Personalized and Adaptive Curriculum Optimization Based on a Performance Correlation Analysis

The paper introduces a "lazy" Data Mining technology, which models students\´ learning characteristics by considering real data instead of deriving ("guessing") their characteristics explicitly. In former work, the authors developed a modeling system for university learning processes, which aims at evaluating and refining university curricula to reach an optimal learning success in terms of the 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. Here, we introduce a complementary technology to mine course characteristics similarities of former students\´ study traces and utilize them to optimize curricula of current students based to their performance traits revealed by their study achievements so far. This way, technology generates suggestions of personalized curricula. Furthermore, this technology is supplemented by an adaptation mechanism, which compares recent data with historical data to ensure that the similarity of mined characteristics follow the dynamic changes affecting curriculum (e.g., revision of course contents and materials, and changes in teachers, etc.).