۱- Introduction

۲- Related work

۳- Problem definition

۴- A Pareto optimal genetic algorithm for the student–supervisor allocation problem

۵- Experiments

۶- Conclusions

References

Abstract

The problem of allocating students to supervisors for the development of a personal project or a dissertation is a crucial activity in the higher education environment, as it enables students to get feedback on their work from an expert and improve their personal, academic, and professional abilities. In this article, we propose a multi-objective and near Pareto optimal genetic algorithm for the allocation of students to supervisors. The allocation takes into consideration the students and supervisors’ preferences on research/project topics, the lower and upper supervision quotas of supervisors, as well as the workload balance amongst supervisors. We introduce novel mutation and crossover operators for the student–supervisor allocation problem. The experiments carried out show that the components of the genetic algorithm are more apt for the problem than classic components, and that the genetic algorithm is capable of producing allocations that are near Pareto optimal in a reasonable time.

Introduction

Every year in higher education (HE) institutions, students undertake individual projects that are supervised by a tutor that offers academic advice and guidance, either as an undergraduate or master dissertation, as part of their coursework, or simply as a summer research project. Students are usually allocated to supervisors for their projects by means of a centralized human decision maker or by means of interactions between students and staff members. The decision makers have to take into consideration the preferences of both students and supervisors with respect to the conduct of the project, as well as departmental constraints such as minimum and maximum levels of workload (in terms of supervision) for each supervisor. This situation results in an extremely time consuming process, and a suboptimal allocation due to a large and complex search space faced by human decision makers. Automating this process by applying artificial intelligence techniques may enhance the process in terms of satisfaction and performance of students with these individual projects. In this article, we present a genetic algorithm for matching students to supervisors according to both students’ and supervisors’ preferences and the constraints of the department. The rationale behind this problem is matching an appropriate student with a supervisor for the development of an individual project.