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A Framework for Learning About Big Data with Mobile Technologies for Democratic Participation: Possibilities, Limitations, and Unanticipated Obstacles

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Abstract

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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Notes

  1. 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.

  2. The curriculum used Deducer (http://www.deducer.org), a graphical user interface for the R language and software environment for statistical computing and graphics (http://www.r-project.org).

  3. 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).

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Acknowledgments

This work was supported by NSF grant MSP-0962919.

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Authors and Affiliations

  1. Graduate School of Education and Information Studies, University of California, Los Angeles, 2339 Moore Hall, Box 951521, Los Angeles, CA, 90095-1521, USA

    Thomas M. Philip, Sarah Schuler-Brown & Winmar Way

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  1. Thomas M. Philip
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  2. Sarah Schuler-Brown
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Correspondence to Thomas M. Philip.

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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

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