Mining coherent evolution patterns in education through biclustering
نویسندگان
چکیده
With the spread of information systems and the increased interest in education, the quantity of data about education has exploded along with a new field Educational Data Mining. Predicting students’ performance has been approached by several techniques, but the combination of supervised and non-supervised techniques appeared as a new tool for improving the results. Biclustering algorithms have been successfully applied in areas such as gene expression data and information retrieval, but not used in the educational context. In this paper, we show how to apply biclustering techniques to educational data and to use its results as features to improve the prediction of student’s performance.
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