Learning analytics: At the intersections between student support, privacy, agency and institutional survival
Paul Prinsloo (University of South Africa, Unisa) @14prinspSharon Slade (Open University, OU) @sharonslade
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Image credit: http://www.yourtango.com/201168184/facebook-relationship-status-what-does-its-complicated-mean
HIGHER EDUCATION
Image credits: Survivor image: Adapted from https://commons.wikimedia.org/wiki/File:Survivor_Bermuda_Logo.jpegMurky middle: https://s-media-cache-ak0.pinimg.com/736x/5c/58/07/5c58072b5f003d7a69b129cb6f8055b6.jpgTriage: https://en.wikipedia.org/wiki/Triage Surveillance camera: http://www.governmentnews.com.au/wp-content/uploads/2014/02/cctv_camera.jpg
HIGHER EDUCATION
• Increasing competition, changing contexts, internationalisation• Rankings and quality regimes/criteria• Increasing funding constraints and austerity measures• Funding follows performance rather than preceding it – the need for
evidence• Persisting concerns about student retention, failure and dropout• History of well-intentioned but often bang-bang approaches to increasing
student retention and success• The mandate and fiduciary duty of higher education• Optimising the student experience, ensuring student success/throughput
Survivor – the Higher Education series (new rules, new contestants, better than ever)
• Determine criteria/characteristics• Calculate cost of care/intervention/return on
investment• Implementation – educational triage• Evaluation
Moving the murky middle/drowning the bunnies
Engaging with (some) assumptions & practices re the need for (more) data • Our (mis)understanding of student
retention, success and failure• Can we assume that knowing more, per
se, results in understanding and care; that more data will necessarily contribute to better teaching and learning?
• The danger of context collapse and the need to ensure context integrity when data collected from disparate sources and for a variety of purposes are combined
• The inherent biases, dangers and potential of algorithmic decision-making
• The scope of students’ right to privacy
Educational triage in practice
• School league tables can lead to a focus on key boundaries– Evidence that the ‘murky middle’
overlooked in favour of those most able to support achievement of key results
• Traditional classroom-based universities – potential focus on ‘seen’ or perceived
need– often driven by individual subject
tutors• Distance learning institutions
– largely reliant on student data to direct support
– often driven by available data and assumed patterns
Analytics in practice at the Open UniversityFramework for consistent support– Drives minimum set of proactive
interventions through curriculum focused Student Support Teams to all students
– Additional core interventions target students based on characteristics (potentially ‘at risk’) and/or study behaviours
– Large number of possible proactive interventions (e.g., missed milestones, etc)
– Prioritising interventions is complex: which characteristics/milestones/behaviours/ modules take precedence? Who decides?
– Results in non-standard support largely not transparent to students and driven by available staff resource
Some considerations…
• We cannot ignore the reality of ‘Survivor: Higher Education’• The impact of funding, resources and contexts on the ‘murky middle’ • The moral implications of our admission requirements: admission without a
reasonable chance of success? The cost of support to make ‘success’ happen?• Educational triage’s potential to exclude students from access/support based
on criteria that disregard context, structural inequalities and inter-generational debt
• The need for transparency re rationales for inclusion/exclusion & decisions made
• The scope of students’ agency: can students refuse advice/support provided they understand the consequences of their opting out?
Image credit: http://www.yourtango.com/201168184/facebook-relationship-status-what-does-its-complicated-mean
Thank youProf Paul PrinslooResearch Professor in Open Distance Learning (ODL)College of Economic and Management Sciences, Office number 3-15, Club 1, Hazelwood, P O Box 392Unisa, 0003, Republic of South Africa
T: +27 (0) 12 433 4719 (office)T: +27 (0) 82 3954 113 (mobile)
Personal blog: http://opendistanceteachingandlearning.wordpress.com
Twitter profile: @14prinsp
Dr Sharon Slade Senior LecturerFaculty of Business and LawThe Open University, Walton Hall, Milton Keynes, MK7 6AA, United Kingdom
T: +44 (0) 1865 486250
www.linkedin.com/profile/view?id=53123496&trk=tab_pro
Twitter profile: @sharonslade
References and additional readingBall, N. (2013, November 11). Big Data follows and buries us in equal measure. [Web log
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Bergstein, B. (2013, February 20). The problem with our data obsession. MIT Technology Review. Retrieved from https://www.technologyreview.com/s/511176/the-problem-with-our-data-obsession/
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References and additional reading (cont.)Biesta, G. (2010). Why ‘what works’ still won’t work: from evidence-based education to
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Booth, M. (2012, July 18). Learning analytics: the new black. EDUCAUSEreview, [online]. Retrieved from http://www.educause.edu/ero/article/learning-analytics-new-black
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References and additional reading (cont.)
References and additional reading (cont.)
Gitelman, L. (ed.). (2013). “Raw data” is an oxymoron. London, UK: MIT Press.Hartfield, T. (2015, May 12 ). Next generation learning analytics: Or, how learning
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References and additional reading (cont.)Joynt, G.M., & Gomersall, C.D. (2005). Making moral decisions when resources are limited
– an approach to triage in ICY patients with respiratory failure. South African Journal of Critical Care (SAJCC), 21(1), 34—44. Retrieved from http://www.ajol.info/index.php/sajcc/article/view/35543
Kitchen, R. (2013). Big data and human geography: opportunities, challenges and risks. Dialogues in Human Geography, 3, 262-267. SOI: 10.1177/2043820613513388
Kitchen, R. (2014). The data revolution. London, UK: SAGE. Kitchen, R., & McArdle, G. (2016). What makes Big Data, Big Data? Exploring the
ontological characteristics of 26 datasets. Big Data & Society, January-June, 1-10. DOI: 10.1177/2053951716631130
Knox, D. (2010). Spies in the house of learning: a typology of surveillance in online learning environments. Paper presented at Edge, Memorial University of Newfoundland, Canada, 12-15 October.
Lagoze, C. (2014). Big Data, data integrity, and the fracturing of the control zone. Big Data & Society (July-December), 1-11.
References and additional reading (cont.)Mayer-Schönberger, V. (2009). Delete. The virtue of forgetting in the digital age.
Princeton, NJ: Princeton University Press.Mayer-Schönberger, V., & Cukier, K. (2013). Big data. London, UK: Hachette.Morozov, E. (2013a, October 23). The real privacy problem. MIT Technology Review.
Retrieved from http://www.technologyreview.com/featuredstory/520426/the-real-privacy-problem/
Morozov, E. (2013b). To save everything, click here. London, UK: Penguin Books. Napoli, P. (2013). The algorithm as institution: Toward a theoretical framework for
automated media production and consumption. In Media in Transition Conference (pp. 1–36). DOI: 10.2139/ssrn.2260923
Nissenbaum, H. (2015). Respecting context to protect privacy: Why meaning matters. Science and engineering ethics. Retrieved from http://link.springer.com/article/10.1007/s11948-015-9674-9
References and additional reading (cont.)
Open University. (2014). Policy on ethical use of student data for learning analytics. Retrieved from http://www.open.ac.uk/students/charter/essential-documents/ethical-use-student-data-learning-analytics-policy
Manning, C. (2012, March 14). Educational triage. [Web log post]. Retrieved from http://colinmcit.blogspot.co.uk/2012/03/educational-triage.html.
Pasquale, F. (2015, October 14). Scores of scores: how companies are reducing consumers to single numbers The Atlantic. Retrieved fromhttp://www.theatlantic.com/business/archive/2015/10/credit-
scores/410350/Pasquale, F. [FrankPasquale]. (2016, February 19). "We know where you are. We know
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References and additional reading (cont.)Pasquale, F. (2015). The black box society. Harvard Publishing, US.Prinsloo, P. (2009). Modelling throughput at Unisa: The key to the successful
implementation of ODL. Retrieved from http://uir.unisa.ac.za/handle/10500/6035Prinsloo, P., Archer, E., Barnes, G., Chetty, Y., & Van Zyl, D. (2015). Big (ger) data as better
data in open distance learning. The International Review of Research in Open and Distributed Learning, 16(1).
Prinsloo, P., & Slade, S. (2014). Educational triage in higher online education: walking a moral tightrope. International Review of Research in Open Distributed Learning (IRRODL), 14(4), pp. 306-331. http://www.irrodl.org/index.php/irrodl/article/view/1881.
Prinsloo, P., & Slade, S. (2016). Student vulnerability, agency, and learning analytics: an exploration. Journal of Learning Analytics, 3(1), 159-182.
References and additional reading (cont.)Prinsloo, P., & Slade, S. (2016). Here be dragons: Mapping student responsibility in
learning analytics, in Mark Anderson and Collette Gavan (eds.), Developing Effective Educational Experiences through Learning Analytics (pp. 174-192). Hershey, Pennsylvania: ICI-Global.
Selwyn, N. (2014). Distrusting educational technology. Critical questions for changing times. New York, NY: Routledge
Slade, S., & Prinsloo, P. (2013). Learning analytics: ethical issues and dilemmas. American Behavioural Scientist, 57(1) pp. 1509–1528.
Slade, S., & Prinsloo, P. (2015). Student perspectives on the use of their data: between intrusion, surveillance and care. European Journal of Open, Distance and Elearning. (pp.16-28). Special Issue. http://www.eurodl.org/materials/special/2015/Slade_Prinsloo.pdf
Subotzky, G., & Prinsloo, P. (2011). Turning the tide: a socio-critical model and framework for improving student success in open distance learning at the University of South Africa. Distance Education, 32(2): 177-19.
References and additional reading (cont.)Tene, O. & Polonetsky, J. (2013). Judged by the Tin Man: Individual rights in the age of Big
Data. J. on Telecomm. & High Tech. L., 11, 351.Totaro, P., & Ninno, D. (2014). The concept of algorithm as an interpretive key of modern
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http://discoversociety.org/2013/10/01/focus-big-data-little-questions/ Wang, T. (2013, January 20). Why Big Data needs thick data. Medium. Retrieved from
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Watters, A. (2013, October 13). Student data is the new oil: MOOCs, metaphor, and money. [Web log post]. Retrieved from http://www.hackeducation.com/2013/10/17/student-data-is-the-new-oil/
Watters, A. (2014). Social justice. [Web log post]. Retrieved from http://hackeducation.com/2014/12/18/top-ed-tech-trends-2014-justice
References and additional reading (cont.)Wigan, M.R., & Clarke, R. (2013). Big data’s big unintended consequences. Computer,
(June), 46-53. Willis, J. E., 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 Retrieved fromhttp://link.springer.com/article/10.1007/s11423-016-9463-4
