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Visual Anomaly Detection in Educational Data

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Artificial Intelligence: Methodology, Systems, and Applications (AIMSA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9883))

Abstract

This paper is dedicated to finding anomalies in short multivariate time series and focus on analysis of educational data. We present ODEXEDAIME, a new method for automated finding and visualising anomalies that can be applied to different types of short multivariate time series. The method was implemented as an extension of EDAIME, a tool for visual data mining in temporal data that has been successfully used for various academic analytics tasks, namely its Motion Charts module. We demonstrate a use of ODEXEDAIME on analysis of computer science study fields.

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Notes

  1. 1.

    http://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen.html.

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Acknowledgments

We thank to the members of Knowledge Discovery Lab at FIMU for their assistance and the anonymous referees for their comments. This work has been supported by Faculty of Informatics, Masaryk University.

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Authors and Affiliations

  1. Knowledge Discovery Lab, Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Jan Géryk, Luboš Popelínský & Jozef Triščík

Authors
  1. Jan Géryk
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  2. Luboš Popelínský
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  3. Jozef Triščík
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Corresponding author

Correspondence to Luboš Popelínský .

Editor information

Editors and Affiliations

  1. Winston-Salem State University, Winston Salem, North Carolina, USA

    Christo Dichev

  2. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Gennady Agre

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Géryk, J., Popelínský, L., Triščík, J. (2016). Visual Anomaly Detection in Educational Data. In: Dichev, C., Agre, G. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016. Lecture Notes in Computer Science(), vol 9883. Springer, Cham. https://doi.org/10.1007/978-3-319-44748-3_10

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  • DOI: https://doi.org/10.1007/978-3-319-44748-3_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44747-6

  • Online ISBN: 978-3-319-44748-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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