Yasemin Zeynep ENGİN, Burçin KURT, Kemal TURHAN

Estimation of student success with artificial neural networks

Yapay sinir ağları (YSA), sınıflandırma, kümeleme ve regresyon problemlerinin çözümünde başarıyla uygulanabilmektedir. Bu sebeple öğrenci ders başarısının erken dönemde tahmini ve gerekli önlemlerin alınabilmesi için de kullanılabilir. Bu çalışmada Karadeniz Teknik Üniversitesi Tıp Fakültesi Dönem I öğrencilerinin kurul sonu çoktan seçmeli üç ara sınav ve öğrenci başarısını etkileyebilecek diğer faktörler eklenerek dönem sonu final sınavı YSA ve regresyon analizi ile tahmin edilmeye çalışılmış ve YSA’nın daha yüksek performans gösterdiği gözlenmiştir. Sonuç olarak daha ileri düzeyde araştırmalar yapılarak erken uyarı sistemleri geliştirilebileceği görülmüştür.

Yapay sinir ağları ile öğrenci başarısı tahmini

Faculties’ counseling systems need to take early action for problematic students by predicting and tracing student success and related problems. In this study, we compared two methods for predicting students’ success to take systematic and proactive actions for detecting and solving problems in early phase. In this study, it is aimed to estimate the marks that the students of the 1st semester of School of Medicine of Karadeniz Technical University, who were 111 in total, 56% of whom were males and 44 % were females. By using their final exam (F1) results that were gathered through multiple choice tests, variables that could be considered as affecting student performance were analyzed. Artificial neural networks and regression analyses were carried out with success in various research areas for the settlement of classification, clustering and regression problems As a result, it is considered that there appears a need of advanced level research and the automatic information system implementations to be developed at the end of those researches in the student performance estimation as also in F1 estimation.

PDF

___

Abraham, A. (2005). Artificial neural networks. Handbook of Measuring System Design. London: John Wiley and Sons Ltd.
Adewale, O. S., Adebiyi, A. B., Solanke, O.O. (2007). Web-based neural network model for university undergraduate admission selection and placement. The Pacific Journal of Science and Technology, 2, 367-384.
Ashby, D. & Kumar, N. (1996). A comparison of neural networks and classical discriminant analysis in anticipating default among high-yield bonds. Association for Information System 1996 Americas Conference, Phoenix, Arizona.
Badri, M. A. (1999). A neural network based DSS for advising students in the school of business. Neural Networks, 99. International Joint Conference, Washington, DC , USA, 5, 3533-3538.
Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford University Press London, UK.
Gorr, W. L., Nagin, D. & Szczypula, A. (1994). Comparative study of artificial neural network and statistical models for predicting student grade point averages. International Journal of Forecasting, 10(2), 17-34.
Greenes, B. P., Bergeron, A. N. & Morse, R. A. (1990). A Generic Neural Network-Based Tutorial Supervisor For Computer Aided Instruction. SCAMC Inc., 435-439.
Lykourentzou, I., Giannoukos, I., Mpardis, G., Nikolopoulos, V. & Loumos, V. (2009). Early and dynamic student achievement prediction in e-learning courses using neural networks.
Journal of The American Society for Information Science and Technology, 60(2), 372–380.
McCulloch, W. S. & Pitts, W. H. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–133.
Monique, F. & Claude, F. (2000). Decision-support and intelligent tutoring systems in medical education. Clin Invest Med., 23(4), 266-269.
Paliwal, M. & Kumar, U. A. (2009). A study of academic performance of business school graduates using neural network and statistical techniques. Expert Systems with Applications, 36, 7865– 7872.
Pulito, A. R., Donnelly, M. B. & Plymale, M. (2007). Factors in faculty evaluation of medical students’ performance. Med Educ., 41(7), 667-75.
Sheel, S. J., Renner, R. S., Dawsey, S. K. (2002). Alternatives to Math Placement Exams: A Look at Discriminant Analysis, Neural Networks and Ensembles of Networks. Department of Computer Science: Coastal Carolina University, Chico Conway, SC.
Shepherd, A. J. (1997). Second-order methods for neural networks: Fast and reliable training methods for multi-layer perceptions. Springer-Verlag., London.
Thobega, M. & Masole, T.M. (2008). Predicting students’ performance on agricultural science examination from forecast grades. US-China Education Review, 5(10), 45-51.
Vendlinski, T. & Stevens, R. (2002). Assessing Student Problem-Solving Skills With Complex Computer-Based Tasks. The Journal of Technology, Learning and Assessment, 1(3), 1-24.
Wilpen, L., Nagin, D., Szczypula, J. (1994). Comparative study of artificial neural network: A statistical models for predicting student grade point averages. International Journal of Forecasting, 10(1), 17-34.
Zaidah, I. & Daliela, R. (2007). Predicting students’ academic performance: comparing artificial neural network, decision tree and linear regression. 21st Annual SAS Malaysia Forum, Kuala Lumpur, 1-16.