Monitoring students' actions and using teachers' expertise in implementing and evaluating the neural network-based fuzzy diagnostic model

dc.contributor.author	Σταθακοπούλου, Ρεγγίνα	el
dc.contributor.author	Γρηγοριάδου, Μαρίλια	el
dc.contributor.author	Σαμαράκου, Μαρία	el
dc.contributor.author	Μητρόπουλος, Δ.	el
dc.date.accessioned	2015-04-27T09:32:54Z
dc.date.available	2015-04-27T09:32:54Z
dc.date.issued	2015-04-27
dc.identifier.uri	http://hdl.handle.net/11400/9055
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://www.sciencedirect.com/	en
dc.subject	Student model
dc.subject	Learning styles
dc.subject	Φοιτητικό μοντέλο
dc.subject	Μαθησιακοί τρόποι
dc.title	Monitoring students' actions and using teachers' expertise in implementing and evaluating the neural network-based fuzzy diagnostic model	en
heal.type	journalArticle
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.identifier.secondary	DOI: 10.1016/j.eswa.2006.02.023
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Ενεργειακής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2007
heal.bibliographicCitation	Stathacopoulou, R., Grigoriadou, M., Samarakou, M. and Mitropoulos, D. (2007). Monitoring students' actions and using teachers' expertise in implementing and evaluating the neural network-based fuzzy diagnostic model. Expert Systems with Applications. 32(4). pp. 955-975. Elsevier: 2007. Available from: http://www.sciencedirect.com/science/article/pii/S095741740600073X	en
heal.abstract	In this paper, the implementation of a neural network-based fuzzy modeling approach to assess aspects of students’ learning style in the discovery learning environment “Vectors in Physics and Mathematics” is presented. Fuzzy logic is used to provide a linguistic description of students’ behavior and learning characteristics, as they have been elicited from teachers, and to handle the inherent uncertainty associated with teachers’ subjective assessments. Neural networks are used to add learning and generalization abilities to the fuzzy model by encoding teachers’ experience through supervised neural-network learning. The neural network-based fuzzy diagnostic model is a general diagnostic model which is implemented in an Intelligent Learning Environment by eliciting teachers’ expertise regarding students’ characteristics based on real students’ observation and on data being collected from students’ interaction. The model has been successfully implemented, trained and tested in the learning environment “Vectors in Physics and Mathematics” by using the recommendations of a group of five experienced teachers. The performance of our model in real classroom conditions has been evaluated during an experiment with an experienced Physics teacher and 49 students of secondary school attending Physics lessons.	en
heal.publisher	Elsevier	en
heal.journalName	Expert Systems with Applications	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true