MOOC datasets have been used to predict academic performance or student dropouts. Academic failure rate is especially high for MOOC as only 25% of highly committed users finish the course. For this reason, several researches focused to provide emails or feedback for those students who are at risk. The issue with these models is that they are highly context-dependent - they are only applicable for the particular environment. Knowing the features that influence academic performance the most can solve extrapolation problem. As Moreno-Marcos suggests sometimes data selection can be more important than the algorithm itself. Forum data, clickstream, course duration and setup, assignments, final exams are among the factors that can be beneficial for this purpose. In this paper the authors have 4 research questions:

RQ1) Which factors, among previous grades, forum variables, course duration, type of assignments and data collection procedures, influence grades predictions and to what extent?

RQ2) How does the presence/absence of clickstream data and interactions with exercises affect prediction results?

RQ3) How does the question format of the final exam (close-ended and open-ended questions) affect prediction results?

RQ4) Is the predictive power greater for predicting the final exam grade rather than the final grade of the course?