Vis2Learning: A scenario-based guide of recommendations for building educational data visualizations

  • Maylon Pires Macedo Universidade Federal de São Carlos
  • Ranilson Oscar Araújo Paiva Universidade Federal de Alagoas
  • Isabela Gasparini Universidade do Estado de Santa Catarina
  • Luciana Aparecida Martinez Zaina Universidade Federal de São Carlos


Information Visualization concerns efforts on creating better solutions to represent data in visual formats and consequently support the user interpretation on it. The literature has shown that there is a gap in the development of educational data visualizations which fulfill end-user needs. This paper presents Vis2Learning a guide of recommendations to support the building of visualizations applied to educational data in e-learning context. Vis2Learning provides a set of scenarios from which educational data visualizations can be developed. Each scenario comprises (i) a visual format of visualization and its characteristics; (ii) examples of how to use it; and (iii) overall recommendations to enhance the user’s understanding on the data. We carried out an online questionnaire from which 34 end-users (Brazilian teachers) evaluated visualizations whichwere constructed by using the guide. The results reveal that the recommendations made the visualizations suitable to be applied in the e-learning context. However, our findings showed that the participants who did not have a former contact with visualizations in e-learning context struggled to understand some visual formats which are not well-known by the audience.
Palavras-chave: information visualization, end-user interaction, e-learning, educational data
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MACEDO, Maylon Pires; PAIVA, Ranilson Oscar Araújo; GASPARINI, Isabela; ZAINA, Luciana Aparecida Martinez. Vis2Learning: A scenario-based guide of recommendations for building educational data visualizations. In: SIMPÓSIO BRASILEIRO SOBRE FATORES HUMANOS EM SISTEMAS COMPUTACIONAIS (IHC), 14. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 350-359.