Knowledge mining for supporting learning processes

Rainer Knauf, Ronald Bock, Sakurai Yoshitaka, Dohi Shinichi, Tsuruta Setsuo
2008 Conference Proceedings / IEEE International Conference on Systems, Man and Cybernetics  
AI technologies for knowledge mining are commonly used in technical environments. Their application for social processes like learning processes, for example, is a quite a new challenge, which is characterized by having "humans in the loop". Humans' desires, preferences and decisions may be unpredictable and thus, not appropriate for modeling -at a first glance. However, in learning processes didactic variants can be anticipated and can become a subject of AI technologies. A semiformal modeling
more » ... semiformal modeling approach called storyboarding, is outlined here. A storyboard represents various opportunities for composing a learning process according to individual circumstances, such as topical prerequisites (educational history), mental prerequisites (preferred learning styles, etc.), performance prerequisites (a requested success level in former learning activities, etc.), and personal aspects (needs, wishes, talents, aims). By storyboarding, various didactic variants can be validated by considering the average learning success associated with the different paths through a storyboard in a case study. Based on validation results, success chances can be derived for the different paths. Here, a concept and an implementation to pre-estimate success chances of intended (future) learning paths through a storyboard are introduced. They are based on a Data Mining technology, and construct a decision tree by analyzing former learners' paths and their degrees of success. Furthermore, this technology generates a supplement to a submitted path, which is optimal according to the success chances. This technology has been tested at a Japanese university, in which students had to compose their individual plan (subject sequences) in advance, and the technology helped them by predicting success chances and suggesting alternatives.
doi:10.1109/icsmc.2008.4811690 dblp:conf/smc/KnaufBSDT08 fatcat:fhw6exlfafdenhfeflq44qwqxq