In this paper, we propose a novel approach to combine domain modelling and student modelling techniques in a single, automated pipeline which does not require expert knowledge and can be used to predict future student performance. Domain modelling techniques map questions to concepts and student modelling techniques generate a mastery score for a concept. We conducted an evaluation using six large datasets from a Python programming course, evaluating the performance of different domain and student modelling techniques. The results showed that it is possible to develop a successful and fully automated pipeline which learns from raw data. The best results were achieved using alternating least squares on hill-climbing Q-matrices as domain modelling and exponential moving average as student modelling. This method outperformed all baselines in terms of accuracy and showed excellent run time. [For the full proceedings, see ED623995.]