Student Performance Classification Using Artificial Intelligence Techniques

  • Nevriye YılmazEmail author
  • Boran Sekeroglu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1095)


Education has vital and increasing importance almost for all countries in order to accelerate their development. Well-educated persons provide more benefits to their countries and for that reason, classification of students’ performance before they enter exams or taking courses is also gained an importance. Improvement of education quality must be performed during the active semester to improve students’ personal performance to response this expectation. To provide this, some of the main indicators are students’ personal information, educational preferences and family properties. In this paper, artificial intelligence techniques are applied to the questionnaire results that consists these main indicators, of three different courses of two faculties in order to classify students’ final grade performances and to determine the most efficient machine learning algorithm for this task. Several experiments are performed and results suggests that Radial-Basis Function Neural Network can be used effectively for this and helps to classify student performance with accuracy of 70%–88%.


Artificial Intelligence Education Radial-Basis Function NN 


  1. 1.
    Uysal, M.P.: Modeling learning styles with fuzzy logic (in Turkish). In: 4th International Computer and Instructional Technologies Symposium, Konya, Turkey, pp. 1040–1045 (2010)Google Scholar
  2. 2.
    Öcal, Ö.: Student modeling with fuzzy logic approach in adaptive intelligent teaching system (in Turkish). Yüksek Lisans Tezi. Marmara Üniversitesi (2016)Google Scholar
  3. 3.
    Sekeroglu, B., Dimililer, K., Tuncal, K.: Student performance prediction and classification using machine learning algorithms. In: 8th International Conference on Educational and Information Technology, Cambridge, UK, pp. 7–11 (2019)Google Scholar
  4. 4.
    Sunter, Z., Altun, H., Sunter, S.: A new approach for harmonic elimination in single-pulse modulated single-phase inverter drive system. J. Fac. Eng. Archit. Gazi Univ. 30(2), 237–247 (2015)Google Scholar
  5. 5.
    Dai, S., Li, L., Li, Z.: Modeling vehicle interactions via modified LSTM models for trajectory prediction. IEEE Access 7, 38287–38296 (2019)CrossRefGoogle Scholar
  6. 6.
    Onat, N.C., Gumus, S., Kucukvar, M., Tatari, O.: Application of the TOPSIS and intuitionistic fuzzy set approaches for ranking the life cycle sustainability performance of alternative vehicle Technologies. Sustain. Prod. Consumption 6, 12–25 (2016)CrossRefGoogle Scholar
  7. 7.
    Akandere, M., Özyalvaç, N.T., Duman, S.: Examination of secondary school students’ attitudes towards physical education course and their academic success motivation. Konya Anatolian High School (in Turkish). Selçuk Univ. J. Soc. Sci. 24, 1–10 (2010)Google Scholar
  8. 8.
    Cox, R.H.: Sport Psychology: Concepts and Applications, 2nd edn. McGraw-Hill Education, New York (1990)Google Scholar
  9. 9.
    Şen, Aİ., Koca, S.A.: Attitudes and reasons of secondary school students towards mathematics and science (in Turkish). Educ. Res. 18, 236–252 (2005)Google Scholar
  10. 10.
    Chen, L.S., Cheng, C.H.: Selecting IS personnel use fuzzy GDSS based on metric distance method. Eur. J. Oper. Res. 160(3), 803–820 (2005)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Dede, C.: Comparison of frameworks for 21st century skills. In: 21st Century Skills: Rethinking How Students Learn, vol. 20, pp. 51–76 (2010)Google Scholar
  12. 12.
    Demirtaş, Z.: The relationship between school culture and student achievement. Educ. Sci. 35(158), 3–13 (2010)Google Scholar
  13. 13.
    Adnan, R., Samad, A.M., Tajjudin, M., Ruslan, F.A.: Modeling of flood water level prediction using improved RBFNN structure. In: 2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE), George Town, pp. 552–556 (2015)Google Scholar
  14. 14.
    Dougherty, G.: Pattern Recognition and Classification. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  15. 15.
    Yuan, Z., Wang, C.: An improved network traffic classification algorithm based on Hadoop decision tree. In: 2016 IEEE International Conference of Online Analysis and Computing Science (ICOACS), Chongqing, pp. 53–56 (2016)Google Scholar
  16. 16.
    Mason, C., Twomey, J., Wright, D., Whitman, L.: Predicting engineering student attrition risk using a probabilistic neural network and comparing results with a backpropagation neural network and logistic regression. Res. High. Educ. 59(3), 382–400 (2018)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Classroom TeachingNear East UniversityNicosiaTurkey
  2. 2.Information Systems EngineeringNear East UniversityNicosiaTurkey

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