The authors constructed different models for the different tasks: classification - predict student category (GP - good performance, SP - satisfactory performance, PP - poor performance) and regression - for predicting exam grades. The authors divided users into categories based on their performances on the exams. The used the 4 features separately for training the model and the results were quite poor as most of students were classified as GP. Classifying student as GP while he/she is PP is especially unsatisfactory. In case of the regression model, they had much better performance with mean difference 0.92 when the maximum grade was 120. Then in order to make regression models comparable with classification one, they used predicted grades to generate categories and results were better. They used only time interval for the regression model as a feature and found that there is strong correlation between exam performances and this feature.