Please use this identifier to cite or link to this item: http://repositorio.ufc.br/handle/riufc/70732
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dc.contributor.authorSilva, Thomaz Edson Veloso da-
dc.contributor.authorVasconcelos, Francisco Herbert Lima-
dc.contributor.authorAlmeida, André Lima Férrer de-
dc.contributor.authorMota, João César Moura-
dc.date.accessioned2023-02-09T17:36:37Z-
dc.date.available2023-02-09T17:36:37Z-
dc.date.issued2012-
dc.identifier.citationMOTA, J. C. M. et al. Multivariate analysis for students' evaluation of teaching effectiveness in teleinformatics engineering. In: INTERNATIONAL CONFERENCE ON TEACHING, ASSESSMENT, AND LEARNING FOR ENGINEERING, 2012, Hong Kong. Anais... Hong Kong: IEEE, 2012. p. 1-6.pt_BR
dc.identifier.urihttp://www.repositorio.ufc.br/handle/riufc/70732-
dc.language.isoenpt_BR
dc.publisherInternational Conference on Teaching, Assessment, and Learning for Engineeringpt_BR
dc.subjectPrincipal components analysispt_BR
dc.subjectEngineering educationpt_BR
dc.subjectEvaluationpt_BR
dc.subjectUniversity teachingpt_BR
dc.titleMultivariate analysis for students' evaluation of teaching effectiveness in teleinformatics engineeringpt_BR
dc.typeArtigo de Eventopt_BR
dc.description.abstract-ptbrIn this work, we propose the use a multivariate analysis tool, called principal components analysis (PCA), to address the problem of Students' Evaluation of Teaching Effectiveness (SETE). We conducted a research with Engineering Students in an undergraduate course. The values obtained after collecting research data were transformed from a 3D array to a 2D array performing an average of students' responses. The PCA was applied in order to take same intrinsic information of the dataset collected. The Cronbach's α validates the PCA application in the dataset. The results show that our study allows an analysis of how students perceive different disciplines about different criteria, which may serve as an indicator for an educational assessment area.pt_BR
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