Business Process Intelligence (BPI) is an emerging topic that has gained popularity in the last decade. It is driven by the need for analysis techniques that allow businesses to understand and improve their processes. One of the most common applications of BPI is reporting, which consists on the structured generation of information (i.e., reports) from raw data. In this article, state-of-the-art process mining techniques are used to periodically produce automated reports that relate the actual performance of students of a Dutch University to their studying behavior. To avoid the tedious manual repetition of the same process mining procedure for each course, we have designed a workflow calling various process mining techniques using RapidProM. To ensure that the actual students’ behavior is related to their actual performance (i.e., grades for courses), our analytic workflows approach leverages on process cubes, which enable the dataset to be sliced and diced based on courses and grades. The article discusses how the approach has been operationalized and what is the structure and concrete results of the reports that have been automatically generated. Two evaluations were performed with lecturers using the real reports. During the second evaluation round, the reports were restructured based on the feedback from the first evaluation round. Also, we analyzed an example report to show the range of insights that they provide.