Cargando…

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,...

Descripción completa

Detalles Bibliográficos
Autores principales: Martins, André Ferreira, Machado, Marcelo, Bernardino, Heder Soares, de Souza, Jairo Francisco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
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
_version_ 1783688684380356608
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
work_keys_str_mv AT martinsandreferreira acomparativeanalysisofmetaheuristicsappliedtoadaptivecurriculumsequencing
AT machadomarcelo acomparativeanalysisofmetaheuristicsappliedtoadaptivecurriculumsequencing
AT bernardinohedersoares acomparativeanalysisofmetaheuristicsappliedtoadaptivecurriculumsequencing
AT desouzajairofrancisco acomparativeanalysisofmetaheuristicsappliedtoadaptivecurriculumsequencing
AT martinsandreferreira comparativeanalysisofmetaheuristicsappliedtoadaptivecurriculumsequencing
AT machadomarcelo comparativeanalysisofmetaheuristicsappliedtoadaptivecurriculumsequencing
AT bernardinohedersoares comparativeanalysisofmetaheuristicsappliedtoadaptivecurriculumsequencing
AT desouzajairofrancisco comparativeanalysisofmetaheuristicsappliedtoadaptivecurriculumsequencing