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Student consent in learning analytics: Finding a way out of the labyrinth

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Invited presentation - ELESIG ‘Conversations’ Webinar 29 January 2018, 11:00 – 12:00 GMT

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Student consent in learning analytics: Finding a way out of the labyrinth

  1. 1. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Student consent in learning analytics: Finding a way out of the labyrinth Image credit: https://pixabay.com/en/maze-graphic-render-labyrinth-2264/ By Paul Prinsloo (University of South Africa) @14prinsp
  2. 2. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Acknowledgements • Since 2013 my thinking about ethical considerations in the collection, analysis and use of student data has been shaped by, inter alia, my collaboration with Dr Sharon Slade (Open University). I am deeply indebted to her for her input and her inspiration • I furthermore don’t own the copyright of any of the images in this presentation. I hereby acknowledge the original copyright and licensing regime of every image and reference used. • This work (excluding the images) is licensed under a Creative Commons Attribution 4.0 International License.
  3. 3. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Image credit: https://www.quora.com/Whats-the-simplest-way-to-create-an-SMS-opt-in-marketing-campaign What is the scope and intention of students’ consent when they sign their learning contracts at registration? Do they provide permission that we may… • combine their demographic and learning behavior data to assess their potential for non-payment, failure or success • inform their teaching staff and course support teams accordingly • allocate or withdraw resources according to the analysis of their data • determine their future enrollment prospects based on their past behavior • personalise/individualise their curricula, assessment, pedagogy and time allowed to complete the course • use their behavioral data and performance in one course to determine the possibility of their success in another course • publish the (anonymised) findings?
  4. 4. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Image credit: http://notbuyinganything.blogspot.co.za/2012/01/opting-out-increasingly.html Will they be allowed to opt out of having their personal identifiable data collected, analysed and used when the collection and analysis will affect their choices, access to resources and standing (whether positively or negatively)? And if allowed, what are the implications for • them – do they understand the risks/value? • the teaching and support team • the institution – our fiduciary and quality assurance duty? • our (and their) understanding the complexities of teaching and learning?
  5. 5. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Image credit: http://chrisschweppe.com/?p=340Image credit: https://www.quora.com/Whats-the-simplest- way-to-create-an-SMS-opt-in-marketing-campaign Thinking in binary terms does not really help. We have to find a more nuanced understanding of student consent and student participation in making sense of their learning journeys
  6. 6. Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367 Towards a (more) nuanced understanding of informed consent in learning analytics Image credit: http://www.yourtango.com/201168184/facebook-relationship-status-what-does-its- complicated-mean ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT
  7. 7. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT How do we talk about student consent in the collection, analysis and use of their data considering…
  8. 8. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Knowing more about them may alert us to students who experience distress, or where their silences/absences in online learning environments may allow us to reach out and make a difference. If only we knew… Knowing more about them may allow us to allocate resources more effectively Knowing more about them and the way they engage, or choose not to engage, may enrich our (and their) understanding of the complexities of learning and challenge some of our/their beliefs and assumptions
  9. 9. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT The asymmetries in power between students and the providing institutions - where students do not know what data are collected, when the data are collected and used by whom and for what purpose, how the data are governed and how this may affect the current and future registrations Image credit: http://faithandheritage.com/2012/02/the-fifth-commandment-versus-egalitarianism/
  10. 10. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT That’s no Learning Management System. That’s a layered system of surveillance, tracking what they do and don’t do, when they access (which) resources, how many times they access these resources, how and when they participate (if), how many videos they watch (till the end), who they interact with (and who not) and then combine that data with their demographic and historical learning and behavioral data to classify them into categories of ‘students-like-you’ and then based on this, shape their learning journeys, access to resources and future registrations.
  11. 11. Source credit: http://2017trends.hackeducation.com/data.html ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT
  12. 12. “Students can [should be able to] see how these systems work, you know – the decisions that are human-made and the decisions that are machine-made and the decisions that are historical and the decisions that are structural. They worry that they are being set up to fail. They worry that their data – their very identities – are being weaponized against them. It’s not simply “the algorithm” that causes educational inequalities to persist. Students know that. Algorithms are just becoming an easier way to justify unjust decision- making” ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Source credit: http://2017trends.hackeducation.com/data.html
  13. 13. Our data are not something separate from our identities, our histories, our beings. Our data are an integral, albeit informational part of our being. Data are therefore not something we own and can give away. We don’t own our data but we are, increasingly, constituted by our data. See Floridi, L. (2005). The ontological interpretation of informational privacy. Ethics and Information Technology, 7(4), 185-200. Image created from https://pixabay.com/en/steampunk-man-male-person- fantasy-1809590/ and https://pixabay.com/en/matrix-network-data- exchange-1013611/ ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT
  14. 14. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT A more nuanced understanding of student consent needs to consider… • The different sources of data • The increasing automation of the collection, analysis and use of (student) data • The scope and intention of ‘consent’ and how it may change in different types of analytics • The brokenness of our data
  15. 15. Three sources of data Directed A digital form of surveillance wherein the “gaze of the technology is focused on a person or place by a human operator” Automated Generated as “an inherent, automatic function of the device or system and include traces …” Volunteered “gifted by users and include interactions across social media and the crowdsourcing of data wherein users generate data” (emphasis added) Kitchen, R. (2013). Big data and human geography: opportunities, challenges and risks. Dialogues in Human Geography, 3, 262-267. SOI: 10.1177/2043820613513388 ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT
  16. 16. (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 Consent in the context of human-algorithm interaction ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT
  17. 17. Source credit: http://timoelliott.com/blog/2013/02/gartnerbi-emea-2013-part-1-analytics-moves-to-the-core.html ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT What do students consent to?
  18. 18. Citation: Pink, S., Ruckenstein, M., Willim, R., & Duque, M. (2018). Broken data: Conceptualising data in an emerging world. Big Data & Society, 5(1), 2053951717753228. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT
  19. 19. Page credit: http://er.educause.edu/articles/2012/7/learning-analytics-the-new-black 2012 ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT
  20. 20. Page credit: https://library.educause.edu/resources/2015/10/the-predictive-learning-analytics-revolution-leveraging-learning-data-for-student- success 2015 ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT
  21. 21. Page credit: https://icde.memberclicks.net/assets/RESOURCES/anne_la_report%20cc%20licence.pdf 2016 Page credit: https://icde.memberclicks.net/assets/RESOURCES/anne_la_report%20cc%20licence.pdf ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT
  22. 22. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Unthinkable, and totally off the radar was any consideration of the notion of student choice to, at a minimum, to provide informed consent for the collection, analysis and use of their data, or at most, participate as equals in the sense-making of their learning journeys Image credit: https://www.quora.com/Whats-the-simplest-way-to-create-an-SMS-opt-in-marketing-campaign
  23. 23. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510-1529. 2013
  24. 24. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Source credit: https://www.open.ac.uk/students/charter/sites/www.open.ac.uk.students.charter/files/files/ecms/web-content/ethical- use-of-student-data-policy.pdf 2014
  25. 25. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Opting out is not an option Image credit: http://notbuyinganything.blogspot.co.za/2012/01/opting-out-increasingly.html
  26. 26. Page credit: http://www.npr.org/sections/ed/2017/01/11/506361845/the-higher-ed-learning-revolution-tracking-each- students-every-move 2017 ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT
  27. 27. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/ Page credit: https://www.edsurge.com/news/2017-06-13-from-high-school-to-harvard-students-urge-for-clarity-on-privacy- rights?utm_content=buffer8dd71&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer 3 June 2017 ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT
  28. 28. Source link: https://twitter.com/SwedishCanary/status/878622084141690881 ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT
  29. 29. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT “Ideally, we should get rid of learning analytics altogether. It is a colonialist, slave-owning, corporatizing, capitalist practice that enacts violence, yes violence, against the sanctity of a learner’s privacy, body and mind” (Hathcock, 2018). Source credit: https://aprilhathcock.wordpress.com/2018/01/24/learning-agency-not-analytics/
  30. 30. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/ Page credit: https://www.amazon.com/Ethical- Futures-Qualitative-Research- International/dp/1598741411 Page credit: https://press.anu.edu.au/publications/series/centre- aboriginal-economic-policy-research-caepr/indigenous-data- sovereignty
  31. 31. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Griffiths, D. (2017, September). An Ethical Waiver for Learning Analytics?. In European Conference on Technology Enhanced Learning (pp. 557-560). Springer, Cham.
  32. 32. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Image credit: https://www.flickr.com/photos/jackskellington101/426791087 “… the ethical processes of academic research seem a straitjacket which prevents them from their methods” (Griffiths, 2017, pp. 2-3) Griffiths, D. (2017, September). An Ethical Waiver for Learning Analytics?. In European Conference on Technology Enhanced Learning (pp. 557-560). Springer, Cham. Learning analytics resemble more Operations Research and as such…
  33. 33. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Source credit: https://www.technologyreview.com/s/609132/dont-let-regulators-ruin-ai/ Vol. 120 | No. 6, p. 73
  34. 34. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT “Policymakers who wish to champion growth should embrace a stance of “permissionless innovation.” Humility, collaboration, and voluntary solutions should trump the outdated “command and control” model of the last century. The age of smart machines needs a new age of smart policy.” Source credit: https://www.technologyreview.com/s/609132/dont-let-regulators-ruin-ai/ Andrea O’Sullivan is a program manager with the Mercatus Center, a free- market-oriented think tank at George Mason University’s Technology Policy Program.
  35. 35. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Higher education has always collected, analysed and used student data – so what has changed? Why is consent suddenly an issue? Image credit: https://en.wikipedia.org/wiki/Scholasticism
  36. 36. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT IN THE PAST AT PRESENT Data sources Demographic and learning data at specific points in the learning journey: data application, registration, class registers, assignments, summative assessment, personal communication Continuous directed and automated collection of data from a range of data sources – student administration, learning management system (LMS), sources outside of the LMS Data use Reporting purposes, operational planning on cohort, group level by management, institutional researchers Descriptive, diagnostic, predictive and prescriptive on group/cohort level Plus individualised, often real- time use of data to inform pedagogy, curriculum, assessment, student support by faculty, students and support staff Who used the data Management, institutional researchers, planners, quality assurance and HR departments Plus researchers, faculty, students and support staff
  37. 37. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT IN THE PAST AT PRESENT Who did the collection, analysis and who used the data Humans Increasingly humans in combination with algorithmic decision-making processes Temporal aim Retrospective/historical data to make predictions with regard to budget, future enrollments & resource allocation on institutional level Plus real-time data for real-time interventions Default Forgetting Remembering Personal identifiers Anonymised, aggregated data Plus re-identifiable data Personal/ised data Oversight/ data governance Broad institutional oversight. Ethical Review Board (ERB) approval for research purposes Approval, oversight and governance highly complex and contested
  38. 38. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Why is it necessary to consider ‘consent’ in the collection, analysis and use of student data? • We have access to greater volumes of data, an increased velocity of data and great granularity of student data from a variety of sources than ever before. With this increased scope of access, the increased capacity of our hardware/software and the dangers of epistemic arrogance • The brokenness of our data • The unintended consequences and the potential of harm • The importance to move away from students as data objects to students as equal partners, as owners of the data • We have a fiduciary duty and moral obligation
  39. 39. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Image credit: https://uvmbored.com/event/office-undergraduate-research-weekly-workshops/2017-12-07/ Central to considering student consent is the question: Is learning analytics….?
  40. 40. 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, 64, 881-901. DOI: 10.1007/s11423- 016-9463-4 http://link.springer.com/article/10.1007/s11423-016-9463-4 Definition, oversight and accountability An interpretative multiple-case study: Indiana University, Open University (UK) and the University of South Africa (Unisa) ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT
  41. 41. Typology: Learning analytics as… Approval/oversight/ accountability Research Formal, well-defined processes An emerging form of research Undefined, unclear Our current processes do not allow for any oversight Scholarship of teaching and learning Undefined, unclear Consent normally not required. Oversight? Student complaints, feedback Dynamic, synchronous and asynchronous sense- making Undefined, unclear Automated Undefined, unclear Participatory process and collaborative sense-making All stakeholders are involved – may need broad, blanket consensus at the beginning of each course – oversight by the highest academic decision making body. Important here is the role of students as collaborators in sharing interpretation, governance, quality assurance, integrity of data
  42. 42. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Griffiths, D. (2017, September). An Ethical Waiver for Learning Analytics?. In European Conference on Technology Enhanced Learning (pp. 557-560). Springer, Cham. Learning analytics has a closer resemblance to Operations Research than traditional education research and therefore …
  43. 43. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Source credit: https://depts.washington.edu/cfar/sites/default/files/uploads/scientific-programs/health-systems/Mini2006OREthicsGloyd.pdf
  44. 44. Source credit: https://depts.washington.edu/cfar/sites/default/files/uploads/scientific-programs/health-systems/Mini2006OREthicsGloyd.pdf
  45. 45. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Sclater, N. (2017, June 30). Consent and the GDPR: what approaches are universities taking? Retrieved from https://analytics.jiscinvolve.org/wp/2017/06/30/consent-and-the-gdpr-what-approaches-are-universities- taking/
  46. 46. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT CONSENT TYPE OF DATA Not ask for consent Non-sensitive data as long as the data and analysis can be considered as of legitimate interest or public interest Ask for consent Sensitive data (under the GDPR, this will be called ‘special category data) Ask for consent When the data and analytics will directly link to interventions that will affect the student Sclater, N. (2017, June 30). Consent and the GDPR: what approaches are universities taking? Retrieved from https://analytics.jiscinvolve.org/wp/2017/06/30/consent-and-the-gdpr-what-approaches-are-universities- taking/
  47. 47. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT • Data are “framed technically, economically, ethically, temporally, spatially and philosophically” and do not exist independently “of the ideas, instruments, practices, contexts and knowledges used to generate, process and analyse them” (Kitchin 2014, p. 2). • Data are never “neutral, objective, and pre-analytic” (Kitchin 2014, p. 2) Source: Prinsloo, P., & Slade, S. (in press). Student consent in learning analytics: the devil in the details? In J. Lester, C. Klein, H. Rangwala, and A. Johri (Eds), Learning analytics in higher education: Current innovations, future potential, and practical applications. Routledge. Under what circumstances do the following data become ‘sensitive’ resulting in potential bias, discrimination and exclusion? • Gender • Race • Home address • Occupation
  48. 48. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Madden, M., Gilman, M., Levy, K., & Marwick, A. (2017). Privacy, Poverty, and Big Data: A Matrix of Vulnerabilities for Poor Americans. Wash. UL Rev., 95, 53.
  49. 49. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Source credit: https://newrepublic.com/article/146710/injustice-algorithms
  50. 50. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Source credit: https://www.commonsense.org/education/privacy/blog/digital-redlining-access-privacy
  51. 51. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Towards a (more) nuanced understanding of informed consent in learning analytics Image credit: https://pixabay.com/en/nuance-swatches-pantone-color-1086726/
  52. 52. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Characteristic Simple consent Informed consent Type of decision Low risk High risk Elements Explanation of intervention, followed by patient agreement or refusal (expressed or implied); other elements, such as discussion of risks, benefits, and alternatives are present when appropriate Discussion of the nature, purpose, risks and benefits of proposed intervention, any alternatives, and no treatment, followed by explicit patient agreement or refusal Whitney, SN., McGuire, AL., & McCullough, LB 2004, ‘A typology of shared decision making, informed consent, and simple consent’, Annals of Internal Medicine, vol.140, no. 1, pp. 54-59.
  53. 53. High Low RISK Certain (1 clear best choice) CERTAINTY Uncertain (˃ 2 alternatives) Quadrant A: high risk (type of data/risk of failure), high certainty Consent type: Informed Shared decision-making: Absent Interaction: Intermediate, enough for an adequately informed decision Example: Financial aid, extended programs Quadrant C: low risk, high certainty Consent type: Simple Shared decision-making: Absent Interaction: Minimal or none Example: Lower diuretic dose for patient with low serum potassium level Quadrant D: low risk, low certainty Consent type: Simple Shared decision-making: Present Interaction: Intermediate Example: Lifestyle changes vs medication for hyperlipidaemia Quadrant B: high risk, low certainty Consent type: Informed Shared decision-making: Present Interaction: Extensive, including discussion of patient values, preferences, hopes and fears Example: Mastectomy or lumpectomy plus radiation for early breast cancer Whitney, SN., McGuire, AL., & McCullough, LB 2004, ‘A typology of shared decision making, informed consent, and simple consent’, Annals of Internal Medicine, vol.140, no. 1, pp. 54-59.
  54. 54. HighLow RISK Certain (1 clear best choice) CERTAINTY Uncertain (˃ 2 alternatives) Quadrant A: high risk (type of data/risk of failure), high certainty Consent type: Informed Shared decision-making: Absent Interaction: Intermediate, enough for an adequately informed decision Example: Financial aid, extended programs Quadrant C: low risk, high certainty Consent type: Simple Shared decision-making: Absent Interaction: Minimal or none Example: Use of aggregated data to offer additional support to broad cohorts, follow up support for missed key milestones Quadrant D: low risk, low certainty Consent type: Simple Shared decision-making: Present Interaction: Intermediate Example: Sending information regarding tutorial classes, alternative programs, reading material Quadrant B: high risk, low certainty Consent type: Informed Shared decision-making: Present Interaction: Extensive, including discussion of student values, preferences, hopes and fears Example: Choice of degree program, suggestion of a different institution, ability to opt out of learning analytics driven support Source: Prinsloo, P., & Slade, S. (in press). Student consent in learning analytics: the devil in the details? In J. Lester, C. Klein, H. Rangwala, and A. Johri (Eds), Learning analytics in higher education: Current innovations, future potential, and practical applications. Routledge.
  55. 55. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Elements of a more nuanced understanding and praxis of student consent • Simple versus informed consent • The level of risk • The certainty of our analysis, peer review, screening out of biases and stereotypes • Shared decision-making • Different levels/intensity of interaction Image credit: https://www.quora.com/Whats-the-simplest-way-to-create-an-SMS-opt-in-marketing-campaign
  56. 56. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT A more nuanced understanding and praxis of student consent will allow us to move beyond compliance towards an ethical and data-informed praxis where students are no longer passive recipients of services or where they have no option to contest the outcome of an analysis or contribute to a more informed position. Image credit: https://www.quora.com/Whats-the-simplest- way-to-create-an-SMS-opt-in-marketing-campaign Image credit: http://chrisschweppe.com/?p=340
  57. 57. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT “Providing people with notice, access, and the ability to control their data is key to facilitating some autonomy in a world where decisions are increasingly made about them with the use of personal data, automated processes, and clandestine rationales, and where people have minimal abilities to do anything about such decisions” (Solove, 2013, p. 1899; emphasis added) (In)conclusions Solove, D.J. 2013. Introduction: Privacy self-management and the consent dilemma. Harvard Law Review, 1880 (2013); GWU Legal Studies Research Paper No. 2012-141; GWU Law School Public Law Research Paper No. 2012-141. Available at SSRN: http://ssrn.com/abstract=2171018
  58. 58. ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT Student consent in learning analytics: Going into the labyrinth to face (tame?) the Minotaur Image credit: https://pixabay.com/en/maze-graphic-render-labyrinth-2264/ Alternative title
  59. 59. THANK YOU Paul Prinsloo (Prof) Research Professor in Open Distance Learning (ODL) College of Economic and Management Sciences, Samuel Pauw Building, Office 5-21, P.O. Box 392 Unisa, 0003, Republic of South Africa T: +27 (0) 12 433 4719 (office) prinsp@unisa.ac.za Skype: paul.prinsloo59 Personal blog: http://opendistanceteachingandlearning.wordpress.com Twitter profile: @14prinsp ELESIG ‘Conversations’ Webinar 29 January 2018 11:00 – 12:00 GMT

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