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International Conference on Computer Supported Education

CSEDU 2016: Computers Supported Education pp 49–70Cite as

Hybrid Matrix Factorization Update for Progress Modeling in Intelligent Tutoring Systems

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 739))

Abstract

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.

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Acknowledgement

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

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Authors and Affiliations

  1. Information Systems and Machine Learning Lab, University of Hildesheim, Hildesheim, Germany

    Carlotta Schatten & Lars Schmidt-Thieme

Authors
  1. Carlotta Schatten
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  2. Lars Schmidt-Thieme
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Corresponding author

Correspondence to Carlotta Schatten .

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Editors and Affiliations

  1. University of Salerno, Fisciano, Italy

    Gennaro Costagliola

  2. Ulster University, Belfast, United Kingdom

    James Uhomoibhi

  3. University of Denver, Denver, Colorado, USA

    Susan Zvacek

  4. Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Bruce M. McLaren

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Schatten, C., Schmidt-Thieme, L. (2017). Hybrid Matrix Factorization Update for Progress Modeling in Intelligent Tutoring Systems. In: Costagliola, G., Uhomoibhi, J., Zvacek, S., McLaren, B. (eds) Computers Supported Education. CSEDU 2016. Communications in Computer and Information Science, vol 739. Springer, Cham. https://doi.org/10.1007/978-3-319-63184-4_4

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  • DOI: https://doi.org/10.1007/978-3-319-63184-4_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63183-7

  • Online ISBN: 978-3-319-63184-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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