Logo do repositório
 
Publicação

Multi-objective clustering algorithm applied to the mathE categorization problem

2026Artigo científico Acesso aberto
Ver registo simples
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg04:Educação de Qualidade
dc.contributor.authorAzevedo, Beatriz Flamia
dc.contributor.authorLeite, Gabriel A.
dc.contributor.authorPacheco, Maria F.
dc.contributor.authorFernandes, Florbela P.
dc.contributor.authorRocha, Ana Maria A. C.
dc.contributor.authorPereira, Ana I.
dc.date.accessioned2026-03-18T12:04:50Z
dc.date.available2026-03-18T12:04:50Z
dc.date.issued2026
dc.description.abstractThis work explores bio-inspired strategies and clustering techniques to propose an automatic clustering algorithm, named Multi-objective Clustering Algorithm (MCA). This algorithm uses a set of measure combinations to define the optimal number of clusters and the partitioning of the elements, minimizing an intra-clustering measure and maximizing an inter-clustering one. The MathE platform is an educational tool whose main objective is to assist students facing challenges in Mathematics at higher education level. Based on previous studies, the opinions of lecturers and students diverge regarding the difficulty level of the questions available on the platform. Therefore, this research aims to explore and develop a new clustering method for question categorization, taking into account the opinions of both lecturers and students about the difficulty levels of the questions. The Multi-objective Clustering Algorithm (MCA) is proposed to group the questions into clusters representing the difficulty level of the platform's questions. Compared with the k-means algorithm, the MCA results exhibit outstanding performance. Through a combination of multi-objective clustering measures, the MCA successfully achieved a set of optimal solutions (Hybrid Pareto front). This method empowers the decision-maker, enabling them to choose the most appropriate solution based on additional insights beyond the model.eng
dc.description.sponsorshipThis work has been supported by FCT Fundação para a Ciência e Tecnologia, I. P. by projects CeDRI, UID/05757/2025 (DOI: 10.54499/UID/05757/2025), and UID/PRR/05757/2025 (DOI: 10.54499/UID/PRR/05757/2025); SusTEC, LA/P/0007/2020 (DOI: 10.54499/LA/P//0007/2020), UIDB/00319 and Erasmus Plus KA2 within the project 2021-1-PT01-KA220-HED-000023288. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021. This work has been partially supported by DataGEMS, funded by the European Union's Horizon Europe Research and Innovation programme, under grant agreement No 101188416.
dc.identifier.citationAzevedo, Beatriz F.; Leite, Gabriel A.; Pacheco, Maria F.; Fernandes, Florbela P.; Rocha, Ana M. A. C.; Pereira, Ana I. (2026). Multi-objective Clustering Algorithm Applied to the MathE Categorization Problem. Information Systems Frontiers. ISSN 1387-3326. 1-17. DOI: 10.1007/10796-025-10674-3
dc.identifier.doi10.1007/10796-025-10674-3
dc.identifier.issn1387-3326
dc.identifier.issn1572-9419
dc.identifier.urihttp://hdl.handle.net/10198/36123
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Science and Business Media LLC
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.relation.ispartofInformation Systems Frontiers
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectMulti-objective-clustering
dc.subjectPartitioning-clustering
dc.subjectActive learning
dc.subjectHigher education
dc.subjecte-learning
dc.titleMulti-objective clustering algorithm applied to the mathE categorization problemeng
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberLA/P/0007/2020
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.citation.endPage17
oaire.citation.startPage1
oaire.citation.titleInformation Systems Frontiers
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameAzevedo
person.familyNameLeite
person.familyNamePacheco
person.familyNameFernandes
person.familyNamePereira
person.givenNameBeatriz Flamia
person.givenNameGabriel A.
person.givenNameMaria F.
person.givenNameFlorbela P.
person.givenNameAna I.
person.identifier.ciencia-id181E-855C-E62C
person.identifier.ciencia-id8A1D-BE60-F58E
person.identifier.ciencia-idF319-DAC3-8F15
person.identifier.ciencia-id501D-6FD0-CC53
person.identifier.ciencia-id0716-B7C2-93E4
person.identifier.orcid0000-0002-8527-7409
person.identifier.orcid0009-0008-3273-0450
person.identifier.orcid0000-0001-7915-0391
person.identifier.orcid0000-0001-9542-4460
person.identifier.orcid0000-0003-3803-2043
person.identifier.ridF-3168-2010
person.identifier.scopus-author-id36802474600
person.identifier.scopus-author-id35179471000
person.identifier.scopus-author-id15071961600
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublication04fa4023-3726-4dd5-8d97-f6b162ceb820
relation.isAuthorOfPublication8bc39973-2684-46ee-b2bd-8661b61b8658
relation.isAuthorOfPublicatione56596ca-3238-4fde-ace1-abb363a222e8
relation.isAuthorOfPublication1f7a9fde-7a4d-4b2c-8f9d-dab571163c33
relation.isAuthorOfPublicatione9981d62-2a2b-4fef-b75e-c2a14b0e7846
relation.isAuthorOfPublication.latestForDiscovery04fa4023-3726-4dd5-8d97-f6b162ceb820
relation.isProjectOfPublication6255046e-bc79-4b82-8884-8b52074b4384
relation.isProjectOfPublication.latestForDiscovery6255046e-bc79-4b82-8884-8b52074b4384

Ficheiros

Principais
A mostrar 1 - 1 de 1
Miniatura
Nome:
Multi-objective Clustering Algorithm.pdf
Tamanho:
3.19 MB
Formato:
Adobe Portable Document Format
Ver/Abrir
Licença
A mostrar 1 - 1 de 1
Miniatura indisponível
Nome:
license.txt
Tamanho:
1.75 KB
Formato:
Item-specific license agreed upon to submission
Descrição:
Ver/Abrir

Coleções

ESTiG - Artigos em Revistas Indexados à WoS/Scopus