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Data mining tool for academic data exploitation: selection of most suitable algorithms

2018Report Open access
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dc.contributor.authorVicario, José
dc.contributor.authorVilanova, Ramon
dc.contributor.authorBazzarelli, M.
dc.contributor.authorPaganoni, Anna
dc.contributor.authorSpagnolini, Umberto
dc.contributor.authorTorrebruno, Aldo
dc.contributor.authorPrada, Miguel Angel
dc.contributor.authorMorán, Antonio
dc.contributor.authorDominguez, Manuel
dc.contributor.authorPereira, Maria João
dc.contributor.authorAlves, Paulo
dc.contributor.authorPodpora, Michal
dc.contributor.authorBarbu, Marian
dc.date.accessioned2019-02-22T09:43:01Z
dc.date.available2019-02-22T09:43:01Z
dc.date.issued2018
dc.description.abstractSPEET project is aimed at exploiting the potential synergy among the huge amount of academic data actually existing at universities and the maturity of data science in order to provide tools to extract information from students’ data. A rich picture can be extracted from this data if conveniently processed. The purpose of this project is to apply data mining algorithms to process this data in order to extract information about and to identify student profiles. In this document, the results obtained at SPEET project under the development of the data mining tools are presented. More specifically, two mechanisms have been developed: a clustering/classification scheme of students in terms of academic performance and a drop-out prediction system. The document starts by addressing the motivation of the development of data mining tools along with the considerations taken into account for academic data gathering. These considerations include the proposed unified dataset format and some details about confidentiality issues. Next, the students’ clustering and classification schemes are presented in detail. More specifically, a description of the considered machine learning algorithms can be found. Besides, a discussion of obtained results when considering data belonging to the different SPEET project’s partners is addressed. Results show how groups of clusters can be automatically identified and how new students can be classified into existing groups with a high accuracy. Finally, the implemented drop-out prediction system is considered by presenting several algorithms alternatives. In this case, the evaluation of the dropout mechanism is focused on one institution, showing a prediction accuracy around 91 %. Algorithms presented at this document are available at repositories or inline code format, as accordingly indicated.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationVicario, Jose; Vilanova, Ramon; Bazzarelli, M.; Paganoni, Anna; Spagnolini, Umberto; Torrebruno, Aldo; Prada, Miguel; Morán, Antonio; Dominguez, Manuel; Pereira, Maria João; Alves, Paulo; Podpora, Michal; Barbu, Marian (2018). Data mining tool for academic data exploitation: selection of most suitable algorithms. ERASMUS+KA2/KA203pt_PT
dc.identifier.isbnISBN 978-989-20-8738-2
dc.identifier.urihttp://hdl.handle.net/10198/18940
dc.language.isoengpt_PT
dc.peerreviewednopt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectAcademic analyticspt_PT
dc.subjectLearning analyticspt_PT
dc.subjectBig data in educationpt_PT
dc.subjectEducational data miningpt_PT
dc.subjectStudent profilept_PT
dc.subjectDropout preventionpt_PT
dc.titleData mining tool for academic data exploitation: selection of most suitable algorithmspt_PT
dc.typereport
dspace.entity.typePublication
oaire.citation.titleERASMUS+KA2/KA203pt_PT
person.familyNamePereira
person.familyNameAlves
person.givenNameMaria João
person.givenNamePaulo
person.identifier.ciencia-idC912-4A49-A3B3
person.identifier.ciencia-idC319-FC42-5B6B
person.identifier.orcid0000-0001-6323-0071
person.identifier.orcid0000-0002-0100-8691
person.identifier.ridG-5999-2011
person.identifier.scopus-author-id13907870300
person.identifier.scopus-author-id55834442100
rcaap.rightsopenAccesspt_PT
rcaap.typereportpt_PT
relation.isAuthorOfPublicationa20ccfa6-4e84-4c25-ab0d-8d6ba196ffc2
relation.isAuthorOfPublication43d3b0cd-8fd9-4194-a9df-9cca66f8726b
relation.isAuthorOfPublication.latestForDiscoverya20ccfa6-4e84-4c25-ab0d-8d6ba196ffc2

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