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 Journal Article
%1 romero07
%A Romero, C.
%A Ventura, S.
%C Tarrytown, NY, USA
%D 2007
%I Pergamon Press, Inc.
%J Expert Syst. Appl.
%K data dm e-learning mining survey webzu
%N 1
%P 135--146
%R http://dx.doi.org/10.1016/j.eswa.2006.04.005
%T Educational data mining: A survey from 1995 to 2005
%U http://portal.acm.org/citation.cfm?id=1223659
%V 33
%X 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,
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.},
added-at = {2008-12-19T16:16:46.000+0100},
address = {Tarrytown, NY, USA},
author = {Romero, C. and Ventura, S.},
biburl = {https://www.bibsonomy.org/bibtex/2746d12e92e58587461ffcb8dc381e283/jaeschke},
description = {Educational data mining},
doi = {http://dx.doi.org/10.1016/j.eswa.2006.04.005},
interhash = {89d843f1a3b181f2a628e881d9210b22},
intrahash = {746d12e92e58587461ffcb8dc381e283},
issn = {0957-4174},
journal = {Expert Syst. Appl.},
keywords = {data dm e-learning mining survey webzu},
number = 1,
pages = {135--146},
publisher = {Pergamon Press, Inc.},
timestamp = {2014-07-28T15:57:31.000+0200},
title = {Educational data mining: A survey from 1995 to 2005},
url = {http://portal.acm.org/citation.cfm?id=1223659},
volume = 33,
year = 2007
}