Yükseköğrenime geçiş yapacak öğrencilerin mesleki geleceklerinin belirlenmesi için yapay öğrenme tabanlı bir sistemin geliştirilmesi
Küçük Resim Yok
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
İnönü Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Bu çalışmanın amacı, öğrencilerin ilgi, yetenek ve kişilik özellikleri ile öğrenim gördükleri bölümleri arasındaki ilişkiyi belirleyerek yükseköğrenime geçiş yapacak öğrencilerin mesleki geleceklerini tahmin edebilen yapay öğrenme tabanlı bir sistemin geliştirilmesidir. Çalışma, 2020 2021 eğitim öğretim yılı bahar döneminde Doğu Anadolu Bölgesinde bulunan bir üniversitenin Eğitim Fakültesinde öğrenim görmekte olan 410 öğrenci ile gerçekleşmiştir. Araştırma, tarama modeli esas alınarak yapılmıştır. Çalışmada, değer anketi, ilgi, yetenek ve kişilik envanteri ile araştırmacı tarafından geliştirilen kişisel bilgi formu veri toplama aracı olarak kullanılmıştır. Veriler, Google Form aracılığıyla toplanmış olup hem istatiksel hem de yapay öğrenme metotları aracılığıyla analiz edilmiştir. İstatistiksel yöntemler aracılığıyla elde edilen bulgular sonucunda çalışmaya katılan öğrencilerin öğrenim gördükleri bölüm ile değer türleri, ilgi türleri ve yetenek alanları arasında bir ilişkinin olduğu, fakülte bölümlerine göre kişilik özelliklerinin farklılaşmadığı tespit edilmiştir. Yapay öğrenme yöntemleri aracığıyla eğitilen modellerin performansları incelendiğinde karar ağacı modelinin %77, doğrusal regresyonun %48, lojistik regresyonun %40 doğruluk performansına sahip olduğu belirlenmiştir. Yapay sinir ağlarında eğitilen modele ilişkin R değeri 0,83 olarak tespit edilmiştir. Öğrencilerin mesleki geleceklerini belirleyebilmek için yapay öğrenme yöntemleri aracılığıyla web tabanlı bir sistem geliştirilmiştir. Veri setinde kategorik verilerin bulunması nedeniyle model lojistik regresyon aracılığıyla eğitilmiş, Python programlama dili ve PyCharm programı aracılığıyla öğrencilerin demografik ve psikolojik özelliklerine göre meslek alanını tahmin edebilen bir sistem oluşturulmuştur.
The aim of this study is to develop a machine learning-based system that can predict the vocational future of students who will transfer to higher education by determining the relationship between students' interests, abilities, personality traits, and departments. The study was carried out with 410 students studying at the Faculty of Education of a university in the Eastern Anatolia Region in the spring semester of the 2020-2021 academic year. The research was based on the survey model. The researcher used, value questionnaires, interest, talent, personality inventory, and personal information form developed by the researcher were used as data collection tools. The data was collected through Google Form and analyzed through both statistical and machine learning methods. As a result of the findings obtained through statistical methods, it has been determined that there is a relationship between the departments of the students participating in the study and the types of values, interests, and talents and that their personality traits don't differ according to the faculty departments. When the performances of the models trained through artificial learning methods were examined, it was determined that the decision tree model had an accuracy of 77%, linear regression of 48%, and logistic regression of 40%. The R-value for the model trained in artificial neural networks was determined as 0.83. To determine the student's vocational future a web-based system has been developed through machine learning methods. Due to the presence of categorical data in the data set, the model was trained through logistic regression and a system was created that can predict the occupational field according to the demographic and psychological characteristics of the students through the Python programming language and the PyCharm program.
The aim of this study is to develop a machine learning-based system that can predict the vocational future of students who will transfer to higher education by determining the relationship between students' interests, abilities, personality traits, and departments. The study was carried out with 410 students studying at the Faculty of Education of a university in the Eastern Anatolia Region in the spring semester of the 2020-2021 academic year. The research was based on the survey model. The researcher used, value questionnaires, interest, talent, personality inventory, and personal information form developed by the researcher were used as data collection tools. The data was collected through Google Form and analyzed through both statistical and machine learning methods. As a result of the findings obtained through statistical methods, it has been determined that there is a relationship between the departments of the students participating in the study and the types of values, interests, and talents and that their personality traits don't differ according to the faculty departments. When the performances of the models trained through artificial learning methods were examined, it was determined that the decision tree model had an accuracy of 77%, linear regression of 48%, and logistic regression of 40%. The R-value for the model trained in artificial neural networks was determined as 0.83. To determine the student's vocational future a web-based system has been developed through machine learning methods. Due to the presence of categorical data in the data set, the model was trained through logistic regression and a system was created that can predict the occupational field according to the demographic and psychological characteristics of the students through the Python programming language and the PyCharm program.
Açıklama
Anahtar Kelimeler
Eğitim ve Öğretim, Education and Training
