Data Mining for Engineering Schools Predicting Students' Performance and Enrollment in Masters Programs

摘要: the supervision of academic performance engineering students is vital during an early stage their curricula. Indeed, grades in specific core/major courses as well cumulative General Point Average (GPA) are decisive when pertaining to ability/condition pursue Masters' studies or graduate from a five-year Bachelor-of- Engineering program. Furthermore, these compelling strict requirements not only significantly affect attrition rates (on top probation and suspension) but also decide grant management, developing courseware, scheduling programs. In this paper, we present study that has twofold objective. First, it attempts at correlating aforementioned issues with students' some key taken stages curricula, then, predictive model presented refined order endow advisors administrators powerful decision-making tool tackling such highly important issues. Matlab Neural Networks Pattern Recognition Classification Regression Trees (CART) fully deployed cross validation testing. Simulation prediction results demonstrated high level accuracy offered efficient analysis information pertinent management schools programs frame perspective.