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Granular Mining of Student’s Learning Behavior in Learning Management System Using Rough Set Technique

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Computational Intelligence for Technology Enhanced Learning

Part of the book series: Studies in Computational Intelligence ((SCI,volume 273))

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Abstract

Pattern multiplicity of user interaction in learning management system can be intelligently examined to diagnose students’ learning style. Such patterns include the way the user navigate, the choice of the link provided in the system, the preferences of type of learning material, and the usage of the tool provided in the system. In this study, we propose mapping development of student characteristics into Integrated Felder Silverman (IFS) learning style dimensions. Four learning dimensions in Felder Silverman model are incorporated to map the student characteristics into sixteen learning styles. Subsequently, by employing rough set technique, twenty attributes have been selected for mapping principle. However, rough set generates a large number of rules that might have redundancy and irrelevant. Hence, in this study, we assess and mining the most significant IFS rules for user behavior by filtering these irrelevant rules. The assessments of the rules are executed by evaluating the rules support, the rules length and the accuracy. The irrelevant rules are further filtered by measuring different rules support, rules length and rules accuracy. It is scrutinized that the rules with the length in between [4,8], and the rules support in the range of [6,43] succumb the highest accuracy with 96.62%.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Nor Bahiah Hj Ahmad & Siti Mariyam Shamsuddin

  2. Centre for Quantifiable Quality of Service in Communication Systems, Norwegian University of Science and Technology, Trondheim, Norway

    Ajith Abraham

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  1. Nor Bahiah Hj Ahmad
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  2. Siti Mariyam Shamsuddin
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  3. Ajith Abraham
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Editor information

Editors and Affiliations

  1. Department of Languages and Informatics Systems, Polytechnic University of Catalonia, Campus Nord, Ed.Omega, C/Jordi Girona 1-3, 08034, Barcelona, Spain

    Fatos Xhafa

  2. Department of Computer Sciences Multimedia and Telecommunications, Open University of Catalonia, Rbla. Poblenou. 156, 08018, Barcelona, Spain

    Santi Caballé  & Thanasis Daradoumis  & 

  3. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071-2259, Auburn, Washington, USA

    Ajith Abraham

  4. Department of Information Sciences, Open University of Catalonia, Av. Tibidabo 39-43, 08035, Barcelona, Spain

    Angel Alejandro Juan Perez

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Ahmad, N.B.H., Shamsuddin, S.M., Abraham, A. (2010). Granular Mining of Student’s Learning Behavior in Learning Management System Using Rough Set Technique. In: Xhafa, F., Caballé, S., Abraham, A., Daradoumis, T., Juan Perez, A.A. (eds) Computational Intelligence for Technology Enhanced Learning. Studies in Computational Intelligence, vol 273. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11224-9_5

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  • DOI: https://doi.org/10.1007/978-3-642-11224-9_5

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