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Comparison between single and multi-objective clustering algorithms: mathE case study
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Orientador(es)
Resumo(s)
This paper compares the results obtained for four single clustering algorithms with a multi-objective clustering approach. For this, a dataset describing the student’s behavior within the Linear Algebra topic on the MathE e-learning platform is used. This dataset aids in understanding student performance and engagement in MathE to support the development of an intelligent system to tailor the platform’s resources to users’s needs. The four algorithms suggested two clusters as the optimal solution for the dataset. However, this binary categorization did not provide meaningful insights into the proposal of the MathE platform; that is, it did not provide a customized system according to individual needs. Thus, this study uses the multi-objective clustering algorithm, which results in a set of non-dominated solutions, providing decision-makers with a broader range of options to choose the solution that best meets their needs. The results demonstrate the main benefits of the proposed human-in-the-loop multi-objective approach since it provides several optimal solutions and allows the decision-maker to apply fundamental knowledge to define the most appropriate solution to the problem based on previous knowledge.
Descrição
Palavras-chave
Multi-objective clustering Automatic clustering Optimization Bio-inspired algorithm
Contexto Educativo
Citação
Azevedo, Beatriz Flamia; Rocha, Ana Maria A.C.; Fernandes, Florbela P.; Pacheco, Maria F.; Pereira, Ana I. (2024). Comparison Between single and multi-objective clustering algorithms: mathE case study. In 4th International Conference, OL2A 2024. Cham: Springer Nature. Part 1, p. 65–80. ISBN 978-3-031-77426-3. DOI: 10.1007/978-3-031-77426-3_5
Editora
Springer Nature