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Resolution to Educational Group Formation Problem Based on Improved Particle Swarm Optimization Using Fuzzy Knowledge

Authors:
Bikhtiyar Hasan
Amine Boufaied

Keywords: Particle Swarm Optimization; Learning group formation problem; Belbin roles; Multi-Parent Order Crossover; Fuzzy Classification

Abstract:
In the educational context, instructors usually partition students into collaborative learning teams to perform collaborative learning tasks. Indeed, one of the grouping criteria most utilized by instructors is based on the students’ roles and on forming similar teams according to the roles of their members, which is costly and complex. This paper addresses the optimization problem of forming automatic learning teams by minimizing the knowledge-difference cost among formed teams. The knowledge index of each group depends on the Belbin roles of their students’ members in the form of a sum of students’ fuzzy rating indexes. The proposed algorithm is called improved particle swarm optimization with multi-parent order crossover (IPSOMPOX). The multi-parent order crossover is used in IPSOMPOX in order to investigate new solutions in the search space and to accelerate the convergence of the proposed algorithm to the best global solution. To evaluate the performance of the proposed algorithm, we apply it to several different experiments with different numbers of teams and students. The results demonstrate the superiority of our proposed performance over the standard PSO.

Pages: 1 to 6

Copyright: Copyright (c) IARIA, 2023

Publication date: November 13, 2023

Published in: conference

ISSN: 2308-4235

ISBN: 978-1-68558-098-8

Location: Valencia, Spain

Dates: from November 13, 2023 to November 17, 2023