Student Grading System using Machine Learning

Abstract

The student’s overall achievement and their pass rates shows the way of coaching given by the college system, this is vital improving the students pass rates and decrease failure rates. Researchers have used two algorithms in which are Decision tree (DT) algorithm and Support Vector Machine (SVM) algorithm in order to search the necessary student characteristics and conclude the student pass rates, but researchers do not forget about the capabilities of student dependency and coefficient of initialization. Therefore, if we look into this study, student grade prediction the use of DT and SVM. The capabilities are taken into consideration to be crucial is this study, we apply improved genetic algorithm to handle best function choice problem. Then device gaining knowledge of algorithm is applied. The results display the test can achieve higher accuracy of prediction. This study is used to help the students who are lagging behind the studies and facing some risks of graduating. 

Keywords:Decision tree algorithm, Support Vector Machine, Genetic Algorithm.