Abstract | ||
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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. |
Year | DOI | Venue |
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2017 | 10.5220/0006317603310338 | PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED EDUCATION (CSEDU), VOL 1 |
Keywords | Field | DocType |
Educational Data Mining, Geographic Information Systems, Visualization, Decision Support, Clustering, Classification, Epidemy Spread | Data science,Data mining,Visualization,Computer science,Knowledge management | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
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John D. Garofalakis | 1 | 176 | 36.73 |
Antonios Maritsas | 2 | 0 | 0.34 |
Flora Oikonomou | 3 | 0 | 1.01 |