Analysis of Machine Learning (Ml) and Deep Learning (Dl) Methods for Predicting Students' Performance

Abstract

Predicting students’ performance is major important areas for education backgrounds which includes of schools and universities because it helps develop effective methods with the purpose of increasing academic results and evading dropout, among other things. Consequently, analyzing and processing this information carefully will be able to provide valuable information regarding the students’ understanding and the association among them and the educational tasks. It is able to be used for various purposes; for instance, a strategic plan is able to be useful for the improvement of a value education.  It might happen due to two major reasons. Primary, the learning of recent prediction methods is still inadequate to recognize the most appropriate algorithms for predicting the results of students. Second is suitable towards the need to study the factors concerning student’s success in exacting courses. This paper we review on Data Mining (DM) techniques for student performance forecast to increase student’s achievements. It reviews regarding the information of DM algorithms with respect to Machine Learning (ML) and Deep Learning (DL) methods in order to predict the performance of students depending on their historical information. It also gives a review on the DM techniques towards student’s performance prediction. It also focuses on how these algorithms are able to be used to recognize the mainly significant features in a student’s data. This review study gives educators an easier access to ML and DL algorithms, enabling each and every one the possibility of their application towards the area of education. This review might bring the benefits and impact to students, educators and educational institutions. Finally it also provides a way to the extension of DL methods for student performance prediction.