Comparing classification methods for predicting distance students' performance
Diego Garcia-Saiz, Marta Zorrilla; JMLR W&CP 17:26-32, 2011.
Virtual teaching is constantly growing and, with it, the necessity of instructors to predict the performance of their students. In response to this necessity, different machine learning techniques can be used. Although there are so many benchmarks comparing their performance and accuracy, there are still very few experiments carried out on educational datasets which have very special features which make them different from other datasets. Therefore, in this work we compare the performance and interpretation level of the output of the different classification techniques applied on educational datasets and propose a meta-algorithm to preprocess the datasets and improve the accuracy of the model, which will be used by virtual instructors for their decision making through the ElWM tool.