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Data-driven fuzzy rule generation and its application for student academic performance evaluation

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Abstract

Several approaches using fuzzy techniques have been proposed to provide a practical method for evaluating student academic performance. However, these approaches are largely based on expert opinions and are difficult to explore and utilize valuable information embedded in collected data. This paper proposes a new method for evaluating student academic performance based on data-driven fuzzy rule induction. A suitable fuzzy inference mechanism and associated Rule Induction Algorithm is given. The new method has been applied to perform Criterion-Referenced Evaluation (CRE) and comparisons are made with typical existing methods, revealing significant advantages of the present work. The new method has also been applied to perform Norm-Referenced Evaluation (NRE), demonstrating its potential as an extended method of evaluation that can produce new and informative scores based on information gathered from data.

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Authors and Affiliations

  1. Department of Computer Science, University of Wales, Aberystwyth, SY23 3DB, United Kingdom

    Khairul A. Rasmani & Qiang Shen

Authors
  1. Khairul A. Rasmani
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  2. Qiang Shen
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Correspondence to Qiang Shen.

Additional information

Khairul Rasmani is a lecturer at the Faculty of Information Technology and Quantitative Sciences, Universiti Teknologi MARA, Malaysia. He received his Masters Degree in Mathematical Education from University of Leeds, UK in 1997 and his Ph.D. degree from University of Wales, Aberystwyth, UK in December 2005. His research interests include fuzzy approximate reasoning, fuzzy rule-based systems and fuzzy classification systems.

Qiang Shen is a Professor and the Director of Research with the Department of Computer Science at the University of Wales, Aberystwyth, UK. He is also an Honorary Fellow at the University of Edinburgh, UK. His research interests include fuzzy systems, knowledge modelling, qualitative reasoning, and pattern recognition. Prof. Shen serves as an associate editor or editorial board member of a number of world leading journals, including the IEEE Transactions on Systems, Man, and Cybernetics (Part B), the IEEE Transactions on Fuzzy Systems, and Fuzzy Sets and Systems. He has acted as a Chair or Co-chair at a good number of major conferences in the field of Computational Intelligence. He has published a book and over 170 peer-refereed articles in international journals and conferences in Artificial Intelligence and related areas.

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Rasmani, K.A., Shen, Q. Data-driven fuzzy rule generation and its application for student academic performance evaluation. Appl Intell 25, 305–319 (2006). https://doi.org/10.1007/s10489-006-0109-9

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