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Authors: John Garofalakis ; Antonios Maritsas and Flora Oikonomou

Affiliation: University of Patras, Greece

ISBN: 978-989-758-239-4

Keyword(s): Educational Data Mining, Geographic Information Systems, Visualization, Decision Support, Clustering, Classification, Epidemy Spread.

Related Ontology Subjects/Areas/Topics: Classroom Management ; Computer-Supported Education ; Information Technologies Supporting Learning

Abstract: Educational Data Mining (EDM) has emerged as an interdisciplinary research area that applies Data Mining (DM) techniques to educational data in order to discover novel and potentially useful information. On the other hand, Geographic Information Systems (GIS) are ones designed to manage spatial data and related attributes and can be used for assisting decision support. This paper proposes an innovative use of DM and visualization GIS techniques for decision support in planning and management of Greek public education focused on high risk groups such as young children. The developed application clusters school units with similar features, such as students’ and teachers’ absences, and represents them on a map, enabling user to make decisions being aware of geographical information. Afterwards, based on real data stored during epidemic spread periods, such as the H1N1 flu pandemic during 2009, the application predicts whether a school should be opened or closed considering students’ and teachers’ absences of a specific time interval. (More)

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Paper citation in several formats:
Garofalakis, J.; Maritsas, A. and Oikonomou, F. (2017). Assisting School Units Management with Data Mining Techniques and GIS Visualization.In Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-239-4, pages 331-338. DOI: 10.5220/0006317603310338

@conference{csedu17,
author={John Garofalakis. and Antonios Maritsas. and Flora Oikonomou.},
title={Assisting School Units Management with Data Mining Techniques and GIS Visualization},
booktitle={Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2017},
pages={331-338},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006317603310338},
isbn={978-989-758-239-4},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Assisting School Units Management with Data Mining Techniques and GIS Visualization
SN - 978-989-758-239-4
AU - Garofalakis, J.
AU - Maritsas, A.
AU - Oikonomou, F.
PY - 2017
SP - 331
EP - 338
DO - 10.5220/0006317603310338

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