Intelligent Student Advisory Framework

Abstract

Improving students and higher education institutes is an important task to increase the quality of the whole higher educational system. In our research, we propose to use educational data mining techniques to discover hidden knowledge from the available educational data. An "Intelligent Student Advisory Framework" is proposed that uses classification and clustering techniques. This system can be used to guide the first year university students to the more suitable educational track. The classification phase will predict the department which is most likely to be chosen by student and the clustering phase will recommend departments to student by showing his expected rate of success for each department, this recommendation is aiming to decrease the high rate of academic failure for first year students. Our approach is tested using a real case study from the Cairo Higher Institute for Engineering, Computer Science, and Management using data collected for a period within 12 years from 2000 – 2012.