Please use this identifier to cite or link to this item: http://hdl.handle.net/11328/3862
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dc.contributor.authorSobral, Sónia Rolland-
dc.contributor.authorOliveira, Catarina Félix de-
dc.date.accessioned2021-12-23T17:09:31Z-
dc.date.available2021-12-23T17:09:31Z-
dc.date.issued2021-12-18-
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.issn2504-2289-
dc.identifier.urihttp://hdl.handle.net/11328/3862-
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.language.isoengpt_PT
dc.publisherMDPI - Multidisciplinary Digital Publishing Institutept_PT
dc.relation.ispartofseries;4-
dc.rightsopenAccesspt_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.typearticlept_PT
dc.peerreviewedyespt_PT
degois.publication.firstPage81pt_PT
degois.publication.volume5pt_PT
degois.publication.titleBig Data and Cognitive Computingpt_PT
dc.identifier.doihttps://doi.org/10.3390/bdcc5040081pt_PT
Appears in Collections:REMIT – Artigos em Revistas Internacionais / Papers in International Journals

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