  Improving students academic performance is not an easy task for the academic
community of higher learning. The academic performance of engineering and
science students during their first year at university is a turning point in
their educational path and usually encroaches on their General Point
Average,GPA in a decisive manner. The students evaluation factors like class
quizzes mid and final exam assignment lab work are studied. It is recommended
that all these correlated information should be conveyed to the class teacher
before the conduction of final exam. This study will help the teachers to
reduce the drop out ratio to a significant level and improve the performance of
students. In this paper, we present a hybrid procedure based on Decision Tree
of Data mining method and Data Clustering that enables academicians to predict
students GPA and based on that instructor can take necessary step to improve
student academic performance.
