Both the International Education Organization (OIE) and UNESCO have stated that promoting collaborative activities is a key competence for sustainable development. This postulate focuses on collaboration with local and international networks. In this line, it is important to mention that, in each teamwork, the members are people who interact sharing objectives, rules and deadlines linked to the activity. Under this reality, it is essential to promote study-team activities in higher education, where students can develop skills to solve problems in multidisciplinary groups. To support the process of generating efficient study-teams, in this investigation we present a system capable of exploring the best alternatives to automatically organize homogeneous study-teams that favor the best performance. Our proposal uses a personalized genetic algorithm (GA), based on student learning styles and academic profile. The experimentation phase has yielded positive results compared to the self-organization method or the teacher imposition method.
|Title of host publication||Advances in Artificial Intelligence, Software and Systems Engineering - Proceedings of the AHFE 2020 Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing|
|Number of pages||8|
|State||Published - 2021|
|Event||AHFE Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing, 2020 - San Diego, United States|
Duration: 16 Jul 2020 → 20 Jul 2020
|Name||Advances in Intelligent Systems and Computing|
|Conference||AHFE Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing, 2020|
|Period||16/07/20 → 20/07/20|
Bibliographical notePublisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
Copyright 2020 Elsevier B.V., All rights reserved.
- Academic profile
- Genetic algorithm
- Higher education
- Study team
- Style learning