Mining Academic Data to Support Students’ Advisors: A Preliminary Study

Abstract

Many universities take measures to reduce the number of students dropping out. To support students’ advisors better becomes crucial. Besides their knowledge that they acquire through experience, which is a very important human factor in that process, advisors usually know very little about how students get along in their studies. In this paper, we present preliminary work to support advisors better when meeting students. The current investigation includes two main parts called “overview” and “typical completing behaviours”. The overview part contains visualizations giving general information about how students manage the degree as well as information contrasting students who complete the degree and students who drop out. Typical completing behaviours are obtained through clustering. In this work, data from 2276 students have been analysed.