Abstract

The student academic performance in Defence University College is of great concern to the higher technical education managements, where several factors may affect the performance. The student academic performance in engineering during their first year at university is a turning point in their educational path and usually encroaches on their general point average in a decisive manner. The students evaluation factors like class quizzes mid and final exam assignment 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. Statistics plays an important role in assessment and evaluation of performance in academics of universities need to have extensive analysis capabilities of student achievement levels in order to make appropriate academic decisions. Academic decisions will result in academic performance changes, which need to be assessed periodically and over span of time. The performance parameters chosen can be viewed at the individual student, department, school and university levels. Data mining is used to extract meaning full information and to develop significant relationships among variables stored in large data set/ data warehouse. In this paper is an attempt to using concepts of data mining like k-Means clustering, Decision tree Techniques, to help in enhancing the quality of the higher technical educational system by evaluating student data to study the main attributes that may affect the performance of student in courses.