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Bölüm ve Öğrenci Arasındaki Uygunluğunu Ölçen bir Eşleştirme Modeli

Year 2020, Volume: 41 Issue: 2, 467 - 471, 25.06.2020

Hidayet Takcı , Kali Gurkahraman , Emre Ünsal , Ahmet Fırat Yelkuvan

https://doi.org/10.17776/csj.410709

Abstract

Özet: Bütün eğitim sistemlerinin
hedefi bilgiyle teçhiz edilmiş öğrenciler yetiştirmektir. Bunun için bir
taraftan müfredat çalışmaları yapılırken diğer taraftan eğitim yönetimi
konusunda çalışmalar yürütülür. Eğitim içeriğinin kalitesi ve eğitim
kurumlarının doğru yönetimiyle de mükemmel sonuç alınacağı düşünülür. Hâlbuki
öğrenciyle bölümü doğru şekilde eşleştirmeyen bir sistemden başarılı sonuçlar
alabilmek olası değildir. Bu çalışmada eğitim sistemlerinin başarısına etki
edeceğini düşündüğümüz bir eşleşme sistemi önerilmiştir. Pilot bölüm olarak
bilgisayar mühendisliği bölümü ele alınmış ve çalışma öğrencinin bilgisayar
mühendisliğine uygun olup olmamasının tespiti için yapılmıştır. Anket verileri
yardımıyla ihtiyaç duyulan veri elde edilmiş ve ardından bu verilerle başarılı
ve başarısız öğrencinin bir modeli oluşturulmuştur. Oluşturulan modelin test
edilmesinde ise veri madenciliği algoritmaları kullanılmıştır.

Keywords

eşleştirme sistemi veri madenciliği tahmin modelleri

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.
  • [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.

A matching model to measure compliance between department and student

Year 2020, Volume: 41 Issue: 2, 467 - 471, 25.06.2020

Hidayet Takcı , Kali Gurkahraman , Emre Ünsal , Ahmet Fırat Yelkuvan

https://doi.org/10.17776/csj.410709

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

matching system data mining predictive models

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.
  • [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.
There are 12 citations in total.

Details

Primary Language English
Journal Section Engineering Sciences
Authors

Hidayet Takcı

Kali Gurkahraman

Emre Ünsal

Ahmet Fırat Yelkuvan

Publication Date June 25, 2020
Submission Date March 29, 2018
Acceptance Date April 22, 2020
Published in Issue Year 2020Volume: 41 Issue: 2

Cite

APA Takcı, H., Gurkahraman, K., Ünsal, E., Yelkuvan, A. F. (2020). A matching model to measure compliance between department and student. Cumhuriyet Science Journal, 41(2), 467-471. https://doi.org/10.17776/csj.410709