Educational data mining: A survey from 1995 to 2005

@article{citeulike:1201711, 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.}, author = {Romero, C. and Ventura, S. }, citeulike-article-id = {1201711}, doi = {http://dx.doi.org/10.1016/j.eswa.2006.04.005}, journal = {Expert Systems with Applications}, keywords = {data-mining-general, education}, month = {July}, number = {1}, pages = {135--146}, posted-at = {2008-03-13 10:40:42}, priority = {2}, title = {Educational data mining: A survey from 1995 to 2005}, url = {http://dx.doi.org/10.1016/j.eswa.2006.04.005}, volume = {33}, year = {2007} }

TY - JOUR ID - citeulike:1201711 L3 - citeulike-article-id:1201711 TI - Educational data mining: A survey from 1995 to 2005 JF - Expert Systems with Applications VL - 33 IS - 1 SP - 135 EP - 146 N2 - 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. KW - data-mining-general KW - education AU - Romero, C AU - Ventura, S PY - 2007/07// UR - http://dx.doi.org/10.1016/j.eswa.2006.04.005 ER -