Polarity, emotions and online activity of students and tutors as features in predicting grades

The lack of physical contact and the demanding need for personalized services has prompted stakeholders in distance learning to benefit from the enormous volume of students’ online traces in the Learning Management Systems. Data mining methodologies are widely applied to analyze data logs and predict trends for early and efficient interventions. Thus, the retention of students in the educational process can be achieved with positive effects on the reputation and finances of the institutions. This work divides the moodle data sets from six different sections of an annual postgraduate program at the Hellenic Open University in six periods for each section, due to the number of written assignments. Then it implements data mining techniques to analyze the activity, polarity and emotions of tutors and students in order to predict students’ grades. The results indicate the algorithm with the highest precision in each prediction. In addition, the research concludes that polarity and emotions as independent variables provide better performance in comparative models. Moreover, tutors’ variables are highlighted as an important factor for more accurate predictions of student grades. Finally, a comparison of actual and predicted grades indicates which students have used a third party to fulfill their assignments.