A matching model to measure compliance between department and student
Abstract
The aim of all education systems is to train students who are equipped with knowledge. In that case, that student is able to determine the most suitable profession for him/her success in education and career that are related to this profession will be higher. Studies done up to this day have been focused on finding out the factors affecting the career choice of the student, but they have not suggested any method for determining the most suitable procession. It is not possible to obtain satisfying results from a system that does not lead students to appropriate higher education departments. In this context, a student- department matching system is proposed which aims to increase the success of the education systems in our study. The department of computer engineering was dealt with as a sample department and the proposed study was examined to determine whether a student was suitable for computer engineering or. The required data was obtained with the help of the questionnaire, and then a model of successful and unsuccessful students was created. Data mining algorithms such as C4.5, C SVC, MLP, and Naïve Bayes are used during the test of the generated model. The best result was obtained by the C-SVC algorithm and the second best result by Naive Bayes. The lowest error rate achieved was 0.2700 and the highest accurate recognition rate was 73.00%.
Keywords
References
- [1] Çelik N, Üzmez U., Evaluation of university students’ affecting factors choice of profession. Electronic Journal of Occupational Improvement and Research (EJOIR), 2(1) (2014) 94-105.
- [2] Sarıkaya T., Khorshid L., Üniversite öğrencilerinin meslek seçimini etkileyen etmenlerin incelenmesi: üniversite öğrencilerinin meslek seçimi. Journal of Turkish Educational Sciences, 7(2) (2009) 393-423.
- [3] Sathapornvajana S., Watanapa B., Factors affecting student’s intention to choose IT program. Procedia Computer Science, 13, (2012), 60-67.
- [4] Çoban, A., Psikometrik testler: Available at: http://www.adnancoban.com.tr/psikometrik_testler.html. Retrieved: July 2015.
- [5] Witten I. H., Frank E., Data mining: practical machine learning tools and techniques with Java implementations. 3rd ed. San Francisco CA: Morgan Kaufmann (2002) 76-77.
- [6] Cha H., Kim Y. S., Park. S. H., Yoon T., Jung Y., Lee J. H., Learning styles diagnosis based on user interface behaviors for the customization of learning interfaces in an intelligent tutoring system. In Proceedings of the 8th International Conference on Intelligent Tutoring Systems, (2006) 513-524.
- [7] Hämäläinen W., Vinni M., Comparison of machine learning methods for intelligent tutoring systems. In International Conference on Intelligent Tutoring Systems, Springer, Berlin (2006) 525-534.
- [8] Perera D., Kay J., Koprinska I., Yacef K., Zaiane O. R., Clustering and sequential pattern mining of online collaborative learning data. IEEE Transactions on Knowledge and Data Engineering, 21(6) (2009) 759-772.
Details
Primary Language
English
Subjects
-
Journal Section
Research Article
Authors
Hidayet Takcı
Türkiye
Kali Gurkahraman
Türkiye
Emre Ünsal
*
Türkiye
Ahmet Fırat Yelkuvan
Türkiye
Publication Date
June 25, 2020
Submission Date
March 29, 2018
Acceptance Date
April 22, 2020
Published in Issue
Year 2020 Volume: 41 Number: 2