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

Yıl 2018, , 142 - 162, 03.12.2018

Sümeyye Kaynak , Baran Kaynak Hayrettin Evirgen

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

Cited By: 2

Öz

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.

Anahtar Kelimeler

artificial neural network, adaptive network fuzzy inference system, student consultancy, course selection

Kaynakça

• Aher, S., & L. M. R. J, L. (2012). A comparative study of association rule algorithms for course recommender system in e-learning. International Journal of Computer Applications, 48-52.
• Babad, E. (2001). Students“course selection : differential considerations for first and last course students” course selection : Differential Considerations for First and Last Course, 42(4), 469–492. Retrieved from http://www.jstor.org/stable/30069473
• Babad, E., Icekson, T., & Yelinek, Y. (2008). Antecedents and correlates of course cancellation in a university “drop and add” period. Research in Higher Education, 49(4), 293–316. http://doi.org/10.1007/s11162-007-9082-3
• Babad, E., & Tayeb, A. (2003). Experimental analysis of students’ course selection. The British Journal of Educational Psychology, 73(Pt 3), 373–393. http://doi.org/Doi 10.1348/000709903322275894
• Babuška, R., & Verbruggen, H. (2003). Neuro-fuzzy methods for nonlinear system identification. Annual Reviews in Control, 27(1), 73–85. http://doi.org/10.1016/S1367-5788(03)00009-9
• Baylari, A., & Montazer, G. a. (2009). Design a personalized e-learning system based on item response theory and artificial neural network approach. Expert Systems with Applications, 36(4), 8013–8021. http://doi.org/10.1016/j.eswa.2008.10.080
• Bozkir, A., Akcapinar Sezer, E., & Gök, B. (2009). Öğrenci seçme sınavında (öss) öğrenci başarımını etkileyen faktörlerin veri madenciliği yöntemleriyle tespiti. Uluslararası İleri Teknolojiler Sempozyumu (IATS’09)
• Caner, M. (2009). Estimation of specific energy factor in marble cutting process using ANFIS and ANN, 221–226.
• Güner, N., & Çomak, E. (2011). Mühendislik öğrencilerinin matematik i derslerindeki başarısının destek vektör makineleri kullanılarak tahmin edilmesi. Pamukkale Univ Muh Bilim Dergisi, 87-96
• Heaton, J. (2008). Introduction to neural networks for C# (2 edition). Heaton Research, Incorporated.
• Jang, J. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685. http://doi.org/10.1109/21.256541
• Jang, J. (1996). Input selection for ANFIS learning. Fuzzy Systems, Proceedings of the Fifth. 1493–1499. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=552396
• Kalejaye, B., Folorunso, O., & Usman, O. (2015). Predicting students’ grade scores using training functions of artificial neural. Journal of Natural Science, Engineering and Technology
• Kardan, A. a., Sadeghi, H., Ghidary, S. S., & Sani, M. R. F. (2013). Prediction of student course selection in online higher education institutes using neural network. Computers & Education, 65, 1–11. http://doi.org/10.1016/j.compedu.2013.01.015
• Lo, J. J., Chan, Y. C., & Yeh, S. W. (2012). Designing an adaptive web-based learning system based on students’ cognitive styles identified online. Computers and Education, 58(1), 209–222. http://doi.org/10.1016/j.compedu.2011.08.018
• 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. http://doi.org/10.1002/asi.20970
• Lykourentzou, I., Giannoukos, I., Nikolopoulos, V., Mpardis, G., & Loumos, V. (2009). Dropout prediction in e-learning courses through the combination of machine learning techniques. Computers and Education, 53(3), 950–965. http://doi.org/10.1016/j.compedu.2009.05.010
• Najah, A. a., El-Shafie, A., Karim, O. a., & Jaafar, O. (2010). Water quality prediction model utilizing integrated wavelet-ANFIS model with cross-validation. Neural Computing and Applications, 21(5), 833–841. http://doi.org/10.1007/s00521-010-0486-1
• Naser, S., Zaqout, I., Ghosh, M., Atallah, R., & Alajrami, E. (2015). Predicting student performance using artificial neural network: in the faculty of engineering and information technology. International Journal of Hybrid Information Technology, 221-228
• Noureldin, A., El-Shafie, A., & Reda Taha, M. (2007). Optimizing neuro-fuzzy modules for data fusion of vehicular navigation systems using temporal cross-validation. Engineering Applications of Artificial Intelligence, 20(1), 49–61. http://doi.org/10.1016/j.engappai.2006.03.002
• Oladokun, V., Adebanjo, A., Sc, B., & Charles-Owaba, O. (2008). Predicting Students ’ academic performance using artificial neural network: a case study of an engineering course. The Pacific Journal of Science and Technology, 72-79
• Seber, G. A. F., & Lee Alan J. (2003). Linear regression analysis. Wiley-Interscience.
• Şahin, Ç., & Arcagök, S. (2013). İlköğretim öğretmenlerinin eğitim araştırmalarına yönelik yaklaşımları. Journal of Computer and Education Research, 1(2), 1-20.
• Şengür, D., & Tekin, A. (2013). Prediction of student’s grade point average by using the data mining methods. Bilişim Teknolojileri Dergisi, 6(3), 7-16.
• Şentürk, M. (2016). Sosyal bilgiler dersinde işbirlikli öğrenme yöntemlerinin akademik başarı üzerindeki etkisi. Journal of Computer and Education Research, 4(8), 205-221.
• Taylan, O., & Karagözoğlu, B. (2009). An adaptive neuro-fuzzy model for prediction of student’s academic performance. Computers & Industrial Engineering, , 732–741.
• Werbos, P. J. (1974). Beyond regression: new tools for prediction and analysis in the behavioral sciences foundations. Retrieved from http://www.citeulike.org/group/1938/article/1055600
• Yan, H., Zou, Z., & Wang, H. (2010). Adaptive neuro fuzzy inference system for classification of water quality status. Journal of Environmental Sciences, 22(12), 1891–1896. http://doi.org/10.1016/S1001-0742(09)60335-1
• Zacharis, N. (2016). Predicting student academic performance in blended learning using artificial neural. International Journal of Artificial Intelligence and Applications (IJAIA)
• Zaidah, I., & Daliela, R. (2007). Predicting students’ academic performance: comparing artificial neural network, decision tree and linear regression. 21st Annual SAS Malaysia Forum.