Automating the Generation of Study Teams Through Genetic Algorithms Based on Learning Styles in Higher Education

Roberto García-Vélez, Bryam Vega Moreno, Angel Ruiz-Ichazu, David Andres Morales Rivera , Esteban Rosero-Perez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationAdvances 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
EditorsTareq Ahram
PublisherSpringer
Pages270-277
Number of pages8
ISBN (Print)9783030513276
DOIs
StatePublished - 2021
EventAHFE Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing, 2020 - San Diego, United States
Duration: 16 Jul 202020 Jul 2020

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1213 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

ConferenceAHFE Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing, 2020
Country/TerritoryUnited States
CitySan Diego
Period16/07/2020/07/20

Bibliographical note

Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Academic profile
  • Genetic algorithm
  • Higher education
  • Study team
  • Style learning

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