Using simulated students for machine learning

dc.contributor.author	Σταθακοπούλου, Ρεγγίνα	el
dc.contributor.author	Γρηγοριάδου, Μαρίλια	el
dc.contributor.author	Σαμαράκου, Μαρία	el
dc.contributor.author	Μαγούλας, Γεώργιος Δ.	el
dc.date.accessioned	2015-04-26T10:59:57Z
dc.date.available	2015-04-26T10:59:57Z
dc.date.issued	2015-04-26
dc.identifier.uri	http://hdl.handle.net/11400/8979
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://link.springer.com/	en
dc.source	http://link.springer.com/chapter/10.1007%2F978-3-540-30139-4_109	en
dc.subject	World Wide Web
dc.subject	Student
dc.subject	Διαδίκτυο
dc.subject	Φοιτητής
dc.title	Using simulated students for machine learning	en
heal.type	conferenceItem
heal.generalDescription	Proceeding of the 7th International Conference on Intelligent Tutoring Systems (ITS2004). Maceio, Brasil 30th August-3rd September 2004. Springer-Verlag: 2004. pp. 889-891.	en
heal.classification	Technology
heal.classification	Computer science
heal.classification	Τεχνολογία
heal.classification	Πληροφορική
heal.classificationURI	http://zbw.eu/stw/descriptor/10470-6
heal.classificationURI	http://data.seab.gr/concepts/77de68daecd823babbb58edb1c8e14d7106e83bb
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Πληροφορική
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh95000541
heal.identifier.secondary	DOI 10.1007/978-3-540-30139-4_109
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Ενεργειακής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2004
heal.bibliographicCitation	Stathacopoulou, R., Grigoriadou, M., Samarakou, M. and Magoulas G. (2004). Using simulated students for machine learning. In: Intelligent Tutoring Systems. Lecture Notes in Computer Science. vol. 3220. pp. 889-891. Springer-Verlag: 2004. Available from: http://link.springer.com/chapter/10.1007%2F978-3-540-30139-4_109	en
heal.abstract	In this paper we present how simulated students have been generated in order to obtain a large amount of labeled data for training and testing a neural network-based fuzzy model of the student in an Intelligent Learning Environment (ILE). The simulated students have been generated by modifying real students’ records and classified by a group of expert teachers regarding their learning style category. Experimental results were encouraging, similar to experts’ classifications.	en
heal.publisher	Springer Berlin Heidelberg	en
heal.fullTextAvailability	false
heal.conferenceName	International Conference on Intelligent Tutoring Systems	en
heal.conferenceItemType	full paper