Course selection can be a daunting task, especially for first year students. Sub-optimal selection can lead to bad performance of students and increase the dropout rate. Given the availability of historic data about student performances, it is possible to aid students in the selection of appropriate courses. Here, we propose a method to compose a personalized curriculum for a given student. We develop a modular approach that combines a context-aware grade prediction with statistical information on the useful temporal ordering of courses. This allows for meaningful course recommendations, both for fresh and senior students. We demonstrate the approach using the data of the computer science Bachelor students at Saarland University. [For the full proceedings, see ED593090.]