Automated System for Student’s Intellectual Performance Prediction

Abstract

In the academic world, in order to ensure that quality education is provided to the students. To prevent or take precautions against dropouts or student failures and to improve the quality of managerial decisions, predicting a student’s intellectual performance is extremely vital not only for the higher education management bodies but also for the students themselves. In our system, different categories of metrics for predictive analytics that would influence the performance of First year engineering students has been collected and used in what-if scenario predictions for their Second Semester exam grading. Various Data mining algorithms and techniques have been studied and the best one which will be most suitable for this particular classification problem has been identified and is used for Predictive Analysis computations. Cloud services are used for storing large voluminous Student Data. Thus, using this student performance model, appropriate and timely warning can be given to students if they are at a risk, educational institutions would also be able to provide them with better additional training, moreover this system would also help in identifying potentially meritorious students and thereby encourage them for academic scholarships.