A combinational incremental ensemble of classifiers as a technique for predicting students’ performance in distance education

The ability to predict a student’s performance could be useful in a great number of different ways associated with university-level distance learning. Students’ marks in a few written assignments can constitute the training set for a supervised machine learning algorithm. Along with the explosive increase of data and information, incremental learning ability has become more and more important for machine learning approaches. The online algorithms try to forget irrelevant information instead of synthesizing all available information (as opposed to classic batch learning algorithms). Nowadays, combining classifiers is proposed as a new direction for the improvement of the classification accuracy. However, most ensemble algorithms operate in batch mode. Therefore a better proposal is an online ensemble of classifiers that combines an incremental version of Naive Bayes, the 1-NN and the WINNOW algorithms using the voting methodology. Among other significant conclusions it was found that the proposed algorithm is the most appropriate to be used for the construction of a software support tool.