Handbook of Learning Analytics
Chapter 4
Handbook of Learning Analytics
First Edition
Ethics and Learning Analytics: Charting the (Un)Charted
Paul Prinsloo & Sharon Slade
Abstract
As the field of learning analytics matures, and discourses surrounding the scope, definition, challenges, and opportunities of learning analytics become more nuanced, there is benefit both in reviewing how far we have come in considering associated ethical issues and in looking ahead. This chapter provides an overview of how our own thinking has developed and maps our journey against broader developments in the field. Against a backdrop of technological advances and increasing concerns around pervasive surveillance and the role and unintended consequences of algorithms, the development of research in learning analytics as an ethical and moral practice provides a rich picture of fears and realities. More importantly, we begin to see ethics and privacy as crucial enablers within learning analytics. The chapter briefly locates ethics in learning analytics in the broader context of the forces shaping higher education and the roles of data and evidence before tracking our personal research journey, highlighting current work in the field, and concluding by mapping future issues for consideration.
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Cabinet Office. (2016, 19 May). Data science ethical framework. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/524298/Data_science_ethics_framework_v1.0_for_publication__1_.pdf
Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007, July/August) Academic analytics: A new tool, a new era. EDUCAUSE Review. http://net.educause.edu/ir/library/pdf/erm0742.pdf
Couldry, N. (2016, September 23). The price of connection: “Surveillance capitalism.” [Web log post]. https://theconversation.com/the-price-of-connection-surveillance-capitalism-64124
Dawson, S., Gašević, D., & Rogers, T. (2016). Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement. Australian Government. http://he-analytics.com/wp-content/uploads/SP13_3249_Dawson_Report_2016-3.pdf
Drachsler, H., & Greller, W. (2012). The pulse of learning analytics understandings and expectations from the stakeholders. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK ʼ12), 29 April–2 May 2012, Vancouver, BC, Canada (pp. 120–129). New York: ACM.
Drachsler, H., & Greller, W. (2016, April). Privacy and analytics: It’s a DELICATE issue — a checklist for trusted learning analytics. Proceedings of the 6th International Conference on Learning Analytics and Knowledge (LAK ʼ16), 25–29 April 2016, Edinburgh, UK (pp. 89–98). New York: ACM.
Engelfriet, A., Manderveld, J., & Jeunink, E. (2015). Learning analytics onder de Wet bescherming persoonsgegevens. SURFnet. https://www.surf.nl/binaries/content/assets/surf/nl/kennisbank/2015/surf_learning-analytics-onder-de-wet-wpb.pdf
FairTest. (n.d.). Just say no to the test. http://www.fairtest.org/get-involved/opting-out
Gašević, D., Dawson, S., & Jovanović, J. (2016). Ethics and privacy as enablers of learning analytics. Journal of Learning Analytics, 3(1), 1–4. http://dx.doi.org/10.18608/jla.2016.31.1
Munoz, C., Smith, M., & Patil, D. J. (2016). Big data: A report on algorithmic systems, opportunity, and civil rights. Executive Office of the President, USA. https://www.whitehouse.gov/sites/default/files/microsites/ostp/2016_0504_data_discrimination.pdf
NMC (New Media Consortium). (2011). The NMC Horizon Report. http://www.educause.edu/Resources/2011HorizonReport/223122
NMC (New Media Consortium). (2016). The NMC Horizon Report. http://www.nmc.org/publication/nmc-horizon-report-2016-higher-education-edition/
O’Brien, A. (2010, September 29). Predictably irrational: A conversation with best-selling author Dan Ariely. [Web log post]. http://www.learningfirst.org/predictably-irrational-conversation-best-selling-author-dan-ariely
Open University. (2014). Policy on ethical use of student data for learning analytics. http://www.open.ac.uk/students/charter/sites/www.open.ac.uk.students.charter/files/files/ecms/web-content/ethical-use-of-student-data-policy.pdf
Prinsloo, P. (2016, September 22). Fleeing from Frankenstein and meeting Kafka on the way: Algorithmic decision-making in higher education. Presentation at NUI, Galway. http://www.slideshare.net/prinsp/feeling-from-frankenstein-and-meeting-kafka-on-the-way-algorithmic-decisionmaking-in-higher-education
Prinsloo, P., Slade, S., & Galpin, F. (2012) Learning analytics: Challenges, paradoxes and opportunities for mega open distance learning institutions. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK ʼ12), 29 April–2 May 2012, Vancouver, BC, Canada (pp. 130–133). New York: ACM.
Prinsloo, P., & Slade, S. (2013). An evaluation of policy frameworks for addressing ethical considerations in learning analytics. Proceedings of the 3rd International Conference on Learning Analytics and Knowledge (LAK ’13), 8–12 April 2013, Leuven, Belgium (pp. 240–244). New York: ACM.
Prinsloo, P., & Slade, S. (2014a). Educational triage in higher online education: Walking a moral tightrope. International Review of Research in Open Distributed Learning (IRRODL), 14(4), 306–331. http://www.irrodl.org/index.php/irrodl/article/view/1881
Prinsloo, P., & Slade, S. (2014b). Student privacy and institutional accountability in an age of surveillance. In M. E. Menon, D. G. Terkla, & P. Gibbs (Eds.), Using data to improve higher education: Research, policy and practice (pp. 197–214). Global Perspectives on Higher Education (29). Rotterdam: Sense Publishers.
Prinsloo, P., & Slade, S. (2015). Student privacy self-management: Implications for learning analytics. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 83–92). New York: ACM.
Prinsloo, P., & Slade, S. (2016a). Student vulnerability, agency, and learning analytics: An exploration. Journal of Learning Analytics, 3(1), 159–182.
Prinsloo, P., & Slade, S. (2016b). Here be dragons: Mapping student responsibility in learning analytics. In M. Anderson & C. Gavan (Eds.), Developing effective educational experiences through learning analytics (pp. 170–188). Hershey, PA: IGI Global.
Ruggiero, D. (2016, May 18). What metrics don’t tell us about the way students learn. The Conversation. http://theconversation.com/what-metrics-dont-tell-us-about-the-way-students-learn-59271
Sclater, N. (2015, March 3). Effective learning analytics. A taxonomy of ethical, legal and logistical issues in learning analytics v1.0. JISC. https://analytics.jiscinvolve.org/wp/2015/03/03/a-taxonomy-of-ethical-legal-and-logistical-issues-of-learning-analytics-v1-0/
Sclater, N., Peasgood, A., & Mullan, J. (2016). Learning analytics in higher education. A review of UK and international practice. JISC. https://www.jisc.ac.uk/reports/learning-analytics-in-higher-education
Shacklock, X. (2016). From bricks to clicks: The potential of data and analytics in higher education. Higher Education Commission. http://www.policyconnect.org.uk/hec/sites/site_hec/files/report/419/fieldreportdownload/frombrickstoclicks-hecreportforweb.pdf
Siemens, G. (2016, April 28). Reflecting on learning analytics and SoLAR. [Web log post]. http://www.elearnspace.org/blog/2016/04/28/reflecting-on-learning-analytics-and-solar/
Siemens, G., & Baker, R. (2012, April). Learning analytics and educational data mining: Towards communication and collaboration. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK ʼ12), 29 April–2 May 2012, Vancouver, BC, Canada (pp. 252–254). New York: ACM.
Slade, S., & Galpin, F. (2012) Learning analytics and higher education: Ethical perspectives (workshop). Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK ʼ12), 29 April–2 May 2012, Vancouver, BC, Canada (pp. 16–17). New York: ACM.
Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(1), 1509–1528.
Slade, S., & Prinsloo, P. (2014). Student perspectives on the use of their data: Between intrusion, surveillance and care. Proceedings of the European Distance and E-Learning Network 2014 Research Workshop (EDEN 2014), 27–28 October 2014, Oxford, UK (pp. 291–300).
Smith, G. J. (2016). Surveillance, data and embodiment: On the work of being watched. Body & Society, 1–32. doi:10.1177/1357034X15623622
Strauss, V. (2016a, January 28). U.S. Education Department threatens to sanction states over test opt-outs. The Washington Post. https://www.washingtonpost.com/news/answer-sheet/wp/2016/01/28/u-s-education-department-threatens-to-sanction-states-over-test-opt-outs/
Strauss, V. (2016b, May 9). “Big data” was supposed to fix education. It didn’t. It’s time for “small data.” The Washington Post. https://www.washingtonpost.com/news/answer-sheet/wp/2016/05/09/big-data-was-supposed-to-fix-education-it-didnt-its-time-for-small-data/
Taneja, H. (2016, September 8). The need for algorithmic accountability. TechCrunch. https://techcrunch.com/2016/09/08/the-need-for-algorithmic-accountability/
van Barneveld, A., Arnold, K., & Campbell, J. (2012). Analytics in higher education: Establishing a common language. EDUCAUSE Learning Initiative, 1, 1–11.
Vitak, J., Shilton, K., & Ashktorab, Z. (2016). Beyond the Belmont principles: Ethical challenges, practices, and beliefs in the online data research community. Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW ’16), 27 February–2 March 2016, San Francisco, CA, USA. New York: ACM. https://terpconnect.umd.edu/~kshilton/pdf/VitaketalCSCWpreprint.pdf
Watters, A. (2016, May 7). Identity, power, and education’s algorithms. [Web log post]. http://hackeducation.com/2016/05/07/identity-power-algorithms
Welsh, S., & McKinney, S. (2015). Clearing the fog: A learning analytics code of practice. In T. Reiners et al. (Eds.), Globally connected, digitally enabled. Proceedings of the 32nd Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education (ASCILITE 2015), 29 November–2 December 2015, Perth, Western Australia (pp. 588–592). http://research.moodle.net/80/
Williamson, B. (2016a). Coding the biodigital child: The biopolitics and pedagogic strategies of educational data science. Pedagogy, Culture & Society, 24(3), 401–416. doi:10.1080/14681366.2016.1175499
Williamson, B. (2016b). Computing brains: Learning algorithms and neurocomputation in the smart city. Information, Communication & Society, 20(1), 81–99. doi:10.1080/1369118X.2016.1181194
Willis, J., Slade, S., & Prinsloo, P. (2016). Ethical oversight of student data in learning analytics: A typology derived from a cross-continental, cross-institutional perspective. Educational Technology Research and Development. doi:10.1007/s11423-016-9463-4
Zhang, S. (2016, May 20). Scientists are just as confused about the ethics of big data research as you. Wired. http://www.wired.com/2016/05/scientists-just-confused-ethics-big-data-research/
Ziewitz, M. (2016). Governing algorithms: Myth, mess, and methods. Science, Technology & Human Values, 41(1), 3–16.
Title
Ethics and Learning Analytics: Charting the (Un)Charted
Book Title
Handbook of Learning Analytics
Pages
pp. 49-57
Copyright
2017
DOI
10.18608/hla17.004
ISBN
978-0-9952408-0-3
Publisher
Society for Learning Analytics Research
Authors
Paul Prinsloo1
Sharon Slade2
Author Affiliations
1. Department of Business Management, University of South Africa, South Africa
2. Faculty of Business and Law, The Open University, United Kingdom
Editors
Charles Lang3
George Siemens4
Alyssa Wise5
Dragan Gašević6
Editor Affiliations
3. Teachers College, Columbia University, USA
4. LINK Research Lab, University of Texas at Arlington, USA
5. Learning Analytics Research Network, New York University, USA
6. Schools of Education and Informatics, University of Edinburgh, UK