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

Grupo de Investigación de Inteligencia Artificial y Tecnologías de Asistencia (GI-IATa)

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

2 Citas (Scopus)

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	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
Editores	Tareq Ahram
Editorial	Springer
Páginas	270-277
Número de páginas	8
ISBN (versión impresa)	9783030513276
DOI	https://doi.org/10.1007/978-3-030-51328-3_38
Estado	Publicada - 2021
Evento	AHFE Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing, 2020 - San Diego, Estados Unidos Duración: 16 jul. 2020 → 20 jul. 2020

Serie de la publicación

Nombre	Advances in Intelligent Systems and Computing
Volumen	1213 AISC
ISSN (versión impresa)	2194-5357
ISSN (versión digital)	2194-5365

Conferencia

Conferencia	AHFE Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing, 2020
País/Territorio	Estados Unidos
Ciudad	San Diego
Período	16/07/20 → 20/07/20

Nota bibliográfica

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.

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

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

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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. En 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 et al. / 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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title = "Automating the Generation of Study Teams Through Genetic Algorithms Based on Learning Styles in Higher Education",

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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author = "Roberto Garc{\'i}a-V{\'e}lez and Moreno, {Bryam Vega} and Angel Ruiz-Ichazu and {Morales Rivera}, {David Andres} and Esteban Rosero-Perez",

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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. En 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, Estados Unidos, 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 et al.
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).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

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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.

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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. En 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). 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

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