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Performance Indicators for Online Secondary Education: A Case Study

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BNAIC 2016: Artificial Intelligence (BNAIC 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 765))

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Abstract

There is little consensus about what variables extracted from learner data are the most reliable indicators of learning performance. The aim of this study is to determine such indicators by taking a wide range of variables into consideration concerning overall learning activity and content processing. A genetic algorithm is used for the selection process and variables are evaluated based on their predictive power in a classification task. Variables extracted from exercise activities turn out to be most informative. Exercises designed to train students in understanding and applying material are found to be especially informative.

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Notes

  1. 1.

    https://www.thiememeulenhoff.nl/voortgezet-onderwijs/mens-en-maatschappij/aardrijkskunde/de-geo-onderbouw-9e-editie.

  2. 2.

    GA implementation from the DEAP library for evolutionary algorithms [2] was used.

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Acknowledgements

The authors would like to thank ThiemeMeulenhoff for providing the resources for this study. Special thanks go to Joost Borsboom, Gilian Halewijn, Wouter van Rennes, Emiel Ubink and Johan Verhaar.

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Authors and Affiliations

  1. Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands

    Pepijn van Diepen & Bert Bredeweg

Authors
  1. Pepijn van Diepen
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  2. Bert Bredeweg
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Corresponding author

Correspondence to Bert Bredeweg .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Tibor Bosse

  2. Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands

    Bert Bredeweg

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van Diepen, P., Bredeweg, B. (2017). Performance Indicators for Online Secondary Education: A Case Study. In: Bosse, T., Bredeweg, B. (eds) BNAIC 2016: Artificial Intelligence. BNAIC 2016. Communications in Computer and Information Science, vol 765. Springer, Cham. https://doi.org/10.1007/978-3-319-67468-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-67468-1_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67467-4

  • Online ISBN: 978-3-319-67468-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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