Mining coherent evolution patterns in education through biclustering.
André Rufino do Vale, Sara C. Madeira, Cláudia Antunes · Educational Data Mining · 2014
Educational Data Mining (EDM) focus in the development of methods for exploring the types of data that come from an educational context. In this dissertation, we studied the inclusion of an unsupervised technique, Biclustering that has been successfully applied in areas such as gene expression and information retrieval, but not used in the educational context. We presented a methodology that allows us to use Biclustering algorithms in educational data to get new patterns and use these results as a complement to the classification. By applying this new technique we can improve the accuracy of the classifiers, similarly to other techniques previously used, finding new types of patterns which until now had never been discovered.