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 2 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%