Curricular analytics to characterize educational trajectories in high-failure rate courses that lead to late dropout

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dc.catalogadorjwg
dc.contributor.authorMuñoz Gama, Jorge
dc.contributor.authorSalazar-Fernandez J.P.
dc.contributor.authorSepĂșlveda M.
dc.contributor.authorNussbaum M.
dc.date.accessioned2024-05-30T17:18:03Z
dc.date.available2024-05-30T17:18:03Z
dc.date.issued2021
dc.description.abstractFeatured Application, In this work, Process Mining techniques are used, with a curricular analytics approach, to understand the educational trajectories of higher education students.", "Late dropout is one of the most pressing challenges currently facing higher education, and the process that each student follows to arrive at that decision usually involves several academic periods. This work presents a curricular analytics approach at the program level, to analyze how educational trajectories of undergraduate students in high-failure rate courses help to describe the process that leads to late dropout. Educational trajectories (n = 10,969) of high-failure rate courses are created using Process Mining techniques, and the results are discussed based on established theoretical frameworks. Late dropout was more frequent among students who took a stopout while having high-failure rate courses they must retake. Furthermore, students who ended in late dropout with high-failure rate courses they must retake had educational trajectories that were on average shorter and less satisfactory. On the other hand, the educational trajectories of students who ended in late dropout without high-failure rate courses they must retake were more similar to those of students who graduated late. Moreover, some differences found among ISCED fields are also described. The proposed approach can be replicated in any other university to understand the educational trajectories of late dropout students from a longitudinal perspective, generating new knowledge about the dynamic behavior of the students. This knowledge can trigger improvements to the curriculum and in the follow-up mechanisms used to increase student retention.
dc.fechaingreso.objetodigital2024-12-16
dc.fuente.origenConveris
dc.identifier.doi10.3390/app11041436
dc.identifier.issn2076-3417
dc.identifier.scopusidSCOPUS_ID:2-s2.0-85100851709
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/86094
dc.identifier.wosidWOS:000632088000001
dc.information.autorucEscuela de IngenierĂ­a; Munoz Gama, Jorge; 0000-0002-6908-3911; 1013863
dc.issue.numero4
dc.language.isoen
dc.nota.accesocontenido completo
dc.pagina.final15
dc.pagina.inicio1
dc.revistaApplied Sciences (Switzerland)
dc.rightsacceso abierto
dc.subject.ddc370
dc.subject.deweyEducaciĂłnes_ES
dc.subject.ods10 Reduced inequalities
dc.subject.odspa10 ReducciĂłn de las desigualdades
dc.titleCurricular analytics to characterize educational trajectories in high-failure rate courses that lead to late dropout
dc.typeartĂ­culo
dc.volumen11
sipa.codpersvinculados16896
sipa.trazabilidadORCID;2024-05-30
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