Human-Data Interaction Syllabus for Undergraduate and Graduate Courses

Authors

DOI:

https://doi.org/10.5753/jis.2024.3251

Keywords:

Human-Data Interaction, Data Science, Data Literacy, Information Visualization

Abstract

The phenomenon of the data deluge is a reality and the volume of data produced by people and companies is much greater than what can be handled and analyzed. Data play a crucial role in guiding the efficient utilization of technological resources for companies, aiding them in product and service management. Moreover, individuals who have become adept data producers and consumers are increasingly orienting their lives toward data. To address this evolving trend, there is a growing imperative to educate Computing professionals. These professionals are required to design technology solutions that facilitate the synergy between individuals and data, a phenomenon known as Human-Data Interaction (HDI). This paper introduces a suggested minimum syllabus for HDI courses, with the aim of addressing the key themes associated with the interaction between individuals and data. The complexity and depth of HDI topics justify a dedicated course, preventing the risk of essential content being fragmented or inadequately covered if dispersed across different courses.

Downloads

Download data is not yet available.

References

Accenture (2014). Big Success With Big Data. pages 1–12.

Accenture (2019). The Power of the DataDriven.

Accenture (2021). Multispeed data and analytics. Accenture. DOI: 10.1201/9781003291800-9.

ACM Data Science Task Force (2021). Computing Competencies for Undergraduate Data Science Curricula. Association for Computing Machinery, New York, NY, USA.

Allard, S. (2012). DataONE: Facilitating eScience through Collaboration. Journal of eScience Librarianship, 1(1):4–17. DOI: 10.7191/jeslib.2012.1004.

Amaral, F. (2016). Introdução à Ciência dos Dados. Alta Books, Rio de Janeiro.

Amo-filva, D., García-peñalvo, F. J., and Chen, J. (2022). Towards an ethical data literacy proficiency : a Moodle logs analytical tool. Proceedings of XII International Conference on Virtual Campus (JICV), (1):2022–2024.

Arass, M. E. and Souissi, N. (2018). Data Lifecycle : From Big Data to Smart Data Data Lifecycle : From Big Data to Smart Data. Proceedings of 5TH Edition International IEEE Congress on Information Science and Technology (CiSt’18), (November):80–87.

Association for Computing Machinery (2020). Computing Curricula 2020. Technical report.

Baig, A. (2020). What is Privacy UX How to Implement Privacy-Aware Design Framework? AltexSoft - Software and Engineering.

Barth, P. (2022). The Seven Principles of Data Literacy. A Blueprint to accelerate your business toward its datadriven future Foreword from Qlik. Accenture.

Basarudin, N. A., Yeon, A. L., Yusoff, Z. M., Dahlan, N. H. M., and Author, N. M. (2017). Smart home users’ information in cloud system: A comparison between Malaysian personal data protection act 2010 and EU general data protection regulation. Malaysian Construction Research Journal, 2(2):209–222.

Beber, M. A., Ferrero, C. A., Fileto, R., and Bogorny, V. (2017). Individual and Group Activity Recognition in Moving Object Trajectories. Journal of Information and Data Management, 8(1):50. DOI: 10.5753/jidm.2017.1606.

Bellamy, B. and Alonso, C. (2016). Reframing data transparency. Centre for Information Policy Leadership and Telefónica Senior Roundtable, 1(June):1–20.

Benyon, D. (2011). Interação Humano Computador. Pearson Education, São Paulo.

Boscarioli, C., Silveira, M. S., Prates, R. O., Bim, S. A., Diniz, S., and Barbosa, J. (2012). Currículos de IHC no Brasil : Panorama Atual e Perspectivas. ... em Computação, ..., (2007):1294–1303.

Cafaro, F. (2012). Using embodied allegories to design gesture suites for human-data interaction. Proceedings of the 2012 ACM Conference on Ubiquitous Computing - UbiComp ’12, page 560. DOI: 10.1145/2370216.2370309.

Cairo, A. (2013). Functional Art, The: An introduction to information graphics and visualization. Analytics Press.

Cavoukian, A. (2020). Understanding how to implement privacy by design, one step at a time. IEEE Consumer Electronics Magazine, 9(2):78–82. DOI: 10.1109/MCE.2019.2953739.

Cham, H., Malek, S., Milow, P., and Song, C. (2022). Developing an ecological visualization system for biodiversity data. All Life, 15(1):500–511. DOI: 10.1080/26895293.2022.2066195.

Choe, E. K., Lee, N. B., Lee, B., Pratt, W., and Kientz, J. A. (2014). Understanding Quantified-Selfers’ Practices in Collecting and Exploring Personal Data. Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems, pages 1143–1152. DOI: 10.1145/2556288.2557372.

Clegg, B. (2017). Big Data: How the Information Revolution Is Transforming Our Lives. Icon Books.

Coleti, T., Morandini, M., and Filgueiras, L. (2022). Inserção de conteúdos de interação humano-dados e privacidade de dados na disciplina de interação humano-computador. In Anais do XXX Workshop sobre Educação em Computação, pages 181–191, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/wei.2022.223236.

Coleti, T. A., Corrêa, P. L. P., Filgueiras, L. V. L., and Morandini, M. (2020). TR-Model. A Meta-data Profile Application for Personal Data Transparency. IEEE Access, 8(1):75184–75209. DOI: 10.1109/ACCESS.2020.2988566.

Columbia University Coms (2022). Systems for human-data interaction.

Cradock, E., Stalla-Bourdillon, S., and Millard, D. (2017). Nobody puts data in a corner? Why a new approach to categorising personal data is required for the obligation to inform. Computer Law and Security Review, 33(2):142–158. DOI: 10.1016/j.clsr.2016.11.005.

Cybis, W. d. A., Holts, A. B., and Faust, R. (2015). Ergonomia e Usabilidade: Conhecimentos, Métodos e Aplicações. Novatec Editora, São Paulo.

Efroni, Z., Metzger, J., Mischau, L., and Schirmbeck, M. (2019). Privacy icons: A risk-based approach to visualisation of data processing. European Data Protection Law Review, 5(3):352–366. DOI: 10.21552/edpl/2019/3/9.

Elmqvist, N. (2011). Embodied Human-Data Interaction. In CHI 2011 Extended Abstracts on Human Factors in Computing Systems, pages 1–4.

Evequoz, F. and Lalanne, D. (2006). Personal Information Management through Interactive Visualizations. Proceedings of Doctoral Colloquium of IEEE Information Visualization Conference (Infovis-DC 2007), (August):158–160.

Few, S. (2016). Data Visualization for Human Perception. The Encyclopedia of Human-Computer Interaction, 2.

Filgueiras, L. V. L., Leal, A. S. F., Coleti, T. A., Morandini, M., Correa, P. L., and Alves-Souza, S. N. (2019). Keep System Status Visible: Impact of Notifications on the Perception of Personal Data Transparency. Human-Computer Interaction. Perspectives on Design, 1:513–530.

Gomes, E. and Braga, F. (2017). Inteligencia Competitiva em Tempos Big Data. Alta Books.

Gorton, I., Bener, A. B., and Mockus, A. (2016). Software engineering for big data systems. IEEE Software, 33(2):32–35. DOI: 10.1109/MS.2016.47.

Haddadi, H., Chaudhry, A., Crowcrof, J., Howard, H., Mortier, R., and Mcauley, D. (2015). Personal Data: Thinking Inside the Box. Aarhus Series on Human Centered Computing, page 8. DOI: 10.7146/aahcc.v1i1.21312.

Hartson, R. and Pyla, P. (2012). UX Book. Process and guidelines for ensuring a quality user experience. Morgan Kaufmann - Elsevier.

Holtz, L. E., Nocun, K., and Hansen, M. (2011). Towards displaying privacy information with icons. IFIP Advances in Information and Communication Technology, 352 AICT:338–348. DOI: 10.1007/978-3-642-20769-327.

Hornung, H., Pereira, R., Baranauskas, M. C. C., and Liu, K. (2015). Challenges for Human-Data Interaction – A Semiotic Perspective BT - Human-Computer Interaction: Design and Evaluation. Human-Computer Interaction: Design and Evaluation, 1:37–48. DOI: 10.1007/978-3-319-20901-2.

Hosseini, M., Shahri, A., Phalp, K., and Ali, R. (2016). Foundations for transparency requirements engineering. In Daneva, M. and Pastor, O., editors, Requirements Engineering: Foundation for Software Quality, pages 225–231, Cham. Springer International Publishing.

Hsieh, O. (2016). Human computer interaction and data visualization. Advanced Writing: Pop Culture Intersections, pages 1–24.

Huang, D., Tory, M., Adriel Aseniero, B., Bartram, L., Bateman, S., Carpendale, S., Tang, A., and Woodbury, R. (2015). Personal visualization and personal visual analytics. IEEE Transactions on Visualization and Computer Graphics, 21(3):420–433. DOI: 10.1109/TVCG.2014.2359887.

Ismail, N. A. and Zainal Abidin, W. (2016). Data Scientist Skills. IOSR Journal of Mobile Computing Application, 03(04):52–61. DOI: 10.9790/0050-03045261.

ISO (1998). Iso 9241-11:1998(en). URL: [link].

ISO/IEC (2011). ISO/IEC 25010:2011, systems and software engineering — systems and software quality requirements and evaluation (square) — system and software quality models.

Janssen, N. (2022). The Data Science Talent Gap: Why It Exists And What Businesses Can Do About It. Forbes Technology Council.

Ji, S., Li, Q., Cao, W., Zhang, P., and Muccini, H. (2020). Quality assurance technologies of big data applications: A systematic literature review. Applied Sciences (Switzerland), 10(22):1–31. DOI: 10.3390/app10228052.

Kandari, J., Jones, E. C., Nah, F. F. H., and Bishu, R. R. (2011). Information quality on the World Wide Web: Development of a framework. International Journal of Information Quality, 2(4):324–343. DOI: 10.1504/IJIQ.2011.043784.

Knaflic, C. N. (2019). Storytelling com dados: Um guia sobre visualização de dados para profissionais de negócios. Alta Books, 2 edition.

Kumar, S. and Jakhar, M. (2010). Understanding user evaluation of Information Quality Dimensions in a digitized world. Proceedings of Production and Operations Management Society, 1:1–10.

Law, D. (2020). Proteção De Dados Do Brasil Lgpd Conhecendo a Lei De Dados Do Brasil.

Lebo, M. S., Sutti, S., and Green, R. C. (2016). ”Big data” gets personal. Science Translational Medicine, 8(322):322fs3–322fs3. DOI: 10.1126/scitranslmed.aad9460.

Lee, Y. W., Strong, D. M., Kahn, B. K., and Wang, R. Y. (2002). AIMQ: A methodology for information quality assessment. Information and Management, 40(2):133–146. DOI: 10.1016/S0378-7206(02)00043-5.

Lieshout, M. and Kool, L. (2008). Privacy implications of RFID: An assessment of threats and opportunites. In The Future of Identity in the Information Society, pages 129–141. Springer US, Boston, MA. DOI: 10.1007/978-0-387-79026-89.

Lopes, F. S., da Silva, L. A., and Breternitz, V. J. (2017). Data Scientists: a Study on Skills and Formation. Proceedings of the 14th CONTECSI International Conference on Information Systems and Technology Management, 14(May):2075–2081. DOI: 10.5748/9788599693131-14contecsi/rf-4642.

Lopes, L. A., Pinheiro, E. G., Zaina, L. A. M., and Álvaro, A. (2016). A interdisciplinaridade entre a Interação Humano Computador e os Métodos Ágeis na visão dos estudantes. Anais do VII Workshop sobre Educação em IHC - WEIHC 2016, pages 7–12.

Mashhadi, A., Kawsar, F., and Acer, U. G. (2014). Human data interaction in iot: The ownership aspect. In 2014 IEEE World Forum on Internet of Things (WF-IoT), pages 159–162. DOI: 10.1109/WF-IoT.2014.6803139.

Maus, G. (2015). Decoding, hacking, and optimizing societies: Exploring potential applications of human data analytics in sociological engineering, both internally and as offensive weapons. Proceedings of the 2015 Science and Information Conference, SAI 2015, pages 538–547. DOI: 10.1109/SAI.2015.7237195.

McAuley, D., Mortier, R., and Goulding, J. (2011). The Dataware manifesto. 2011 3rd International Conference on Communication Systems and Networks, COMSNETS 2011. DOI: 10.1109/COMSNETS.2011.5716491.

McGill, M. M., Decker, A., and Abbott, Z. (2018). Improving research and experience reports of pre-College computing activities: A gap analysis. SIGCSE 2018 - Proceedings of the 49th ACM Technical Symposium on Computer Science Education, 2018-January(February):964–969. DOI: 10.1145/3159450.3159481.

Ministério da Educação Superior - Conselho Nacional de Educação (2016). Resolução nº 5, de 16 de Novembro de 2016. 2016:1–9.

Morrow, J. (2021). Be Data Literate: The Data Literacy Skills Everyone Needs to Succeed. Kogan Page.

Mortier, R., Haddadi, H., Henderson, T., McAuley, D., and Crowcroft, J. (2015). Human-Data Interaction: The Human Face of the Data-Driven Society. DOI: 10.2139/ssrn.2508051.

Mortier, R., Haddadi, H., Henderson, T., Mcauley, D., Crowcroft, J., and Crabtree, A. (2016). Human-Data Interaction. The Encyclopedia of Human-Computer Interaction, pages 1–48.

Murmann, P. and Fischer-Hübner, S. (2017). Tools for Achieving Usable Ex Post Transparency: A Survey. IEEE Access, 5:22965–22991. DOI: 10.1109/ACCESS.2017.2765539.

Neil, T. (2014). Mobile Design Pattern Gallery. OReilly, 2 edition.

Peek, S. (2023). Big Data, Big Problem: Coping With a Shortage of Talent in Data Analysis. business.com.

Pekala, S. (2017). Privacy and user experience in 21st century library discovery. Information Technology and Libraries, 36(2):48–58. DOI: 10.6017/ital.v36i2.9817.

Rocha, H. V. and Baranauskas, M. C. C. (2003). Design e Avaliação de Interfaces Humano-Computador. Instituto de Computação - Universidade Estadual de Campinas.

Rogers, Y., Sharp, H., and Preece, J. (2013). Design de interação: além da interação humano-computador. Bookman, 3 edition.

Rutgers School of Arts and Science (2022). Data interaction and visual analytics.

Santos, P., Salgado, L., and Viterbo, J. (2018). Assessing the Communicability of Human-Data Interaction Mechanisms in Transparency Enhancing Tools. Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, 15:897–906. DOI: 10.15439/2018f174.

Sindiy, O., Litomisky, K., Davidoff, S., and Dekens, F. (2013). Introduction to information visualization (Info-Vis) techniques for model-based systems engineering. Procedia Computer Science, 16(December 2013):49–58. DOI: 10.1016/j.procs.2013.01.006.

Swinburne University of Technology (2022). Reserach programa in human-data interaction.

Taibi, D., Fernandez-Sanz, L., Pospelova, V., Leon-Urrutia, M., Marjanovic, U., Splendore, S., and Urbsiene, L. (2021). Developing Data Literacy Competences at University: The experience of the DEDALUS project. 2021 1st Conference on Online Teaching for Mobile Education, OT4ME 2021, pages 112–113. DOI: 10.1109/OT4ME53559.2021.9638912.

Toledo, M. D. E. (2020). Lei Geral de Proteção de Dados. um guia completo.

Tom, J., Sing, E., and Matulevičius, R. (2018). Conceptual representation of the GDPR: Model and application directions. Lecture Notes in Business Information Processing, 330(January):18–28. DOI: 10.1007/978-3-319-99951-72.

United Nations Development Group (2017). UNDG Guidance Note on Big Data for Achievement of the 2030 Agenda : Data Privacy, Ethics, and Protection. page 16.

Vivacqua, A. S., França, J. B. d. S., and Dias, A. F. d. S. (2019). Promoting Data Literacy in Brazilian Schools. Proceedings of CLIHC ’19, 4:1–4. DOI: 10.1145/3358961.3359003.

Wroblewski, L. (2011). Mobile First. DOI: 978-1-937557-02-7.

Zorzo, A. F., Nunes, D., Ecivaldo, S., and Martins, S. (2017). Referenciais de Formação para os Cursos de Graduação em Computação 2017. Sociedade Brasileira de Computação.

Downloads

Published

2024-01-01

How to Cite

COLETI, T. A.; MORANDINI, M.; FILGUEIRAS, L. V. L. Human-Data Interaction Syllabus for Undergraduate and Graduate Courses. Journal on Interactive Systems, Porto Alegre, RS, v. 15, n. 1, p. 36–54, 2024. DOI: 10.5753/jis.2024.3251. Disponível em: https://sol.sbc.org.br/journals/index.php/jis/article/view/3251. Acesso em: 29 apr. 2024.

Issue

Section

Regular Paper

Most read articles by the same author(s)