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International Journal of Innovation and Applied Studies
ISSN: 2028-9324     CODEN: IJIABO     OCLC Number: 828807274     ZDB-ID: 2703985-7
 
 
Friday 24 May 2019

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Data Mining: Impact of Daily Activities on Student Performance


[ Minería de datos: Impacto de Actividades Cotidianas en el Rendimiento Estudiantil ]

Volume 14, Issue 4, February 2016, Pages 927–935

 Data Mining: Impact of Daily Activities on  Student Performance

Huerta Luis1, Ruiz Juan2, Cabrera Nubia3, Montiel Luis4, Benítez Felipe5, and Ramírez Víctor6

1 Department of Informatics, University of Istmo, Ciudad Ixtepec, Oaxaca, Mexico
2 Department of Informatics, University of Istmo, Ciudad Ixtepec, Oaxaca, Mexico
3 Department of Informatics, University of Istmo, Ciudad Ixtepec, Oaxaca, Mexico
4 Department of Informatic, University of Istmo, Ciudad Ixtepec, Oaxaca, Mexico
5 Department of Informatics, University of Istmo, Ciudad Ixtepec, Oaxaca, Mexico
6 Department of Informatic, University of Istmo, Ciudad Ixtepec, Oaxaca, Mexico

Original language: Spanish

Received 3 September 2015

Copyright © 2016 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


The student performance has been affected for different factors, many of them are unobvious. The habits or daily activities undoubtly have a deep effects on the student performance. In this work, the study of student daily activities, and the relationship with his academic performance, using Data Mining techniques was done. In the attribute selection phase, 5-13 attributes from the 35 total were selected. The students were classified in four classes related with their academic performance: low, regular, good and high; the classification accuracy was near to 90%, using algorithms like MLP, KNN and tree algorithms like Random Forest, Random Tree and J48. The activities and factors presented for low and high performance students, also the tendency of activities and factors in the four classes, are reported.

Author Keywords: Student Performance, Classify, Attribute Selection.


How to Cite this Article


Huerta Luis, Ruiz Juan, Cabrera Nubia, Montiel Luis, Benítez Felipe, and Ramírez Víctor, “Data Mining: Impact of Daily Activities on Student Performance,” International Journal of Innovation and Applied Studies, vol. 14, no. 4, pp. 927–935, February 2016.