DocumentCode :
2751108
Title :
Evolutionary algorithms for subgroup discovery applied to e-learning data
Author :
Carmona, C.J. ; Gonzá, P. ; del Jesus, M.J. ; Romero, C. ; Ventura, S.
Author_Institution :
Dept. of Comput. Sci., Univ. of Jaen, Jaen, Spain
fYear :
2010
fDate :
14-16 April 2010
Firstpage :
983
Lastpage :
990
Abstract :
This work presents the application of subgroup discovery techniques to e-learning data from learning management systems (LMS) of andalusian universities. The objective is to extract rules describing relationships between the use of the different activities and modules available in the e-learning platform and the final mark obtained by the students. For this purpose, the results of different classical and evolutionary subgroup discovery algorithms are compared, showing the adequacy of the evolutionary algorithms to solve this problem. Some of the rules obtained are analyzed with the aim of extract knowledge allowing the teachers to take actions to improve the performance of their students.
Keywords :
computer aided instruction; data mining; educational administrative data processing; educational institutions; evolutionary computation; andalusian universities; e-learning data; evolutionary algorithms; knowledge extraction; learning management systems; subgroup discovery; Application software; Association rules; Computer science; Data mining; Electronic learning; Evolutionary computation; Least squares approximation; Navigation; Numerical analysis; Performance analysis; Subgroup discovery; e-learning systems; educational data mining; evolutionary algorithms; fuzzy rules;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Education Engineering (EDUCON), 2010 IEEE
Conference_Location :
Madrid
Print_ISBN :
978-1-4244-6568-2
Electronic_ISBN :
978-1-4244-6570-5
Type :
conf
DOI :
10.1109/EDUCON.2010.5492470
Filename :
5492470
Link To Document :
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