The increasing (im)possibilities of justice and care in open, distance learning - Paul Prinsloo #EDENRW9
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Presentation shared by author at the 9th EDEN Research Workshop "Forging new pathways of research and innovation in open and distance learning: Reaching from the roots" held on 4-6 October 2016, in Oldenburg, Germany. Find out more on #EDENRW9 here: http://www.eden-online.org/2016_oldenburg/
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The increasing (im)possibilities of justice and care in open, distance learning - Paul Prinsloo #EDENRW9
- 1. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 The increasing (im)possibilities of justice and care in open, distance learning Image credit: https://pixabay.com/en/street-sign-note-direction-possible-141396/ By Paul Prinsloo @14prinsp University of South Africa (Unisa)
- 3. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 I do not own the copyright of any of the images in this presentation. I therefore acknowledge the original copyright and licensing regime of every image used. This presentation (excluding the images) is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
- 9. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 • The South African government spends a mere 0.6% of GDP on its universities, lagging behind many other countries (Russia at 1.8%, Argentina at 1.4% and India at 1.3%)” (Govender, 2016). • Undergraduate courses are subsidised 50% compared to face-to- face, residential higher education • Course/module success rate of 68% • Cohort completion rates for 3-year undergraduate degrees: 23-27% dropout/non-return in the first year. Only 6.4% complete the qualification in 5.1 years • Cohort completion rates for 4 year undergraduate degrees: 27% dropout/non-return with only 15.8% completing the qualification in 6 years Open distance learning in the context of higher education in South Africa:
- 11. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 Distance education’s traditional response to the revolving door and poor attrition rates was to increase personal tutorial support. “This appears to be the least cost-effective way of helping students” (Daniel, Kanwar, & Uvalić-Trumbić, 2009, p. 34). Source: Molapo, M., & van Zyl, D. (2014). An overview of Unisa’s October/November 2014 Exam Sitting Results. Unpublished report
- 13. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 What are the potential, the challenges and ethical implications in learning analytics and using algorithms, Artificial Intelligence and machine learning to address issues of cost, quality, access and care?
- 21. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 Image sources: https://twitter.com/urbandata/status/695261718344290304 https://za.pinterest.com/barbaralley/fair-is-not-equal/ What are the potential, challenges and ethical implications in learning analytics and using algorithms, Artificial Intelligence and machine learning to address issues of cost, quality, justice, access and care?
- 22. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 Access, funding and rankings Justice, care and student support in a resource- constrained world The future of learning: Digital, distributed, data-driven – but … increasingly unequal
- 23. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 Access Cost Quality What are the potential, challenges and ethical implications in learning analytics and using algorithms, Artificial Intelligence and machine learning to address issues of cost, quality, justice, access and care? Images from: http://www.bbc.com/news/technology-34066941
- 24. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 Student data as the ‘new black”, as oil, as a resource to be mined Image credit: http://fpif.org/wp-content/uploads/2013/01/great-oil-swindle-peak-oil- world-energy-outlook.jpg
- 29. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 Imagine what we could learn if we put a tracker on everyone and everything (Jurdak, 2016) Image credit: https://www.flickr.com/photos/jeepersmedia/13966485507
- 30. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 We know where you are. We know where you’ve been. We can more or less know what you're thinking about (@FrankPasquale, 2016) Image credit: https://en.wikipedia.org/wiki/Surveillance
- 32. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 How much (more) student data do we need? ‘how much is enough data to solve my problem?’ (Adryan, 2015) Image credit: https://www.flickr.com/photos/uncle- leo/1341913549
- 33. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 An (im)possible balancing act We need to ensure the sustainability of higher education in the light of • funding constraints • increased competition • the socioeconomic downturn • student needs • increased need for efficiency/effectiveness • audit & quality assurance regimes • #FeesMustFall The fiduciary duty of higher education to • care • create supportive, appropriate and effective teaching and learning environments • ethical collection, analysis and use of student data • transparency Also see: Prinsloo, P., & Slade, S. (2014). Educational triage in open distance learning: Walking a moral tightrope. The International Review of Research in Open and Distributed Learning, 15(4), 306-331. Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/1881/3060 Image crediet: https://upload.wikimedia.org/wikipedia/commons/d/d0/John_Reynolds,_9th_Street_NW_-_Washington,_D.C..jpg
- 34. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 We need to critically consider the ethical implications of … • With having access to more information about our students’ identity, life-worlds and learning journey, it is important that we know the limitations of the data, our samples, our models, our analyses and recognise our assumptions, biases, perceptions and lack of understanding • Knowing more about our students does not, necessarily, result in understanding • When we know and understand more, responding in appropriate ways may be outside our locus of control, outside of our budget, or outside our job descriptions and performance criteria
- 35. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 • Students’ digital lives are but a minute part of a bigger whole – so we should not pretend as if our data represent the whole • The data we collect are never ‘raw’, ‘uncontaminated’, or just ‘scraped’… Our samples, choices, timing and tools change and impact on data. “Data are in fact framed technically, economically, ethically, temporally, spatially and philosophically. Data do not exist independently of the ideas, instruments, practices, contexts and knowledges used to generate, process and analyse them” (Kitchen, 2014, p. 2) We need to critically consider the ethical implications of … (2)
- 36. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 We need to critically consider the ethical implications of … (3) • Data have contexts. To re-use data outside of the original context and purpose for which it was collected impacts on the contextual integrity. • Knowing ‘what’ is happening, does not necessarily tell us the ‘why’… • Education is an open, recursive system (Biesta 2007, 2010) where multiple variables not only intersect but often also constitute one another. Let us therefore tread carefully between correlation and causation…
- 37. Caught between correlation and causation Image credit: http://www.tylervigen.com/spurious-correlations
- 38. Caught between correlation and causation (cont.) Image credit: http://www.tylervigen.com/spurious-correlations
- 39. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 While Artificial Intelligence (AI) “tools are producing compelling advances in complex tasks, with dramatic improvements in energy consumption, audio processing, and leukemia detection”, we are also faced with the reality that “AI systems are already making problematic judgements that are producing significant social, cultural, and economic impacts in people’s everyday lives” (Crawford and Whittaker, 2016, par. 1).
- 40. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 “Just as we learn our biases from the world around us, AI will learn its biases from us” (Collins, 2016)
- 41. (1) Humans perform the task (2) Task is shared with algorithms (3) Algorithms perform task: human supervision (4) Algorithms perform task: no human input Seeing Yes or No? Yes or No? Yes or No? Yes or No? Processing Yes or No? Yes or No? Yes or No? Yes or No? Acting Yes or No? Yes or No? Yes or No? Yes or No? Learning Yes or No? Yes or No? Yes or No? Yes or No? Danaher, J. (2015). How might algorithms rule our lives? Mapping the logical space of algocracy. [Web log post]. Retrieved from http://philosophicaldisquisitions.blogspot.com/2015/06/how-might-algorithms-rule-our-lives.html Human-algorithm interaction in the collection, analysis and use of student data
- 42. (1) Humans perform the task (2) Task is shared with algorith ms (3) Algorithms perform task: human supervision (4) Algorithms perform task: no human input Seeing Yes or No? Yes or No? Yes or No? Yes or No? Processi ng Yes or No? Yes or No? Yes or No? Yes or No? Acting Yes or No? Yes or No? Yes or No? Yes or No? Learnin g Yes or No? Yes or No? Yes or No? Yes or No? Danaher, J. (2015). How might algorithms rule our lives? Mapping the logical space of algocracy. [Web log post]. Retrieved from http://philosophicaldisquisitions.blogspot.com/2015/06/how-might-algorithms-rule-our-lives.html Some possibilities (with their own set of challenges…) • Admission: Addressing inter-generational disadvantage, ‘red-lining’, but what about ‘open’? • Fit between students’ choice, aspirations, potential, career choice, • Learning journey structure, content, resources, just-in-time feedback, ‘personalisation’, formative assessment, etc • Allocation of resources Important to note that there is not a one-size-fits-all and disciplinary context, and the the impact of bias, downstream impact and unintended consequences must be considered.
- 43. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 Access Cost Quality What are the potential, challenges and ethical implications in learning analytics and using algorithms, Artificial Intelligence and machine learning to address issues of cost, quality, justice, access and care? Images from: http://www.bbc.com/news/technology-34066941
- 44. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 Image sources: https://twitter.com/urbandata/status/695261718344290304 https://za.pinterest.com/barbaralley/fair-is-not-equal/ So, what are the possibility, limitations and ethical challenges for open, distance learning to use advances in technology to actually remove barriers, achieve (more) justice and care?
- 45. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 The way forward (some pointers) • Rule 1: Do no harm. • Rule 2: Read rule 1 • Students have a right to know who designs our algorithms, for what purposes, using what data, how they are affected, and make an informed decision to opt-in • Provide students access to information and data held about them, to verify and/or question the conclusions drawn, and where necessary, provide context • Provide access to a neutral ombudsperson • Opting in/opting out • Ethical oversight? Accountability? (See Prinsloo & Slade, 2015; Slade & Prinsloo, 2013; Willis, Slade & Prinsloo 2016)
- 46. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 Thank you 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) prinsp@unisa.ac.za Personal blog: http://opendistanceteachingandlearning.wordpress.com Twitter profile: @14prinsp
- 47. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 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 . 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 Boffey, D. (2016, October 1). Student loans ‘increasing the divide between rich and poor’. The Guardian. Retrieved from https://www.theguardian.com/education/2016/oct/01/student-loans- increasing-rich-poor-divide Booth, M. (2012, July 18). Learning analytics: the new black. EDUCAUSEreview, [online]. Retrieved from http://www.educause.edu/ero/article/learning-analytics-new-black References and additional reading
- 48. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 Booth, M. (2012, July 18). Learning analytics: the new black. EDUCAUSEreview, [online]. Retrieved from http://www.educause.edu/ero/article/learning-analytics-new-black Bothwell, E. (2016, September 15). Nordic higher education in decline? Times Higher Education. Retrieved from https://www.timeshighereducation.com/features/is-nordic-higher-education-in- decline boyd, D., & Crawford, K. (2013). Six provocations for Big Data. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1926431 Bozzoli, B. (2015, October 19). Behind the university funding crisis. Politics Web. Retrieved from http://www.politicsweb.co.za/news-and-analysis/behind-the-university-funding-crisis Citron, D.K., & Pasquale, F. (2014). The scored society: Due process for automated predictions. http://ssrn.com/abstract=2376209 Collins, N. (2016, September 1). Artificial Intelligence will be as biased and prejudiced as its human creators. Pacific Standard. Retrieved from https://psmag.com/artificial-intelligence-will-be-as- biased-and-prejudiced-as-its-human-creators-38fe415f86dd#.p15q3xmow Coughland, S. (2016, September 29). Tuition fees heading over £9,500. BBC News. Retrieved from http://www.bbc.com/news/education- 37510744?utm_source=twitterfeed&utm_medium=twitter Crawford, K. (2014, May 30). The anxieties of Big Data. The New Inquiry. Retrieved from http://thenewinquiry.com/essays/the-anxieties-of-big-data References and additional reading (cont)
- 49. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 References and additional reading (cont) Crawford, K., & Whittaker, M. (2016, September 12). Artificial intelligence is hard to see. Why we urgently need to measure AI’s societal impacts. [Web log post]. Medium. Retrieved from https://medium.com/@katecrawford/artificial-intelligence-is-hard-to-see- a71e74f386db#.wi7sq5l3a 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 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 Dascalu, M. I., Bodea, C. N., Mihailescu, M. N., Tanase, E. A., & Ordoñez de Pablos, P. (2016). Educational recommender systems and their application in lifelong learning. Behavior & Information Technology, 35(4), 290-297. de Freitas, S., Gibson, D., Du Plessis, C., Halloran, P., Williams, E., Ambrose, M., Dunwell, I., & Arnab, S. (2015). Foundations of dynamic learning analytics: Using university student data to increase retention. British Journal of Educational Technology, 46(6), 1175-1188. Del Rey, E., & Schiopu, I. (2015). Student debt in selected countries. EENEE Analytics Report No 25. Prepared for the European Commission.
- 50. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 References and additional reading (cont) 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. Doctorow, C. (2016, September 15). Rules for trusting "black boxes" in algorithmic control systems. Retrieved from http://boingboing.net/2016/09/15/rules-for-trusting-black-box.html Domingos, P. (2015). The master algorithm. How the quest for the ultimate learning machine will remake our world. New York, NY: Perseus Books. Drachsler, H., Hummel, H. G., & Koper, R. (2008). Personal recommender systems for learners in lifelong learning networks: the requirements, techniques and model. International Journal of Learning Technology, 3(4), 404-423. Espinoza, J. (2015, June 25). Thousands of new graduates out of work, figures show. Retrieved from http://www.telegraph.co.uk/education/educationnews/11699095/Thousands-of-new-graduates- out-of-work-figures-show.html 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 Feldstein, M. (2012, May 6) What is machine learning good for? [Web log post]. Retrieved from http://mfeldstein.com/what-is-machine-learning-good-for/
- 51. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 References and additional reading (cont) Ferguson, R., Brasher, A., Clow, D., Griffiths, D., & Drachsler, H. (2016). Learning analytics: visions of the future. Paper delivered at the 6th International Learning Analytics and Knowledge (LAK) Conference, 25-29 April, Edinburgh, Scotland. Retrieved from http://oro.open.ac.uk/45312/ Fleming, (2016, April 1). Artificial intelligence and machine learning in education – a glimpse of what that might mean. Microsoft. Retrieved from https://blogs.msdn.microsoft.com/education/2016/04/01/how-will-your-staff-or-students-use- this/ Floridi, L. (2012). Big data and their epistemological challenge. Philosophy & Technology, 1-3. Gitelman, L. (ed.). (2013). “Raw data” is an oxymoron. London, UK: MIT Press. Govender, P. (2016, August 15). More than 1 200 academics plead with government to address funding crisis. Mail & Guardian. Retrieved from http://mg.co.za/article/2016-08-15-00-more-than-1-200- academics-plead-with-government-to-address-funding-crisis Grosz, B.J., Altman, R., Horvitz, E., Mackworth, A., Mitchell, T., Mulligan, D., & Shoham, Y. (2016). One hundred year study on Artificial Intelligence. Artificial Intelligence and life in 2030. Stanford University. Retrieved from https://ai100.stanford.edu/sites/default/files/ai_100_report_0831fnl.pdf 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/
- 52. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 References and additional reading (cont) 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 Howells, C. (2016, February 15). Can algorithms replace academics? Insead Knowledge. Retrieved from http://knowledge.insead.edu/operations/can-algorithms-replace-academics-4518 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 Lagoze, C. (2014). Big Data, data integrity, and the fracturing of the control zone. Big Data & Society (July-December), 1-11. Leonhard, G. (2016). Technology vs. humanity: The coming clash between man and machine. Fast Future Publishing Ltd. Mager, A. (2012). Algorithmic ideology: How capitalist society shapes search engines. Information, Communication & Society, 15(5), 769-787. Mager, A. (2015). Glocal search: Search technology at the intersection of global capitalism and local socio-political cultures. Vienna: Institute of Technology Assessment (ITA), Austrian Academy of Sciences. Retrieved from http://www.astridmager.net/wp- content/uploads/2015/11/Abschlussbericht-OeNB_Mager.pdf
- 53. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 References and additional reading (cont) Manning, C. (2012, March 14). Educational triage. [Web log post]. Retrieved from http://colinmcit.blogspot.co.uk/2012/03/educational-triage.html Markoff, J. (2015). Machines of loving grace: The quest for common ground between humans and robots. New York, NY: HarperCollins Publishing. 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. Merceron, A., Blikstein, P., & Siemens, G. (2016). Learning analytics: from Big Data to meaningful data. Journal of Learning Analytics, 2(3), 4-8. Miller, C.C. (2013, August 24). Addicted to apps. The New York Times. Retrieved from http://www.nytimes.com/2013/08/25/sunday-review/addicted-to-apps.html Miller, C. C. (2015, July 9). When algorithms discriminate. The New York Times. Retrieved from http://www.nytimes.com/2015/07/10/upshot/when-algorithms-discriminate.html 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. Muñoz, C., Smith, M., & Patil, D.J. (2016, May). Big data: A report on algorithmic systems, opportunity, and civil rights. Executive Office of the President. Retrieved from https://www.whitehouse.gov/sites/default/files/microsites/ostp/2016_0504_data_discrimination. pdf
- 54. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 References and additional reading (cont) 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 O’Neil, C. (2016a, September 1). How algorithms rule our working lives. The Guardian. Retrieved from https://www.theguardian.com/science/2016/sep/01/how-algorithms-rule-our-working-lives O’Neil, C. (2016b). Weapons of math destruction. How big data increases inequality and threatens democracy. UK: Allen Lane. 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 Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438-450. 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/
- 55. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 References and additional reading (cont) 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 Pasquale, F. (2015). The black box society. Harvard Publishing, US. Perrotta, C., & Williamson, B. (2016). The social life of Learning Analytics: cluster analysis and the ‘performance’of algorithmic education. Learning, Media and Technology, 1-14. PewResearch. (2016). Smartphone ownership and Internet usage continues to climb in emerging economies. Retrieved from http://www.pewglobal.org/2016/02/22/smartphone-ownership-and- internet-usage-continues-to-climb-in-emerging-economies/ 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 (2016). Evidence-based decision making as séance: implications for learning and student support. In Jan Botha & Nicole Muller (eds.), Institutional Research in support of evidence-based decision-making in Higher Education in Southern Africa. Stellenbosch, South Africa: SUN Media. In press.
- 56. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 References and additional reading (cont) 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. (2015, March). Student privacy self-management: implications for learning analytics. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 83-92). ACM. Retrieved from http://dl.acm.org/citation.cfm?id=2723585 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 Mark Anderson and Collette Gavan (eds.), Developing Effective Educational Experiences through Learning Analytics (pp. 174-192). Hershey, Pennsylvania: ICI-Global. Sclater, N., Peasgood, A., & Mullan, J. (2016). Learning analytics in higher education. JISC. Retrieved from https://www.jisc.ac.uk/sites/default/files/learning-analytics-in-he-v3.pdf Selwyn, N. (2014). Distrusting educational technology. Critical questions for changing times. New York, NY: Routledge Siemens, G. (2016, May 12). The future of learning: digital, distributed, data-driven. [Web log post]. Retrieved from http://www.elearnspace.org/blog/2016/05/12/the-future-of-learning-digital- distributed-data-driven/
- 57. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 References and additional reading (cont) Skiti, S. (2016, April 24). Tragedy of SA youth who put education first. Sunday Times. Retrieved from http://www.timeslive.co.za/sundaytimes/stnews/2016/04/24/Tragedy-of-SA-youth-who-put- education-first Slade, S., & Prinsloo, P. (2013). Learning analytics: ethical issues and dilemmas. American Behavioral 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. Stack, M. (2016a, February 26). Who and what gets left out of world university rankings. Times Higher Education. Retrieved from https://www.timeshighereducation.com/blog/who-and-what-gets-left- out-world-university-rankings Stack, M. (2016b). Global University Rankings and the Mediatization of Higher Education. Springer. 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
- 58. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 References and additional reading (cont) Uprichard, E. (2013, October 1). Big data, little questions. Discover Society. Retrieved from http://discoversociety.org/2013/10/01/focus-big-data-little-questions/ Vander Ark, T. (2015, November 25). 8 ways machine learning will improve education. [Web log post]. Retrieved from http://blogs.edweek.org/edweek/on_innovation/2015/11/8_ways_machine_learning_will_impro ve_education.html Vikmane. L., & Antonescu, A. (2016, May 27). Higher education funding – Towards greater inequality? University World News. Retrieved from http://www.universityworldnews.com/article.php?story=20160524143025838 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 Wigan, M.R., & Clarke, R. (2013). Big data’s big unintended consequences. Computer,(June), 46-53. Williamson, B. (2016). Silicon startup schools: technocracy, algorithmic imaginaries and venture philanthropy in corporate education reform. Critical Studies in Education, 1-19.
- 59. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 References and additional reading (cont) 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 World Bank. (2016). Digital dividends. Washington: International Bank for Reconstruction and Development / The World Bank. Retrieved from http://www.worldbank.org/en/publication/wdr2016
Editor's Notes
- Balancing access to higher education with providing a caring and enabling environment to students, had always been central in the evolution of distance education. Achieving both justice and care in ways outside of the frame of reference of residential education, had always been a key characteristic of distance education, throughout its evolution. From the early days in distance education, we were concerned with those on the outside, those who never had access to opportunities for whatever reason. We spent time and resources on removing barriers. We cared, despite the distance, for those who wanted and/or needed another chance in life. The choice of the title of this presentation grew from considering that, on the one hand, especially with regard to the affordances of technology, and the rich history of theoretical and empirical research in distance education, the possibilities of us embracing justice and care have never been greater. We can, possibly care more than ever before. On the other hand, there is a range of factors making justice and care increasingly impossible in open, distance learning contexts.
- Allow me to also provide context for my presentation and specifically the title of the presentation: When I raise the issue of the increasing (im)possibilities of justice and care in open distance learning, I do so from the specific context of a developing world context with massive inequalities and the often unbearable inter-generational weight and legacy of colonialism and apartheid. I also cannot ignore the fact that my understanding of the (im)possibilities of justice and care is shaped by my own location in this debate as a 57 years old, white, gay male. I cannot and do not want to pretend that the weight and the privileges of whiteness do not matter, and possibly, disqualify myself from participating in this debate. In this specific context of a post-apartheid society and various initiatives to address the inter-generational legacy of colonialism and apartheid, education is often seen as the key initiative to address inequalities and unemployment. Now that I have located and declared my own positionality in the context of talking about the (im)possibilities of justice and care in open distance learning, let me disclose some of my beliefs central to my approach to this keynote: I do not belief that education, on its own, can address the structural inequalities and inter-generational legacy of colonialism and apartheid. We should not expect of education to solve all of societies’ problems. Yes, education can make a difference and without education we will not be able to address the vast inequalities in society and in the world. We need multiple stakeholders to engage in addressing inequality. Governments cannot de-fund higher education on the one hand, and on the other hand, expect of higher education to, somehow, contribute to erasing inequalities and injustices. We cannot be expected to increase participation rates, and access to students often underprepared for higher and distance education, while government actively defund and underestimate the costs of providing caring and just environments. I further believe that while higher education, and specifically open, distance learning should be committed to justice and addressing injustices, justice is not enough. While providing access to higher education is a key aspect of embracing justice, access is not enough. For justice to be realised, we also have to consider an ethics of care. To allow students to enter higher education without an ethics of care, may constitute justice deferred.
- In the context of South Africa, Govender (2016) reports that in the period between 1994 and 2014, “the number of students in our public universities more than doubled. During the same period the proportion of black students at universities increased from 52% to 81% of the student population.” Despite evidence of widening access, national government subsidies to university budgets fell “from an already low 49% in 2000 to 40% in 2012.” (Govender, 2016). Except for the increased pressure on infrastructure due to the increase in student numbers “employment of full-time academic staff has not matched increases in student numbers” (Govender, 2016).
- We appointed more staff, often in contract positions to provide support to students, and evidence suggests that this immensely increased the salary budgets, and that the impact of this initiative, however well-intended, was limited. In attempting to address student attrition and failure, we often followed and follow a “bang-bang” approach, shooting at noises in the dark, not sure where we are aiming, not sure what causes the noise, but at least we can count the number of bullets spent.
- I would like to make it very clear that I don’t subscribe to the Silicon Valley narrative that education is broken and that technology can solve it. My research provides record of my constant skepticism and wariness when we subscribe to a strategy of “to save everything, click here” (Morozov, 2013). I do, however, belief that advances in technology and specifically data, algorithms, Artificial Intelligence and machine learning can address some of the issues we face. So the issue is not if technology can make a difference, but under what conditions…
- “To help with his class this spring, a Georgia Tech professor hired Jill Watson, a teaching assistant unlike any other in the world. Throughout the semester, she answered questions online for students, relieving the professor’s overworked teaching staff.” The article continues to state that students remarked about the timeliness of responses, the care received and of course, the fact that she was on duty 24/7. We should, however, not miss the point that this male professor created a female bot. There do not seem to be any limits to male perceptions that they teach while females can do the caring. Even if she is a bot.
- Talking about algorithms, Artifical Intelligence and machine learning immediately raises a number of serious issues, and one of the most dominant issues in the popular press is the question whether robots will replace teachers…Except for the fact that this is a crude representation of the potential of Artificial Intelligence, it does allow for some light relief...
- We have to consider the question against the backdrop of increased access, decreasing funding and the increasing impact of Global University Rankings. How do we talk about justice and care in a university sector where global university rankings determine funding, access, definitions of excellence and where the more students get rejected by a particular institution, “the higher it scores on student selectivity” (Stack, 2016). “They [rankings] play a pivotal role in the dramatic increase in higher education institutions’ spend on marketing and public relations” (Stack, 2016). As Stack (2016) and others indicate, the data in the global university rankings are not objective, the criteria represents very specific epistemologies and power interests, and impact negatively on “equity and access, furthering the marginalization of oppressed peoples and constraining ways of knowing” (Estera & Shahjahan, 2016). A disturbing figure of 60% of the world’s population is still offline and many of the opportunities and benefits of being and conversing online “are offset by emerging risks” (World Bank, 2016, p. 3). The Report mentions the fact that in advanced economies technology polarizes labor markets and increases inequality – “in part because technology augments higher skills while replacing routine jobs, forcing many workers to compete for low-paying jobs” (p. 3). Due to “the absence of accountable institutions” technology amplifies “the voice of elites, which can result in policy capture and greater state control” (p. 3). The “economics of the Internet favor natural monopolies, [and] the absence of a competitive business environment can result in more concentrated markets, benefiting incumbent firms.” And the following statement is most probably the most disturbing. The Report (2016) states that “Not surprisingly, the better educated, well connected, and more capable have received most of the benefits—circumscribing the gains from the digital revolution” (p. 3).