An Intelligent System Based on Genetic Algorithms to Generate Study Groups Using Personality Traits and Academic Profiles in Higher Education

Garcia-Velez, R.; Robles-Bykbaev, Vladimir; Lopez-Notes, M.; Calle-Lopez, D.; Barros-Ponton, M.; Galán-Mena, Jorge Andrés

doi:10.1109/coniiti.2018.8587070

Cited by 3 publications

(7 citation statements)

References 12 publications

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“…There is a tendency to use distance computational methods such as genetic algorithms to assess the group formation problem [10,15,32,[37][38][39][40][41], while network-based approaches are rarely used for that purpose: in two recent reviews on group formation problem, Maqtary et al…”

Section: Discussionmentioning

confidence: 99%

“…The former is computationally simple, but for the latter we elaborated an algorithm based on the work by Moreno et al [39]. We selected the genetic algorithm because it is severally used in the literature for automatic group formation [10,15,20,32,[37][38][39][40][41].…”

Section: Methodsmentioning

confidence: 99%

“…Table 1 shows the results obtained by comparing the answers given for the 6 scales of the questionnaire by the MECG and the random grouping. In general, studies assessing the group formation problem that involve real students use as a control group a random grouping [17,35,39,40] or also self-selection [14,[37][38][39][40]. The dataset is available online [63].…”

Section: Experiments With Real Studentsmentioning

confidence: 99%

“…Notwithstanding, there is a tendency in the literature to use mainly genetic algorithms [10,15,32,[37][38][39][40][41] and also particle swarm optimization algorithms [42][43][44][45]. In a genetic algorithm, the partition of the groups evolves by swapping the students in the different groups in a three-step iteration-selection, crossover and mutation-to achieve an optimal grouping of students.…”

Section: Introductionmentioning

confidence: 99%

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Minimum entropy collaborative groupings: A tool for an automatic heterogeneous learning group formation

Vallès-Català

Martín

2023

PLoS ONE

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For some decades now, theories on learning methodologies have advocated collaborative learning due to its good results in terms of effectiveness and learning types and its promotion of educational and social values. This means that teachers need to be able to apply different criteria when forming heterogeneous groups of students and to use automated techniques to assist them. In this study, we have created an approach based on complex network theory to design an algorithm called Minimum Entropy Collaborative Groupings (MECG) in order to form these heterogeneous groups more effectively. The algorithm was tested firstly under a synthetic framework and secondly in a real situation. In the first case, we generated 30 synthetic classrooms of different sizes and compared our approach with a genetic algorithm and a random grouping. In the latter case, the approach was tested on a group of 200 students on two subjects of a master’s degree in teacher training. For each subject there were 4 large groups of 50 students each, in which collaborative groups of 4 students were created. Two of these large groups were used as random groups, another group used the CHAEA test and the fourth group used the LML test. The results showed that the groups created with MECG were more effective, had less uncertainty and were more interrelated and mature. It was observed that the randomized groups did not obtain significantly better LML results and that this cannot be related to any emotional or motivational effect because the students performed the test as a placebo measure. In terms of learning styles, the results were significantly better with LML than with CHAEA, whereas no significant difference was observed in the randomized groups.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Experiments With Real Studentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Minimum entropy collaborative groupings: A tool for an automatic heterogeneous learning group formation

Vallès-Català

Martín

2023

PLoS ONE

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“…Garcia-Velez et al [29] in their work present an intelligent system that determines the best alternatives to automatically generate student groups to address practice activities and problems. The proposal uses a genetic algorithm to analyze the students' personality 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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A Novel Genetic Algorithm for Efficient Task Scheduling in Cloud Environment

Basahel

Yamin

2022

2022 9th International Conference on Computing for Sustainable Global Development (INDIACom)

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An Intelligent System Based on Genetic Algorithms to Generate Study Groups Using Personality Traits and Academic Profiles in Higher Education

Cited by 3 publications

References 12 publications

Minimum entropy collaborative groupings: A tool for an automatic heterogeneous learning group formation

Minimum entropy collaborative groupings: A tool for an automatic heterogeneous learning group formation

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

A Novel Genetic Algorithm for Efficient Task Scheduling in Cloud Environment

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