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Articles

Interoperability and data standards in the K-12 education sector: intersections with data justice

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Pages 324-336 | Received 31 May 2022, Accepted 02 Mar 2023, Published online: 15 Mar 2023

References

  • Abualghaib, O., N. Groce, N. Simeu, M. T. Carew, and D. Mont. 2019. “Making Visible the Invisible: Why Disability-Disaggregated Data is Vital to “Leave No-One Behind”.” Sustainability 11: 3091. doi:10.3390/su11113091.
  • Allensworth, E. M., and J. Q. Easton. 2007. What Matters for Staying on-Track and Graduating in Chicago Public High Schools: A Close Look at Course Grades, Failures, and Attendance in the Freshman Year. Chicago, IL: University of Chicago Consortium on Chicago School Research.
  • Anagnostopoulos, D., and J. Bautista-Guerra. 2013. “Trust and Numbers: Constructing and Contesting Statewide Student Information Systems.” In The Infrastructure of Accountability: Data use and the Transformation of American Education, edited by D. Anagnostpoulos, S. A. Rutledge, and R. Jacobsen, 41–56. Cambridge, MA: Harvard Education Press.
  • Anagnostopoulos, D., S. Rutledge, and V. Bali. 2013. “State Education Agencies, Information Systems, and the Expansion of State Power in the era of Test-Based Accountability.” Educational Policy 27 (2): 217–247. doi:10.1177/0895904813475713.
  • Archibald, R. A. 2022. “The Rise of the Airport Metal Detector: Colorblind Racism, Police Discretion, and Surveillance Across Borders.” Journal of Social History 56: 637–671. doi:10.1093/jsh/shac049.
  • Austin, C. C., T. Bloom, S. Dallmeier-Tiessen, V. K. Khodiyar, F. Murphy, A. Nurnberger, L. Raymond, et al. 2016. “Key Components of Data Publishing: Using Current Best Practices to Develop a Reference Model for Data Publishing.” International Journal on Digital Libraries 18: 77–92. doi:10.1007/s00799-016-0178-2.
  • Balfanz, R., L. Herzog, and D. J. Mac Iver. 2007. “Preventing Student Disengagement and Keeping Students on the Graduation Path in Urban Middle-Grades Schools: Early Identification and Effective Interventions.” Educational Psychologist 42 (4): 223–235. doi:10.1080/00461520701621079.
  • Chan Zuckerberg Initiative. 2023. Whole Child Approach to Education. https://chanzuckerberg.com/education/whole-child-approach-to-education/#:~:text=A%20whole%20child%20approach%20to,adults%20to%20engage%20and%20thrive.
  • Charteris, J. 2022. “Post-panoptic Accountability: Making Data Visible Through ‘Data Walls’ for Schooling Improvement.” British Journal of Sociology of Education 43 (3): 333–348. doi:10.1080/01425692.2021.2018651.
  • Cho, V., and J. C. Wayman. 2014. “Districts’ Efforts for Data use and Computer Data Systems: The Role of Sensemaking in System use and Implementation.” Teachers College Record 116: 1–45. doi:10.1177/016146811411600203.
  • Cho, V., and J. C. Wayman. 2015. “Assumptions, Strategies, and Organization: Central Office Implementation of Computer Data Systems.” Journal of School Leadership 25: 1203–1236. doi:10.1177/105268461502500607.
  • Clarke, R. 1988. “Information Technology and Dataveillance.” Communications of the ACM 31 (5): 498–512. doi:10.1145/42411.42413.
  • Clutterbuck, J. 2022. “Data Infrastructures and the Governance of Their Accompanying Narratives.” British Journal of Sociology of Education 43 (1): 120–139. doi:10.1080/01425692.2021.2003184.
  • Clutterbuck, J., I. Hardy, and S. Creagh. 2021. “Data Infrastructures as Sites of Preclusion and Omission: The Representation of Students and Schooling.” Journal of Education Policy 38: 93–114. doi:10.1080/02680939.2021.1972166.
  • Conron, K. J., Scout, and S. B. Austin. 2008. “‘Everyone has a Right to, Like, Check Their Box:’ Findings on a Measure of Gender Identity from a Cognitive Testing Study with Adolescents.” Journal of LGBT Health Research 4 (1): 1–9. doi:10.1080/15574090802412572.
  • Copur-Gencturk, Y., J. R. Cimpian, S. T. Lubienski, and I. Thacker. 2019. “Teachers’ Bias Against the Mathematical Ability of Female, Black, and Hispanic Students.” Educational Researcher 49 (1): 30–43. doi:10.3102/0013189X19890577.
  • Corbeil, M. E., J. R. Corbeil, and B. H. Khan. 2019. “A Framework for Implementing Responsible Data Mining and Analytics in Education.” In Responsible Analytics and Data Mining in Education: Global Perspectives on Quality, Support, and Decision Making, edited by B. H. Khan, J. R. Corbeil, and M. E. Corbeil, 3–15. New York, NY: Routledge, Taylor & Francis Group.
  • Decuypere, M. 2021. “The Topologies of Data Practices: A Methodological Introduction.” Journal of New Approaches in Educational Research 10 (1): 67–84. doi:10.7821/naer.2021.1.650.
  • Dencik, L., A. Hintz, and J. Cable. 2016. “Towards Data Justice? The Ambiguity of Anti-Surveillance Resistance in Political Activism.” Big Data & Society 3 (2): 1–12. doi:10.1177/2053951716679678.
  • Dencik, L., A. Hintz, J. Redden, and E. Treré. 2019. “Exploring Data Justice: Conceptions, Applications and Directions.” Information, Communication & Society 22 (7): 873–881. doi:10.1080/1369118X.2019.1606268.
  • Dexter, S., A. Francisco, and C. L. Luna. 2021. “Five Leading-Edge K-12 Districts’ Decision-Making Processes for EdTech Innovations.” Journal of Educational Administration 59 (3): 352–366. doi:10.1108/JEA-10-2020-0222.
  • Ed-Fi Alliance. 2023. https://www.ed-fi.org/.
  • Educating the Whole Child Summit. 2023. https://ismhi.indiana.edu/events/ishmi-whole-child-summit.html.
  • Education360. 2023. https://education360.com.au/.
  • Ehlers, L. 2020. Seeing the “Whole Child” Data Picture, June 17. https://www.illuminateed.com/blog/2020/06/seeing-the-whole-child-data-picture/.
  • Endrew F. v. Douglas County School Dist. RE-1, 580 U.S. ___. 2017.
  • Eubanks, V. 2018. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. New York, NY: St. Martin’s Press.
  • Eynon, R. 2013. “The Rise of Big Data: What Does it Mean for Education, Technology, and Media Research?” Learning, Media and Technology 38 (3): 237–240. doi:10.1080/17439884.2013.771783.
  • Fu, S., and K. King. 2021. “Data Disaggregation and its Discontents: Discourses of Civil Rights, Efficiency and Ethnic Registry.” Discourse: Studies in the Cultural Politics of Education 42 (2): 199–214. doi:10.1080/01596306.2019.1602507.
  • Goldhaber, D., K. L. Holden, and C. Grout. 2019. “Errors in Administrative Education Data: A Cautionary Tale.” Educational Researcher 48 (3): 179–182. doi:10.3102/0013189X19837598.
  • Gordon, W. J., and C. Catalini. 2018. “Blockchain Technology for Healthcare: Facilitating the Transition to Patient-Driven Interoperability.” Computational and Structural Biotechnology Journal 16: 224–230. doi:10.1016/j.csbj.2018.06.003.
  • Gulson, K. N., and S. Sellar. 2019. “Emerging Data Infrastructures and the new Topologies of Education Policy.” Environment and Planning D: Society and Space 37 (2): 350–366. doi:10.1177/0263775818813144.
  • Hartong, S. 2016. “Between Assessments, Digital Technologies and big Data: The Growing Influence of ‘Hidden’ Data Mediators in Education.” European Educational Research Journal 15 (5): 523–536. doi:10.1177/1474904116648966.
  • Hawn Nelson, A., D. Jenkins, S. Zanti, M. Katz, E. Berkowitz, T. Burnett, and D. Culhane. 2020. A Toolkit for Centering Racial Equity Throughout Data Integration. Philadelphia, PA: Actionable Intelligence for Social Policy, University of Pennsylvania. https://www.aisp.upenn.edu/wp-content/uploads/2020/08/AISP-Toolkit_5.27.20.pdf.
  • Iwama, J., Y. Irizarry, A. Ernstes, M. Ripepi, A. A. Peguero, J. M. Bondy, and J. S. Hong. 2022. “Segregation, Securitization, and Bullying: Investigating the Connections Between Policing, Surveillance Punishment, and Violence.” Race and Justice 1–32. doi:10.1177/21533687221105906.
  • Jaffe, R. 2020. “Rethinking Metadata’s Value and how it is Evaluated.” Technical Services Quarterly 37 (4): 432–443. doi:10.1080/07317131.2020.1810443.
  • Jarke, J., and A. Breiter. 2019. “Editorial: The Datafication of Education.” Learning, Media and Technology 44 (1): 1–6. doi:10.1080/17439884.2019.1573833.
  • Karapetyan, A. 2018. Ensuring Student Success with 360-Degree Data Views, July 20. https://www.salesforce.org/blog/student-success-in-k-12-education-case-study-with-boston-day-and-evening-academy/.
  • Kerssens, N., and J. van Dijck. 2022. “Governed by Edtech? Valuing Pedagogical Autonomy in a Platform Society.” Harvard Educational Review 92 (2): 284–303. doi:10.17763/1943-5045-92.2.284.
  • Kitchin, R. 2014. The Data Revolution: Big Data, Open Data, Data Infrastructures & Their Consequences. Los Angeles, CA: SAGE.
  • Komljenovic, J. 2021. “The Rise of Education Rentiers: Digital Platforms, Digital Data and Rents.” Learning, Media and Technology 46 (3): 320–332. doi:10.1080/17439884.2021.1891422.
  • Krachman, S. B., R. LaRocca, and C. Gabrieli. 2018. “Accounting for the Whole Child.” Educational Leadership 75 (5). https://www.ascd.org/el/articles/accounting-for-the-whole-child.
  • Landwehr, M., A. Borning, and V. Wulf. 2021. “Problems with Surveillance Capitalism and Possible Alternative for IT Infrastructure.” Information, Communication & Society 26: 70–85. doi:10.1080/1369118X.2021.2014548.
  • Lavis, T., and N. Brewer. 2017. “Effects of a Proven Error on Evaluations of Witness Testimony..” Law and Human Behavior 41 (3): 314–323. doi:10.1037/lhb0000217.
  • Leonelli, S. 2019. “Data — from Objects to Assets.” Nature 574: 317–320. doi:10.1038/d41586-019-03062-w.
  • Lindh, M., and J. Nolin. 2016. “Information we Collect: Surveillance and Privacy in the Implementation of Google Apps for Education.” European Educational Research Journal 15 (6): 644–663. doi:10.1177/1474904116654917.
  • Mandinach, E. B., and K. Schildkamp. 2021. “Misconceptions About Data-Based Decision Making in Education: An Exploration of the Literature.” Studies in Educational Evaluation 69. doi:10.1016/j.stueduc.2020.100842.
  • McKinney, E. H., and C. J. Yoos. 2010. “Information About Information: A Taxonomy of Views.” MIS Quarterly 34 (2): 329–344. doi:10.2307/20721430.
  • Murray, S. D., J. Hurley, and S. R. Ahmed. 2015. “Supporting the Whole Child Through Coordinated Policies, Processes, and Practices.” Journal of School Health 85: 795–801. doi:10.1111/josh.12306.
  • Noura, M., M. Atiquzzaman, and M. Gaedke. 2019. “Interoperability in Internet of Things: Taxonomies and Open Challenges.” Mobile Networks and Applications 24: 796–809. doi:10.1007/s11036-018-1089-9.
  • Office of Special Education Programs. n.d. Slides to Explain Results Driven Accountability (RDA) [Part B]. Washington, DC: Office of Special Education Programs, U.S. Department of Education. https://www2.ed.gov/about/offices/list/osers/osep/rda/part-b-rda-comprehensive-overview.ppt.
  • Pang, V. O., P. H. Han, and J. M. Pang. 2011. “Asian American and Pacific Islander Students: Equity and the Achievement Gap.” Educational Researcher 40 (8): 378–389. doi:10.3102/0013189X11424222.
  • Pang, C., and D. Szafron. 2014. “Single Source of Truth (SSOT) for Service Oriented Architecture (SOA).” Proceedings of the 12th International Conference on Service Oriented Computing 8831: 575–589. doi:10.1007/978-3-662-45391-9_50.
  • Pangrazio, L., N. Selwyn, and B. Cumbo. 2022. “A Patchwork of Platforms: Mapping Data Infrastructures in Schools.” Learning, Media and Technology 48: 65–80. doi:10.1080/17439884.2022.2035395.
  • Perrotta, C., K. N. Gulson, B. Williamson, and K. Witzenberger. 2021. “Automation, APIs and the Distributed Labour of Platform Pedagogies in Google Classroom.” Critical Studies in Education 62 (1): 97–113. doi:10.1080/17508487.2020.1855597.
  • Phelps, D., and R. Santo. 2022. “Debugging Inequities: Data use, “Gumshoe Work,” and Problem Identification in District-Wide Computer Science Education Initiatives.” Policy Futures in Education 1–23. doi:10.1177/14782103221126317.
  • Piety, P. J. 2019. “Components, Infrastructures, and Capacity: The Quest for the Impact of Actionable Data use on P-20 Educator Practice.” Review of Research in Education 43: 394–421. doi:10.3102/0091732X18821116.
  • Rafalow, M. H., and C. Puckett. 2022. “Sorting Machines: Digital Technology and Categorical Inequality in Education.” Educational Researcher 51 (4): 274–278. doi:10.3102/0013189X211070812.
  • Ralyea, D., and A. Rice. 2022. Data Neighborhoods: Preserving Context Around Educational Data, April 4. https://www.edanalytics.org/blog/data-neighborhoods-preserving-context-around-educational-data.
  • Rowe, D. A., C. H. Fowler, C. D’Agord, F. Horiuchi, M. Kawatachi, G. C. Norbert, and S. K. Avoke. 2021. “State Systemic Improvement Planning: Impact on System and Student Outcomes.” Journal of Disability Policy Studies 32 (2): 131–141. doi:10.1177/1044207320932548.
  • Schueler, B. E., and M. R. West. 2021. “Federalism, Race, and the Politics of Turnaround: U.S. Public Opinion on Improving low-Performing Schools and Districts.” Educational Researcher 51 (2): 122–133. doi:10.3102/0013189X211053317.
  • Scott, T. M., N. Gage, R. Hirn, and H. Han. 2019. “Teacher and Student Race as a Predictor for Negative Feedback During Instruction.” School Psychology 34 (1): 22–31. doi:10.1037/spq0000251.
  • Sellar, S. 2015. “Data Infrastructure: A Review of Expanding Accountability Systems and Large-Scale Assessments in Education.” Discourse: Studies in the Cultural Politics of Education 36 (5): 765–777. London, UK: Bloomsbury Academic. doi:10.1080/01596306.2014.931117.
  • Sellar, S. 2017. “Making Network Markets in Education: The Development of Data Infrastructure in Australian Schooling.” Globalisation, Societies and Education 15 (3): 341–351. doi:10.1080/14767724.2017.1330137.
  • Sellar, S., and K. N. Gulson. 2019. “Dispositions and Situations of Education Governance: The Example of Data Infrastructure in Australian Schooling.” In Education Governance and Social Theory: Interdisciplinary Approaches to Research, edited by A. Wilkins, and A. Olmedo, 63–79. New York, NY: Bloomsbury Academic.
  • Selwyn, N. 2014. “Data Entry: Towards the Critical Study of Digital Data and Education.” Learning, Media and Technology 40 (1): 64–82. doi:10.1080/17439884.2014.921628.
  • Selwyn, N. 2016. “‘There’s so Much Data’: Exploring the Realities of Data-Based School Governance.” European Educational Research Journal 15 (1): 54–68. doi:10.1177/1474904115602909.
  • Selwyn, N. 2021. “The Human Labour of School Data: Exploring the Production of Digital Data in Schools.” Oxford Review of Education 47 (3): 353–368. doi:10.1080/03054985.2020.1835628.
  • Selwyn, N. 2022. “‘Just Playing Around with Excel and Pivot Tables’ – The Realities of Data-Driven Schooling.” Research Papers in Education 37 (1): 95–114. doi:10.1080/02671522.2020.1812107.
  • Selwyn, N., L. Pangrazio, and B. Cumbo. 2022. “Knowing the (Datafied) Student: The Production of the Student Subject Through School Data.” British Journal of Educational Studies 70 (3): 345–361. doi:10.1080/00071005.2021.1925085.
  • Smith, P., A. Kumi-Yeboah, R. Chang, J. Lee, and P. Frazier. 2019. “Rethinking “(Under) Performance” for Black English Speakers: Beyond Achievement to Opportunity.” Journal of Black Studies 50 (6): 528–554. doi:10.1177/0021934719851870.
  • State Educational Technology Directors Association. 2018. State Education Leadership Interoperability: Leveraging Data for Academic Excellence (ED590233) ERIC. http://files.eric.ed.gov/fulltext/ED590233.pdf.
  • Taylor, L. 2017. “What is Data Justice? The Case for Connecting Digital Rights and Freedoms Globally.” Big Data & Society 4: 1–14. doi:10.1177/2053951717736335.
  • U.S. Census Bureau. 2022. About the Topic of Race. https://www.census.gov/topics/population/race/about.html.
  • Weatherly, R., and M. Lipsky. 1977. “Street-level Bureaucrats and Institutional Innovation: Implementing Special-Education Reform.” Harvard Educational Review 47 (2): 171–197. doi:10.17763/haer.47.2.v870r1v16786270x.
  • Whole Child Conference. 2023. https://www.njascd.org/store/p/5th-annual-whole-child-conference.
  • Whole Child Summit. 2023. https://www.characterstrong.com/wholechildsummit2023/.
  • Wikman, C., M. W. Allodi, and L. A. Ferrer-Wreder. 2022. “Self-concept, Prosocial School Behaviors, Well-Being, and Academic Skills in Elementary School Students: A Whole-Child Perspective.” Education Sciences 12: 298–315. doi:10.3390/educsci12050298.
  • Williamson, B. 2015. “Governing Software: Networks, Databases and Algorithmic Power in the Digital Governance of Public Education.” Learning, Media and Technology 40 (1): 83–105. doi:10.1080/17439884.2014.924527.
  • Williamson, B. 2016. “Digital Education Governance: Data Visualization, Predictive Analytics, and ‘Real-Time’ Policy Instruments.” Journal of Education Policy 31 (2): 123–141. doi:10.1080/02680939.2015.1035758.
  • Williamson, B. 2022. “Big Edtech.” Learning, Media and Technology 47 (2): 157–162. doi:10.1080/17439884.2022.2063888.
  • Williamson, B., K. N. Gulson, C. Perrotta, and K. Witzenberger. 2022. “Amazon and the new Global Connective Architectures of Education Governance.” Harvard Educational Review 92 (2): 231–256. doi:10.17763/1943-5045-92.2.231.
  • Williamson, B., and N. Piattoeva. 2018. “Objectivity as Standardization in Data-Scientific Education Policy, Technology and Governance.” Learning, Media and Technology 44 (1): 64–76. doi:10.1080/17439884.2018.1556215.

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