The short period between the first and second broadcast of the MOOC has everything to do with its popularity, says Van der Aalst. “After we closed the MOOC, we received many complaints of people who wanted to complete the course at their own pace. Those people can continue in April.”
 
Setting up the MOOC was hard work, but a rerun isn’t taxing, according to van der Aalst. “That’s the great thing about this concept: all classes and questions are done, and tests are scored automatically. We don’t have to do anything for that.” Apart from the questions during lectures, weekly tests, and the final exam, there’s one practical assignment for which students have to analyze an actual data set using the methods from the MOOC. That assignment isn’t tested through multiple choice questions, but correcting it doesn’t take up too much time, either. Van der Aalst: “We work with peer reviewing, so students correct each other’s work. If their corrections show a uniform outcome, we don’t really have to go over the assignment again. We only do so when the outcome is irregular.”
 
The data science MOOC is about process mining, mostly: a method to distill useful data from event logs of digital systems that are stored automatically. Van der Aalst is an international authority in that field. It should come as no surprise, then, that he took the opportunity to subject the digital tracks of both his MOOC students and those of colleague Bert Blocken (aerodynamics) to the methods treated during the course. “It’s a way for us to analyze in detail how our students follow our MOOCs exactly.”