Fuzzy Logic Based Gregorc Learning Style Model Inference and Statistical Evaluation

Ali Özdemir; Vildan Çınarlı Ergene

doi:https://doi.org/10.14445/22315373/IJMTT-V66I7P505

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 66 | Issue 7 | Year 2020 | Article Id. IJMTT-V66I7P505 | DOI : https://doi.org/10.14445/22315373/IJMTT-V66I7P505

Fuzzy Logic Based Gregorc Learning Style Model Inference and Statistical Evaluation

Ali Özdemir, Vildan Çınarlı Ergene

Citation :

Ali Özdemir, Vildan Çınarlı Ergene, "Fuzzy Logic Based Gregorc Learning Style Model Inference and Statistical Evaluation," International Journal of Mathematics Trends and Technology (IJMTT), vol. 66, no. 7, pp. 33-39, 2020. Crossref, https://doi.org/10.14445/22315373/IJMTT-V66I7P505

Abstract

Fuzzy logic-based Gregorc learning style model was developed to determine the learning styles of the students. In the Gregorc learning style model, a four-input and one-out Mamdani-type fuzzy logic algorithm is used to find out which intelligence type belongs to the student in Gregorc learning style. This system is modeled using the fuzzy logic method in Matlab computer software environment and C # programming language is used in the development phase of the model. The research was carried out with 151 students. Gregorc Learning style questionnaire was used to collect the information. The Kolmogorov-Smirnov test was used to determine whether the quantitative variables in the data obtained were suitable for normal distribution. The groups were compared with Mann Whitney U or Kruskal Wallis H test because they did not conform to normal distribution in terms of quantitative variables. Students' learning styles and ages, learning styles and departments, learning styles and grade levels, learning styles and differences between high school types were determined.

Keywords

Fuzzy Logic, Gregorc Learning Style, Statistics

References

[1] Anand, J. and Dilip Kumar S.M., Responsive Web Design for Distributed Resource Sharing and E-Learning among Universities using Fixed and Mobile Devices SSRG International Journal of Mobile Computing & Application (SSRG – IJMCA) – Volume 3 Issue 3 Sep to Dec. page 18-26. 2016.
[2] Cınarlı, V., Mulayım, N., Ozdemır, A. and Alaybeyoglu, A, Fuzzy Logic Based Gregorc Learning, System International Conference On Education in Mathematics, Science & Technology (Icemst), April 23 - 26, Antalya, Turkey,2015.
[3] Elmas C, Fuzzy Logic Controllers, Seçkin Publishing, Ankara, 230 s. 2003.
[4] Gunal, U, Fuzzy Logic, Automation Journal, No.55,56, s.50-55, 1997.
[5] Karasakal, O., Online Rule Weighting Methods for Fuzzy PID Controllers, thesis, Istanbul Technical University, İstanbul, 130 s. 2012.
[6] Kazu Y, and Ozdemir O, Determining the Individual Characteristics of Students with Artificial Intelligence, Academic Informatics ’09 - XI. Academic Informatics Conference Proceedings Harran University, Şanlıurfa, 2009.
[7] Klir, G. J. and Yuan, Fuzzy Sets and Fuzzy Logic. Theory and Applications, Prentice Hall. 1995.
[8] Sahin, H. and Ekici, G., Investigation of Prospective Teachers' Learning Styles with Gregorc Learning Style Model, Gazi University, Ministry of National Education,190-191,2012.
[9] Zadeh, L. A., Fuzzy sets, Information and Control, 8, 338–353,1965.