A Neuro-fuzzy Approach in Student Modeling
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.
KeywordsFuzzy Logic Contact Force Student Knowledge Student Modeling Human Tutor
Unable to display preview. Download preview PDF.
- 1.Arons A. B. (1990) A guide to introductory physics teaching. Washington: John Wiley & Sons, Inc.Google Scholar
- 2.Brown J. S., Collins A., Duguid P. (1989) Situated cognition and the culture of learning. Educational researcher 18 32–34.Google Scholar
- 3.Driver R. (1983) The pupil as a scientist? Milton Keynes: Open University Press.Google Scholar
- 4.Grigoriadou M., Mitropoulos D., Samarakou M., Solomonidou C., Stavridou E. (1999). Methodology for the Design of Educational Software in Mathematics and Physics for Secondary Education. Computer Based Learning in Science, Conf. Proc. 1999 pB3.Google Scholar
- 6.Jameson A.,(1996) Numerical Uncertainty Management In User and Student Modeling: An Overview of Systems and Issues. User Modeling and User-Adapted Interaction, 5, p. 193–251.Google Scholar
- 8.Mitchell T. M. Machine learning (1997), McGraw-Hill, cp.4, pp. 81–117.Google Scholar
- 10.Vanlehn K., Niu Z. (2001). Bayesian student modeling, user interfaces and feedback: A sensitivity analysis. Inter. Journal of Artificial Intelligence in Education 12 154–184.Google Scholar
- 11.Vosniadou S. (1994) From cognitive theory to educational technology. In: Vosniadou S., De Corte E., Mandl H. (Ed), Technology-Based Learning Environments, Psychological and Educational Foundations. NATO ASI Series. F, vol. 137, 11–17. Berlin: Springer-Verlag.Google Scholar
- 12.Yasdi R.(2000). A Litetature Syrvey on Applications of Neural Networks for Human-Computer Interaction. Neural Computing & Applications, 9, p.245–258.Google Scholar
- 13.Zadeh L.A. (1989). Knowledge Representation in Fuzzy Logic. In Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems Advances in Fuzzy Systems-Application and Theory, Vol 6. Editors G.J. Klir & B. Yuan.Google Scholar