Quality Improvements in Online Education System by Using Data Mining Techniques

In the present era online education has become one of the popular higher education alternatives. With the busy and competitive life-style it continues to expanding. This gave rise a need for determining and maintaining the quality in the process of designing, developing and delivering online education. In traditional Face-To-Face teaching method it is easier for a faculty to monitor the performance of a student. In online learning it is difficult to monitor a student's performance. That is why there is a requirement for a prediction system. The suggested dropout prediction system helps in tracking student progress in the course and acts as an alert system for the teacher to improve student's performance in the course. The prediction can be done by clustering algorithms like Random forest, C4.5.