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

doi:10.1007/978-3-030-51328-3_38

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

Artificial Intelligence and Assistive Technologies Research Group (GI-Iata)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
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
Editors	Tareq Ahram
Publisher	Springer
Pages	270-277
Number of pages	8
ISBN (Print)	9783030513276
DOIs	https://doi.org/10.1007/978-3-030-51328-3_38
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

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1213 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	AHFE Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing, 2020
Country/Territory	United States
City	San Diego
Period	16/07/20 → 20/07/20

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-51328-3_38

Cite this

García-Vélez, R., Moreno, B. V., Ruiz-Ichazu, A., Morales Rivera , D. A., & Rosero-Perez, E. (2021). Automating the Generation of Study Teams Through Genetic Algorithms Based on Learning Styles in Higher Education. In T. Ahram (Ed.), 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 (pp. 270-277). (Advances in Intelligent Systems and Computing; Vol. 1213 AISC). Springer. https://doi.org/10.1007/978-3-030-51328-3_38

García-Vélez, Roberto ; Moreno, Bryam Vega ; Ruiz-Ichazu, Angel ; Morales Rivera , David Andres ; Rosero-Perez, Esteban. / Automating the Generation of Study Teams Through Genetic Algorithms Based on Learning Styles in Higher Education. 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. editor / Tareq Ahram. Springer, 2021. pp. 270-277 (Advances in Intelligent Systems and Computing).

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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.",

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García-Vélez, R, Moreno, BV, Ruiz-Ichazu, A, Morales Rivera , DA & Rosero-Perez, E 2021, Automating the Generation of Study Teams Through Genetic Algorithms Based on Learning Styles in Higher Education. in T Ahram (ed.), 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. Advances in Intelligent Systems and Computing, vol. 1213 AISC, Springer, pp. 270-277, AHFE Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing, 2020, San Diego, United States, 16/07/20. https://doi.org/10.1007/978-3-030-51328-3_38

Automating the Generation of Study Teams Through Genetic Algorithms Based on Learning Styles in Higher Education. / García-Vélez, Roberto; Moreno, Bryam Vega; Ruiz-Ichazu, Angel; Morales Rivera , David Andres; Rosero-Perez, Esteban.

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. ed. / Tareq Ahram. Springer, 2021. p. 270-277 (Advances in Intelligent Systems and Computing; Vol. 1213 AISC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Automating the Generation of Study Teams Through Genetic Algorithms Based on Learning Styles in Higher Education

AU - García-Vélez, Roberto

AU - Moreno, Bryam Vega

AU - Ruiz-Ichazu, Angel

AU - Morales Rivera , David Andres

AU - Rosero-Perez, Esteban

N1 - 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.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Academic profile

KW - Genetic algorithm

KW - Higher education

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KW - Style learning

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DO - 10.1007/978-3-030-51328-3_38

M3 - Conference contribution

AN - SCOPUS:85088530987

SN - 9783030513276

T3 - Advances in Intelligent Systems and Computing

SP - 270

EP - 277

BT - 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

A2 - Ahram, Tareq

PB - Springer

Y2 - 16 July 2020 through 20 July 2020

ER -

García-Vélez R, Moreno BV, Ruiz-Ichazu A, Morales Rivera DA, Rosero-Perez E. Automating the Generation of Study Teams Through Genetic Algorithms Based on Learning Styles in Higher Education. In Ahram T, editor, 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. Springer. 2021. p. 270-277. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-030-51328-3_38

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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