Investigating Factors Affecting Success in University Placement Using Artificial Intelligence Methods

Ayhan Doğan; Cihan Ünal

Article Details

Investigating Factors Affecting Success in University Placement Using Artificial Intelligence Methods

Authors:

Ayhan Doğan | Cihan Ünal

Publisher:

Gazi University

Main Topics:

Eğitim Teknolojisi ve Bilgi İşlem

Journal:

Türk Eğitim Bilimleri Dergisi Vol.22 Issue 3, pp.1678-1698

Document Type:

Article

Publication Date:

12/2024

Document Language:

Turkish

Document ID (Org No.):

2001331295

pISSN:

2459-1912

Keywords:

Abstract:

 There are several factors affecting students success. This study investigates the factors that affect university placement success. A forty-question questionnaire, prepared with the opinions of experts, was applied to university students from various fields. The data obtained from the questionnaire was analyzed using artificial intelligence methods to predict the most important factors affecting success. The study evaluated the success of the prediction models with performance measurement metrics using four different machine learning methods. It is important to note that the most successful method varied depending on the performance metrics used for evaluation. The Random Forest method had the best results with 4.95 MSE and 2.22 RMSE values, followed by the Extreme Gradient Boosting method with a 1.60 MAE value and the Linear Regression method with a 0.36 MAPE value. Based on all metrics, the success rate of the Support Vector Machines method was relatively lower than that of other methods. The study suggests that by considering the factors affecting university placement success in order of importance, it can be possible to increase the students success. Therefore, educators, families, counselors, and students can make improvements, conduct studies in the relevant areas, and take necessary measures to account for this order of importance.

 There are several factors affecting students success. This study investigates the factors that affect university placement success. A forty-question questionnaire, prepared with the opinions of experts, was applied to university students from various fields. The data obtained from the questionnaire was analyzed using artificial intelligence methods to predict the most important factors affecting success. The study evaluated the success of the prediction models with performance measurement metrics using four different machine learning methods. It is important to note that the most successful method varied depending on the performance metrics used for evaluation. The Random Forest method had the best results with 4.95 MSE and 2.22 RMSE values, followed by the Extreme Gradient Boosting method with a 1.60 MAE value and the Linear Regression method with a 0.36 MAPE value. Based on all metrics, the success rate of the Support Vector Machines method was relatively lower than that of other methods. The study suggests that by considering the factors affecting university placement success in order of importance, it can be possible to increase the students success. Therefore, educators, families, counselors, and students can make improvements, conduct studies in the relevant areas, and take necessary measures to account for this order of importance.

 There are several factors affecting students success. This study investigates the factors that affect university placement success. A forty-question questionnaire, prepared with the opinions of experts, was applied to university students from various fields. The data obtained from the questionnaire was analyzed using artificial intelligence methods to predict the most important factors affecting success. The study evaluated the success of the prediction models with performance measurement metrics using four different machine learning methods. It is important to note that the most successful method varied depending on the performance metrics used for evaluation. The Random Forest method had the best results with 4.95 MSE and 2.22 RMSE values, followed by the Extreme Gradient Boosting method with a 1.60 MAE value and the Linear Regression method with a 0.36 MAPE value. Based on all metrics, the success rate of the Support Vector Machines method was relatively lower than that of other methods. The study suggests that by considering the factors affecting university placement success in order of importance, it can be possible to increase the students success. Therefore, educators, families, counselors, and students can make improvements, conduct studies in the relevant areas, and take necessary measures to account for this order of importance.

 Öğrencilerin akademik başarılarını etkilediği düşünülen birçok faktör bulunmaktadır. Bu çalışmada özellikle üniversite yerleşme başarısını etkileyen faktörler araştırılmıştır. Uzmanların görüşleri alınarak kırk soruluk bir anket hazırlanmış ve çeşitli alanlarda eğitim görmekte olan üniversite öğrencilerine uygulanmıştır. Anketten elde edilen veriler kullanılarak başarıyı en çok etkileyen faktörler önem sırasına göre yapay zekâ yöntemleriyle tahmin edilmiştir. Bu tahminlerin başarıları performans ölçüm metrikleriyle değerlendirilmiştir. Çalışmada dört farklı makine öğrenimi kullanılmıştır. Tahminlere ait performans metriklerine göre en başarılı yöntemler değişmektedir. Metrikler incelendiğinde en iyi sonuçlar, 4,95 MSE ve 2,22 RMSE değerleriyle Rastgele Orman yöntemi, 1,60 MAE değeriyle Aşırı Gradyan Yükseltme yöntemi, 0,36 MAPE değeriyle Lineer Regresyon yöntemidir. Destek Vektör Makineleri yönteminin başarısı ise tüm metriklere göre diğer yöntemlerden görece daha düşüktür. Çalışma sonucunda önem sırasına göre üniversite yerleşme başarısına etki eden faktörler dikkate alınarak öğrencilerin başarısını artırmanın mümkün olabileceği düşünülmektedir. Böylece eğitimciler, aileler, danışmanlar ve öğrenciler bu önem sırasını dikkate alacak şekilde mümkün olan alanlarda iyileştirmeler ve çalışmalar yapabilir, gerekli önlemleri alabilirler.

Article Details

Investigating Factors Affecting Success in University Placement Using Artificial Intelligence Methods

Authors:

Ayhan Doğan | Cihan Ünal

Publisher:

Gazi University

Main Topics:

Eğitim Teknolojisi ve Bilgi İşlem

Journal:

Türk Eğitim Bilimleri Dergisi Vol.22 Issue 3, pp.1678-1698

Document Type:

Article

Publication Date:

12/2024

Document Language:

Turkish

Document ID (Org No.):

2001331295

pISSN:

2459-1912

Machine learning

Artificial intelligence

University placement success

Factors affecting successMakine öğrenimi

Yapay zekâ

Üniversite yerleşme başarısı

Başarıyı etkileyen faktörler

Issues

Article Details

Authors: Ayhan Doğan | Cihan Ünal

Publisher: Gazi University

Main Topics: Eğitim Teknolojisi ve Bilgi İşlem

Journal: Türk Eğitim Bilimleri Dergisi Vol.22 Issue 3, pp.1678-1698

Document Type: Article

Publication Date: 12/2024

Document Language: Turkish

Document ID (Org No.): 2001331295

pISSN: 2459-1912

Issues

Authors:

Ayhan Doğan | Cihan Ünal

Publisher:

Gazi University

Main Topics:

Eğitim Teknolojisi ve Bilgi İşlem

Journal:

Türk Eğitim Bilimleri Dergisi Vol.22 Issue 3, pp.1678-1698

Document Type:

Article

Publication Date:

12/2024

Document Language:

Turkish

Document ID (Org No.):

2001331295

pISSN:

2459-1912