Online voluntary mentoring: Optimising the assignment of students and mentors

TL;DR

This paper presents an optimization-based approach for assigning volunteer mentors to students during the COVID-19 pandemic, using integer programming to improve matching efficiency and effectiveness.

Contribution

It introduces a novel integer programming method for optimal mentor-student pairing in a web-based volunteer coordination system.

Findings

Effective matching achieved in simulations with real and synthetic data.

The scheme adapts well to different parameter settings.

Demonstrates the potential of optimization in volunteer-student assignments.

Abstract

After the closure of the schools in Hungary from March 2020 due to the pandemic, many students were left at home with no or not enough parental help for studying, and in the meantime some people had more free time and willingness to help others in need during the lockdown. In this paper we describe the optimisation aspects of a joint NGO project for allocating voluntary mentors to students using a web-based coordination mechanism. The goal of the project has been to form optimal pairs and study groups by taking into the preferences and the constraints of the participants. In this paper we present the optimisation concept, and the integer programming techniques used for solving the allocation problems. Furthermore, we conducted computation simulations on real and generated data for evaluate the performance of this dynamic matching scheme under different parameter settings.