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

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dc.contributor.author Iglesias Maqueda, Ana María
dc.contributor.author Martínez Fernández, Paloma
dc.contributor.author Aler, Ricardo
dc.contributor.author Fernández Rebollo, Fernando
dc.date.accessioned 2013-09-13T09:41:52Z
dc.date.available 2013-09-13T09:41:52Z
dc.date.issued 2003-12-03
dc.identifier.bibliographicCitation Méndez 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.isbn 84-96212-09-2 (colección)
dc.identifier.isbn 84-96212-10-6 (vol. I)
dc.identifier.other D.L. BA-072-2003 (vol. I)
dc.identifier.uri http://hdl.handle.net/10016/17547
dc.description Proceedings of: 2nd Intemational Conference on Multimedia and Infonnation & Communication Technologies in Education (m-ICTE2003) Badajoz, Spain, December 3-6th 2003
dc.description.abstract One 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.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Junta De Extremadura. Consejería de Educación, Ciencia y Tecnología
dc.relation.ispartofseries Sociedad de la Información
dc.rights © Los autores & Junta de Extremadura
dc.subject.other Adaptive and intelligent educational systems
dc.subject.other Reinforcement learning
dc.subject.other Database design methodologies
dc.title Analysing the Advantages of Using Exploration and Exploitation Strategies in an Adaptive and Intelligent Educational System
dc.type conferenceObject
dc.type bookPart
dc.subject.eciencia Informática
dc.rights.accessRights openAccess
dc.type.version publishedVersion
dc.relation.eventdate December 3-6th, 2003
dc.relation.eventplace Badajoz (Spain)
dc.relation.eventtitle International Conference on Multimedia and Information & Communication Technologies in Education (m-ICTE 2003)
dc.identifier.publicationfirstpage 489
dc.identifier.publicationlastpage 493
dc.identifier.publicationtitle Advances in technology-based education: towards a knowledge based society: II International Conference on Multimedia ICT's in Education (m-ICTE2003)
dc.identifier.publicationvolume 1
dc.identifier.uxxi CC/0000019471
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