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. © 2017 IEEE.