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On Modeling the Affective Effect on Learning

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Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7080))

Abstract

This paper presents an approach to modeling the affective state of a student. The model represents the learning process used by the student including the affective state. The student modeling is built upon an ontology of machine learning strategies. This paper describes how the ontology is extended to include affective knowledge. The recommended learning strategies for a situation are ordered based on the affective state of the learner.

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Author information

Authors and Affiliations

  1. Department of Computer Technology and Applications, Coimbatore Institute of Technology, Coimbatore, India

    Arunkumar Balakrishnan

Authors
  1. Arunkumar Balakrishnan
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Editor information

Editors and Affiliations

  1. Faculty of Informatics, Mahasarakham University, Khamreing, 44150, Kantarawichai, Mahasarakham, Thailand

    Chattrakul Sombattheera

  2. Department of Computers and Information Science, University of Hyderabad, 500046, Hyderabad, Andhra Pradesh, India

    Arun Agarwal

  3. Department of Computer and Information Sciences, University of Hyderabad, 500046, Hyderabad, India

    Siba K Udgata

  4. School of Information Technology, King Mongkut’s University of Technology, Thonburi, Bangkok, Thailand

    Kittichai Lavangnananda

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© 2011 Springer-Verlag Berlin Heidelberg

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Balakrishnan, A. (2011). On Modeling the Affective Effect on Learning. In: Sombattheera, C., Agarwal, A., Udgata, S.K., Lavangnananda, K. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2011. Lecture Notes in Computer Science(), vol 7080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25725-4_20

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  • DOI: https://doi.org/10.1007/978-3-642-25725-4_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25724-7

  • Online ISBN: 978-3-642-25725-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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