A review on feature selection methods for improving the performance of classification in educational data mining. (29th March 2021)

• Record Type: Journal Article
• Authors: Zaffar, Maryam; Hashmani, Manzoor Ahmed; Savita, K.S.; Khan, Sameer Ahmad
• Is Part Of: International journal of information technology and management. Volume 20:Number 1/2(2021)
• Page Start: 110
• Page End: 131
• Publication Date: 2021-03-29
• Subjects: educational data mining -- EDM -- classification algorithms -- feature selection in educational data mining -- filter feature selection -- wrapper feature selection; Management information systems -- Periodicals; Information technology -- Periodicals; Management -- Data processing -- Periodicals; 658.403805
• Languages: English
• ISSNs: 1461-4111

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.