Targeting the right students using data mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Educational data mining: A survey from 1995 to 2005
Expert Systems with Applications: An International Journal
Clustering and Sequential Pattern Mining of Online Collaborative Learning Data
IEEE Transactions on Knowledge and Data Engineering
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Comparison of machine learning methods for intelligent tutoring systems
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Correlation of grade prediction performance and validity of self-evaluation comments
Proceedings of the 14th annual ACM SIGITE conference on Information technology education
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Educational Data Mining (EDM) is an emerging multidisciplinary research area, in which methods and techniques for exploring data originating from various educational information systems have been developed. EDM is both a learning science, as well as a rich application area for data mining, due to the growing availability of educational data. EDM contributes to the study of how students learn, and the settings in which they learn. It enables data-driven decision making for improving the current educational practice and learning material. We present a brief overview of EDM and introduce four selected EDM papers representing a crosscut of different application areas for data mining in education.