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Educational data mining for tutoring support in Higher Education: a web-based tool case study in engineering degrees

2020Journal article Open access
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dc.contributor.authorPrada, Miguel Angel
dc.contributor.authorDominguez, Manuel
dc.contributor.authorVicario, Jose Lopez
dc.contributor.authorAlves, Paulo
dc.contributor.authorBarbu, Marian
dc.contributor.authorPodpora, Michal
dc.contributor.authorSpagnolini, Umberto
dc.contributor.authorPereira, Maria João
dc.contributor.authorVilanova, Ramon
dc.date.accessioned2021-03-30T09:50:17Z
dc.date.available2021-03-30T09:50:17Z
dc.date.issued2020
dc.description.abstractThis paper presents a web-based software tool for tutoring support of engineering students without any need of data scientist background for usage. This tool is focused on the analysis of students' performance, in terms of the observable scores and of the completion of their studies. For that purpose, it uses a data set that only contains features typically gathered by university administrations about the students, degrees and subjects. The web-based tool provides access to results from different analyses. Clustering and visualization in a low-dimensional representation of students' data help an analyst to discover patterns. The coordinated visualization of aggregated students' performance into histograms, which are automatically updated subject to custom filters set interactively by an analyst, can be used to facilitate the validation of hypotheses about a set of students. Classification of students already graduated over three performance levels using exploratory variables and early performance information is used to understand the degree of course-dependency of students' behavior at different degrees. The analysis of the impact of the student's explanatory variables and early performance in the graduation probability can lead to a better understanding of the causes of dropout. Preliminary experiments on data of the engineering students from the 6 institutions associated to this project were used to define the final implementation of the web-based tool. Preliminary results for classification and drop-out were acceptable since accuracies were higher than 90% in some cases. The usefulness of the tool is discussed with respect to the stated goals, showing its potential for the support of early profiling of students. Real data from engineering degrees of EU Higher Education institutions show the potential of the tool for managing high education and validate its applicability on real scenarios.pt_PT
dc.description.sponsorshipThis work was supported by the Erasmus+ Key Action 2 Strategic Partnerships KA203, funded by the European Commission, under Grant 2016-1-ES01-KA203-025452.
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationPrada, Miguel Angel; Dominguez, Manuel; Vicario, Jose Lopez; Alves, Paulo; Barbu, Marian; Podpora, Michal; Spagnolini, Umberto; Pereira, Maria João; Vilanova, Ramon (2020). Educational data mining for tutoring support in Higher Education: a web-based tool case study in engineering degrees. IEEE Access. ISSN 2169-3536. 8, p. 212818-212836pt_PT
dc.identifier.doi10.1109/ACCESS.2020.3040858pt_PT
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/10198/23514
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/pt_PT
dc.subjectDrop-out predictionpt_PT
dc.subjectEducational data miningpt_PT
dc.subjectPerformance predictionpt_PT
dc.subjectVisual analyticspt_PT
dc.titleEducational data mining for tutoring support in Higher Education: a web-based tool case study in engineering degreespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage212836pt_PT
oaire.citation.startPage212818pt_PT
oaire.citation.titleIEEE Accesspt_PT
oaire.citation.volume8pt_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.typearticlept_PT
relation.isAuthorOfPublication43d3b0cd-8fd9-4194-a9df-9cca66f8726b
relation.isAuthorOfPublicationa20ccfa6-4e84-4c25-ab0d-8d6ba196ffc2
relation.isAuthorOfPublication.latestForDiscovery43d3b0cd-8fd9-4194-a9df-9cca66f8726b

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