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Analysing the Advantages of Using Exploration and Exploitation Strategies in an Adaptive and Intelligent Educational System

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Computación Evolutiva y Redes Neuronales (EVANNAI)es
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Human Language and Accessibility Technologies (HULAT)es
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Planificación y Aprendizajees
dc.contributor.authorIglesias Maqueda, Ana María
dc.contributor.authorMartínez Fernández, Paloma
dc.contributor.authorAler, Ricardo
dc.contributor.authorFernández Rebollo, Fernando
dc.date.accessioned2013-09-13T09:41:52Z
dc.date.available2013-09-13T09:41:52Z
dc.date.issued2003-12-03
dc.descriptionProceedings of: 2nd Intemational Conference on Multimedia and Infonnation & Communication Technologies in Education (m-ICTE2003) Badajoz, Spain, December 3-6th 2003
dc.description.abstractOne of the most important issues in Adaptive and Intelligent Educational Systems (AIES) is to define pedagogical strategies for tutoring studies according to their needs. In previous papers we have proposed to use a pedagogical knowledge representation based on a Reinforcement Learning (RL) model. Using the reinforcement learning model, the system is able to automatically learn which is the best pedagogical way to teach student individually based only on acquired experience other students with similar learning characteristics, like a human tutor does. In this paper we study the viability of the application of the RL model in a DataBase Design (DBD) AIES using in this study simulated students. The viability is measured on 1hree important issues. First, we are going to check that the system converges to a pedagogical policy when it interacts with simulated students with different leaming characteristics. Second, we are going to prove that tite system leans an optimal pedagogical strategy, measured in number of actions that the system must execute to teach all the contents to tbe student. And third, we are going to prove that the system does not need many students to leanr to teach optimally. Choosing a good exploration and exploitation strategy is determinant for the three elements defined above, so two typical exploration/exploitatíon policies in RL problems have been used for the experiments in order to analyze tbe differences between them when the system teaches simulated students: the e-greedy and the Boltzmann exploration strategies.
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dc.identifier.bibliographicCitationMéndez Villas, Antonio; Mesa González, José Antonio; Mesa González, Julián. Advances in Technology-based education: towards a knowledge-based society: Proceedings of 2nd Intemational Conference on Multimedia and Infonnation & Communication Technologies in Education: m-ICTE2003. Junta De Extremadura, Consejería de Educación, Ciencia y Tecnología, 2003, pp. 489-493. ISBN 84-96212-10-6 (vol. I)
dc.identifier.isbn84-96212-09-2 (colección)
dc.identifier.isbn84-96212-10-6 (vol. I)
dc.identifier.otherD.L. BA-072-2003 (vol. I)
dc.identifier.publicationfirstpage489
dc.identifier.publicationlastpage493
dc.identifier.publicationtitleAdvances in technology-based education: towards a knowledge based society: II International Conference on Multimedia ICT's in Education (m-ICTE2003)
dc.identifier.publicationvolume1
dc.identifier.urihttps://hdl.handle.net/10016/17547
dc.identifier.uxxiCC/0000019471
dc.language.isoeng
dc.publisherJunta De Extremadura. Consejería de Educación, Ciencia y Tecnología
dc.relation.eventdateDecember 3-6th, 2003
dc.relation.eventplaceBadajoz (Spain)
dc.relation.eventtitleInternational Conference on Multimedia and Information & Communication Technologies in Education (m-ICTE 2003)
dc.relation.ispartofseriesSociedad de la Información
dc.rights© Los autores & Junta de Extremadura
dc.rights.accessRightsopen access
dc.subject.ecienciaInformática
dc.subject.otherAdaptive and intelligent educational systems
dc.subject.otherReinforcement learning
dc.subject.otherDatabase design methodologies
dc.titleAnalysing the Advantages of Using Exploration and Exploitation Strategies in an Adaptive and Intelligent Educational System
dc.typeconference paper*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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