538638705
curtin.departmentSchool of Education
curtin.accessStatusFulltext not available


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