TY  - JOUR
IS  - 1-2
N2  - 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. Copyright © 2021 Inderscience Enterprises Ltd.
ID  - scholars15809
KW  - Classification (of information); Feature extraction; Forecasting; Predictive analytics; Students
KW  -  Academic performance; Accuracy of classifications; Classification algorithm; Educational data mining; Educational data minings (EDM); Feature selection algorithm; Feature selection methods; Prediction model
KW  -  Data mining
Y1  - 2021///
A1  - Zaffar, M.
A1  - Hashmani, M.A.
A1  - Savita, K.S.
A1  - Khan, S.A.
JF  - International Journal of Information Technology and Management
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104058268&doi=10.1504%2fIJITM.2021.114161&partnerID=40&md5=df9668a2254ed54d260b210c1e5cd956
VL  - 20
AV  - none
N1  - cited By 3
SP  - 110
TI  - A review on feature selection methods for improving the performance of classification in educational data mining
PB  - Inderscience Publishers
SN  - 14614111
EP  - 131
ER  -