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 aid 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 regression algorithms.

• Type: Conference
• Year: 2005
• Where: ICALT
• Authors: Sotiris B. Kotsiantis, Panayiotis E. Pintelas
• Keywords: ICALT 2005, Learning Process, Machine Learning, Regression Algorithms, Student’s Learning Process