Student Performance Analysis With Data Mining In Distance Education Synchronous, Asynchronous And Hybrid Courses In The Pandemic Process

Mehmet Yıldırım; Serdar Kırışoğlu

Makale detayları

Student Performance Analysis With Data Mining In Distance Education Synchronous, Asynchronous And Hybrid Courses In The Pandemic Process

Yazarlar:

Mehmet Yıldırım | Serdar Kırışoğlu

Yayıncı:

Duzce University

Ana Başlıklar:

Mühendislik

Dergi:

Düzce Üniversitesi Bilim ve Teknoloji Dergisi Vol.12 Issue 1, pp.89-111

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001310327

pISSN:

2148-2446

Anahtar kelimeler:

Özet:

 Today, the rate of university students access to education and training materials from the internet has increased considerably. There has been an increase in the data set that can be analyzed due to the use of the internet in education and the increase in access to course materials. One of these data sets planned or urgently is the homework, exam, project, performance, attendance grades and the like accumulated in the distance education systems of the universities that started the distance education process. In the new Corona Virus pandemic, with the recommendation of the Higher Education Council, universities continued their education by using Asynchronous, Synchronous and Hybrid methods and even had to take the exams in the distance education system. In this research, data obtained from the Distance Education Application and Research Center system of Kayseri University was used. There are 8319 processed data within the scope of the research. Using the automatic modeling feature of RapidMiner program, which is used in Data Mining field, on these data, predictions for the future were made with default algorithms. Deep Learning, Naive Bayes, Gradient Boosted Trees, Logistic Regression, which gave the best results among the default algorithms, were used and the k-NN algorithm, which is not included in this automatic modeling, was also included in the study. It has been tried to obtain better results by making changes on the parameters of these 5 algorithms. The model established with Logistic Regression gave the best estimation result according to student success. The effect of all the participation methods Synchronous, Asynchronous and Hybrid on student achievement was compared with the Confusion Matrix method and it was seen that the most reliable method was Hybrid. With this research, inferences were made about which of the methods of attending lectures in universities would be more reliable for students. Therefore, with the inferences made, it has been possible to predict which of the methods of participation in the course is more reliable in order to increase the level of student achievement for the next academic term.

 Today, the rate of university students access to education and training materials from the internet has increased considerably. There has been an increase in the data set that can be analyzed due to the use of the internet in education and the increase in access to course materials. One of these data sets planned or urgently is the homework, exam, project, performance, attendance grades and the like accumulated in the distance education systems of the universities that started the distance education process. In the new Corona Virus pandemic, with the recommendation of the Higher Education Council, universities continued their education by using Asynchronous, Synchronous and Hybrid methods and even had to take the exams in the distance education system. In this research, data obtained from the Distance Education Application and Research Center system of Kayseri University was used. There are 8319 processed data within the scope of the research. Using the automatic modeling feature of RapidMiner program, which is used in Data Mining field, on these data, predictions for the future were made with default algorithms. Deep Learning, Naive Bayes, Gradient Boosted Trees, Logistic Regression, which gave the best results among the default algorithms, were used and the k-NN algorithm, which is not included in this automatic modeling, was also included in the study. It has been tried to obtain better results by making changes on the parameters of these 5 algorithms. The model established with Logistic Regression gave the best estimation result according to student success. The effect of all the participation methods Synchronous, Asynchronous and Hybrid on student achievement was compared with the Confusion Matrix method and it was seen that the most reliable method was Hybrid. With this research, inferences were made about which of the methods of attending lectures in universities would be more reliable for students. Therefore, with the inferences made, it has been possible to predict which of the methods of participation in the course is more reliable in order to increase the level of student achievement for the next academic term.

 Today, the rate of university students access to education and training materials from the internet has increased considerably. There has been an increase in the data set that can be analyzed due to the use of the internet in education and the increase in access to course materials. One of these data sets planned or urgently is the homework, exam, project, performance, attendance grades and the like accumulated in the distance education systems of the universities that started the distance education process. In the new Corona Virus pandemic, with the recommendation of the Higher Education Council, universities continued their education by using Asynchronous, Synchronous and Hybrid methods and even had to take the exams in the distance education system. In this research, data obtained from the Distance Education Application and Research Center system of Kayseri University was used. There are 8319 processed data within the scope of the research. Using the automatic modeling feature of RapidMiner program, which is used in Data Mining field, on these data, predictions for the future were made with default algorithms. Deep Learning, Naive Bayes, Gradient Boosted Trees, Logistic Regression, which gave the best results among the default algorithms, were used and the k-NN algorithm, which is not included in this automatic modeling, was also included in the study. It has been tried to obtain better results by making changes on the parameters of these 5 algorithms. The model established with Logistic Regression gave the best estimation result according to student success. The effect of all the participation methods Synchronous, Asynchronous and Hybrid on student achievement was compared with the Confusion Matrix method and it was seen that the most reliable method was Hybrid. With this research, inferences were made about which of the methods of attending lectures in universities would be more reliable for students. Therefore, with the inferences made, it has been possible to predict which of the methods of participation in the course is more reliable in order to increase the level of student achievement for the next academic term.

 Günümüzde üniversite öğrencilerinin eğitime ve eğitim materyallerine internetten erişim oranları oldukça artmıştır. Eğitimde internetin kullanılması ve ders materyallerine erişimin artmasına bağlı olarak analiz edilebilecek veri setinde artış meydana gelmiştir. Bu veri setlerinden bir tanesi de planlı veya acilen uzaktan eğitim sürecine geçen üniversitelerin, uzaktan eğitim sistemlerinde biriken ödev, sınav, proje, performans, devam notları ve benzeridir. Yeni Korona Virüs Covid-19 pandemisinde Yüksek Öğretim Kurumunun YÖK tavsiyesi ile üniversiteler eğitimlerine uzaktan Asenkron, Senkron ve Hibrit yöntemlerini kullanarak devam etmiş, hatta sınavları uzaktan eğitim sisteminde yapmak zorunda kalmışlardır. Bu araştırmada, Kayseri Üniversitesinin Uzaktan Eğitim Uygulama ve Araştırma Merkezi KAYUZEM sisteminden alınmış veriler kullanılmıştır. Araştırma kapsamında 8319 işlenmiş veri bulunmaktadır. Bu veriler üzerinde Veri Madenciliği VM alanında kullanılan RapidMiner programının otomatik modelleme özelliği kullanılarak varsayılan algoritmalarla geleceğe yönelik tahminleme işlemi yapılmıştır. Varsayılan algoritmalar arasından en iyi sonucu veren Derin Öğrenme, Naive Bayes, Gradient Boosted Trees, Lojistik Regresyon kullanılmış ve bu otomatik modelleme de yer almayan k-En Yakın Komşu k-NN algoritması da çalışmaya dahil edilmiştir. Bu 5 algoritmanın parametreleri üzerinde değişiklikler yapılarak daha iyi sonuçlar elde edilmeye çalışılmıştır. Öğrenci başarısına göre en iyi tahminleme sonucunu, Lojistik Regresyon ile kurulan model vermiştir. Derse katılma yöntemlerinin tümünün Senkron, Asenkron ve Hibrit öğrenci başarısına etkisi Karışıklık Matrisi yöntemiyle karşılaştırılmıştır ve en güvenilir yöntemin Hibrit olduğu görülmüştür. Bu çalışma ile üniversitelerde derse katılma yöntemlerinden hangisinin, öğrenciler açısından daha güvenilir olacağına yönelik çıkarımlarda bulunulmuştur. Dolayısıyla yapılan çıkarımlar ile birlikte bir dahaki akademik dönem için öğrenci başarı düzeyinin artması yönünde, derse katılma yöntemlerinden hangisinin daha güvenilir olduğu konusunda tahminleme mümkün olmuştur.

Makale detayları

Student Performance Analysis With Data Mining In Distance Education Synchronous, Asynchronous And Hybrid Courses In The Pandemic Process

Yazarlar:

Mehmet Yıldırım | Serdar Kırışoğlu

Yayıncı:

Duzce University

Ana Başlıklar:

Mühendislik

Dergi:

Düzce Üniversitesi Bilim ve Teknoloji Dergisi Vol.12 Issue 1, pp.89-111

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001310327

pISSN:

2148-2446

Data mining

Distance education

Pandemic

Covid-19

RapidMinerVeri madenciliği

Uzaktan eğitim

Pandemi

Covid-19

RapidMiner

Sayı

Makale detayları

Yazarlar: Mehmet Yıldırım | Serdar Kırışoğlu

Yayıncı: Duzce University

Ana Başlıklar: Mühendislik

Dergi: Düzce Üniversitesi Bilim ve Teknoloji Dergisi Vol.12 Issue 1, pp.89-111

Belge Tipi: Makale

Yayın Tarihi: 1/2024

Belge Dili: Türkçe

Document ID (Org No.): 2001310327

pISSN: 2148-2446

Sayı

Yazarlar:

Mehmet Yıldırım | Serdar Kırışoğlu

Yayıncı:

Duzce University

Ana Başlıklar:

Mühendislik

Dergi:

Düzce Üniversitesi Bilim ve Teknoloji Dergisi Vol.12 Issue 1, pp.89-111

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001310327

pISSN:

2148-2446