Machine Learning Based Slow Learner Prediction in Educational Sector

Abstract

The educational sector has been proved to be a sector where improvement measures are a must to improve the system, the curriculum which changes every few years, student’s state in order to cope with the changing curriculum every now and then, how much a student has been able to achieve academically, why are they falling behind, etc. Education is said to be the backbone of a nation, if the students are falling behind that means the nation is falling behind. So, the first and foremost duty of the education ministry, education ministry related personnel’s, teachers is to improve the malfunctioning educational system and come up with a system which can help the students truly in a sense of letting them achieve their dreams. Our purpose of the study is to predict the slow learners in the university level which is the last paddock of their study life and the step where they must acquire skills to face the professional life. We have acquired genuine university student information. The information has been acquired from the CSE (computer science and engineering) department students. In order to achieve the prediction and accuracy outcomes, Machine learning classification-based algorithms will be applied.