Análise Automatizada da Originalidade de Design de Interfaces de Usuário no Contexto Educacional: Um Mapeamento da Literatura
Resumo
Com o objetivo de contribuir ao desenvolvimento de habilidades importantes no século XXI, como a criatividade pelo ensino de computação na Educação Básica, visa-se ensinar ao aluno a desenvolver apps originais com App Inventor. Porém, se questiona como avaliar a originalidade especificamente em relação ao design de interface de usuário (UI) para acompanhar o progresso da aprendizagem. Assim, este artigo apresenta um mapeamento sistemático das abordagens existentes para automaticamente avaliar a originalidade de design de UI de apps Android usando técnicas de Inteligência Artificial, que podem ser utilizadas por professores para a avaliação da criatividade.
Palavras-chave:
criatividade, avaliação automatizada
Referências
Alves, N. da C., Alberto, M., Gresse von Wangenheim, C. (2021) Análise Automatizada da Originalidade de Aplicativos Android no Contexto Educacional: Um Mapeamento da Literatura. Anais do 29º WEI – Workshop sobre Educação em Computação, online.
Alves, N. da C., Gresse von Wangenheim, C., Alberto, M.; Martins-Pacheco, L. H. (2020) Uma Proposta de Avaliação da Originalidade do Produto no Ensino de Algoritmos e Programação na Educação Básica. Anais do Xxxi Simpósio Brasileiro de Informática na Educação. SBC. http://dx.doi.org/10.5753/cbie.sbie.2020.41.
Alves, N. da C., Gresse von Wangenheim, C., Hauck, J. C.R. (2019) Approaches to Assess Computational Thinking Competences Based on Code Analysis in K-12 Education: a systematic mapping study. Informatics In Education, 18(1), p. 17-39. http://dx.doi.org/10.15388/infedu.2019.02.
Basu, S. (2019) Using Rubrics Integrating Design and Coding to Assess Middle School Students' Open-ended Block-based Programming Projects. Proceedings Of The 50Th Acm Technical Symposium On Computer Science Education, p. 1211-1217. ACM. http://dx.doi.org/10.1145/3287324.3287412.
Beaty, R., Johnson, D. R. (2020) Automating Creativity Assessment with SemDis: an open platform for computing semantic distance. Psyarxiv, 53, p. 757-780. http://dx.doi.org/10.31234/osf.io/nwvps.
Cavallo, D., Singer, H., Gomes, A., Bittencourt,I., Silveira, I. (2016). Inovação e Criatividade na Educação Básica: Dos conceitos ao ecossistema. Revista Brasileira de Informática na Educação, 24(2).
Ferreira, M. N. F., Gresse von Wangenheim, C., Goncalves, B. S., Hauck, J.C.R., Medeiros, G. (2020) Ensino de Design Visual de Aplicativos Móveis no Ensino Fundamental. In: Anais da XI Conferência Computer on the Beach, Balneário Camboriú/SC, p. 199-205.
Gomes, K. P. e Matos, S. N. (2020). Detection of Programming Plagiarism in Computing Education: A Systematic Mapping Study. In: Anais do Simpósio ui; Brasileiro de Informática na Educação, Natal, Brasil.
Hu, R. M., Cai, L., Chen, W. (2020) Detection and Segmentation of Graphical Elements on GUIs for Mobile Apps Based on Deep Learning. Lecture Notes Of The Institute For Computer Sciences, Social Informatics And Telecommunications Engineering, p. 187-197. Springer International Publishing. http://dx.doi.org/10.1007/978-3-030-64214-3_13.
Ichinco, M. Kelleher, C. (2018) The Example Guru: Suggesting Examples to Novice Blocks Programmers in an Artifact-Based Context Increases Use of New Blocks. In: Blocks + 2018, Boston, p. 1-2.
Jeong, J., Kim, N., In, H. P. (2020) Detecting usability problems in mobile applications on the basis of dissimilarity in user behavior. International Journal Of Human-Computer Studies, v. 139, p. 102364. Elsevier BV. http://dx.doi.org/10.1016/j.ijhcs.2019.10.001.
Li, L., Bissyandé, T. F., Wang, H., Klein, J. (2019) On Identifying and Explaining Similarities in Android Apps. Journal Of Computer Science And Technology, [S.L.], v. 34, n. 2, p. 437-455. Springer Science and Business Media LLC. http://dx.doi.org/10.1007/s11390-019-1918-8.
Lyu, F., Lin, Y., Yang, J., Zhou, J. (2016) SUIDroid: an efficient hardening-resilient approach to android app clone detection. 2016 Ieee Trustcom/Bigdatase/Ispa, [S.L.], p. 511-518. IEEE. http://dx.doi.org/10.1109/trustcom.2016.0104.
Martins, O. P. H. R. (2019) Desenvolvimento de um Modelo para Avaliação da Estética Visual de Interfaces de Usuários de Aplicativos Usando Deep Learning. 2019. 116 f. TCC (Graduação) - Curso de Ciência da Computação, Universidade Federal de Santa Catarina. Florianópolis.
MIT. (2020) MIT App Inventor, http://appinventor.mit.edu/, Outubro, 2020.
Patton, E. W., Tissenbaum, M., Harunani, F. (2019) MIT App Inventor: objectives, design, and development. Computational Thinking Education, 3, p. 31-49. http://dx.doi.org/10.1007/978-981-13-6528-7_3.
Petersen, K., Vakkalanka, S., Kuzniarz, L. (2015) Guidelines for conducting systematic mapping studies in software engineering: an update. Information and Software Technology, 64, p. 1-18. http://dx.doi.org/10.1016/j.infsof.2015.03.007.
Piasecki, J., Waligora, M. & Dranseika, V. (2018) Google Search as an Additional Source in Systematic Reviews. Sci Eng Ethics 24, p. 809–810.
Ritchie, G. (2001) Assessing Creativity. Proceedings of the AISB symposium on AI and creativity in arts and science. York: The Society for the Study of Artificial Intelligence and Simulation of Behaviour. p. 3–11.
Runco, M. A., Jaeger, G. J. (2012) The Standard Definition of Creativity. Creativity Research Journal, 24(1), p. 92-96. http://dx.doi.org/10.1080/10400419.2012.650092.
Skager, R. W.; Schultz, C. B.; Klein, S. P. (1966) Points of view about preference as tools in the analysis of creative products. Perceptual and Motor Skills, v. 22, p. 83-94.
Soh, C., Tan, H. B. K., Arnatovich, Y. L., Wang, L. (2015) Detecting Clones in Android Applications through Analyzing User Interfaces. Ieee 23Rd International Conference On Program Comprehension, p. 163-173. IEEE. http://dx.doi.org/10.1109/icpc.2015.25.
Snyder, H. T., Hammond, J. A., Grohman, M. G., Katz-Buonincontro, J. (2019) Creativity measurement in undergraduate students from 1984–2013: A systematic review. Psychology of Aesthetics, Creativity, and the Arts, 13(2), 133–143.
WEF. (2020) The Future of Jobs Report 2020. 2020. Disponível em: https://www.weforum.org/reports/the-future-of-jobs-report-2020, Junho, 2021.
Yadav, Aman; Cooper, Steve. (2017) Fostering creativity through computing. Communications of the ACM, 60(2), p. 31-33. http://dx.doi.org/10.1145/3029595.
Zen, M., Vanderdonckt, J. (2016) Assessing User Interface Aesthetics based on the Inter-subjectivity of Judgment. International BCS Human Computer Interaction Conference, BCS Learning & Development, p. 1-12. http://dx.doi.org/10.14236/ewic/hci2016.25.
Zhang, T., Liu, Y., Gao, J., Gao, L. P., Cheng, J. (2020) Deep Learning-Based Mobile Application Isomorphic GUI Identification for Automated Robotic Testing. Ieee Software, v. 37, n. 4, p. 67-74. Institute of Electrical and Electronics Engineers (IEEE). http://dx.doi.org/10.1109/ms.2020.2987044.
Alves, N. da C., Gresse von Wangenheim, C., Alberto, M.; Martins-Pacheco, L. H. (2020) Uma Proposta de Avaliação da Originalidade do Produto no Ensino de Algoritmos e Programação na Educação Básica. Anais do Xxxi Simpósio Brasileiro de Informática na Educação. SBC. http://dx.doi.org/10.5753/cbie.sbie.2020.41.
Alves, N. da C., Gresse von Wangenheim, C., Hauck, J. C.R. (2019) Approaches to Assess Computational Thinking Competences Based on Code Analysis in K-12 Education: a systematic mapping study. Informatics In Education, 18(1), p. 17-39. http://dx.doi.org/10.15388/infedu.2019.02.
Basu, S. (2019) Using Rubrics Integrating Design and Coding to Assess Middle School Students' Open-ended Block-based Programming Projects. Proceedings Of The 50Th Acm Technical Symposium On Computer Science Education, p. 1211-1217. ACM. http://dx.doi.org/10.1145/3287324.3287412.
Beaty, R., Johnson, D. R. (2020) Automating Creativity Assessment with SemDis: an open platform for computing semantic distance. Psyarxiv, 53, p. 757-780. http://dx.doi.org/10.31234/osf.io/nwvps.
Cavallo, D., Singer, H., Gomes, A., Bittencourt,I., Silveira, I. (2016). Inovação e Criatividade na Educação Básica: Dos conceitos ao ecossistema. Revista Brasileira de Informática na Educação, 24(2).
Ferreira, M. N. F., Gresse von Wangenheim, C., Goncalves, B. S., Hauck, J.C.R., Medeiros, G. (2020) Ensino de Design Visual de Aplicativos Móveis no Ensino Fundamental. In: Anais da XI Conferência Computer on the Beach, Balneário Camboriú/SC, p. 199-205.
Gomes, K. P. e Matos, S. N. (2020). Detection of Programming Plagiarism in Computing Education: A Systematic Mapping Study. In: Anais do Simpósio ui; Brasileiro de Informática na Educação, Natal, Brasil.
Hu, R. M., Cai, L., Chen, W. (2020) Detection and Segmentation of Graphical Elements on GUIs for Mobile Apps Based on Deep Learning. Lecture Notes Of The Institute For Computer Sciences, Social Informatics And Telecommunications Engineering, p. 187-197. Springer International Publishing. http://dx.doi.org/10.1007/978-3-030-64214-3_13.
Ichinco, M. Kelleher, C. (2018) The Example Guru: Suggesting Examples to Novice Blocks Programmers in an Artifact-Based Context Increases Use of New Blocks. In: Blocks + 2018, Boston, p. 1-2.
Jeong, J., Kim, N., In, H. P. (2020) Detecting usability problems in mobile applications on the basis of dissimilarity in user behavior. International Journal Of Human-Computer Studies, v. 139, p. 102364. Elsevier BV. http://dx.doi.org/10.1016/j.ijhcs.2019.10.001.
Li, L., Bissyandé, T. F., Wang, H., Klein, J. (2019) On Identifying and Explaining Similarities in Android Apps. Journal Of Computer Science And Technology, [S.L.], v. 34, n. 2, p. 437-455. Springer Science and Business Media LLC. http://dx.doi.org/10.1007/s11390-019-1918-8.
Lyu, F., Lin, Y., Yang, J., Zhou, J. (2016) SUIDroid: an efficient hardening-resilient approach to android app clone detection. 2016 Ieee Trustcom/Bigdatase/Ispa, [S.L.], p. 511-518. IEEE. http://dx.doi.org/10.1109/trustcom.2016.0104.
Martins, O. P. H. R. (2019) Desenvolvimento de um Modelo para Avaliação da Estética Visual de Interfaces de Usuários de Aplicativos Usando Deep Learning. 2019. 116 f. TCC (Graduação) - Curso de Ciência da Computação, Universidade Federal de Santa Catarina. Florianópolis.
MIT. (2020) MIT App Inventor, http://appinventor.mit.edu/, Outubro, 2020.
Patton, E. W., Tissenbaum, M., Harunani, F. (2019) MIT App Inventor: objectives, design, and development. Computational Thinking Education, 3, p. 31-49. http://dx.doi.org/10.1007/978-981-13-6528-7_3.
Petersen, K., Vakkalanka, S., Kuzniarz, L. (2015) Guidelines for conducting systematic mapping studies in software engineering: an update. Information and Software Technology, 64, p. 1-18. http://dx.doi.org/10.1016/j.infsof.2015.03.007.
Piasecki, J., Waligora, M. & Dranseika, V. (2018) Google Search as an Additional Source in Systematic Reviews. Sci Eng Ethics 24, p. 809–810.
Ritchie, G. (2001) Assessing Creativity. Proceedings of the AISB symposium on AI and creativity in arts and science. York: The Society for the Study of Artificial Intelligence and Simulation of Behaviour. p. 3–11.
Runco, M. A., Jaeger, G. J. (2012) The Standard Definition of Creativity. Creativity Research Journal, 24(1), p. 92-96. http://dx.doi.org/10.1080/10400419.2012.650092.
Skager, R. W.; Schultz, C. B.; Klein, S. P. (1966) Points of view about preference as tools in the analysis of creative products. Perceptual and Motor Skills, v. 22, p. 83-94.
Soh, C., Tan, H. B. K., Arnatovich, Y. L., Wang, L. (2015) Detecting Clones in Android Applications through Analyzing User Interfaces. Ieee 23Rd International Conference On Program Comprehension, p. 163-173. IEEE. http://dx.doi.org/10.1109/icpc.2015.25.
Snyder, H. T., Hammond, J. A., Grohman, M. G., Katz-Buonincontro, J. (2019) Creativity measurement in undergraduate students from 1984–2013: A systematic review. Psychology of Aesthetics, Creativity, and the Arts, 13(2), 133–143.
WEF. (2020) The Future of Jobs Report 2020. 2020. Disponível em: https://www.weforum.org/reports/the-future-of-jobs-report-2020, Junho, 2021.
Yadav, Aman; Cooper, Steve. (2017) Fostering creativity through computing. Communications of the ACM, 60(2), p. 31-33. http://dx.doi.org/10.1145/3029595.
Zen, M., Vanderdonckt, J. (2016) Assessing User Interface Aesthetics based on the Inter-subjectivity of Judgment. International BCS Human Computer Interaction Conference, BCS Learning & Development, p. 1-12. http://dx.doi.org/10.14236/ewic/hci2016.25.
Zhang, T., Liu, Y., Gao, J., Gao, L. P., Cheng, J. (2020) Deep Learning-Based Mobile Application Isomorphic GUI Identification for Automated Robotic Testing. Ieee Software, v. 37, n. 4, p. 67-74. Institute of Electrical and Electronics Engineers (IEEE). http://dx.doi.org/10.1109/ms.2020.2987044.
Publicado
22/11/2021
Como Citar
SOUZA, Angélica Siqueira de; DA CRUZ ALVES, Nathalia; GRESSE VON WANGENHEIM, Christiane; KREUCH, Leonardo.
Análise Automatizada da Originalidade de Design de Interfaces de Usuário no Contexto Educacional: Um Mapeamento da Literatura. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 32. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 1140-1151.
DOI: https://doi.org/10.5753/sbie.2021.217542.
