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

Year 2019, , 1875 - 1892, 15.09.2019

Elif Kartal , Sezer Köse Biber Mahir Biber Melodi Özyaprak İrfan Şimşek Tuncer Can

https://doi.org/10.24106/kefdergi.2863

Cited By: 1

Abstract

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.

Keywords

Kolb öğrenme stilleri envanteri, makine öğrenmesi, öğrenme stilleri

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

Abstract

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.

Keywords

Kolb’s learning styles inventory, learning styles, machine learning techniques

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