An Intelligent System for Modeling and Supporting Academic Educational Processes

Abstract

University has a complicated system of course offerings, registration rules, and prerequisite courses, which should be matched to students’ dynamic learning needs, and desires. We address this problem by developing an Educational-Learning System called “Dynamic Storyboarding System”. Besides modeling learning processes, this system aims at evaluating and refining university curricula to reach an optimum of learning success in terms of best possible ac-cumulative grade point average (GPA). This is performed by applying Educational Data Mining (EDM) to former students curricula and their degree of success (GPA) and thus, uncovering golden didactic knowledge for successful education. It consists of mining a decision tree (DT) and applying it to curricula planned by current students. Students receive an estimation of the GPA they are likely to receive along with a recommendation to supplement a partial path to reach optimal success. Our approach includes individual learner profiles. The profiling concept initially uses the per-university educational history and is dynamically extended by the students’ university study results. The profiles are used by applying the EDM technology to students with profiles of a high similarity to the student under consideration. A feasibility study showed the usefulness of the system. The effect has been validated by cross-validation with about 200 students’ records. The mean of the difference between the original grade point average (GPA) and the estimated one was 0.43 with a standard deviation of 0.30.