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8367The 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 @14prinspUniversity of South Africa (Unisa)

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Modified Image: https://c2.staticflickr.com/6/5587/14218024197_be5857a509_b.jpg

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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

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Image credit: https://pixabay.com/en/street-sign-note-direction-possible-141396/

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Site credit: https://www.theguardian.com/world/2016/oct/04/south-africa-students-attack-police-protests-tuition-fees-escalate

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Site credit: http://mg.co.za/article/2016-08-15-00-more-than-1-200-academics-plead-with-government-to-address-funding-crisis

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Site credit: http://www.politicsweb.co.za/news-and-analysis/behind-the-university-funding-crisis

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Site credit: http://www.politicsweb.co.za/news-and-analysis/behind-the-university-funding-crisis

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• 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:

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Image credit: http://worldofdtcmarketing.com/wp-content/uploads/2014/09/904168_-houston-we-have-a-problem.jpg

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8367Distance 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

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Image credit: https://upload.wikimedia.org/wikipedia/commons/thumb/f/fd/Keep-calm-and-click-edit.svg/2000px-Keep-calm-and-click-edit.svg.png

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8367What 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?

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Site credit: https://www.washingtonpost.com/news/innovations/wp/2016/05/11/this-professor-stunned-his-students-when-he-revealed-the-secret-identity-of-his-teaching-assistant/

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Site credit: http://qz.com/653084/microsofts-disastrous-tay-experiment-shows-the-hidden-dangers-of-ai/

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Site credit: http://www.bbc.com/news/technology-34066941

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Site credit: http://www.bbc.com/news/technology-34066941

Site credit: http://www.bbc.com/news/technology-34066941

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Site credit: http://www.bbc.com/news/technology-34066941

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Image sources: https://twitter.com/urbandata/status/695261718344290304 https://za.pinterest.com/barbaralley/fair-is-not-equal/

Getting from here…

To here…

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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?

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8367Access,

funding and rankings

Justice, care and student support in a resource-constrained world

The future of learning: Digital, distributed, data-driven – but … increasingly unequal

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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

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8367Student 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

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Site credit: http://insider.foxnews.com/2016/01/31/oklahoma-college-forcing-students-wear-fitbits

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Site credit: https://dzone.com/articles/are-university-campuses-turning-into-big-brother

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Site credit: http://www.theguardian.com/higher-education-network/2015/nov/27/our-obsession-with-metrics-turns-academics-into-data-drones

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Site credit: http://www.ft.com/cms/s/2/634624c6-312b-11e5-91ac-a5e17d9b4cff.html#slide0

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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

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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

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Source: http://www.chronicle.com/article/What-Clicks-From-70000/237704/?key=3E28u5V_kVLLINFdIng14ArzhfOapBHcCtJa0JA29Cl6h1B4PR-WbNBpaTBJOFtlRFVCZFU4NElnZEx4em9IdDVJNzc5WHBMbzVXdHdOejU4ZUZxenNhMG9hVQ

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8367How much (more) student data do we need?

‘how much is enough data to solve my problem?’

(Adryan, 2015)

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8367An (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

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8367We 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

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• 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)

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8367We 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…

Caught between correlation and causation

Image credit: http://www.tylervigen.com/spurious-correlations

Caught between correlation and causation (cont.)

Image credit: http://www.tylervigen.com/spurious-correlations

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8367While 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).

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“Just as we learn our biases from the world around us, AI will learn its biases from us” (Collins, 2016)

(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

(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?

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

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.

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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

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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?

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8367The 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)

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8367Thank you

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 AfricaT: +27 (0) 12 433 4719 (office)prinsp@unisa.ac.za

Personal blog: http://opendistanceteachingandlearning.wordpress.comTwitter profile: @14prinsp

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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

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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

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)

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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.

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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/

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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/

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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, 173-191Howells, 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

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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

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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/

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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.

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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/

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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

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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_improve_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.

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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