Based on previous works, in dropout prediction task CNN combined with DNN have outperformed other models like Random Forest, SVM. Even though dropouts are important, even more important are the students who do not drop out but fail the course. In order to forecast at-risk students models will typically predict exam or assignment grades. Actually there are few studies that have focused on predicting final exam grade. Features are one of the most important factors for developing model with reasonable accuracy. While student demographics can be good features to add to the model, they might have low predictive power compared to student interactions. It was found that one of the strongest predictors are exercises in the MOOC. Apart from that, from where the data comes from can influence the model performance. Additionally one of the previous studies achieved good performance on predicting certificate earners and assignment grades solely based on demographic data.