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The ability to predict a student's performance could be useful in a great number of different ways associated with university-level distance learning. Students' key demographic characteristics and their marks on a few written assignments can constitute the training set for a supervised machine learning algorithm. The learning algorithm could then be able to predict the performance of new students, thus becoming a useful tool for identifying predicted poor performers. The scope of this work is to compare some of the state of the art learning algorithms. Two experiments have been conducted with six algorithms, which were trained using data sets provided by the Hellenic Open University. Among other significant conclusions, it was found that the Naïve Bayes algorithm is the most appropriate to be used for the construction of a software support tool, has more than satisfactory accuracy, its overall sensitivity is extremely satisfactory, and is the easiest algorithm to implement.Computers do not learn as well as people do, but many machine-learning algorithms have been found that are effective for some types of learning tasks. They are especially useful in poorly understood domains where humans might not have the knowledge needed to develop effective knowledge-engineering algorithms. Generally, machine learning (ML) explores

This paper reports the results of a survey conducted to examine the root causes leading to student dropout at a Greek distance education university. Data was gathered from two different courses – an undergraduate course leading to a Bachelors degree in Informatics (characterized by high dropout rates), and a postgraduate course leading to a Masters degree in education (characterized by low dropout rates). A comparative analysis of these two different courses revealed important similarities in dropout percentages and the reasons cited by students for dropping out. Our analysis also revealed important differences as well. This paper presents the results of a survey designed to investigate the relationship between dropout with intrinsic (student-related) factors such as sickness, work/ school conflict etc., and extrinsic (institutional-related) factors such as study methods and materials, educational approach, and tutor influence.

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