Extraction of rules based on students’ questionnaires

dc.contributor.author	Χάλαρης, Μανώλης	el
dc.contributor.author	Χάλαρης, Ιωάννης	el
dc.contributor.author	Σκουρλάς, Χρήστος Π.	el
dc.contributor.author	Τσολακίδης, Αναστάσιος	el
dc.date.accessioned	2015-05-14T19:24:31Z
dc.date.available	2015-05-14T19:24:31Z
dc.date.issued	2015-05-14
dc.identifier.uri	http://hdl.handle.net/11400/10427
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/science?_ob=ArticleListURL&_method=list&_ArticleListID=-789754701&_st=5&filterType=&searchtype=a&originPage=rslt_list&_origin=&_mlktType=&md5=87744d4a267f72265e7ea4e9408e1f0f	el
dc.subject	Data mining
dc.subject	Higher education
dc.subject	Association rules
dc.subject	Κανόνες συσχέτισης
dc.subject	Εξόρυξη Δεδομένων
dc.subject	Τριτοβάθμια εκπαίδευση
dc.subject	Student Retention
dc.subject	Διατήρηση φοιτητών
dc.subject	Student intervention
dc.subject	Παρέμβαση φοιτητών
dc.subject	Rules’ extraction
dc.subject	Εξόρυξη κανόνων
dc.title	Extraction of rules based on students’ questionnaires	en
heal.type	journalArticle
heal.generalDescription	Δημοσιευμένο και στο: The 2nd International Conference on Integrated Information	el
heal.classification	Education
heal.classification	Computer science
heal.classification	Εκπαίδευση
heal.classification	Πληροφορική
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85040989
heal.classificationURI	http://skos.um.es/unescothes/C00750
heal.classificationURI	N/A-Εκπαίδευση
heal.classificationURI	N/A-Πληροφορική
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh97002073
heal.keywordURI	http://skos.um.es/unescothes/C01791
heal.language	en
heal.access	campus
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Πληροφορικής Τ.Ε.	el
heal.publicationDate	2013
heal.bibliographicCitation	Chalaris, Μ., Chalaris, Ι., Skourlas, C. and Tsolakidis, A. (2013). Extraction of rules based on students’ questionnaires. The 2nd International Conference on Integrated Information . "Procedia - Social and Behavioral Sciences", 73 . pp. 510 - 517. Available from: http://www.sciencedirect.com/science?_ob=ArticleListURL&_method=list&_ArticleListID=-789754701&_st=5&filterType=&searchtype=a&originPage=rslt_list&_origin=&_mlktType=&md5=87744d4a267f72265e7ea4e9408e1f0f.	en
heal.abstract	There are many students in the Greek Higher Education that are still “lingering” in their Departments beyond the six years. The length of studies beyond 6 years has not been justified, and this study focuses on this problem. We also study another problem: The percentage of graduates scoring about 8.5/10 or more is extremely low. Association rules mining is a well known data analysis method for extracting associations between data in a wide range of different fields. In this paper, we focus on the generation of the appropriate association rules based on students’ questionnaires in Higher Education. A sample of 50.000 questionnaires was filled by 10.000 students in the TEI of Athens. Various interesting rules could be extracted related to learning goals, practices, years required for graduation, etc. These rules and clustering techniques could be used for solving the problem of the students that are still “lingering”, and the problem of the low “scoring” of the graduates.	en
heal.publisher	Elsevier Ltd	el
heal.journalName	Procedia - Social and Behavioral Sciences	el
heal.journalType	peer-reviewed
heal.fullTextAvailability	true