Use of reorderable matrices and heatmaps to support data analysis of students transcripts

  • Rafael Tavares Carvalho Barros Universidade Estadual de Campinas
  • Thiago Gonçalves Mendes Universidade Estadual de Campinas
  • Celmar Guimarães da Silva Universidade Estadual de Campinas

Resumo


For a course coordinator, the analysis of several students’ transcripts to identify the situation of subjects or students is often an old-fashioned process executed through a textual and numerical approach. This work is part of a larger project aimed at choosing appropriate visual representations to help course coordinators to analyze sets of students transcripts. In this work, we developed a system that allows the visualization of student transcripts through a heatmap of student grades per subject. The heatmap represent grades based on a user-defined color scale. To assist in the analysis, it is possible to reorder subjects and students using the optimal leaf order algorithm, or even to reorder according to the grades of a specific subject or student. In addition, some features have been developed to meet visual guidelines, such as overview, zoom, filter and details-on-demand.

Palavras-chave: Heatmap, reorderable matrices, educational data mining, student transcripts

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Publicado
28/10/2019
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BARROS, Rafael Tavares Carvalho; MENDES, Thiago Gonçalves; DA SILVA, Celmar Guimarães. Use of reorderable matrices and heatmaps to support data analysis of students transcripts. In: WORKSHOP DE TRABALHOS DA GRADUAÇÃO - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 32. , 2019, Rio de Janeiro. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 219-222. DOI: https://doi.org/10.5753/sibgrapi.est.2019.8334.