Hidayet TAKCI, Kali GÜRKAHRAMAN, Ahmet Fırat YELKUVAN, Emre ÜNSAL

A matching model to measure compliance between department and student

The aim of all education systems is to train students who are equipped with knowledge. In thatcase, that student is able to determine the most suitable profession for him/her success ineducation and career that are related to this profession will be higher. Studies done up to thisday have been focused on finding out the factors affecting the career choice of the student, butthey have not suggested any method for determining the most suitable procession. It is notpossible to obtain satisfying results from a system that does not lead students to appropriatehigher education departments. In this context, a student- department matching system isproposed which aims to increase the success of the education systems in our study. Thedepartment of computer engineering was dealt with as a sample department and the proposedstudy 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 ofsuccessful 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 resultwas obtained by the C-SVC algorithm and the second best result by Naive Bayes. The lowesterror rate achieved was 0.2700 and the highest accurate recognition rate was 73.00%.

PDF

___

[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_testl er.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.
[9] Pardos Z. A., Gowda S. M., Baker S.J.d R., Heffernan N. T., The sum is greater than the parts: ensembling models of student knowledge in educational software. ACM SIGKDD Explorations Newsletter, 13(2) (2011) 37-44.
[10] Salton G., Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley, (1989).
[11] Kuzgun Y., Mesleki ve Teknik Öğretim Kurumları ve Meslekler Rehberi. National Education Press, Istanbul, (2006).
[12] Rakotomalala R., TANAGRA: a free software for research and academic purposes. Proceedings of EGC'2005, RNTI-E-3, 2 (2005) 697-702.