Abstract. Using the factorial analysis technique, a set of factors or latent components can be discovered from quantitative variables to characterize programs written by students. Representing each program by a set of latent components, we call this representation of student profile. In order to classify students profiles, this paper proposes a non-random selection method based on Graph Clustering to select representative profiles from the diversity of latent components to build a training set for classification models. The results achieved demonstrate that the combination of factorial analysis with Graph Clustering improves outcomes of profiles classification.
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