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A review on feature selection methods for improving the performance of classification in educational data mining

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  • Maryam Zaffar
  • Manzoor Ahmed Hashmani
  • K.S. Savita
  • Sameer Ahmad Khan

Abstract

Educational data mining (EDM) evaluates and predicts students' performance that assists to discover important factors affecting students' academic performance and also guides educational managers to make appropriate decisions accordingly. The most common technique for discovering meaningful information from the educational database is classification. The accuracy of classification algorithms on educational data can be increased by applying feature selection algorithms. Feature selection algorithms help in selecting robots and meaningful features for predicting students' performance with high accuracy. This paper presents different EDM approaches for forecasting students' performance using different data mining techniques. In addition, this paper also presents an evaluation of recent classification algorithms and feature selection algorithms used in educational data mining. Furthermore, the paper will guide the researchers on new and possible dimensions in building a prediction model in EDM.

Suggested Citation

  • Maryam Zaffar & Manzoor Ahmed Hashmani & K.S. Savita & Sameer Ahmad Khan, 2021. "A review on feature selection methods for improving the performance of classification in educational data mining," International Journal of Information Technology and Management, Inderscience Enterprises Ltd, vol. 20(1/2), pages 110-131.
    • Handle: RePEc:ids:ijitma:v:20:y:2021:i:1/2:p:110-131
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    Cited by:

    1. Claudia C. Tusell-Rey & Oscar Camacho-Nieto & Cornelio Yáñez-Márquez & Yenny Villuendas-Rey & Ricardo Tejeida-Padilla & Carmen F. Rey Benguría, 2022. "A Priori Determining the Performance of the Customized Naïve Associative Classifier for Business Data Classification Based on Data Complexity Measures," Mathematics, MDPI, vol. 10(15), pages 1-19, August.

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