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Improving prediction of students’ performance in intelligent tutoring systems using attribute selection and ensembles of different multimodal data sources

dc.contributor.authorChango, W.
dc.contributor.authorCerezo Menéndez, Rebeca 
dc.contributor.authorSánchez Santillán, Miguel 
dc.contributor.authorAzevedo, R.
dc.contributor.authorRomero, C.
dc.date.accessioned2022-03-11T07:29:52Z
dc.date.available2022-03-11T07:29:52Z
dc.date.issued2021
dc.identifier.citationJournal of Computing in Higher Education, 33(3), p. 614-634 (2021); doi:10.1007/s12528-021-09298-8
dc.identifier.issn1042-1726
dc.identifier.urihttp://hdl.handle.net/10651/62219
dc.description.sponsorshipThe authors acknowledge the financial subsidy provided by the Spanish Ministry of Science and Innovation “Deteccion Temprana e Intervencion en Dificultades del Aprendizaje Específicas desde el Modelo RtI” (PID2019-107201GB-I009), and the Spanish Ministry of Science and Innovation in the project “Improving Data Science User’s Experience with Computational Intelligence (INTENSE)” (PID2020-115832GB-I00).
dc.format.extentp. 614-634
dc.language.isoeng
dc.relation.ispartofJournal of computing in higher education
dc.rights© The Author(s) 2021
dc.rightsCC Reconocimiento 4.0 Internacional
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceScopus
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85118119545&doi=10.1007%2fs12528-021-09298-8&partnerID=40&md5=ad99946aaf03093817fc5ef9bc74b72b
dc.titleImproving prediction of students’ performance in intelligent tutoring systems using attribute selection and ensembles of different multimodal data sources
dc.typejournal article
dc.identifier.doi10.1007/s12528-021-09298-8
dc.local.notesOA ATUO21
dc.relation.projectIDMICINN/PID2019-107201GB-I009
dc.relation.projectIDMICINN/PID2020-115832GB-I00
dc.relation.publisherversionhttp://dx.doi.org/10.1007/s12528-021-09298-8
dc.rights.accessRightsopen access
dc.type.hasVersionVoR


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