Student Consultancy Service: Prediction of Course Grades in Course Selection Phases Using Artificial Intelligence Techniques

Sümeyye KAYNAK; Baran KAYNAK; Hayrettin EVİRGEN

doi:https://doi.org/10.18009/jcer.421123

Makale detayları

Student Consultancy Service: Prediction of Course Grades in Course Selection Phases Using Artificial Intelligence Techniques

Yazarlar:

Sümeyye KAYNAK | Baran KAYNAK | Hayrettin EVİRGEN

Yayıncı:

Tamer KUTLUCA

Ana Başlıklar:

Sosyoloji & antropoloji

Dergi:

Journal of Computer and Education Research 2018 Vol.6 Issue 12, pp.142-162

Belge Tipi:

Makale

Yayın Tarihi:

12/2018

Belge Dili:

İngilizce

Document ID (Org No.):

2000495714

Sıklık:

İki Yıllık

eISSN:

2148-2896

DOI:

https://doi.org/10.18009/jcer.421123

Özet:

 


Universities offer
technical elective courses to allow students to improve themselves in various
parts of their majors. Each semester, the students make a decision regarding
these technical electives, and the most common expectations students have in
this context include, getting education at a better school, getting a better
job, and getting higher grades with a view to securing admission into more
advanced degree programs. Electing a course on the basis of the interests and
skills of the student will naturally translate into achievement. Advisors, in
this context, play a major role. Yet, the substantial workload advisors have
already assumed prevent them dedicating enough time for exploring the interests
and skills of the students, and hence hinder the development of the required
relationship between students and their advisors. This study attempts
to estimate the achievement level a student intends to elect, on the basis of
graduate data received from the database of students of Sakarya University,
Faculty of Computer and Information Sciences, and led to the development of a
decision-support system. The application used ANFIS and artificial neural
network methods among the artificial intelligence techniques, alongside the
linear regression model as the mathematical model, whereupon the performance of
the methods were compared over the application. In conclusion, it was observed
that artificial intelligence techniques provided more relevant results compared
to mathematical models, and that, among the artificial intelligence techniques
feed forward backpropagation neural network model offered a lower standard
deviation compared to ANFIS model.


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Makale detayları

Student Consultancy Service: Prediction of Course Grades in Course Selection Phases Using Artificial Intelligence Techniques

Yazarlar: Sümeyye KAYNAK | Baran KAYNAK | Hayrettin EVİRGEN

Yayıncı: Tamer KUTLUCA

Ana Başlıklar: Sosyoloji & antropoloji

Dergi: Journal of Computer and Education Research 2018 Vol.6 Issue 12, pp.142-162

Belge Tipi: Makale

Yayın Tarihi: 12/2018

Belge Dili: İngilizce

Document ID (Org No.): 2000495714

Sıklık: İki Yıllık

eISSN: 2148-2896

DOI: https://doi.org/10.18009/jcer.421123

Sayı

Yazarlar:

Sümeyye KAYNAK | Baran KAYNAK | Hayrettin EVİRGEN

Yayıncı:

Tamer KUTLUCA

Ana Başlıklar:

Sosyoloji & antropoloji

Dergi:

Journal of Computer and Education Research 2018 Vol.6 Issue 12, pp.142-162

Belge Tipi:

Makale

Yayın Tarihi:

12/2018

Belge Dili:

İngilizce

Document ID (Org No.):

2000495714

Sıklık:

İki Yıllık

eISSN:

2148-2896

DOI:

https://doi.org/10.18009/jcer.421123