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In former work, the authors developed a modeling system for university learning processes, which aims at evaluating and refining university curricula to reach an optimum of learning success in terms of best possible best possible grade point average (GPA). This is performed by applying an Educational Data Mining (EDM) technology to former students curricula and their degree of success (GPA) and thus, uncovering golden didactic knowledge for successful education. We shifted strategy from an "eager" strategy of holding an explicit model towards a "lazy" strategy of mining with data, which is really available, holds empirically, and is not a result of "guesses" about the students' general characteristics. In particular, we utilize the educational history of the students and vocational ambitions for student modeling.