Makine Öğrenmesi Tekniklerini ve Kolb Öğrenme Stilleri Envanterini Kullanarak Öğrencilerin Öğrenme Stillerinin Belirlenmesi için Bir Model Önerisi

Öğrenme stillerini önceden belirlemek, öğrenme ortamının tasarımında, öğretim üyesinin ders içeriğini hazırlamasında ve özellikle öğrencinin öğrenme sürecinde önemli bir rol oynamaktadır. Kolb Öğrenme Stilleri Envanteri (KÖSE), öğrenme stillerini belirlemede en yaygın kullanılan araçlardan birisidir; ancak diğer araştırmalar, ölçekler veya psikolojik testlerde olduğu gibi KÖSE’nin de uygulama ve değerlendirme aşamalarında, soruların yanlış anlaşılması veya boş geçilmesi gibi bazı problemlerle karşılaşılabilir. Bu çalışmada; makine öğrenmesi teknikleri ve KÖSE Versiyon III (KÖSE-III) kullanılarak öğrencilerin öğrenme stillerini belirlemeye yönelik bir model önerisi geliştirmek ve bu modeli temel alan, web ve mobilden erişilebilen bir uygulama geliştirmek amaçlanmaktadır. Bu amaçla, KÖSE-III’te verilen durumlara yönelik Kolb’un orijinal değerlendirme yönteminden farklı olarak öğrencilerden kendilerine en uygun gelen seçeneği seçmeleri istenmiş ve öğrencilerin yaş ve cinsiyet bilgileri de alınarak araştırmanın veri seti oluşturulmuştur. Makine öğrenmesi tekniklerinden k-En Yakın Komşu Algoritması, C4.5 Karar Ağacı Algoritması ve Naive Bayes Sınıflandırıcısı kullanılarak en iyi performansı gösteren model seçilmiştir. Araştırma kapsamında geliştirilen uygulama e-öğrenme sistemlerine kolaylıkla entegre edilebileceğinden; öğreticilerin, öğrencilerin öğrenme stillerini belirleme süreçlerini kolaylaştırması, buna bağlı olarak eğitim etkinliklerinin öğrenci merkezli tasarlanmasına imkân tanıması ve daha çok öğrenciye ulaşılan bilimsel çalışmaların yapılabilmesi açısından bu çalışmanın önemli olduğu düşünülmektedir.

A Model Proposal to Determine Learning Styles of Students by Using Machine Learning Techniques and Kolb Learning Styles Inventory

Determining the learning styles in advance plays an important role in the design of the learning environment, in the preparation of the instructor’s course content, and in the learning process of the learner in particular. Kolb’s Learning Style Inventory (KLSI) is one of the most widely used tools to determine learning styles. However, some problems such as misunderstood or unanswered questions can be encountered in application and evaluation stages of the KLSI as in the other questionnaires, scales or psychological tests. The aim of this study is to develop a model proposal for determining learning styles of students by using machine learning techniques and KLSI Version III (KLSI-III) and based on this model to develop an application that can be accessible both online and on mobile devices. For this purpose, data set of this research was created by adding the age and gender attributes to the answers given as the most appropriate option to KLSI-III (unlike Kolb’s original evaluation method). Machine learning techniques such as k-Nearest Neighbor Algorithm, C4.5 Decision Tree Algorithm and Naive Bayes Classifier were applied to this data set and the model with the highest performance has been selected out of this data set. As the application developed within the scope of this study can be easily integrated into e-learning systems; it is thought that it is important for the teachers to facilitate the process of determining the learning styles of the students and accordingly to enable the student-centered design of the training activities and the scientific studies reaching more students.

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Kastamonu Eğitim Dergisi-Cover
  • ISSN: 1300-8811
  • Yayın Aralığı: Yılda 4 Sayı
  • Başlangıç: 1992
  • Yayıncı: -
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