Towards educational sustainability: An AI system for identifying and preventing student dropout

dc.contributor.authorBrand C, Erika J.
dc.contributor.authorRamírez, Gabriel M.
dc.contributor.authorDiaz, Jaime
dc.contributor.authorMoreira, Fernando
dc.date.accessioned2024-04-08T15:17:47Z
dc.date.available2024-04-08T15:17:47Z
dc.date.issued2024-03-25
dc.description.abstractThe design and development of a web application to identify a high or low probability of student dropout at the National Learning Service (SENA) in Colombia, aiming to streamline the process of identifying and supporting potential candidates for assistance provided by the institution through the student welfare department. Throughout the development, socioeconomic variables with the highest impact on characterized academic dropout processes to create a dataset. This dataset was then utilized with various artificial intelligence techniques explored in Machine Learning (Decision Trees, K-means, and Regression), ultimately determining the most effective algorithm for integration into the Software. The decision tree classification technique emerged as the most effective, achieving an impressive accuracy of 91% and a minimal error rate of 9%, substantiating its state-of-the-art standing. As a result, this Software has optimized processes within the Student Welfare Department at SENA and is adaptable for use in any higher education institution.
dc.identifier.citationBrand C, E. J., Ramírez, G. M., Diaz, J., & Moreira, F. (2024). Towards educational sustainability: An AI system for identifying and preventing student dropout. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, (Published online: 25 march 2024), 1-11. https://doi.org/10.1109/RITA.2024.3381850. Repositório Institucional UPT. https://hdl.handle.net/11328/5576
dc.identifier.issn1932-8540
dc.identifier.urihttps://hdl.handle.net/11328/5576
dc.language.isoeng
dc.publisherIEEE
dc.relation.hasversionhttps://doi.org/10.1109/RITA.2024.3381850
dc.rightsrestricted access
dc.subjectArtificial Intelligence
dc.subjectMachine Learning School Dropout
dc.subjectHigher Education
dc.subjectColombia
dc.subject.fosCiências Naturais - Ciências da Computação e da Informação
dc.titleTowards educational sustainability: An AI system for identifying and preventing student dropout
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage11
oaire.citation.issuePublished online: 25 march 2024
oaire.citation.startPage1
oaire.citation.titleIEEE Revista Iberoamericana de Tecnologias del Aprendizaje
person.affiliation.nameUniversidade Portucalense
person.familyNameMoreira
person.givenNameFernando
person.identifier.ciencia-id7B1C-3A29-9861
person.identifier.orcid0000-0002-0816-1445
person.identifier.ridP-9673-2016
person.identifier.scopus-author-id8649758400
relation.isAuthorOfPublicationbad3408c-ee33-431e-b9a6-cb778048975e
relation.isAuthorOfPublication.latestForDiscoverybad3408c-ee33-431e-b9a6-cb778048975e

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