Sotiris Kotsiantis|Christos Pierrakeas|Panagiotis Pintelas
PREDICTING STUDENTS' PERFORMANCE IN DISTANCE LEARNING USING MACHINE LEARNING TECHNIQUES
ability predict students performance could useful great number different ways associated universitylevel distance learning students key demographic characteristics marks written assignments constitute training set supervised machine learning algorithm learning algorithm could able predict performance new students thus becoming useful tool identifying predicted poor performers scope work compare state art learning algorithms two experiments conducted six algorithms trained using data sets provided hellenic open university among significant conclusions found nave bayes algorithm appropriate used construction software support tool satisfactory accuracy overall sensitivity extremely satisfactory easiest algorithm implement