Interpretable success prediction in a computer networks curricular unit using machine learning

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dc.contributor.authorOliveira, Catarina Félix de
dc.contributor.authorSobral, Sónia Rolland
dc.contributor.authorFerreira, Maria João
dc.contributor.authorMoreira, Fernando
dc.date.accessioned2024-07-29T16:48:06Z
dc.date.available2024-07-29T16:48:06Z
dc.date.issued2024-07-25
dc.description.abstractToday, higher education institutions are focused on understanding which factors are associated with the failure or success of students to, early on, be able to implement measures that can reduce the low performance of students and even dropout. The retention rate is positively and negatively influenced by factors belonging to several dimensions (personal, environmental, and institutional). We aim to use information from those dimensions to identify students enrolled in a Computer Networks course at risk of failing the subject. Besides, this needs to happen as early as possible, to be able to provide the students, for example, with extra support or resources to try to prevent that negative outcome. For predicting the grade level on the first test, the best accuracy obtained was 55%. However, most C-level grades were correctly classified, with 63% accuracy in predicting the students that are most at risk of failing, which is one of our main objectives. As for the prediction of the second test’s grade level, the best accuracy obtained was 89% and concerned data regarding the students’ interaction with the LMS together with students’ grades history. All the C-level grades were correctly classified (100% accuracy) and so we were able to correctly predict every student at a high risk of failing. Using the procedure described in this paper, we are able to anticipate the students needing extra support, and provide them with different resources, to try to prevent their negative outcome.
dc.identifier.citationOlveira, C. F., Sobral, S. R., Ferreira, M. J., & Moreira, F. (2024). Interpretable success prediction in a computer networks curricular unit using machine learning. Procedia Computer Science, 239, 598-605. https://doi.org/10.1016/j.procs.2024.06.212. Repositório Institucional UPT. https://hdl.handle.net/11328/5855
dc.identifier.issn1877-0509
dc.identifier.urihttps://hdl.handle.net/11328/5855
dc.language.isoeng
dc.publisherElsevier
dc.relation.hasversionhttps://doi.org/10.1016/j.procs.2024.06.212
dc.rightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectLearning Analytics
dc.subjectEducational Data Mining
dc.subjectHigher education
dc.subjectDropout
dc.subjectRetention
dc.subject.fosCiências Sociais - Ciências da Educação
dc.titleInterpretable success prediction in a computer networks curricular unit using machine learning
dc.typejournal article
dcterms.referenceshttps://www.sciencedirect.com/science/article/pii/S1877050924014558
dspace.entity.typePublication
oaire.citation.endPage605
oaire.citation.issuePublished online: 25 july 2024
oaire.citation.startPage598
oaire.citation.titleProcedia Computer Science
oaire.citation.volume239
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.affiliation.nameREMIT – Research on Economics, Management and Information Technologies
person.affiliation.nameREMIT – Research on Economics, Management and Information Technologies
person.affiliation.nameUniversidade Portucalense
person.familyNameSobral
person.familyNameFerreira
person.familyNameMoreira
person.givenNameSónia Rolland
person.givenNameMaria João
person.givenNameFernando
person.identifier.ciencia-idED15-C9EC-5996
person.identifier.ciencia-id5C16-639B-5E48
person.identifier.ciencia-id7B1C-3A29-9861
person.identifier.orcid0000-0002-5041-3597
person.identifier.orcid0000-0003-4274-8845
person.identifier.orcid0000-0002-0816-1445
person.identifier.ridG-2227-2014
person.identifier.ridO-3023-2015
person.identifier.ridP-9673-2016
person.identifier.scopus-author-id37091626900
person.identifier.scopus-author-id57193559489
person.identifier.scopus-author-id8649758400
relation.isAuthorOfPublication2eea0284-22be-4cb8-8a14-192e56671b77
relation.isAuthorOfPublication4b6dcd84-a387-474c-a23b-299984fdcc92
relation.isAuthorOfPublicationbad3408c-ee33-431e-b9a6-cb778048975e
relation.isAuthorOfPublication.latestForDiscovery2eea0284-22be-4cb8-8a14-192e56671b77

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