Uncovering student profiles. An explainable cluster analysis approach to PISA 2022

Educational data mining (EDM) applied to the wealth of data generated from international large-scale assessments (ILSAs) shows potential for identifying successful educational initiatives. Despite limited research on clustering methods in ILSAs, leveraging these methods to uncover student profiles c...

Descripción completa

Detalles Bibliográficos
Autores: Alvarez-Garcia, Miguel, Arenas-Parra, Mar, Ibar-Alonso, Raquel
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universidad de Oviedo (UNIOVI)
Repositorio:RUO. Repositorio Institucional de la Universidad de Oviedo
Idioma:inglés
OAI Identifier:oai:digibuo.uniovi.es:10651/76089
Acceso en línea:https://hdl.handle.net/10651/76089
https://dx.doi.org/10.1016/j.compedu.2024.105166
Access Level:acceso abierto
Palabra clave:Educational data mining
Explainable cluster analysis
Student profiles
International large-scale assessments
PISA
id ES_2481eae2be4afcb1b85fda89debb8f21
oai_identifier_str oai:digibuo.uniovi.es:10651/76089
network_acronym_str ES
network_name_str España
repository_id_str
spelling Uncovering student profiles. An explainable cluster analysis approach to PISA 2022Alvarez-Garcia, MiguelArenas-Parra, MarIbar-Alonso, RaquelEducational data miningExplainable cluster analysisStudent profilesInternational large-scale assessmentsPISAEducational data mining (EDM) applied to the wealth of data generated from international large-scale assessments (ILSAs) shows potential for identifying successful educational initiatives. Despite limited research on clustering methods in ILSAs, leveraging these methods to uncover student profiles can help decision-making in designing tailored programs. This study aims to identify and characterize 15-year-old student profiles using PISA 2022 data and reveal insights into the relationship between these profiles and factors such as ICT availability and use, gender, academic performance, and educational expectations. We analyzed PISA 2022 Spanish student data (n = 30,800) with a selection of 74 contextual variables, applying an end-to-end explainable cluster analysis methodology that integrates different machine learning (ML) and explainable artificial intelligence (XAI) techniques. This methodology covered data pre-processing, dimensionality reduction, clustering, and classification to ensure data quality and result explainability. We obtained 16 derived variables, 7 student clusters, and an optimal XGBoost classifier with a global accuracy of 0.8643. Using local and global SHAP values, we interpreted clusters, finding that socio-economic status and ICT availability and use at home are the most important factors differentiating student profiles. Our findings suggest the need to emphasize (i) proper ICT accessibility and use, as well as student support networks to improve academic performance, (ii) gender-specific well-being programs, and (iii) the encouragement of educational expectations tailored to students’ gender and their exposure to higher education. These results pave the way for personalized academic policies and programs through ML-based tools for uncovering student profiles.Universidad de Oviedo. PAPI-23-GR-2011-004920242024-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articlehttps://hdl.handle.net/10651/76089https://dx.doi.org/10.1016/j.compedu.2024.105166reponame:RUO. Repositorio Institucional de la Universidad de Oviedoinstname:Universidad de Oviedo (UNIOVI)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessoai:digibuo.uniovi.es:10651/760892026-06-07T06:38:51Z
dc.title.none.fl_str_mv Uncovering student profiles. An explainable cluster analysis approach to PISA 2022
title Uncovering student profiles. An explainable cluster analysis approach to PISA 2022
spellingShingle Uncovering student profiles. An explainable cluster analysis approach to PISA 2022
Alvarez-Garcia, Miguel
Educational data mining
Explainable cluster analysis
Student profiles
International large-scale assessments
PISA
title_short Uncovering student profiles. An explainable cluster analysis approach to PISA 2022
title_full Uncovering student profiles. An explainable cluster analysis approach to PISA 2022
title_fullStr Uncovering student profiles. An explainable cluster analysis approach to PISA 2022
title_full_unstemmed Uncovering student profiles. An explainable cluster analysis approach to PISA 2022
title_sort Uncovering student profiles. An explainable cluster analysis approach to PISA 2022
dc.creator.none.fl_str_mv Alvarez-Garcia, Miguel
Arenas-Parra, Mar
Ibar-Alonso, Raquel
author Alvarez-Garcia, Miguel
author_facet Alvarez-Garcia, Miguel
Arenas-Parra, Mar
Ibar-Alonso, Raquel
author_role author
author2 Arenas-Parra, Mar
Ibar-Alonso, Raquel
author2_role author
author
dc.subject.none.fl_str_mv Educational data mining
Explainable cluster analysis
Student profiles
International large-scale assessments
PISA
topic Educational data mining
Explainable cluster analysis
Student profiles
International large-scale assessments
PISA
description Educational data mining (EDM) applied to the wealth of data generated from international large-scale assessments (ILSAs) shows potential for identifying successful educational initiatives. Despite limited research on clustering methods in ILSAs, leveraging these methods to uncover student profiles can help decision-making in designing tailored programs. This study aims to identify and characterize 15-year-old student profiles using PISA 2022 data and reveal insights into the relationship between these profiles and factors such as ICT availability and use, gender, academic performance, and educational expectations. We analyzed PISA 2022 Spanish student data (n = 30,800) with a selection of 74 contextual variables, applying an end-to-end explainable cluster analysis methodology that integrates different machine learning (ML) and explainable artificial intelligence (XAI) techniques. This methodology covered data pre-processing, dimensionality reduction, clustering, and classification to ensure data quality and result explainability. We obtained 16 derived variables, 7 student clusters, and an optimal XGBoost classifier with a global accuracy of 0.8643. Using local and global SHAP values, we interpreted clusters, finding that socio-economic status and ICT availability and use at home are the most important factors differentiating student profiles. Our findings suggest the need to emphasize (i) proper ICT accessibility and use, as well as student support networks to improve academic performance, (ii) gender-specific well-being programs, and (iii) the encouragement of educational expectations tailored to students’ gender and their exposure to higher education. These results pave the way for personalized academic policies and programs through ML-based tools for uncovering student profiles.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10651/76089
https://dx.doi.org/10.1016/j.compedu.2024.105166
url https://hdl.handle.net/10651/76089
https://dx.doi.org/10.1016/j.compedu.2024.105166
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:RUO. Repositorio Institucional de la Universidad de Oviedo
instname:Universidad de Oviedo (UNIOVI)
instname_str Universidad de Oviedo (UNIOVI)
reponame_str RUO. Repositorio Institucional de la Universidad de Oviedo
collection RUO. Repositorio Institucional de la Universidad de Oviedo
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869404703804096512
score 15.811543

Similares en LA Referencia