Ethical challenges of edtech, big data and personalized learning: twenty-first century student sorting and tracking
- 17 Downloads
With the increase in the costs of providing education and concerns about financial responsibility, heightened consideration of accountability and results, elevated awareness of the range of teacher skills and student learning styles and needs, more focus is being placed on the promises offered by online software and educational technology. One of the most heavily marketed, exciting and controversial applications of edtech involves the varied educational programs to which different students are exposed based on how big data applications have evaluated their likely learning profiles. Characterized most often as ‘personalized learning,’ these programs raise a number of ethical concerns especially when used at the K-12 level. This paper analyzes the range of these ethical concerns arguing that characterizing them under the general rubric of ‘privacy’ oversimplifies the concerns and makes it too easy for advocates to dismiss or minimize them. Six distinct ethical concerns are identified: information privacy; anonymity; surveillance; autonomy; non-discrimination; and ownership of information. Particular attention is paid to whether personalized learning programs raise concerns similar to those raised about educational tracking in the 1950s. The paper closes with discussion of three themes that are important to consider in ethical and policy discussions.
KeywordsPrivacy Discrimination Big data Autonomy Education technology Personalized learning
The research for this paper was funded by the eQuality Partnership Grant from the Social Science and Humanities Research Council of Canada. For information on the grant, please see: http://www.equalityproject.ca/. An earlier version of this paper, “Big Data in the Education Arena: 21st Century Student Sorting and Tracking” co-authored by Priscilla M. Regan, Jolene Jesse and Elsa Talat Khwaja, was presented at the Surveillance Studies Network Conference in April 2016. The author acknowledges the assistance of Elsa Talat Khwaja in preparation of this paper. This material is based upon work supported while employed at the US National Science Foundation. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
- Alarcon, A., Zeide, E., Rosenblat, A., Wikelius, K., boyd, d., Gangadharan, S. P., & Yu, C. (2014). Data & Civil Rights: Education primer, produced for Data & Civil Rights Conference. Accessed March 15, 2016, from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2542268.
- Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review 104, 671–732.Google Scholar
- Bennett, C. J. (1992). Regulating privacy: Data protection and public policy in Europe and the United States. Ithaca: Cornell University Press.Google Scholar
- Bienkowski, M. (2017). Implications of privacy concerns for using student data for research: Panel summary. Workshop on Big Data in Education. Accessed March 4, 2018, from https://naeducation.org/wp-content/uploads/2017/05/Bienkowski-FINAL.pdf.
- Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational analytics and data mining. Center for Technology and Learning, SRI International. Accessed March 1, 2018, from https://tech.ed.gov/wp-content/uploads/2014/03/edm-la-brief.pdf.
- Bogle, A. (2014). What the failure of InBloom means for the student-data industry. Slate Future Tense Blog. Accessed March 8, 2016, from http://www.slate.com/blogs/future_tense/2014/04/24/what_the_failure_of_inbloom_means_for_the_student_data_industry.html.
- Boninger, F., Molnar, A., & Murray, K. (2017). Asleep at the Switch: Schoolhouse commercialism, student privacy, and the failure of policymaking. National Education Policy Center: Report on Schoolhouse Commercialization Trends. Accessed February 1, 2018, from http://nepc.colorado.edu/files/publications/RB%20Trends%202017_2.pdf.
- Bulger, M. (2016). Personalized learning: The conversations we’re not having, Working Paper 07.22.2016. Data and Society Research Institute. Accessed February 3, 2018, from https://datasociety.net/pubs/ecl/PersonalizedLearning_primer_2016.pdf.
- Bulger, M., McCormick, P., & Pitcan, M. (2017). The legacy of inBloom, Working Paper 02.02.2017. Data and Society Research Institute. Accessed February 3, 2018, from https://datasociety.net/pubs/ecl/InBloom_feb_2017.pdf.
- Burris, C. C., & Garrity, D. T. (2008). Detracking for excellence and equity,” see especially Chap. 2 “What Tracking is and How to Start Dismantling It.” Accessed March 15, 2016, from http://www.ascd.org/publications/books/108013/chapters/What-Tracking-Is-and-How-to-Start-Dismantling-It.aspx.
- Campolo, A., Sanfilippo, M., Whittaker, M., & Crawford, K. (2017). AI Now 2017 Report. Accessed March 3, 2018, from https://ainowinstitute.org/AI_Now_2017_Report.pdf.
- Citron, D. K., & Pasquale, F. (2014). The scored society: Due process for automated predictions. Washington Law Review, 89, 101–133.Google Scholar
- Crawford, K., & Schultz, J. (2014). Big data and due process: Toward a framework to redress predictive privacy harms. Boston College Law Review, 55, 93–128.Google Scholar
- Data Quality Campaign. (2015). State student data privacy legislation: What happened in 2015, and what is next? Accessed February 15, 2016, from https://2pido73em67o3eytaq1cp8au-wpengine.netdna-ssl.com/wp-content/uploads/2016/03/DQC-Student-Data-Laws-2015-Sept23.pdf.
- Data Quality Campaign. (2017). Education data legislation review: 2017 state activity. Accessed January 28, 2018, from https://2pido73em67o3eytaq1cp8au-wpengine.netdna-ssl.com/wp-content/uploads/2017/09/DQC-Legislative-summary-0926017.pdf.
- Davis, M. R. (2015). Lessons learned from security breaches. Education Week, 35(9), S6–S7.Google Scholar
- Doran, L. (2017). Ransomware attacks force school districts to shore up–or pay up. Education Week, 36(17), 1–10.Google Scholar
- Effrem, K. R. (2018). 6 Key takeaways from Congress’ hearing on protecting student data. The National Pulse. Accessed August 15, 2018, from https://thenationalpulse.com/commentary/6-key-takeaways-congress-hearing-protecting-student-data/.
- English, W. (2016). Two cheers for nudging. Georgetown Journal of Law and Public Policy, 14, 829–840.Google Scholar
- Flaherty, D. (1989). Protecting privacy in surveillance societies: The Federal Republic of Germany, Sweden, France, Canada, and the United States. Chapel Hill: University of North Carolina Press.Google Scholar
- Future of Privacy Forum and Software & Information Industry Association. (2018). K-12 school service provider pledge to safeguard student privacy. Accessed August 20, 2018, from https://studentprivacypledge.org/privacy-pledge/.
- Gellman, R. M. (1993). Fragmented, incomplete, and discontinuous: The failure of federal privacy regulatory proposals and institutions. Software Law Journal, 6, 199.Google Scholar
- Hartzog, W., & Selinger, E. (2013a). Big data in small hands. Stanford Law Review Online, 66, 81–88.Google Scholar
- Hartzog, W., & Selinger, E. (2013b). Obscurity: A better way to think about your data than privacy, Atlantic. Accessed January 10, 2016, from http://www.theatlantic.com/technology/archive/2013/01/obscurity-a-better-way-to-think-about-your-data-than-privacy/267283/.
- Herold, B. (2014). InBloom to shut down amid growing data-privacy concerns, Education Week. Accessed March 8, 2016, from http://blogs.edweek.org/edweek/DigitalEducation/2014/04/inbloom_to_shut_down_amid_growing_data_privacy_concerns.html.
- Herold, B. (2017). Personalized learning: Modest gains, big challenges, RAND study finds, Education Week. https://blogs.edweek.org/edweek/DigitalEducation/2017/07/personalized_learning_research_implementation_RAND.html. Accessed 10 February 2018.
- Horn, M. B., & Staker, H. (2011). The rise of K-12 blended learning. Innosight Institute. Accessed January 20, 2018, from https://www.christenseninstitute.org/publications/the-rise-of-k-12-blended-learning/.
- Horn, M. B., Staker, H., & Christensen, C. (2013). Is K-12 blended learning disruptive? An introduction to the theory of hybrids. Accessed January 20, 2018, from https://www.christenseninstitute.org/publications/hybrids/.
- Kelly, J. T. (2016). Non-paternalist nudges. Georgetown Journal of Law and Public Policy, 14, 807–816.Google Scholar
- Kerr, I., & Earle, J. (2013). Prediction, preemption, presumption: How big data threatens big picture privacy. Stanford Law Review Online, 66, 65–72.Google Scholar
- Kohli, S. (2014). Modern-day segregation in public schools, The Atlantic. Accessed March 1, 2016, from http://www.theatlantic.com/education/archive/2014/11/modern-day-segregation-in-public-schools/382846/.
- Lerman, J. (2013). Big data and its exclusions. Stanford Law Review Online, 66, 55–63.Google Scholar
- Lichtenberg, J. (2016). For your own good: Informing, nudging, coercing. Georgetown Journal of Law and Public Policy, 14, 663–682.Google Scholar
- Loveless, T. (2013). The resurgence of ability grouping and persistence of tracking, (Part II of the 2013 Brown Center Report on American Education), Brookings Report. Accessed March 3, 2016, from http://www.brookings.edu/research/reports/2013/03/18-tracking-ability-grouping-loveless.
- Mirel, J. (2006). The traditional high school: Historical debates over its nature and function, Education Next (Winter) Vol 6, No. 1, pp. 14–21. http://eric.ed.gov/?id=EJ763310. Accessed 1 March 2016.
- National Academy of Education Workshop Summary. (2017). Big data in education: Balancing the benefits of educational research and student privacy. Accessed February 23, 2018, from http://naeducation.org/wp-content/uploads/2017/05/NAEd_BD_Booklet_FINAL_051717_3.pdf.
- National Association of State Boards of Education. (2015). Comparison of 2015 Federal Education Data Privacy Bills. Accessed August 15, 2018, from http://www.nasbe.org/wp-content/uploads/2015-Federal-Education-Data-Privacy-Bills-Comparison-2015.07.22-Public.pdf.
- New, J. (2016). Building a data-driven education system in the United States. Center for Data Innovation. Accessed August 15, 2018, from http://www2.datainnovation.org/2016-data-driven-education.pdf.
- Organization for Economic Cooperation and Development (OECD). (2015). Students, computers and learning: Making the connection. Programme for International Student Assessment, OECD Publishing. Accessed August 16, 2018, from https://read.oecd-ilibrary.org/education/students-computers-and-learning_9789264239555-en#page1.
- Pane, J. F., Steiner, E. D., Baird, M. D., Hamilton, L. S., & Pane, J. D. (2017). Informing progress: Insights on personalized learning implementation and effects. RAND Report. Accessed February 13, 2018, from https://www.rand.org/pubs/research_reports/RR2042.html.
- Pariser, E. (2011). The filter bubble: How the new personalized web is changing what we read and how we think. New York: Penguin Books.Google Scholar
- President’s Council of Advisors on Science and Technology. (2014). Big data and privacy: A technological perspective. Accessed December 10, 2015, from http://www.whitehouse.gov/sites/default/files/microsites/ostp/PCAST/pcast_big_data_and_privacy_-_may_2014.pdf.
- Regan, P. M. (1995). Legislating privacy: Technology, social values, and public policy. Chapel Hill: University of North Carolina Press.Google Scholar
- Regan, P. M. (2017). Big data and privacy. In J. Bachner, K. W. Hill, & B. Ginsberg (Eds.), Analytics, policy and governance. New Haven: Yale University Press.Google Scholar
- Regan, P. M., & Bailey, J. (2018). Big data, privacy and education applications. In Presented at Surveillance Studies Network Conference, June 2018. Denmark: Aarhus University.Google Scholar
- Regan, P. M., & Khwaja, E. T. (2017). Ethical implementation of big data in education: Policy and practices in the US and Canada. In Presented at the Law and Society Association Annual Conference, June 2017, Mexico City.Google Scholar
- Roberts-Mahoney, H., Means, A. J., & Garrison, M. J. (2016). Netflixing human capital development: Personalized learning technology and the corporatization of K-12 education, Journal of Education Policy 1–16.Google Scholar
- Roscorla, T. (2016). 3 Student Data Privacy Bills that Congress Could Act On, Government Technology: Center for Digital Education. Accessed August 15, 2018, from http://www.govtech.com/education/k-12/3-Student-Data-Privacy-Bills-That-Congress-Could-Act-On.html.
- Schwartz, P. M. (2000). Internet privacy and the state. Connecticut Law Review, 32, 815.Google Scholar
- Singer, N. (2013). Deciding Who Sees Students’ Data, The New York Times. Accessed February 16, 2016, from http://www.nytimes.com/2013/10/06/business/deciding-who-sees-students-data.html.
- Singer, N. (2014). InBloom student data repository to close. The New York Times Bit Blog. http://bits.blogs.nytimes.com/2014/04/21/inbloom-student-data-repository-to-close/?_r=0. Accessed 8 March 2016.
- Solove, D. (2008). Understanding privacy. Cambridge: Harvard University Press.Google Scholar
- Strauss, V. (2013). The bottom line on student tracking, The Washington Post. Accessed March 8, 2016, from https://www.washingtonpost.com/news/answer-sheet/wp/2013/06/10/the-bottom-line-on-student-tracking/.
- Sunstein, C. R. (2015). The ethics of nudging. Yale Journal on Regulation, 32(2), 413–450.Google Scholar
- Sweeney, L. (2000). Uniqueness of simple demographics in the US population (Laboratory for International Data Privacy, Working Paper LIDAP-WP4). Accessed December 10, 2015, from http://dataprivacylab.org/projects/identifiability/index.html.
- Sweeney, L., Abu, A., & Winn, J. (2013). Identifying participants in the personal genome project by name, Harvard University Data Privacy Lab, White Paper 1021-1 (April 24). Accessed December 10, 2015, from http://dataprivacylab.org/projects/pgp/1021-1.pdf.
- Tene, O., & Polonetsky, J. (2013). Big data for all: Privacy and user control in the age of analytics. Northwestern Journal of Technology and Intellectual Property, 11(5), 239–273,253.Google Scholar
- Tucker, M. (2015). Student tracking vs academic pathways: Different… or the same? Education Week (October 15). Accessed March 8, 2016, from http://blogs.edweek.org/edweek/top_performers/2015/10/tracking_vs_pathways_differentor_the_same.html.
- West, D. M. (2012). Big data for education: Data mining, data analytics, and web dashboards, Governance Studies at Brookings (September). Accessed March 5, 2016, from http://www.brookings.edu/~/media/research/files/papers/2012/9/04-education-technology-west/04-education-technology-west.pdf.
- Westin, A. (1967). Privacy and freedom. New York: Atheneum.Google Scholar
- Zeide, E. (2016). Student privacy principles for the age of big data: Moving beyond FERPA and FIPPS. Drexel Law Review, 8, 101–160.Google Scholar
- Zeide, E. (2017). The limits of education purpose limitations. University of Miami Law Review, 41, 494–527.Google Scholar