A Comparative Study of Classifiers in the Context of Critical Students Management

Abstract

Education can determines the standard of society, it also make the nation empowered providing new thoughts and implementation. In the last decades, it is found that number of higher level educational institutes grows rapidly in Bangladesh. Besides ensuring quality this increasing number causes tight competition of attracting students to get admitted in the institutes. This institute have higher rating tendency to fill all the available seats emphasizing on counting the number of students not on their academic excellence. Therefore, a remarkable number of student drop the course due to inability of adjustment with the academics which causing an ultimate loss to the family, society and educational institute. None knows the proper reason of their leave and what percent or who of student is going to become critical student. This paper investigated the prediction of dropout student through data mining approaches. The study predicts critical students applying different classification algorithm who tend to need support and essential guidelines from the different perspective. The outcomes are compared with each and also the models with the best.