FIT
2011 - 2015
Intelligent tutoring systems (ITS) have been getting more and more popular. The applicability of ITSs is typically restricted to well-defined domains where a domain formalization is easily possible. For ill-defined domains, human tutors still by far outperform the performance of ITSs, or the latter are not applicable at all. The goal of the FIT project was to develop novel ITS methods which extend the applicability of ITS systems to ill-defined domains by means of machine learning techniques which can autonomously infer structures and feedback options from given data (e.g., student solutions). For this purpose, prototype based machine learning methods and recent developments for general non-vectorial data structures were extended such that they allow to simultaneously structure solution spaces, learn metrics for structures, align student solutions with clusters of other solutions, and infer appropriate feedback based thereon. In this project which was part of the DFG priority programme "Autonomous Learning", the CSES group cooperated with the research group of Prof. Dr. Barbara Hammer which is located at the Faculty of Technology at Bielefeld University.
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