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A Neuro-fuzzy Approach in Student Modeling

  • Regina Stathacopoulou
  • Maria Grigoriadou
  • George D. Magoulas
  • Denis Mitropoulos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2702)

Abstract

In this paper, a neural network-based fuzzy modeling approach to assess student knowledge is presented. Fuzzy logic is used to handle the subjective judgments of human tutors with respect to student observable behavior and their characterizations of student knowledge. Student knowledge is decomposed into pieces and assessed by combining fuzzy evidences, each one contributing to some degree to the final assessment. The neuro-fuzzy synergism helps to represent teacher experience in an interpretable way, and allows capturing teacher subjectivity. The proposed approach was used to assess knowledge and misconceptions of simulated students interacting with the exploratory learning environment “Vectors in Physics and Mathematics”, which is used by high school pupils to learn about vectors. In our experiments, this approach provided significant improvement in student diagnosis compared with previous attempts.

Keywords

Fuzzy Logic Contact Force Student Knowledge Student Modeling Human Tutor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Regina Stathacopoulou
    • 1
  • Maria Grigoriadou
    • 1
  • George D. Magoulas
    • 2
  • Denis Mitropoulos
    • 1
  1. 1.Department of Informatics and TelecommunicationsUniversity of AthensAthensGreece
  2. 2.Department of Information Systems and ComputingBrunel UniversityUxbridgeUK

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