Applying Representation Learning for Educational Data Mining

Milagro Teruel, Laura Alonso Alemany

Feb 18, 2016 (modified: Feb 18, 2016) ICLR 2016 workshop submission readers: everyone
  • 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

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