A priori evaluation & refinement of curricula by data mining over storyboards

In university studies, there is a flexible but complicated learning system of subject offers, enrollment rules for particular subject combinations, and prerequisites to meet for taking particular subjects, which need to be matched with students' needs and desires. Students need assistance in the jungle of such learning opportunities and limitations at today's universities. To face this problem, we employed our formerly developed storyboard concept and used it to develop, maintain, and evaluate curricula. Storyboarding is based on the idea of formally representing, processing, evaluating and refining didactic knowledge. This concept is utilized to supplement an educational system called Dynamic Learning Needs Reflection System (DLNRS) of the School of Information Environment of Tokyo Denki University, Japan. Didactic knowledge of DLNRS can be represented by storyboarding and used for supporting dynamic learning activities of students. Here, we introduce an additional benefit of storyboarding. By using data mining-like methods to evaluate storyboard paths, we are able to estimate success chances of storyboard paths. Based on this evaluation we will be able to rate planned (future) paths and thus, to prevent students from failing by non-appropriate curricula. Moreover, besides the evaluation, the estimation can be used for computer enforced suggestions to complete a path towards optimal success chances.


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