Student agency in a data-driven educational ecosystem

Student agency in a data-driven educational ecosystem
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Contributors (1)
Published
May 29, 2019

Student agency in a data-driven educational ecosystem

By Velislava Hillman

The data standard framework1 a public school district would typically use to organise school data can have over 700 fields2, which compile operational, academic, and personal student data. Additionally, a public school district3 may be in agreement with over one hundred, usually for-profit, vendors who also generate student data, often considered proprietary and therefore inaccessible to schools. Students may only see a fraction of any of this data on electronic dashboards and in school reports.

While much discussion surrounds issues stemming from the fact that the Internet was not designed with children in mind, studies of children’s understanding, interpretation, and agency over their school data are meagre. Schools, like the Internet, are platforms for learning, discovery, expression, and connection. With the growing digitization of schools (Collins & Halverson, 2018), algorithm-based applications provide new venues for instruction, practice, and assessment. But they also automate these processes, normalise constant surveillance of students as performers, and blur the definition of learning assessment, and of learning itself. To find strategies and creative solutions to protect children from algorithmic bias and inequality should not exclude the educational system, because, like the Internet, school is not immune to bias and perpetuating inequalities (Illich, 1970).

This proposal presents two objectives. First, I aim to deconstruct and provide some clarity about the data generated about children in school. There is no taxonomy of school data that aims to help children understand and be aware of what is collected about them. For example, from the 700+ fields of data according to SIF4, it remains unclear what this data looks like, how frequently it is collected, what purpose it serves and to whom, beyond measuring school accountability5, which data is critical to be shared and with whom, which data is critical to be kept private and therefore under whose control. Some make efforts to provide such clarity. Data Quality Campaign6, for example, disseminates information about school data to various concerned stakeholders. However, a more transparent and participatory approach should be made available to students. This leads to the second objective outlined in this proposal.

An opportunity must be created whereby students can view, understand, interact with, and even query the data that is generated about them in school. A decentralised system can be designed that enables such clarity and transparency about the otherwise disparate data across schools and vendors. For example, schools generate data about student conduct. Applications such as Class Dojo (Manolev et al., 2018), gather similar data. Teachers may further report behaviour using Swivl7. Schools at district, state, or federal level do not have comprehensive access to all data related to student conduct. Less so do students. Instead, disparate behaviour data exists scattered across silos with unclear use and impact.

The scale, source, and nature of school data makes its interoperability impractical, resulting in an inability to assess the true impact of educational technologies on children’s learning. While data may help teachers improve work (Mandinach & Jackson, 2012), an increasingly data-driven decision-making process (Zeide, 2017) suggests that student dimensions of learning and equitable participation in curriculum design become secondary. Finding a balance between increasingly data-driven decision making and student voice and choice is critical for an efficient educational ecosystem.

A new layer with techno-social functionality is necessary that is independent, fair, and transparent about school data, one that allows students to gradually become data informed and in full control of their data.



Footnotes

  1. Schools at district and state level may use any data framework. Some of the most commonly used include the Standard Interoperability Framework (SIF), more information about it can be found here: https://www.a4l.org/ and Ed-Fi, https://www.ed-fi.org/, a non-profit organisation, fully funded by Michael and Susan Dell Foundation.

  2. For the research of this paper I collaborate with Cambridge Public School district (CPS) who use SIF. Their data schemas can be found here: http://specification.sifassociation.org/Implementation/NA/4.0/IndexOfTables.html#IndexOfTables

  3. A full list of the vendors CPS has agreements with can be found here: https://sdpc.a4l.org/district_listing.php?districtID=457

  4. http://specification.sifassociation.org/Implementation/NA/4.0/IndexOfTables.html#IndexOfTables. CEDS, the data standard framework for school reporting at federal level are based on SIF. Their data schemas can be found here: https://ceds.ed.gov/domainEntitySchema.aspx

  5. Measuring school quality and accountability originally emanates from the United States federal law, which mandates schools at district, state, and federal level to collect student data to serve as ‘accountability’ metric for assessing school effectiveness. See Elementary and Secondary Education Act, 2001, 2010.

  6. https://dataqualitycampaign.org/

  7. The application enables teacher live recording of reflection and observations. More can be found here: https://www.swivl.com/K-12-swivl-uses/

References

  1. Collins, A. & Halverson, R. (2018). Re-thinking education in the age of technology: The digital revolution and schooling in America, Teachers College Press: New York.

  2. Illich, I. (1970). Deschooling society. Marion Boyars Publishers: London.

  3. Mandinach, B. E., & Jackson, S. S. (2012). Transforming Teaching and Learning through Data-Driven Decision Making. SAGE: London.

  4. Manolev, J. Sullivan A., & Slee, R. (2018). The datafication of discipline: Class Dojo, surveillance and a performative classroom culture. Learning, Media and Technology, 44(1), 36-51.

  5. Zeide, E. (2017). The structural consequences of big data-driven education. Big Data, 5(2), 164-172.

About the author

I am currently a fellow at Berkman Klein Centre for Internet & Society, researching in the field of school data and children. Along with three collaborators (From MIT DCI and Yale Open Lab (NUS)) I am developing a proof-of-concept for a decentralised data management system that addresses data interoperability issues faced by schools today. Our project aims to provide student and school agency over disparate data for data governance and auditability. Cambridge Public School, Access for Learning (SIF) and the Student Data Privacy Consortium are partnering and helping in the process.

Before coming to Berkman Klein Centre I had just completed my PhD which focused on children’s perspectives and agency over digital tools in the classroom. I developed a model (and designed ICT curriculum for primary schools) that aimed to foster self-directed learning through making projects and stories using various digital applications. A paper presenting this research is accepted at the forthcoming Connected Learning Summit at University of California, Irvine.

My focus on children and young people’s voices and perspectives goes beyond the academic work. Back at home in Malta, I created and distributed a magazine for and by young people. I generated advertising revenue and remunerated young contributors to develop the content and design.

I am a mom of three young children who are my greatest wonder and happiness.

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