Artificial intelligence tools for academic management: assigning students to academic supervisors

[EN] In the last few years, there has been a broad range of research focusing on how learning should take place both in the classroom and outside the classroom. Even though academic dissertations are a vital step in the academic life of both students, as they get to employ all their knowledge and skills in an original project, there has been limited research on this topic. In this paper we explore the topic of allocating students to supervisors, a time-consuming and complex task faced by many academic departments across the world. Firstly, we discuss the advantages and disadvantages of employing different allocation strategies from the point of view of students and supervisors. Then, we describe an artificial intelligence tool that overcomes many of the limitations of the strategies described in the article, and that solves the problem of allocating students to supervisors. The tool is capable of allocating students to supervisors by considering the preferences of both students and supervisors with regards to research topics, the maximum supervision quota of supervisors, and the workload balance of supervisors.Sanchez-Anguix, V.; Chalumuri, R.; Alberola Oltra, JM.; Aydogan, R. (2020). Artificial intelligence tools for academic management: assigning students to academic supervisors. IATED. 4638-4644. https://doi.org/10.21125/inted.2020.1284S4638464

A near Pareto optimal approach to student supervisor allocation with two sided preferences and workload balance

[EN] 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.This work is partially supported by funds of the Faculty of Engineering and Computing at Coventry University, United Kingdom, and funds from EU ICT-20-2015 Project SlideWiki granted by the European Commission.Sanchez-Anguix, V.; Chalumuri, R.; Aydogan, R.; Julian Inglada, VJ. (2019). A near Pareto optimal approach to student supervisor allocation with two sided preferences and workload balance. Applied Soft Computing. 76:1-15. https://doi.org/10.1016/j.asoc.2018.11.049S1157