| J |
Romero, C. & Ventura, S.
(2007):
Educational data mining: A survey from 1995 to 2005.
In: Expert Syst. Appl.,
Ausgabe/Number: 1,
Vol. 33,
Verlag/Publisher: Pergamon Press, Inc..
Erscheinungsjahr/Year: 2007.
Seiten/Pages: 135-146.
[Volltext] [Kurzfassung] [BibTeX]
[Endnote]
Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community. This paper surveys the application of data mining to traditional educational systems, particular web-based courses, well-known learning content management systems, and adaptive and intelligent web-based educational systems. Each of these systems has different data source and objectives for knowledge discovering. After preprocessing the available data in each case, data mining techniques can be applied: statistics and visualization; clustering, classification and outlier detection; association rule mining and pattern mining; and text mining. The success of the plentiful work needs much more specialized work in order for educational data mining to become a mature area.
@article{romero07,
author = {Romero, C. and Ventura, S.},
title = {Educational data mining: A survey from 1995 to 2005},
journal = {Expert Syst. Appl.},
publisher = {Pergamon Press, Inc.},
address = {Tarrytown, NY, USA},
year = {2007},
volume = {33},
number = {1},
pages = {135--146},
url = {http://portal.acm.org/citation.cfm?id=1223659},
doi = {http://dx.doi.org/10.1016/j.eswa.2006.04.005},
issn = {0957-4174},
keywords = {data, dm, e-learning, mining, survey, webzu},
abstract = {Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community. This paper surveys the application of data mining to traditional educational systems, particular web-based courses, well-known learning content management systems, and adaptive and intelligent web-based educational systems. Each of these systems has different data source and objectives for knowledge discovering. After preprocessing the available data in each case, data mining techniques can be applied: statistics and visualization; clustering, classification and outlier detection; association rule mining and pattern mining; and text mining. The success of the plentiful work needs much more specialized work in order for educational data mining to become a mature area.}
}
%0 = article
%A = Romero, C. and Ventura, S.
%C = Tarrytown, NY, USA
%D = 2007
%I = Pergamon Press, Inc.
%T = Educational data mining: A survey from 1995 to 2005
%U = http://portal.acm.org/citation.cfm?id=1223659
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