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Student Performance Classification Using Artificial Intelligence Techniques

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10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-2019 (ICSCCW 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1095))

Abstract

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%.

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Author information

Authors and Affiliations

  1. Department of Classroom Teaching, Near East University, Nicosia, TRNC, Mersin 10, Turkey

    Nevriye Yılmaz

  2. Information Systems Engineering, Near East University, Nicosia, TRNC, Mersin 10, Turkey

    Boran Sekeroglu

Authors
  1. Nevriye Yılmaz
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  2. Boran Sekeroglu
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Corresponding author

Correspondence to Nevriye Yılmaz .

Editor information

Editors and Affiliations

  1. Joint MBA Program, Azerbaijan State Oil and Industry University, Baku, Azerbaijan

    Rafik A. Aliev

  2. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  4. Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, USA

    Mo Jamshidi

  5. Azerbaijan State Oil and Industry University, Baku, Azerbaijan

    Mustafa B. Babanli

  6. Department of Mechatronics, Near East University, Nicosia, Turkey

    Fahreddin M. Sadikoglu

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Yılmaz, N., Sekeroglu, B. (2020). Student Performance Classification Using Artificial Intelligence Techniques. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Babanli, M., Sadikoglu, F. (eds) 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-2019. ICSCCW 2019. Advances in Intelligent Systems and Computing, vol 1095. Springer, Cham. https://doi.org/10.1007/978-3-030-35249-3_76

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