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Improving students academic performance is not an easy task for the academiccommunity of higher learning. The academic performance of engineering andscience students during their first year at university is a turning point intheir educational path and usually encroaches on their General PointAverage,GPA in a decisive manner. The students evaluation factors like classquizzes mid and final exam assignment lab work are studied. It is recommendedthat all these correlated information should be conveyed to the class teacherbefore the conduction of final exam. This study will help the teachers toreduce the drop out ratio to a significant level and improve the performance ofstudents. In this paper, we present a hybrid procedure based on Decision Treeof Data mining method and Data Clustering that enables academicians to predictstudents GPA and based on that instructor can take necessary step to improvestudent academic performance.

An Approach of Improving Students Academic Performance by using k means clustering algorithm and Decision tree