Spectral Clustering in Educational Data Mining

Spectral Clustering is a graph theoretic technique to represent data in such a way that clustering on this new representation is reduced to a trivial task. It is especially useful in complex datasets where traditional clustering methods would fail to find groupings. In previous work we have shown the utility of using K-means clustering for exploiting structure in the data to affect a significant improvement in prediction accuracy on educational datasets. In this work we show that by using Spectral Clustering we are able to further improve the student performance prediction. We evaluate an educational data mining prediction task: predicting student state test scores from student features derived from a tutor and also explore some other EDM tasks using spectral clustering.