Clustering Algorithm to Measure Student Assessment Accuracy: A Double Study

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dc.contributor.authorOliveira, Catarina Félix de
dc.contributor.authorSobral, Sónia Rolland
dc.date.accessioned2021-12-23T17:09:31Z
dc.date.available2021-12-23T17:09:31Z
dc.date.issued2021-12-18
dc.description.abstractSelf-assessment is one of the strategies used in active teaching to engage students in the entire learning process, in the form of self-regulated academic learning. This study aims to assess the possibility of including self-evaluation in the student’s final grade, not just as a self-assessment that allows students to predict the grade obtained but also as something to weigh on the final grade. Two different curricular units are used, both from the first year of graduation, one from the international relations course (N = 29) and the other from the computer science and computer engineering courses (N = 50). Students were asked to self-assess at each of the two evaluation moments of each unit, after submitting their work/test and after knowing the correct answers. This study uses statistical analysis as well as a clustering algorithm (K-means) on the data to try to gain deeper knowledge and visual insights into the data and the patterns among them. It was verified that there are no differences between the obtained grade and the thought grade by gender and age variables, but a direct correlation was found between the thought grade averages and the grade level. The difference is less accentuated at the second moment of evaluation—which suggests that an improvement in the self-assessment skill occurs from the first to the second evaluation momentpt_PT
dc.identifier.citationSobral, S. R., & Oliveira, C. F. (2021). Clustering Algorithm to Measure Student Assessment Accuracy: A Double Study. Big Data and Cognitive Computing, 5(4), 81. doi: https://doi.org/10.3390/bdcc5040081. Disponível no Repositório UPT, http://hdl.handle.net/11328/3862pt_PT
dc.identifier.doihttps://doi.org/10.3390/bdcc5040081pt_PT
dc.identifier.issn2504-2289
dc.identifier.urihttp://hdl.handle.net/11328/3862
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPI - Multidisciplinary Digital Publishing Institutept_PT
dc.relation.ispartofseries;4
dc.rightsopen accesspt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectSelf-assessmentpt_PT
dc.subjectSelf-evaluationpt_PT
dc.subjectHigher educationpt_PT
dc.subjectClusteringpt_PT
dc.subjectAccuracypt_PT
dc.titleClustering Algorithm to Measure Student Assessment Accuracy: A Double Studypt_PT
dc.typejournal articlept_PT
degois.publication.firstPage81pt_PT
degois.publication.titleBig Data and Cognitive Computingpt_PT
degois.publication.volume5pt_PT
dspace.entity.typePublicationen
person.affiliation.nameREMIT – Research on Economics, Management and Information Technologies
person.familyNameSobral
person.givenNameSónia Rolland
person.identifier.ciencia-idED15-C9EC-5996
person.identifier.orcid0000-0002-5041-3597
person.identifier.ridG-2227-2014
person.identifier.scopus-author-id37091626900
relation.isAuthorOfPublication2eea0284-22be-4cb8-8a14-192e56671b77
relation.isAuthorOfPublication.latestForDiscovery2eea0284-22be-4cb8-8a14-192e56671b77

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