Predicting drop-out from social behaviour of students

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F12%3A00060271" target="_blank" >RIV/00216224:14330/12:00060271 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Predicting drop-out from social behaviour of students
Original language description
This paper focuses on predicting drop-out and school failure when student data has been enriched with data derived from students social behaviour. These data describe social dependencies gathered from e-mail and discussion boards conversation, among other sources. We describe an extraction of new features from both student data and behaviour data (or more precisely from social graph which we construct). Then we introduce a novel method for learning classier for student failure prediction that employs cost-sensitive learning to lower the number of incorrectly classified unsuccessful students. We show that a use of social behaviour data results in significant prediction accuracy increase.
Czech name
—
Czech description
—

Project
<a href="/en/project/LA09016" target="_blank" >LA09016: Czech Republic membership in the European Research Consortium for Informatics and Mathematics (ERCIM)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 5th International Conference on Educational Data Mining - EDM 2012
ISBN
9781742102764
ISSN
—
e-ISSN
—
Number of pages
7
Pages from-to
103-109
Publisher name
www.educationaldatamining.org
Place of publication
Řecko
Event location
Chania, Řecko
Event date
Jan 1, 2012
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

Similar results(10)