Hybrid Matrix Factorization Update for Progress Modeling in Intelligent Tutoring Systems

  • Carlotta SchattenEmail author
  • Lars Schmidt-Thieme
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 739)


Intelligent Tutoring Systems often profit of intelligent components, which allow to personalize the proposed contents’ characteristics and sequence. Adaptive sequencing, in particular, requires either a detrimental data collection for users or extensive domain information provided by experts of the educational area. In this paper we propose an efficient domain independent method to model student progress that can be later used to sequence tasks in large commercial systems. The developed method is based on the integration of domain independent Matrix Factorization Performance Prediction with Kalman Filters state modeling abilities. Our solution not only reduces the prediction error, but also possesses a more computationally efficient model update. Finally, we give hints about a potential interpretability of student’s state computed by Matrix Factorization, that, because of its implicit modeling, did not allow human experts, to monitor user’s knowledge acquisition.


Progress modeling Kalman filerts Matrix factorization Performance prediction Sequencing 



This research has been co-funded by the Seventh Framework Programme of the European Commission, through project iTalk2Learn (\(\#\)318051). This paper is an extended version of [21] presented at the 8th International Conference on Computer Supported Education.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Information Systems and Machine Learning LabUniversity of HildesheimHildesheimGermany

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