Performance analysis of feature selection algorithm for educational data mining

TLDR

Educational institutions prioritize student academic performance, and emerging Educational Data Mining uses feature selection to remove irrelevant data and improve classifier performance, making feature relevance a critical issue for stakeholders. The study analyzes the performance of feature selection algorithms on a student dataset to guide researchers and improve educational quality. The authors evaluate various feature selection algorithms on a student dataset to assess their impact on classification. The results identify effective combinations of feature selection algorithms and classifiers, aiding researchers in selecting optimal models.

Abstract

Student's academic performance is the main focus of all educational institutions. Educational Data Mining (EDM) is an emerging research area help the educational institutions to improve the performance of their students. Feature Selection (FS) algorithms remove irrelevant data from the educational dataset and hence increases the performance of classifiers used in EDM techniques. This paper present an analysis of the performance of feature selection algorithms on student data set. The obtained results of the different FS algorithms and classifiers will also help the new researchers in finding the best combinations of FS algorithms and classifiers. Selecting relevant features for student prediction model is very sensitive issue for educational stakeholders, as they have to take decisions on the basis of results of prediction models. Furthermore our paper is an attempt of playing a positive role in the improvement of education quality, as well as guides new researchers in making academic intervention.