Comparative analysis of algorithms for student characteristics classification using a methodological framework

Data clustering and mining have evolved into a major research topic. The techniques used to cluster data, are applicable in a number of subjects. The education arena offers a fertile ground for data mining applications, since there are multiple sources of data and diverse interest groups. Thus, modeling student performance, can be a great tool for both educators as well as students, in order to make correct adjustments to the curriculum as well as the teaching methods. As a testbed for this study, four classification algorithms were chosen for comparison, namely the k-means algorithm, the - Nearest Neighbors algorithm, Support Vector Machines and the Naive Bayes Classifier. Though research algorithmic on performance has already being performed, a concrete conclusion has yet to be drawn. Therefore the focus of this study is to determine any differences and similarities that these algorithms may have on the data mining of student characteristics. To achieve this, several classification models were used, while a regression model was also introduced.