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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

Publicada em: Educação
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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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