Predicting students marks in Hellenic Open University

The ability to provide assistance for a student at the appropriate level is invaluable in the learning process. Not only does it aids the student´s learning process but also prevents problems, such as student frustration and floundering. Students´ key demographic characteristics and their marks in a small number of written assignments can constitute the training set for a regression method in order to predict the student´s performance. The scope of this work compares some of the state of the art regression algorithms in the application domain of predicting students´ marks. A number of experiments have been conducted with six algorithms, which were trained using datasets provided by the Hellenic Open University. Finally, a prototype version of software support tool for tutors has been constructed implementing the M5rules algorithm, which proved to be the most appropriate among the tested algorithms.