Um framework de classificação de complexidade para infográficos

Authors

  • Kamila Takayama Lyra Universidade de São Paulo
  • Rachel Reis Universidade de São Paulo; Universidade Federal de Viçosa
  • Wilmax Marreiro Cruz Universidade de São Paulo
  • Seiji Isotani Universidade de São Paulo

DOI:

https://doi.org/10.5753/rbie.2019.27.01.196

Keywords:

Infográficos, Classificação, Complexidade, Aprendizado, Framework

Abstract

Os infográficos passaram a ser empregados como material de apoio ao ensino devido a sua natureza informativa. As evidências empíricas mostram que o uso desse formato de visualização afeta o aprendizado e o interesse dos alunos. No entanto, ainda é necessário fornecer maiores detalhes sobre os parâmetros que garantem os resultados positivos. Um desses parâmetros é complexidade dos infográficos. Logo, é preciso obter informações sobre o impacto de infográficos com diferentes níveis de complexidade no aprendizado. Observando a ausência de diretrizes que pudessem ser aplicadas para classificar a complexidade de infográficos, este trabalho propôs um framework que considera três dimensões (visual, verbal e conceitual) e o utiliza para pontuar infográficos, gerando uma medida de complexidade. Para avaliar o framework foi realizado um experimento controlado cujo objetivo foi verificar se a complexidade medida representa a real complexidade pro usuário. Analisando os resultados verificou-se que, de fato, os usuários aprenderam mais a partir de infográficos classificados como de baixa e média complexidade. Conclui-se que o fator complexidade do infográfico afeta o aprendizado e deve ser considerado por professores e criadores de conteúdo na decisão de se utilizar infográficos como materiais de aprendizagem.

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Referências

Albers, M. J. (2014). Infographics: Horrid chartjunk or quality communication. InProfes-sional communication conference (ipcc)(pp. 1–4). Pittsburgh, PA, USA: IEEE. doi: 10.1109/IPCC.2014.7020344. [GS Seach].

Albers, M. J. (2015). Infographics and communicating complex information. InInternationalconference of design, user experience, and usability(pp. 267–276). Los Angeles, CA, USA:Springer, Cham. doi:10.1007/978-3-319-20898-5_26 [GS Seach].

Andrews, G., & Halford, G. S. (2002). A cognitive complexity metric applied to cognitivedevelopment.Cognitive Psychology,45(2), 153–219. doi: 10.1016/S0010-0285(02)00002-6 [GS Seach].

Andrews, G., Halford, G. S., Bunch, K. M., Bowden, D., & Jones, T. (2003). Theory of mindand relational complexity.Child development,74(5), 1476–1499. doi: 10.1111/1467-8624.00618. [GS Seach].

Baddeley, A. (1994). The magical number seven: Still magic after all these years?PsychologicalReview,101(2), 353-356. doi: 10.1037/0033-295X.101.2.353. [GS Seach].

Bertin, J. (2011).Semiology of graphics: Diagrams, networks, maps. ESRI Press. [GS Seach].

Brehmer, M., & Munzner, T. (2013). A multi-level typology of abstract visualization tasks.IEEE Transactions on Visualization and Computer Graphics,19(12), 2376–2385. doi:10.1109/TVCG.2013.124 [GS Seach].

Burkhard, R. A. (2004). Learning from architects: the difference between knowledge visuali-zation and information visualization. InProceedings. eighth international conference oninformation visualisation, 2004. iv 2004.(p. 519-524). doi: 10.1109/IV.2004.1320194 [GS Seach]

Card, S. K., Mackinlay, J. D., & Shneiderman, B. (1999).Readings in information visualization:using vision to think. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc. [GS Seach]

Carr, D. (1999). Guidelines for designing information visualization applications. InProceedingsof the ericsson conference on usability engineering ecue(pp. 1–3). Stockholm, Sweden.[GS Seach]

Chen, C. (2005). Top 10 unsolved information visualization problems.IEEE Computer Graphicsand Applications,25(4), 12–16. doi: 10.1109/MCG.2005.91 [GS Seach]

Clark, R., Nguyen, F., & Sweller, J. (2011).Efficiency in learning: Evidence-based guidelines tomanage cognitive load. John Wiley & Sons. [GS Seach]

Cleveland, W. S. (1994).The elements of graphing data. AT&T Bell Laboratories. [GS Seach]

Collins-Thompson, K., & Callan, J. (2004). A language modeling approach to predicting readingdifficulty.In Proceedings of the Human Language Technology Conference of the NorthAmerican Chapter of the Association for Computational Linguistics: HLT-NAACL 2004.[GS Seach]

Cowan, N.(2010).The magical mystery four: How is working memory capacity li-mited, and why?Current directions in psychological science,19(1), 51–57.doi: 10.1177/0963721409359277 [GS Seach]

Cox, R. (1999). Representation construction, externalised cognition and individual differences.Learning and Instruction,9(4), 343–363. doi:10.1016/S0959-4752(98)00051-6 [GS Seach]

Cuevas, H. M., Fiore, S. M., & Oser, R. L. (2002). Scaffolding cognitive and metacognitiveprocesses in low verbal ability learners: Use of diagrams in computer-based training envi-ronments.Instructional Science,30(6), 433–464. doi:10.1023/A:1020516301541 [GS Seach]

Dale, E., & Chall, J. S. (1948). A formula for predicting readability: Instructions.EducationalResearch Bulletin,27(2), 37–54. Retrieved from http://www.jstor.org/stable/1473669 [GS Seach]

Diakopoulos, N., Kivran-swaine, F., & Naaman, M.(2011, may).Playable data : Cha-racterizing the design space of game-y infographics. InAcm chi conference on humanfactors in computing systems(pp. 1717–1726). Vancouver, BC, Canada: ACM. doi:10.1145/1978942.1979193 [GS Seach]

Elizabeth Thomson (1996, Dec). Mit research - brain processing of visual information.MITNews. Retrieved from [Link]. [GS Search]

Figueiras, A. (2013, July). A typology for data visualization on the web. InInformation visua-lization (iv), 2013 17th international conference(pp. 351–358). London, UK: IEEE. doi:10.1109/IV.2013.45 [GS Seach]

Flesch, R. (1948). A new readability yardstick.Journal of Applied Psychology,32(3), 221. doi:10.1037/h0057532 [GS Seach]

Freitas, C. M. D. S., Chubachi, O. M., Luzzardi, P. R. G., & Cava, R. A. (2001). Introduçãoà visualização de informações.Revista de Informática Teórica e Aplicada,8(2), 143–158.[GS Seach]

Fry, E. (1968). A readability formula that saves time.Journal of Reading,11(7), 513–578. [GS Seach]

Gärtner, J., Miksch, S., & Carl-McGrath, S. (2002, April). Vico: a metric for the complexityof information visualizations. InInternational conference on theory and application ofdiagrams(pp. 249–263). Callaway Gardens, GA, USA: Springer, Berlin, Heidelberg.doi:10.1007%2F3-540-46037-3_25. [GS Seach]

Gerber, R., Boulton-Lewis, G., & Bruce, C. (1995). Children’s understanding of graphic repre-sentations of quantitative data.Learning and Instruction,5(1), 77–100. [GS Seach]

Gyselinck, V., & Tardieu, H. (1999). The role of illustrations in text comprehension: what, when,for whom, and why?Lawrence Erlbaum Associates Publishers, 195-218. [GS Seach]

Halford, G. S., Andrews, G., & Jensen, I. (1998). Category induction and hierarchical classifica-tion assessed by property inference: The influence of complexity.ERIC. [GS Seach]

Halford, G. S., Wilson, W. H., & Phillips, S. (1998). Processing capacity defined by rela-tional complexity: implications for comparative, developmental, and cognitive psycho-logy.The Behavioral and brain sciences,21(6), 803–831; discussion 831–864. doi:10.1017/S0140525X98001769 [GS Seach]

Hegarty, M., Carpenter, P. A., & Just, M. A. (1991). Diagrams in the comprehension of scientifictexts. ,2, 641-668. [GS Seach]

Jonassen, D. H., Reeves, T. C., Hong, N., Harvey, D., & Peters, K. (1997). Concept mapping ascognitive learning and assessment tools.Journal of interactive learning research,8(3), 289.[GS Seach]

Kincaid, J. P., Fishburne Jr, R. P., Rogers, R. L., & Chissom, B. S. (1975). Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula)for navy enlisted personnel.Institute for Simulation and Training, University of CentralFlorida. [GS Seach]

King, D. B., & Wertheimer, M. (2005).Max wertheimer and gestalt theory. Transaction Publishers. [GS Seach]

Larkin, J. H., & Simon, H. A. (1987). Why a diagram is (sometimes) worth ten thousand words.Cognitive Science,11(1), 65 - 100. doi:10.1016/S0364-0213(87)80026-5 [GS Seach]

Lee, E.-J., & Kim, Y. W. (2016). Effects of infographics on news elaboration, acquisition, andevaluation: Prior knowledge and issue involvement as moderators.New Media & Society,18(8), 1579-1598. doi:10.1177/1461444814567982 [GS Seach]

Leturia, E. (1998). ¿ qué es infografía.Revista Latina de Comunicación Social,4(10). [GS Seach]

Lyra, K., & Isotani, S. (2017). Impacto do uso de infográficos como materiais de aprendizagem esuas correlações com satisfação, estilos de aprendizagem e complexidade visual. InAnaisdos workshops do congresso brasileiro de informática na educação (cbie)(Vol. 6, p. 46).doi:10.5753/cbie.wcbie.2017.46 [GS Seach]

Lyra, K. T. (2017, 4). Impacto do uso de infográficos como materiais de aprendizagem e suascorrelações com satisfação, estilos de aprendizagem e complexidade visual.Dissertaçãode Mestrado. Instituto de Ciências Matemáticas e de Computação - Universidade de SãoPaulo. doi:10.11606/D.55.2017.tde-02082017-104605 [GS Seach]

Lyra, K. T., Isotani, S., Reis, R. C. D., Marques, L. B., Pedro, L. Z., Jaques, P. A., & Bitencourt,I. I. (2016, July). Infographics or graphics+text: Which material is best for robust lear-ning? In2016 ieee 16th international conference on advanced learning technologies (icalt)(p. 366-370). doi:10.1109/ICALT.2016.83 [GS Seach]

Lyra, K. T., Oliveira, B. R., Reis, R. C., Cruz, W. M., Nakagawa, E. Y., & Isotani, S. (2016). In-fográficos versus materiais de aprendizagem tradicionais: uma investigação empírica.RE-NOTE - Revista Novas Tecnologias na Educação,14(2). doi:10.22456/1679-1916.70653 [GS Seach]

Machado, V., Margarida, L., & Tarouco, R. (2010). Infográfico : características , autoria e uso educacional.Novas Tecnologias na Educação,8(3). [GS Seach]

Martins, T. B. F., Ghiraldelo, C. M., Nunes, M. G. V., & Júnior, O. N. O. (1996).ReadabilityFormulas Applied to Textbooks in Brazilian Portuguese. Notas do ICMSC-USP ICMC. [GS Seach]

Masud, L., Valsecchi, F., Ciuccarelli, P., Ricci, D., & Caviglia, G. (2010). From data to kno-wledge: Visualizations as transformation processes within the data-information-knowledgecontinuum.Proceedings of the International Conference on Information Visualisation, 445–449. doi:10.1109/IV.2010.68 [GS Seach]

Mayer, R. E. (2003). The promise of multimedia learning: using the same instructional de-sign methods across different media.Learning and instruction,13(2), 125–139. doi:10.1016/S0959-4752(02)00016-6 [GS Seach]

Mayer, R. E., Bove, W., Bryman, A., Mars, R., & Tapangco, L. (1996). When less is more:Meaningful learning from visual and verbal summaries of science textbook lessons.Journalof educational psychology,88(1), 64. [GS Seach]

McCormick, B. H., DeFanti, T. A., & Brown, M. D. (1987). Visualization in scientific computing.IEEE Computer Graphics and Applications,7(10), 69–69. [GS Seach]

McLoud, S. (2000). Reinventing comics: How imagination and technology are revolutionizingan art form.Perennial, New York, 118–122. [GS Seach]

Montgomery, D. C. (2008).Design and analysis of experiments. John Wiley & Sons. [GS Seach]

Naps, T., Cooper, S., Koldehofe, B., Leska, C., Rößling, G., Dann, W., . . . McNally, M. (2003).Evaluating the educational impact of visualization. InAcm sigcse bulletin(Vol. 35, pp.124–136). [GS Seach]

Paas, F., Renkl, A., & Sweller, J. (2004). Cognitive load theory: Instructional implications of theinteraction between information structures and cognitive architecture.Instructional science,32(1-2), 1–8. [GS Seach]

Ribas, B. (2004). Infografia multimídia: um modelo narrativo para o webjornalismo.Anais do IISBPJor (CD-ROM). Salvador-BA/Brasil. [GS Seach]

Risch, J. S. (2008). On the role of metaphor in information visualization.arXiv preprint ar-Xiv:0809.0884, 20. [GS Seach]

Schwarm, S. E., & Ostendorf, M. (2005). Reading Level Assessment Using Support VectorMachines and Statistical Language Models.Annual Meeting of the Association for Computational Linguistics(June), 523–530. doi:10.3115/1219840.1219905 [GS Seach]

Sebrechts, M. M. (2005). Visualizing information in virtual space: Prospects and pitfalls. InKnowledge and information visualization(pp. 136–166). Springer. [GS Seach]

Smiciklas, M. (2012).The power of infographics: Using pictures to communicate and connectwith your audiences. Que Publishing. doi:10.4324/9780203075609 [GS Seach]

Stenner, A. J. (1996). Measuring reading comprehension with the lexile framework.ERIC. [GS Seach]

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning.Cognitive Science,12(2), 257–285. doi:10.1016/0364-0213(88)90023-7 [GS Seach]

Sweller, J., Van Merrienboer, J. J., & Paas, F. G. (1998). Cognitive architecture and instructionaldesign.Educational psychology review,10(3), 251–296. [GS Seach]

Toth, C. (2013). Revisiting a Genre: Teaching Infographics in Business and ProfessionalCommunication Courses.Business Communication Quarterly,76(4), 446–457.doi:10.1177/1080569913506253. [GS Seach]

Van Merrienboer, J. J., & Sweller, J. (2005). Cognitive load theory and complex learning: Recentdevelopments and future directions.Educational psychology review,17(2), 147–177. [GS Seach]

Van Merriënboer, J. J., & Sweller, J. (2010). Cognitive load theory in health professional educa-tion: design principles and strategies.Medical education,44(1), 85–93. [GS Seach]

Wohlin, C., Runeson, P., Höst, M., Ohlsson, M. C., Regnell, B., & Wesslén, A. (2012).Experi-mentation in software engineering. Springer Science & Business Media. [GS Seach]

Wolfe, J. M., & Horowitz, T. S. (2004). What attributes guide the deployment of visual attentionand how do they do it?Nature reviews neuroscience,5(6), 495–501. [GS Seach]

Zhang-Kennedy, L., Chiasson, S., & Biddle, R. (2013, sep). Password advice shouldn’t be boring:Visualizing password guessing attacks.2013 APWG eCrime Researchers Summit, 1–11. doi:10.1109/eCRS.2013.6805770 [GS Seach]

Zhu, Y. (2007). Measuring effective data visualization. In G. Bebis et al. (Eds.),Advancesin visual computing(pp. 652–661). Berlin, Heidelberg: Springer Berlin Heidelberg. [GS Seach]

Zhu, Y., Suo, X., & Owen, G. S. (2007). Complexity analysis for information visualization designand evaluation. In G. Bebis et al. (Eds.),Advances in visual computing(pp. 576–585).Berlin, Heidelberg: Springer Berlin Heidelberg. [GS Seach]

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Published

2019-01-01

Como Citar

LYRA, K. T.; REIS, R.; CRUZ, W. M.; ISOTANI, S. Um framework de classificação de complexidade para infográficos. Revista Brasileira de Informática na Educação, [S. l.], v. 27, n. 1, p. 196–223, 2019. DOI: 10.5753/rbie.2019.27.01.196. Disponível em: https://journals-sol.sbc.org.br/index.php/rbie/article/view/4762. Acesso em: 21 nov. 2024.

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