Adaptive Content Sequencing without Domain Information
Carlotta Schatten, Lars Schmidt-Thieme · 2014
In this paper we show that a performance prediction method can be used to sequence contents ameliorating sequencing over common domain informed strategies. Our developed sequencer is able to sequence content without knowledge on the domain, i.e. without knowing the number of skills involved in a content and the relative difficulties. Moreover, we discuss if a synthetic learning process can be modeled in a plausible way in order to facilitate testing with sequencing, which is generally difficult to evaluate on real students. In conclusion we show that Matrix Factorization is able to deal with all the important actual problems of Intelligent Tutoring Systems: personalization, multiple skill content modeling, and difficulty.