A multi-objective evolutionary proposal for matching students to supervisors

Abstract

In the last few years there has been a growing interest in the use of artificial intelligence to improve different areas of education such as student team formation, learning analytics, intelligent tutoring systems, or the recommendation of learning resources. This paper presents a genetic algorithm that aims to improve the allocation of students to supervisors while taking both the students’ and supervisors’ preferences with regards to research topics, and by providing a balanced allocation for supervisors’ workload. A Pareto optimal genetic algorithm has been designed and tested for the resolution of this problem.