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 a 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 used learner profiles to personalize this technology. After a short introduction to this technology, we discuss the result of a practical application and draw conclusions. In particular, we could not obtain sufficient data to establish this kind of learner profiles. Therefore, we shifted our strategy from an “eager” one of holding an explicit model towards a “lazy” strategy of mining with data, which is really available without making “guesses” what they mean (profiles). In particular, we utilize the educational history of the students and vocational ambitions for student modeling.
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