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A comparative analysis of metaheuristics applied to adaptive curriculum sequencing
The effective adoption of online learning depends on user satisfaction as distance education approaches suffer from a lack of commitment that may lead to failures and dropouts. The adaptive learning literature argues that an alternative to achieve student satisfaction is to treat them individually,...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8099996/ https://www.ncbi.nlm.nih.gov/pubmed/33972824 http://dx.doi.org/10.1007/s00500-021-05836-9 |
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author | Martins, André Ferreira Machado, Marcelo Bernardino, Heder Soares de Souza, Jairo Francisco |
author_facet | Martins, André Ferreira Machado, Marcelo Bernardino, Heder Soares de Souza, Jairo Francisco |
author_sort | Martins, André Ferreira |
collection | PubMed |
description | The effective adoption of online learning depends on user satisfaction as distance education approaches suffer from a lack of commitment that may lead to failures and dropouts. The adaptive learning literature argues that an alternative to achieve student satisfaction is to treat them individually, delivering the educational content in a personalized manner. In addition, the sequencing of this content—called Adaptive Curriculum Sequencing (ACS)—is important to avoid cognitive overload and disorientation. The search for an optimal sequence from ever-growing databases is an NP-Hard combinatorial optimization problem. Although some approaches have been proposed, it is challenging to assess their contributions due to the lack of benchmark data available. This paper presents a procedure to create synthetic dataset to evaluate ACS approaches and, as a concept proof, analyzes metaheuristics usually used in ACS approaches: Genetic Algorithm, Particle Swarm Optimization (PSO) and Prey–Predator Algorithm using student’s learning goals and their extrinsic and intrinsic information. We also propose an approach based on Differential Evolution (DE). The computational experiments include synthetic datasets with a varied amount of learning materials and real-world datasets for comparison. The results show that DE performed better than the other methods when less than 500 learning materials are used while PSO performed better for larger problems. |
format | Online Article Text |
id | pubmed-8099996 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-80999962021-05-06 A comparative analysis of metaheuristics applied to adaptive curriculum sequencing Martins, André Ferreira Machado, Marcelo Bernardino, Heder Soares de Souza, Jairo Francisco Soft comput Application of Soft Computing The effective adoption of online learning depends on user satisfaction as distance education approaches suffer from a lack of commitment that may lead to failures and dropouts. The adaptive learning literature argues that an alternative to achieve student satisfaction is to treat them individually, delivering the educational content in a personalized manner. In addition, the sequencing of this content—called Adaptive Curriculum Sequencing (ACS)—is important to avoid cognitive overload and disorientation. The search for an optimal sequence from ever-growing databases is an NP-Hard combinatorial optimization problem. Although some approaches have been proposed, it is challenging to assess their contributions due to the lack of benchmark data available. This paper presents a procedure to create synthetic dataset to evaluate ACS approaches and, as a concept proof, analyzes metaheuristics usually used in ACS approaches: Genetic Algorithm, Particle Swarm Optimization (PSO) and Prey–Predator Algorithm using student’s learning goals and their extrinsic and intrinsic information. We also propose an approach based on Differential Evolution (DE). The computational experiments include synthetic datasets with a varied amount of learning materials and real-world datasets for comparison. The results show that DE performed better than the other methods when less than 500 learning materials are used while PSO performed better for larger problems. Springer Berlin Heidelberg 2021-05-06 2021 /pmc/articles/PMC8099996/ /pubmed/33972824 http://dx.doi.org/10.1007/s00500-021-05836-9 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Application of Soft Computing Martins, André Ferreira Machado, Marcelo Bernardino, Heder Soares de Souza, Jairo Francisco A comparative analysis of metaheuristics applied to adaptive curriculum sequencing |
title | A comparative analysis of metaheuristics applied to adaptive curriculum sequencing |
title_full | A comparative analysis of metaheuristics applied to adaptive curriculum sequencing |
title_fullStr | A comparative analysis of metaheuristics applied to adaptive curriculum sequencing |
title_full_unstemmed | A comparative analysis of metaheuristics applied to adaptive curriculum sequencing |
title_short | A comparative analysis of metaheuristics applied to adaptive curriculum sequencing |
title_sort | comparative analysis of metaheuristics applied to adaptive curriculum sequencing |
topic | Application of Soft Computing |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8099996/ https://www.ncbi.nlm.nih.gov/pubmed/33972824 http://dx.doi.org/10.1007/s00500-021-05836-9 |
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