Student Information System and Performance Prediction in Educational Data Mining

The main focus of data mining is to collect different data from databases or data warehousesand the information collected that had never been known before, it is valid and operational. Educational institutes can use this to maintain all the information of student academics easily which is critically important. The performance of students in their academics is a turning point for their brightest career. Predicting student academic performance has been an important research topic in Educational Data Mining (EDM) which uses machine learning and data mining techniques to explore data from educational settings. Measuring student academic performance is challenging since it depends on various factors. Classification and Prediction are among the major techniques in data mining and plays a vital role in EDM. The need for this is to enable the university to intervene and provide assistance to low achievers as early as possible. In this study we develop a classification model usingC4.5 algorithm for domain wise performance evaluation system for engineering students. It also brings connectivity between teachers, students and parents by keeping them updated with their child performance regularly. The whole system will be available through a secure, online interface embedded in college website.