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Using Simulated Students for Machine Learning

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Intelligent Tutoring Systems (ITS 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3220))

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Abstract

In this paper we present how simulated students have been generated in order to obtain a large amount of labeled data for training and testing a neural network-based fuzzy model of the student in an Intelligent Learning Environment (ILE). The simulated students have been generated by modifying real students’ records and classified by a group of expert teachers regarding their learning style category. Experimental results were encouraging, similar to experts’ classifications.

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References

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Author information

Authors and Affiliations

  1. Department of Informatics and Telecommunications, University of Athens, Panepistimiopolis, GR-15784, Athens, Greece

    Regina Stathacopoulou & Maria Grigoriadou

  2. Department of Energy Technology, Technological Education Institute of Athens, Ag. Spyridonos Str, GR 12210, Egaleo, Athens, Greece

    Maria Samarakou

  3. School of Computer Science and Information Systems, Birkbeck College, University of London, Malet Street, London, WC1E 7HX, United Kingdom

    George D. Magoulas

Authors
  1. Regina Stathacopoulou
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  2. Maria Grigoriadou
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  3. Maria Samarakou
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  4. George D. Magoulas
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Editor information

Editors and Affiliations

  1. Department of Computer Science, North Carolina State University Raleigh, NC 27695, USA

    James C. Lester

  2. Instituto de Informática, Universidade Federal do Rio Grande do Sul (UFRGS), Po. B 15.064, 91.501-970, Porto Alegre, RS, Brazil

    Rosa Maria Vicari

  3. Departamento de Tecnologia da Informação, Universidade Federal de Alagoas, Maceió - AL, Brazil

    Fábio Paraguaçu

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© 2004 Springer-Verlag Berlin Heidelberg

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Stathacopoulou, R., Grigoriadou, M., Samarakou, M., Magoulas, G.D. (2004). Using Simulated Students for Machine Learning. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds) Intelligent Tutoring Systems. ITS 2004. Lecture Notes in Computer Science, vol 3220. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30139-4_109

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  • DOI: https://doi.org/10.1007/978-3-540-30139-4_109

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22948-3

  • Online ISBN: 978-3-540-30139-4

  • eBook Packages: Springer Book Archive

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