As Big Data becomes increasingly important in policy-making, research, marketing, and commercial applications, we argue that literacy in this domain is critical for engaged democratic participation and that peer-generated data from mobile technologies offer rich possibilities for students to learn about this new genre of data. Through the lens of what we term the paradigms of technology and cutting-edge content as an educational end, means, and equalizer, we explore how learning about Big Data with mobile technologies exists at the critical intersection of issues such as the purpose of schooling, global competitiveness, corporate profit, student agency, and democratic participation. These competing interests surface tensions at the classroom, institutional, and societal levels. Engaging these tensions, we offer a framework of student objectives for learning about Big Data with mobile technologies. Through a reflection on the challenges we continue to encounter as we attempt to implement innovative curriculum within the constraints of urban public schools, we hope to prompt dialogue and changes in practice with respect to what it means to learn for democratic participation using Big Data.
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Additional information about this project can be found at http://www.mobilizingcs.org. While we have benefitted tremendously from our conversations with our colleagues about the ideas explored here, we take sole responsibility for the views that are presented.
The study and use of data is becoming increasingly interdisciplinary and multidisciplinary, and arguably cannot be understood adequately if we confine ourselves to disciplines, such as statistics or the sciences, with which data are commonly associated. We adopt the term, data science, which in recent years has become more common, to describe interdisciplinary and multidisciplinary approaches to the study, use, and application of data. For additional information, see Loukides (2010).
Amichaihamburger, Y., McKenna, K., & Tal, S. (2008). E-empowerment: Empowerment by the internet. Computers in Human Behavior, 24(5), 1776–1789.
Anderson, J. Q., & Rainie, L. (2012). Big data: Experts say new forms of information will help people be more nimble and adaptive, but worry over humans’ capacity to understand and use these new tools well. Washington, D.C.: Pew Research Center’s Internet and American Life Project.
Arnold, S. E. (2011). Big data: The new information challenge. Online, 35(2), 27–29.
Boaler, J., & Greeno, J. G. (2000). Identity, agency, and knowing in mathematical worlds. In J. Boaler (Ed.), Multiple perspectives on mathematics teaching and learning (pp. 45–82). Stamford, CT: Ablex.
Carr, N. (2010). The shallows: What the internet is doing to our brains. New York, NY: W.W. Norton & Company, Inc.
Castells, M. (2007). Communication, power and counter-power in the network society. International Journal of Communication, 1(1), 238–266.
Cobb, P., Gresalfi, M. S., & Hodge, L. (2009). An interpretative scheme for analyzing the identities that students develop in mathematics classrooms. Journal for Research in Mathematics Education, 40(1), 40–68.
Cuban, L. (1986). Teachers and machines: The classroom use of technology since 1920. New York, NY: Teachers College Press.
Delpit, L. (1995). Other people’s children: Cultural conflict in the classroom. New York: The New Press.
Duhigg, C. (2012, February 16). How companies learn your secrets. The New York Times. Retrieved from http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=all.
Freire, P. (2001). Pedagogy of the oppressed. New York, NY: Continuum International Publishing Group Inc.
Giroux, H. A. (2001). Theory and resistance in education: Towards a pedagogy for the opposition. Westport, CT: Bergin & Garvey.
Goode, E. (2011, August 15). Sending the police before there is a crime. The New York Times. Retrieved from http://www.nytimes.com/2011/08/16/us/16police.html.
Gramsci, A. (1971). Prison notebooks. New York: International Publishers.
Hall, S. (1982). The rediscovery of ‘ideology’: Return of the repressed in media studies. In M. Gurevitch, T. Bennet, J. Curran & J. Wollacott (Eds.), Culture, society and the media (pp. 56–90). London: Methuen.
Harel, I., & Papert, S. (Eds.). (1991). Constructionism. Norwood, NJ: Ablex Publishing.
Helft, M. (2008, November 11). Google uses searches to track flu’s spread. The New York Times, p. A1.
Hochschild, J., & Scovronick, N. (2003). The American dream and the public schools. New York, NY: Oxford University Press.
Howard, P. N. (2005). Deep democracy, thin citizenship: The impact of digital media in political campaign strategy. The ANNALS of the American Academy of Political and Social Science, 597(1), 153–170.
Ito, M., Okabe, D., & Matsuda, M. (2005). Personal, portable, pedestrian: Mobile phones in Japanese life. Cambridge, MA: MIT Press.
Klein, H. K., & Kleinmann, D. L. (2002). The social construction of technology: Structural considerations. Science, Technology and Human Values, 27(1), 28–52.
Kozol, J. (1991). Savage inequalities: Children in America’s schools. New York: Crown.
Lipman, P. (2004). High stakes education: Inequality globalization, and urban school reform. New York: Routledge Falmer.
Lipman, P., & Hursh, D. (2007). Renaissance 2010: The reassertion of ruling-class power through neoliberal policies in Chicago. Policy futures in education, 5(2), 160.
Loukides, M. (2010). What is data science? The future belongs to the companies and people that turn data into products. Retrieved from http://radar.oreilly.com/2010/06/what-is-data-science.html.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. Retrieved from http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation.
Martin, D. (2009). Researching race in mathematics education. Teachers College Record, 111(2), 295–338.
Mayer, R. E. (2005). Introduction to mulitmedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 1–18). New York, NY: Cambridge University Press.
McLaren, P. (1994). Life in schools. White Plains, NY: Longman.
Nasir, N. S., & Hand, V. M. (2006). Exploring sociocultural perspectives on race, culture, and learning. Review of Educational Research, 76(4), 449–475.
National Commission on Excellence in Education. (1983). A nation at risk: The imperative for educational reform. Retrieved from: http://www2.ed.gov/pubs/NatAtRisk/index.html.
Neill, M. (1995). Computers, thinking and schools in the new world order. In J. Brook & I. A. Boal (Eds.), Resisting the virtual life: The culture and politics of information (pp. 181–194). San Francisco, CA: City Lights.
Newman, A. A. (2011, December 22). Using Google’s data to reach consumers. The New York times, p. B3.
Office of Science and Technology Policy. (2012). Obama administration unveils “Big Data” initiative: Announces $200 million in new R&D investments. Retrieved from: http://www.whitehouse.gov/…/big_data_press_release_final_2.pdf.
Office of the White House Press Secretary. (2009). President Obama launches “Educate to Innovate” campaign for excellence in science, technology, engineering & math (STEM) education. Retrieved from: http://www.whitehouse.gov/the-press-office/president-obama-launches-educate-innovate-campaign-excellence-science-technology-en.
Partnership for 21st Century Skills. (2009). P21 framework definitions. Retrieved from http://www.p21.org/documents/P21_Framework_Definitions.pdf.
Rogoff, B. (1995). Observing sociocultural activity on three planes: Participatory appropriation, guided participation, and apprenticeship. In J. V. Wertsch, P. d. Rio, & A. Alvarez (Eds.), Sociocultural studies of mind (pp. 139–164). Cambridge, UK: Cambridge University Press.
Rogoff, B. (2003). The cultural nature of human development. Oxford, UK: Oxford University Press.
Rose, M. (2009). Why school? New York, NY: The New Press.
Said, E. (1979). Orientalism. New York: Vintage Books.
Selwyn, N., Gorard, S., & Williams, S. (2001). Digital divide or digital opportunity? The role of technology in overcoming social exclusion in U.S. education. Educational Policy, 15(2), 258–277.
Shekhar, S. (2011). What is special about mining spatial and spatio-temporal datasets? Paper presented at The International Symposium on Spatial-Temporal Analysis and Data Mining. London, UK: University College.
Thackeray, R., & Hunter, M. (2010). Empowering youth: Use of technology in advocacy to affect social change. Journal of Computer-Mediated Communication, 15(4), 575–591. doi:10.1111/j.1083-6101.2009.01503.x.
Thrift, N. (2006). Re-inventing invention: New tendencies in capitalist commodification. Economy and Society, 35(2), 279–306.
Tuhiwai-Smith, L. (2002). Decolonizing methodologies: Research and indigenous peoples. New York, NY: Zed Books Ltd.
Tyack, D. (1974). The one best system: A history of American urban education. Cambridge, MA: Harvard University Press.
Tyack, D., & Cuban, L. (2001). Progress or regress? In L. Iura (Ed.), The Jossey-Bass reader on school reform (pp. 5–42). San Francisco, CA: Wiley Company.
Valaitis, R. K. (2005). Computers and the internet: Tools for youth empowerment. Journal of Medical Internet Research, 7(5), e51.
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.
Wenger, E. (1998). Communities of practice: Learning, meaning, and identity. Cambridge, UK: Cambridge University Press.
Wider Opportunities for Women. (2010). The basic economic security tables for the United States. Retrieved from http://wowonline.org/documents/BESTIndexforTheUnitedStates2010.pdf.
Wraga, W. G. (1994). Democracy’s high school: The comprehensive high school and educational reform in the United States. Lanhan, MD: University Press of America.
Wresch, W. (1996). Disconnected: Haves and have-nots in the information age. New Brunswich, NJ: Rutgers University Press.
This work was supported by NSF grant MSP-0962919.
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Philip, T.M., Schuler-Brown, S. & Way, W. A Framework for Learning About Big Data with Mobile Technologies for Democratic Participation: Possibilities, Limitations, and Unanticipated Obstacles. Tech Know Learn 18, 103–120 (2013). https://doi.org/10.1007/s10758-013-9202-4
- Big Data
- Mobile technology
- Democratic participation