- Abstract: Educational Data Mining is an area of growing interest, given the increase of available data and generalization of online learning environments. In this paper we present a first approach to integrating Representation Learning techniques in Educational Data Mining by adding autoencoders as a preprocessing step in a standard performance prediction problem. Preliminary results do not show an improvement in performance by using autoencoders, but we expect that a fine tuning of parameters will provide an improvement. Also, we expect that autoencoders will be more useful combined with different kinds of classifiers, like multilayer perceptrons.
- Conflicts: unc.edu.ar