By this way, it is aimed to increase students success in education considerably.
📌 3 Key Takeaways
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Objective & Hypothesis:In this study, fuzzy logic based Gregorc learning system is proposed to characterize learning styles of the students who have various own learning skills, intelligence levels and learning styles.
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Key Findings & Data:Gregorc learning system helps student to notice their different ways of perceiving and ordering information.
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Conclusion & Impact:By this way, it is aimed to increase students success in education considerably.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Gregorc learning style · fuzzy logic
Abstract
In this study, fuzzy logic based Gregorc learning system is proposed to characterize learning styles of the students who have various own learning skills, intelligence levels and learning styles. Gregorc learning system helps student to notice their different ways of perceiving and ordering information. Such that student can learn by reasoning logically and intuitively; by seeing and hearing; by reflecting and acting or by analyzing and visualizing. The goal of this system is to categorize students learning style and to make instructor be able to match his teaching style with student's learning style. By this way, it is aimed to increase students success in education considerably.
Cınarlı, V., Mulayım, N., Ozdemır, A., Alaybeyoglu, A. (2015). FUZZY LOGIC BASED GREGORC LEARNING SYSTEM. The Eurasia Proceedings of Educational and Social Sciences, Volume: 2. https://ajindex.com/articles/1550480
Kazu, İ.Y., Özdemir, O. (2009). Öğrencilerin Bireysel Özelliklerinin Yapay Zeka ile Belirlenmesi (Bulanık Mantık Örneği). Akademik Bilişim, ss: 457-466.
Bloom, B.S.(1979). İnsan Nitelikleri ve Okulda Öğrenme (Çeviren: Özçelik, D.A.). Milli Eğitim Basımevi. Ankara.
Kazu İ., Kazu H., Özdemir O., (2005). The Effects of Mastery Learning Model on the Success of the Students Who Attended “Usage of Basic Information Technologies” Course. Journal of Educational Technology & Society. 8 (4): 233-243.
Sever, S. (1997). Türkçe Öğretimi ve Tam Öğrenme. Anı Yayıncılık. Ankara.
Kolb, D.A. (1999). The Kolb Learning Style İnventory. Hay Resources Direct, 6-25.
Mc Carthy, B. (1982). Improving Staff Development Through CBAM and 4MAT. Educational Leadership, 40(1): 20-25.
Akman, N. H. Muğan, C.Ş. (2004). “Muhasebe Eğitiminde Öğretim Ve Öğrenim Yöntemleri İle Ders Başarısı Arasındaki İlişki: Pilot Çalışma,” XXIII. Türkiye Muhasebe Eğitimi Sempozyumu, Mayıs 2004, Antalya, http://web.cortland.edu/andersmd/learning/Gregorc.htm Novák, V., Perfilieva, I. and Močkoř, J. (1999) Mathematical principles of fuzzy logic Dodrecht: Kluwer Academic. ISBN 0-7923-8595-0
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.
Kazu, İ.Y., Özdemir, O. (2009). Öğrencilerin Bireysel Özelliklerinin Yapay Zeka ile Belirlenmesi (Bulanık Mantık Örneği). Akademik Bilişim, ss: 457-466.
Bloom, B.S.(1979). İnsan Nitelikleri ve Okulda Öğrenme (Çeviren: Özçelik, D.A.). Milli Eğitim Basımevi. Ankara.
Kazu İ., Kazu H., Özdemir O., (2005). The Effects of Mastery Learning Model on the Success of the Students Who Attended “Usage of Basic Information Technologies” Course. Journal of Educational Technology & Society. 8 (4): 233-243.
Sever, S. (1997). Türkçe Öğretimi ve Tam Öğrenme. Anı Yayıncılık. Ankara.
Kolb, D.A. (1999). The Kolb Learning Style İnventory. Hay Resources Direct, 6-25.
Mc Carthy, B. (1982). Improving Staff Development Through CBAM and 4MAT. Educational Leadership, 40(1): 20-25.
Akman, N. H. Muğan, C.Ş. (2004). “Muhasebe Eğitiminde Öğretim Ve Öğrenim Yöntemleri İle Ders Başarısı Arasındaki İlişki: Pilot Çalışma,” XXIII. Türkiye Muhasebe Eğitimi Sempozyumu, Mayıs 2004, Antalya, http://web.cortland.edu/andersmd/learning/Gregorc.htm Novák, V., Perfilieva, I. and Močkoř, J. (1999) Mathematical principles of fuzzy logic Dodrecht: Kluwer Academic. ISBN 0-7923-8595-0
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.