Discovery and evaluation of student's profiles with machine learning

Abstract

Higher education institutions are overwhelmed with huge amounts of information regarding student's enrollment, number of courses completed, achievement in each course, performance indicators and other data. This has led to an increasingly complex analysis process of the growing volume of data and to the incapability to take decisions regarding curricula reform and restructuring. On the other side, educational data mining is a growing field aiming at discovering knowledge from student's data in order to thoroughly understand the learning process and take appropriate actions to improve the student's performance and the quality of the courses delivery. This paper presents a thorough analysis process performed on student's data through machine learning techniques. Experiments performed on a very large real-world dataset of students performance on all courses of a university, reveal interesting and important students profiles with clustering and surprising relationships among the courses performance with association rule mining.