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Learning analytics: At the intersections between
student support, privacy, agency and institutional
survival
Paul Prinsloo...
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...
HIGHER EDUCATION
• Increasing competition, changing contexts, internationalisation
• Rankings and quality regimes/criteria...
• Determine criteria/characteristics
• Calculate cost of care/intervention/return on
investment
• Implementation – educati...
Engaging with (some) assumptions &
practices re the need for (more) data
• Our (mis)understanding of student
retention, su...
Educational triage in practice
• School league tables can lead to a
focus on key boundaries
– Evidence that the ‘murky mid...
Analytics in practice at the Open University
Framework for consistent support
– Drives minimum set of proactive
interventi...
Some considerations…
• We cannot ignore the reality of ‘Survivor: Higher Education’
• The impact of funding, resources and...
Image credit: http://www.yourtango.com/201168184/facebook-relationship-status-
what-does-its-complicated-mean
Thank you
Prof Paul Prinsloo
Research Professor in Open Distance
Learning (ODL)
College of Economic and Management
Science...
References and additional reading
Ball, N. (2013, November 11). Big Data follows and buries us in equal measure. [Web log
...
References and additional reading
(cont.)
Biesta, G. (2010). Why ‘what works’ still won’t work: from evidence-based educat...
Danaher, J. (2015, June 15). How might algorithms rule our lives? Mapping the logical
space of algocracy. [Web log post]. ...
References and additional reading
(cont.)
Gitelman, L. (ed.). (2013). “Raw data” is an oxymoron. London, UK: MIT Press.
Ha...
References and additional reading
(cont.)
Joynt, G.M., & Gomersall, C.D. (2005). Making moral decisions when resources are...
References and additional reading
(cont.)
Mayer-Schönberger, V. (2009). Delete. The virtue of forgetting in the digital ag...
References and additional reading
(cont.)
Open University. (2014). Policy on ethical use of student data for learning anal...
References and additional reading
(cont.)
Pasquale, F. (2015). The black box society. Harvard Publishing, US.
Prinsloo, P....
References and additional reading
(cont.)
Prinsloo, P., & Slade, S. (2016). Here be dragons: Mapping student responsibilit...
References and additional reading
(cont.)
Tene, O. & Polonetsky, J. (2013). Judged by the Tin Man: Individual rights in th...
References and additional reading
(cont.)
Wigan, M.R., & Clarke, R. (2013). Big data’s big unintended consequences.
Comput...
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Learning analytics: At the intersections between student support, privacy, agency and institutional survival

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Presentation by Paul Prinsloo (Unisa) and Sharon Slade (OU) at ALT-C conference, University of Warwick, United Kingdom, 6-8 September 2016

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Learning analytics: At the intersections between student support, privacy, agency and institutional survival

  1. 1. Learning analytics: At the intersections between student support, privacy, agency and institutional survival Paul Prinsloo (University of South Africa, Unisa) @14prinsp Sharon Slade (Open University, OU) @sharonslade Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367
  2. 2. Image credit: http://www.yourtango.com/201168184/facebook-relationship-status- what-does-its-complicated-mean
  3. 3. HIGHER EDUCATION Image credits: Survivor image: Adapted from https://commons.wikimedia.org/wiki/File:Survivor_Bermuda_Logo.jpeg Murky middle: https://s-media-cache-ak0.pinimg.com/736x/5c/58/07/5c58072b5f003d7a69b129cb6f8055b6.jpg Triage: https://en.wikipedia.org/wiki/Triage Surveillance camera: http://www.governmentnews.com.au/wp-content/uploads/2014/02/cctv_camera.jpg
  4. 4. 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)
  5. 5. • Determine criteria/characteristics • Calculate cost of care/intervention/return on investment • Implementation – educational triage • Evaluation Moving the murky middle/drowning the bunnies
  6. 6. 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
  7. 7. 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
  8. 8. Analytics in practice at the Open University Framework 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
  9. 9. 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?
  10. 10. Image credit: http://www.yourtango.com/201168184/facebook-relationship-status- what-does-its-complicated-mean
  11. 11. Thank you Prof Paul Prinsloo Research Professor in Open Distance Learning (ODL) College of Economic and Management Sciences, Office number 3-15, Club 1, Hazelwood, P O Box 392 Unisa, 0003, Republic of South Africa T: +27 (0) 12 433 4719 (office) T: +27 (0) 82 3954 113 (mobile) prinsp@unisa.ac.za Personal blog: http://opendistanceteachingandlearning .wordpress.com Twitter profile: @14prinsp Dr Sharon Slade Senior Lecturer Faculty of Business and Law The Open University, Walton Hall, Milton Keynes, MK7 6AA, United Kingdom T: +44 (0) 1865 486250 sharon.slade@open.ac.uk www.linkedin.com/profile/view?id=53 123496&trk=tab_pro Twitter profile: @sharonslade
  12. 12. References and additional reading Ball, N. (2013, November 11). Big Data follows and buries us in equal measure. [Web log post]. Retrieved from http://www.popmatters.com/feature/175640-this-so-called- metadata/ Beauchamp T. L., & Childress J.F. (2001). Principles of Biomedical Ethics. (5th ed). Oxford: Oxford University Press. 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/ Bertolucci, J. (2014, July 28). Deep data trumps Big Data. Information Week. Retrieved from http://www.informationweek.com/big-data/big-data-analytics/deep-data- trumps-big-data/d/d-id/1297588 Biesta, G. (2007). Why “what works” won’t work: evidence-based practice and the democratic deficit in educational research, Educational Theory, 57(1),1–22. DOI: 10.1111/j.1741-5446.2006.00241.x .
  13. 13. References and additional reading (cont.) Biesta, G. (2010). Why ‘what works’ still won’t work: from evidence-based education to value-based education, Studies in Philosophy of Education, 29, 491–503. DOI 10.1007/s11217-010-9191-x. Booth, M. (2012, July 18). Learning analytics: the new black. EDUCAUSEreview, [online]. Retrieved from http://www.educause.edu/ero/article/learning-analytics-new-black boyd, D., & Crawford, K. (2013). Six provocations for Big Data. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1926431 Crawford, K. (2014, May 30). The anxieties of Big Data. The New Inquiry. Retrieved from http://thenewinquiry.com/essays/the-anxieties-of-big-data Danaher, J. (2014, January 6). Rule by algorithm? Big Data and the threat of algocracy.[Web log post]. Retrieved from http://philosophicaldisquisitions.blogspot.com/2014/01/rule-by-algorithm-big-data- and-threat.html
  14. 14. Danaher, J. (2015, June 15). How might algorithms rule our lives? Mapping the logical space of algocracy. [Web log post]. Retrieved from http://philosophicaldisquisitions.blogspot.co.za/2015/06/how-might-algorithms- rule-our-lives.html Diakopoulos, N. (2014). Algorithmic accountability. Digital Journalism. DOI: 10.1080/21670811.2014.976411 Diefenbach, T. (2007). The managerialistic ideology of organisational change management, Journal of Organisational Change Management, 20(1), 126 — 144. Eubanks, V. (2014, January 15). Want to predict the future of surveillance? Ask poor communities. The American Prospect. Retrieved from http://prospect.org/article/want-predict-future-surveillance-ask-poor-communities Floridi, L. (2012). Big data and their epistemological challenge. Philosophy & Technology, 1-3. References and additional reading (cont.)
  15. 15. 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 analytics is passé. [Web log post]. Retrieved from http://timothyharfield.com/blog/2015/05/12/next-generation-learning-analytics- or-how-learning-analytics-is-passe/ Hartley, D. (1995). The ‘McDonaldisation’ of higher education: food for thought? Oxford Review of Education, 21(4), 409—423. Henman, P. (2004). Targeted!: Population segmentation, electronic surveillance and governing the unemployed in Australia. International Sociology, 19, 173-191 Johnson, J.A. (2015, October 7). How data does political things: The processes of encoding and decoding data are never neutral. [Web log post]. Retrieved from http://blogs.lse.ac.uk/impactofsocialsciences/2015/10/07/how-data-does-political- things/
  16. 16. 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.
  17. 17. 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
  18. 18. 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 where you’ve been. We can more or less know what you're thinking about. http://www.theatlantic.com/technology/archive/2016/02/google-cute-evil/463464/ … #Jigsaw [Tweet]. Retrieved from https://twitter.com/FrankPasquale/status/700473628605947904
  19. 19. 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/10500/6035 Prinsloo, 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.
  20. 20. 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.
  21. 21. 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 rationality. Theory Culture Society 31, pp. 29—49. DOI: 10.1177/0263276413510051 Uprichard, E. (2013, October 1). Big data, little questions. Discover Society. Retrieved from 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 https://medium.com/ethnography-matters/why-big-data-needs-thick-data- b4b3e75e3d7#.4jbatgurh 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
  22. 22. 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

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