ANALYSIS OF FEATURES FOR AUTOMATIC CLASSIFICATION OF ACADEMIC PERFORMANCE

Year 2022, Volume: 30 Issue: 2, 234 - 241, 18.08.2022

Hakan Alp Eren , Efnan Şora Günal

https://doi.org/10.31796/ogummf.1037272

Abstract

This paper analyzes the contributions of features widely used in the automatic classification of students’ academic performance. In this classification problem, the relationship between various features and classifiers is analyzed using an exhaustive feature selection strategy. In this way, the optimal subset of features providing the highest classification performance is obtained. For this purpose, an academic performance dataset consisting of 15 distinct features and 480 samples is used. The features mainly belong to four different categories, including demographic, academic background, parent participation, and behavioral. The samples are from three different classes corresponding to the low, middle, and high levels of students’ success. For evaluations, 10 different classification algorithms are employed. Extensive experimental analysis reveals that the accuracy of the classification of students’ academic performance can be improved up to 79.40% using only 8 features rather than all.

Keywords

Academic performance, Data mining, Classification, Feature selection

AKADEMİK PERFORMANSIN OTOMATİK SINIFLANDIRILMASI İÇİN ÖZNİTELİKLERİN ANALİZİ

Abstract

Bu makale, öğrencilerin akademik performansının otomatik olarak sınıflandırılmasında yaygın olarak kullanılan özelliklerin katkılarını analiz etmektedir. Bu sınıflandırma probleminde, çeşitli öznitelikler ve sınıflandırıcılar arasındaki ilişki, kapsamlı bir öznitelik seçim stratejisi kullanılarak analiz edilmiştir. Bu şekilde, en yüksek sınıflandırma performansını sağlayan optimal öznitelik alt kümesi elde edilmiştir. Bu amaçla 15 farklı öznitelik ve 480 örnekten oluşan bir akademik performans veri seti kullanılmıştır. Öznitelikler demografik, akademik geçmiş, ebeveyn katılımı ve davranışsal olmak üzere dört farklı kategoriye aittir. Örnekler, öğrenci başarısının düşük, orta ve yüksek seviyelerine karşılık gelen üç farklı sınıftandır. Değerlendirmeler için 10 farklı sınıflandırma algoritması kullanılmıştır. Kapsamlı deneysel analizler, öğrencilerin akademik performansını sınıflandırma doğruluğunun, özniteliklerin tamamı yerine yalnızca 8 tanesi kullanılarak, %79.40'a kadar artırılabileceğini ortaya koymaktadır.

Keywords

Academic performance, Data Mining, Classification, Feature Selection

References

• Aggarwal, C. C. (2015). Data mining: the textbook. Springer.
• Amrieh, E.A., Hamtini, T.M., & Aljarah, I. (2015). Preprocessing and analyzing educational data set using X-API for improving student's performance. 2015 IEEE Jordan Conf. on Applied Electrical Engineering and Computing Technologies (AEECT), 1-5.
• Amrieh, E.A., Hamtini, T.M., & Aljarah, I. (2016). Mining Educational Data to Predict Student’s academic Performance using Ensemble Methods. International Journal of Database Theory and Application Vol.9, No.8 (2016), pp.119-136.
• Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
• Gunal, S., & Edizkan, R. (2008). Subspace based feature selection for pattern recognition. Information Sciences, 178(19), 3716-3726.
• Gunal, S., Gerek, O. N., Ece, D. G., & Edizkan, R. (2009). The search for optimal feature set in power quality event classification. Expert Systems with Applications, 36(7), 10266-10273.
• Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of machine learning research, 3(Mar), 1157-1182.
• Han, J., Kamber, M., & Pei, J. (2011). Data mining concepts and techniques third edition. The Morgan Kaufmann Series in Data Management Systems, 5(4), 83-124.
• Huang, S., & Fang, N. (2013). Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models. Computers & Education, 61, 133-145.
• Hussain, S., Dahan, N. A., Ba-Alwib, F. M., & Ribata, N. (2018). Educational data mining and analysis of students’ academic performance using WEKA. Indonesian Journal of Electrical Engineering and Computer Science, 9(2), 447-459.
• Rahman, M. H., & Islam, M. R. (2017). Predict Student's Academic Performance and Evaluate the Impact of Different Attributes on the Performance Using Data Mining Techniques. In 2017 2nd International Conference on Electrical & Electronic Engineering (ICEEE) (pp. 1-4). IEEE.
• Sana, Siddiqui, I. F. & Arain, Q. A. (2019). Analyzing Students’ Academic Performance through Educational Data Mining. 3C Tecnología. Glosas de innovación aplicadas a la pyme. Special Issue, May 2019, pp. 402–421.
• Theodoridis, S., & Koutroumbas, K., (2009). Pattern Recognition (4th Ed.). Academic Press.
• Zaffar, M., Hashmani, M. A., & Savita, K. S. (2017). Performance analysis of feature selection algorithm for educational data mining. In 2017 IEEE Conference on Big Data and Analytics (ICBDA) (pp. 7-12). IEEE.