@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.}, address = {Tarrytown, NY, USA}, author = {Romero, C. and Ventura, S.}, doi = {http://dx.doi.org/10.1016/j.eswa.2006.04.005}, interhash = {89d843f1a3b181f2a628e881d9210b22}, intrahash = {746d12e92e58587461ffcb8dc381e283}, issn = {0957-4174}, journal = {Expert Syst. Appl.}, number = 1, pages = {135--146}, publisher = {Pergamon Press, Inc.}, title = {Educational data mining: A survey from 1995 to 2005}, url = {http://portal.acm.org/citation.cfm?id=1223659}, volume = 33, year = 2007 }