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Predicting Learning Styles in a Conversational Intelligent Tutoring System

  • Conference paper
Advances in Web-Based Learning – ICWL 2010 (ICWL 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6483))

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Abstract

This paper presents Oscar, a conversational intelligent tutoring system (CITS) which dynamically predicts and adapts to a student’s learning style throughout the tutoring conversation. Oscar aims to mimic a human tutor to improve the effectiveness of the learning experience by leading a natural language tutorial and modifying the tutoring style to suit an individual’s learning style. Intelligent solution analysis and support have been incorporated to help students establish a deeper understanding of the topic and boost confidence. Oscar CITS with its natural dialogue interface and classroom tutorial style is more intuitive to learners than learning systems designed specifically to capture learning styles. An initial study is reported which produced encouraging results in predicting several learning styles and positive test score improvements in all students across the sample.

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Author information

Authors and Affiliations

  1. Intelligent Systems Group, Department of Computing & Mathematics, Manchester Metropolitan University, Manchester, M1 5GD, UK

    Annabel Latham, Keeley Crockett & David McLean

  2. Centre for Policy Modelling, Manchester Metropolitan University, Aytoun Building, Aytoun Street, Manchester, M1 3GH, UK

    Bruce Edmonds

Authors
  1. Annabel Latham
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  2. Keeley Crockett
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  3. David McLean
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  4. Bruce Edmonds
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Editor information

Editors and Affiliations

  1. Shanghai University, Xingjian Building, No. 149 Yanchang Road, 200072, China

    Xiangfeng Luo

  2. Department D5: Databases and Information Systems, MaxPlanck Institute for Informatics, Campus E1 4, 66123, Saarbrücken, Germany

    Marc Spaniol

  3. Pervasive Technology Institute, Indiana University, 2719 East 10th Street, 47408, Bloomington, IN, USA

    Lizhe Wang

  4. Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong SAR

    Qing Li

  5. L3S Research Center, Leibniz University Hannover, Appelstr. 4, 30167, Hannover, Germany

    Wolfgang Nejdl

  6. School of Computer Engineering and Science, Shanghai University, Xingjian Building, No. 149 Yanchang Road, 200072, Shanghai, China

    Wu Zhang

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© 2010 Springer-Verlag Berlin Heidelberg

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Latham, A., Crockett, K., McLean, D., Edmonds, B. (2010). Predicting Learning Styles in a Conversational Intelligent Tutoring System. In: Luo, X., Spaniol, M., Wang, L., Li, Q., Nejdl, W., Zhang, W. (eds) Advances in Web-Based Learning – ICWL 2010. ICWL 2010. Lecture Notes in Computer Science, vol 6483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17407-0_14

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  • DOI: https://doi.org/10.1007/978-3-642-17407-0_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17406-3

  • Online ISBN: 978-3-642-17407-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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