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Using academic analytics to predict dropout risk in engineering courses

2018Conference object Open access
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dc.contributor.authorLima, Jhonny de
dc.contributor.authorAlves, Paulo
dc.contributor.authorPereira, Maria João
dc.contributor.authorAlmeida, Simone
dc.date.accessioned2020-07-31T10:05:49Z
dc.date.available2020-07-31T10:05:49Z
dc.date.issued2018
dc.description.abstractThe increase of data generated and stored in the educational databases makes it possible to obtain essential information about the teaching and learning process. School dropout and performance problems continue to represent issues which challenge teachers, researchers and higher education institutions to seek solutions. Through the use of academic analytics techniques for data analysis, a sample of 1,844 students between graduates and dropouts on the period between 2007 and 2015 were used as the basis. The methodology followed is essentially quantitative and it allowed to compare student profiles and degrees based on scores, number of attempts and other performance indicators. The data set was processed using Excel software for statistical analysis and R software for data mining using the k-Means and C5.0 algorithms. The propose of a model based on decision trees has as main objectives the generation of standardized instructions, easy interpretation and allow the addition of several possible outcomes, contributing to the decision-making process. The results of this study resulted in contributions which enable higher education institutions to identify students with performance problems and those at risk of dropout and, therefore, allow teachers and course directors to adopt better strategies to increase success and reduce dropout.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationLima, Jhonny; Alves, Paulo; Pereira, Maria João; Almeida, Simone (2018). Using academic analytics to predict dropout risk in engineering courses. In 17th European Conference on e Learning ECEL 2018. Atenaspt_PT
dc.identifier.issn2048-8645
dc.identifier.urihttp://hdl.handle.net/10198/22583
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherAcademic Conferences and Publishing International Limitedpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectAcademic analyticspt_PT
dc.subjectHigher educationpt_PT
dc.subjectDropoutpt_PT
dc.subjectEducationpt_PT
dc.subjectEngineeringpt_PT
dc.titleUsing academic analytics to predict dropout risk in engineering coursespt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceAtenaspt_PT
oaire.citation.endPage35pt_PT
oaire.citation.startPage34pt_PT
oaire.citation.title17th European Conference on e Learning ECEL 2018pt_PT
person.familyNameAlves
person.familyNamePereira
person.givenNamePaulo
person.givenNameMaria João
person.identifier.ciencia-idC319-FC42-5B6B
person.identifier.ciencia-idC912-4A49-A3B3
person.identifier.orcid0000-0002-0100-8691
person.identifier.orcid0000-0001-6323-0071
person.identifier.ridG-5999-2011
person.identifier.scopus-author-id55834442100
person.identifier.scopus-author-id13907870300
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication43d3b0cd-8fd9-4194-a9df-9cca66f8726b
relation.isAuthorOfPublicationa20ccfa6-4e84-4c25-ab0d-8d6ba196ffc2
relation.isAuthorOfPublication.latestForDiscovery43d3b0cd-8fd9-4194-a9df-9cca66f8726b

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