Dergiler / The Eurasia Proceedings of Educational and Social Sciences / 2026 / Volume: 4

A LEARNING STYLE INFERENCE SYSTEM BASED ON FUZZY LOGIC TECHNIQUE

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Yayın Tarihi
2016-09-01
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:There are many reasons why education is very important in daily life and business.
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Temel Bulgular & Veri: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.
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Sonuç & Katkı: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.
🔬Tür:Research Article
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Dunn learning style · fuzzy logic · inference system

Abstract

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.

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Bu Makaleye Atıf Yap

Uysal, M., Balbal, K. F., Mulayım, N., Özdemir, A., Alaybeyoglu, A. (2016). A LEARNING STYLE INFERENCE SYSTEM BASED ON FUZZY LOGIC TECHNIQUE. The Eurasia Proceedings of Educational and Social Sciences, Volume: 4. https://ajindex.com/articles/1550429

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Kaynakça (11)

  1. https://essays.pw/essay/understanding-of-learning-styles-is-useful-education-essay-105446
  2. Kazu, İ.Y., Özdemir, O. (2009). Öğrencilerin Bireysel Özelliklerinin Yapay Zeka ile Belirlenmesi (Bulanık Mantık Örneği). Akademik Bilişim, ss: 457-466.
  3. Voskoglou, M.G. Fuzzy logic and uncertainty in mathematics education. Int. J. Appl. Fuzzy Sets Artif. Intell. 2011, 1, 45–64.
  4. Voskoglou, M.G. Stochastic and Fuzzy Models in Mathematics Education, Artificial Intelligence and Management; Lambert Academic Publishing: Saarbrucken, Germany, 2011
  5. 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
  6. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353 https://essays.pw/essay/understanding-of-learning-styles-is-useful-education-essay-105446
  7. Kazu, İ.Y., Özdemir, O. (2009). Öğrencilerin Bireysel Özelliklerinin Yapay Zeka ile Belirlenmesi (Bulanık Mantık Örneği). Akademik Bilişim, ss: 457-466.
  8. Voskoglou, M.G. Fuzzy logic and uncertainty in mathematics education. Int. J. Appl. Fuzzy Sets Artif. Intell. 2011, 1, 45–64.
  9. Voskoglou, M.G. Stochastic and Fuzzy Models in Mathematics Education, Artificial Intelligence and Management; Lambert Academic Publishing: Saarbrucken, Germany, 2011
  10. 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
  11. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353