Automated Student Advisory using Machine Learning

Abstract

Educational data mining is a specific data mining field applied to data originating from educational environments, it relies on different approaches to discover hidden knowledge from the available data. Among these approaches are machine learning techniques which are used to build a system that acquires hidden knowledge from previous data. Machine learning can be applied to solve different regression, classification, clustering and optimization problems. In our research, we propose a "Student Advisory Framework" that utilizes classification and clustering. 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 a student and the clustering phase will recommend a department to student by showing his expected rate of success for each department, this recommendation aims to decrease the high rate of academic failure for first year students. Our approach is tested using a real case study from "Cairo Higher Institute for Engineering, Computer Science, and Management" using data collected for a period within 12 years from 2000-2012.