Abstract
The current study proposes an intelligent approach to compose optimal learning groups in which the members have different domain backgrounds. The approach is based on a well-known evolutionary algorithm - Particle Swarm Optimization. The authors claim that quantifying various indicators, such as background diversity and similarity between the type of interest of the participants, within a group and between groups can positively impact on building learning groups.
The algorithm is integrated in an ontology-based e-learning system, designed to create self-built educating communities, in which a trainees goes through the education process, gains points through achievements and ultimately becomes a trainer. When creating a new account, the newly created trainee is asked to self asses himself by filling out a form. The resulting profile is used to assign the user to the most suitable learning group. We propose to assign him by the following rule: maximizing the diversity within a group (due to the fact that multidisciplinary teams are more challenging) and minimizing the diversity between groups (all the groups should have similar composition), meaning a group will have members with similar interests.
The study is presented in the context of group building strategies in adults' education. (C) 2013 Elsevier Ltd. All rights reserved.