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; Rivera, David Morales; Rosero-Perez, Esteban

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

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…García-Vélez et al [23] in their research present a system capable of exploring the best alternatives to automatically organize homogeneous study groups that favor the best performance. The proposal uses a personalized genetic algorithm, based on the students' learning styles and their academic profiles.…”

Section: Related Workmentioning

confidence: 99%

Group formation in collaborative learning contexts based on personality traits: An empirical study in initial Programming courses

Sánchez¹,

Ordóñez²,

Duque³

2021

IxD&A

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Considering that group formation is one of the key processes when developing activities in collaborative learning contexts, this paper aims to propose a technique based on an approach of genetic algorithms to achieve homogeneous groups, considering the students' personality traits as grouping criteria. For its validation, an experiment was designed with 132 first semesters engineering students, quantifying their personality traits through the “Big Five Inventory”, forming workgroups and developing a collaborative activity in initial Programming courses. The experiment made it possible to compare the results obtained by the students applying the proposed approach to those obtained through other group formation strategies. It was demonstrated through the experiment that the homogeneous groups generated by the proposed technique produce better academic results compared to the grouping technique by students’ preference, traditionally used by the teachers when developing a collaborative activity.

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Section: Related Workmentioning

confidence: 99%

Group formation in collaborative learning contexts based on personality traits: An empirical study in initial Programming courses

Sánchez¹,

Ordóñez²,

Duque³

2021

IxD&A

View full text Add to dashboard Cite

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On the Design of Student Employment Module in Higher Education Management System Based on Genetic Algorithm

Gao

2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Revolutionizing Higher Education: The Transformative Impact of Artificial Intelligence on Teaching, Learning, and Career Preparation

Farooq,

Malik,

Syed

2024

ShodhKosh J. Vis. Per. Arts

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Artificial Intelligence (AI) is revolutionizing higher education, reshaping teaching methodologies, assessment practices, and career preparation. This paper explores AI's transformative role, focusing on its ability to personalize learning, automate administrative tasks, and enhance the efficiency and fairness of assessments. AI-driven systems such as adaptive learning platforms and automated grading tools enable personalized education tailored to individual needs while providing timely feedback to students and easing educators' workloads. Additionally, AI is influencing the job market by creating demand for advanced technical skills like data science and machine learning, alongside essential human-centric abilities like creativity and emotional intelligence. However, the integration of AI in education also presents challenges. Ethical concerns such as data privacy, algorithmic bias, and the diminishing role of human judgment in critical educational processes require careful consideration. Cognitively, AI's limitations in understanding context and providing empathy underscore the need for a balanced approach where AI complements human insights rather than replaces them. To address these challenges, this paper recommends investing in teacher training, prioritizing ethical AI use, fostering skill development for an AI-driven workforce, and ensuring equitable access to AI technologies. The findings highlight the need for proactive strategies in higher education to harness AI's potential while maintaining its human-centered focus. By aligning AI's capabilities with educational goals, institutions can prepare students to thrive in an evolving global workforce while addressing ethical and cognitive implications.

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

Cited by 3 publications

References 7 publications

Group formation in collaborative learning contexts based on personality traits: An empirical study in initial Programming courses

Group formation in collaborative learning contexts based on personality traits: An empirical study in initial Programming courses

On the Design of Student Employment Module in Higher Education Management System Based on Genetic Algorithm

Revolutionizing Higher Education: The Transformative Impact of Artificial Intelligence on Teaching, Learning, and Career Preparation

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