A LEARNING STYLE INFERENCE SYSTEM BASED ON FUZZY LOGIC TECHNIQUE

Yıl 2016, Cilt: 4 , 590 - 597, 01.09.2016

Muhammet Uysal Kadriye Filiz Balbal Naciye Mulayım Ali Özdemir Aysegul Alaybeyoglu

Öz

There
are many reasons why education is very important in daily life and business.
Such that learning will continue every time parallel to the rapid developments
and changes in innovation and the technology. In this study, fuzzy logic based
dunn learning style inference system is developed to measure student’s success
in learning. Dunn learning style identifies five important factors on which
student learning style differs; namely environmental, emotional, sociological,
physiological, and psychological. In this study, a software system is developed and an interface which includes some
questions in relation with Dunn learning style is designed.  Answers of the students are rated and given
as an input to the proposed fuzzy logic engine. The proposed software
system inferences Education Style, Learning Status and the Level of Learning
Style of the student. By this way, the instructor will be able to match
his teaching style with student's learning style which contributes to student’s
success in education field.

Anahtar Kelimeler

Dunn learning style, fuzzy logic, inference system

Kaynakça

• https://essays.pw/essay/understanding-of-learning-styles-is-useful-education-essay-105446 Kazu, İ.Y. , Özdemir, O. (2009). Öğrencilerin Bireysel Özelliklerinin Yapay Zeka ile Belirlenmesi (Bulanık Mantık Örneği). Akademik Bilişim, ss: 457-466. Voskoglou, M.G. Fuzzy logic and uncertainty in mathematics education. Int. J. Appl. Fuzzy Sets Artif. Intell. 2011, 1, 45–64. Voskoglou, M.G. Stochastic and Fuzzy Models in Mathematics Education, Artificial Intelligence and Management; Lambert Academic Publishing: Saarbrucken, Germany, 2011 Chahid Fourali (1997) Using Fuzzy Logic in Educational Measurement: The Case of Portfolio Assessment, Evaluation & Research in Education, 11:3, 129-148, James R. Nolan, “A Prototype Application of Fuzzy Logic and Expert Systems in Education Assessment ”, AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative Applications of Artificial Intelligence, pp: 1134-1139 http://wps.prenhall.com/wps/media/objects/863/884633/Volume_medialib/dunn.pdf Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353