Higher Education Student Dropout Prediction and Analysis through Educational Data Mining
The high rate of student’s dropout in a registered course has been a major threat to many educational institutions or universities. Using machine learning technology analyse the given data set and also examine the reason for student drop out at an early state and predict whether the student will drop or not.
The existing method is very time consuming and not very accurate and focuses on only specific factors. There is no early warning system to know the potential drop out student beforehand. Even though the data is available with us all the time, the authorities were acting only after the dropout happened.
A system is proposed for the early deduction of college dropout students using machine learning techniques. The proposed method is a combined approach which takes into consideration factors such as demographics, academic performance, health issues, place of residence etc. which increases the accuracy and implements methods that reduce the time taken for prediction.