Sahela presentation 5 july2013 final
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Sahela presentation 5 july2013 final
- 1. Dr Paul Prinsloo, Education Consultant and Researcher, Directorate for Curriculum and Learning Development, University of South Africa ETHICS AND LEARNING ANALYTICS AS A FAUSTIAN PACT: BETWEEN ORWELL, HUXLEY, KAFKA AND THE DEEP BLUE SEA
- 5. OVERVIEW OF THE PRESENTATION • Some questions to ponder on… • Learning analytics and ethics –introductory remarks • A short history of profiling • Ethics in learning analytics – different approaches • Orwell, Huxley, Kafka and Faust • A short overview of different frameworks • Learning analytics as moral practice • Some questions to ponder on… (revisited) • (In)conclusions
- 6. SOME QUESTIONS TO PONDER ON… 1. What are some of the dangers in learning analytics? 2. Is “raw data” an oxymoron? 3. Should students be allowed to opt-out of having their personal digital footprints harvested and analysed? 4. To what extent should students have access to the content of their digital dossiers, who have access to these dossiers, and what it is used for? 5. How complete and permanent a picture do our data provide about students?
- 7. SOME QUESTIONS TO PONDER ON… (cont.) 6. To what extent do we provide students the option to update their digital dossiers and provide extra (possibly qualitative) data? 7. Do students have the right to request that their digital dossiers be deleted on graduation? 8. If we outsource the collection (and analysis) of student digital data to companies, do students need to give consent? *Who owns a student’s data?+ 9. Is bigger data sets always better or provide more complete pictures? 10.What responsibility comes with ‘knowing’?
- 10. Amidst the hype and potential… Learning analytics, like knowledge and progress, is not an unqualified good – “it can be used as much as a curse as a blessing” (Gray 2004, p. 70 referring to knowledge and progress)
- 11. The dilemma… Finding the signal in the noise, patterns in the chaos (Silver, 2012)
- 12. The information network… An elaborate lattice of information networks creates digital collages and biographies from shards of data to form digital dossiers used by governments, employers, higher education institutions and the corporate sector (Solove, 2004)
- 13. The gaps… “We are more than the bits of data we give off as we go about our lives. Our digital biography is revealing of ourselves but in a rather standardized way” (p. 46). Our digital biographies are therefore not only (to some extent) unauthorized, but also to a huge extent reductive, partial and often inaccurate. (Solove, 2004)
- 14. Six provocations of Big Data… 1. “Automating research changes the definition of knowledge” (p.3) 2. “Claims to objectivity and accuracy are misleading” (p. 4) 3. “…bigger data are not always better data” (p. 6) 4. “Not all data are equivalent” (p. 8) 5. “Just because it is accessible doesn’t make it ethical” (p. 10) 6. “Limited access to big data creates new digital divides” (p. 12) (boyd & Crawford, 2013)
- 15. Contested objectivity… “Data and data sets are not objective; they are creations of human design. We give numbers their voice, draw inferences from them, and define their meaning through our interpretations” (Crawford, 2013, para.2). “…raw data is an oxymoron” (Crawford, 2013, para. 7).
- 16. A number of disclaimers… I am not going to refer to • Aristotle, Emmanuel Kant, John Stuart Mill, and John Rawl • Various international and South African laws on privacy, protection of personal information, personal and national security • Institutional review boards’ criteria and processes I will • Share my personal sense-making of an extremely complex and volatile field • Provoke and question some of my own and possibly our assumptions about data, surveillance and our current techno determinism and reductionist approaches to interpreting student data
- 17. A short overview of the history of profiling
- 18. http://www.understandingrace.org/images/482x270/science/early_class.jpg From Aristotle (4 BCE), to those in the 18th century and beyond… Lavatar, Lombroso, Linnaeus, Blumenbach, Bernier…
- 22. http://boingboing.net/images/_wikipedia_commons_3_34_Head-Measurer_of_Tremearne_%28side_view%29.jpg
- 27. But analysis of data and the compilation of profiles have also allowed us to stop…
- 30. How can learning analytics assist us to stop the revolving door in higher education? https://fbcdn-sphotos-d-a.akamaihd.net/hphotos-ak-ash3/p480x480/601036_626579104038059_1576248974_n.jpg
- 31. A brief overview of different approaches to thinking about ethics in learning analytics
- 32. Different approaches to discussing ethical implications of surveillance, and profiling: • Philosophical approaches – e.g. Willis, Campbell and Pistilli (2013) • Legal approaches – Marx (1998, 2001); Solove (2004) • A rights-based approach versus a discursive-disclosive approach (Stoddard, 2012) • Tensions between “public” and “private” (Marx, 2001) • Borders of personal information (Marx & Muschert, 2007) • Socio-critical (Bauman & Lyon, 2013; Prinsloo & Slade, 2013; Slade & Prinsloo, 2013)
- 34. “Metaphors are tools of shared cultural understanding” (Balkin, 1998, in Solove, 2004, p. 28) Metaphors don’t function “to render a precise descriptive presentation of the problem; rather, they capture our concerns over privacy in a way that is palpable, potent, and compelling” (Solove, 2004, p. 28).
- 35. TOWARDS A FRAMEWORK FOR ETHICS IN LEARNING ANALYTICS • 1973 – “Code of fair information practices” • 20 questions by Marx (1998) • 9 Principles by Pounder (2008)
- 36. 1. There must be no personal-date record-keeping systems whose very existence is secret. 2. There must be a way for an individual to find out what information about him (sic) is in the record and how it is used. 3. There must be a way for an individual to prevent information obtained about him for one purpose for being used or made available for other purposes without his consent. 4. There must be a way for an individual to correct or amend a record of identifiable information about him. 5. Any organisaton creating, maintaining, sing, or disseminating records of indentifiable personal data must assure the reliability of the data for their intended use and must take reasonable precautions to prevent misue of the data. 1973 Code of fair information practices
- 37. 20 questions by Marx (1998) Means: Harm, boundaries, trust, personal relationships, validity Data collection contexts: Awareness, consent, golden rule, minimisation, public decision making, human review, right of inspection, right to challenge, redress and sanction, stewardship and protection, unintended precedents, etc. Uses: Beneficiary, proportionality, alternative means, consequences of inaction, protections, etc.
- 38. 9 Principles by Pounder (2008) Principle 1:The justification principle Principle 2:The approval principle Principle 3:The separation principle Principle 4:The adherence principle Principle 5:The reporting principle Principle 6: The independent supervision principle Principle 7: The privacy principle Principle 8: The compensation principle Principle 9: The unacceptability principle
- 39. 6 Principles for learning analytics as moral practice (Slade & Prinsloo, 2013) 1.Learning analytics as moral practice 2.Students as agents 3.Student identity and performance as temporal dynamic constructs 4.Student success is a complex and multidimensional phenomenon 5.Transparency 6.Higher education cannot afford not to use data
- 40. A number of considerations for an ethics architecture for learning analytics (Slade & Prinsloo, 2013) 1.Who benefits and under what conditions? 2.Conditions for consent, de-identification, and opting out – including considerations regarding vulnerability and harm 3.Data collection, analyses, access and storage 4.Governance and resource allocation
- 41. SOME QUESTIONS TO PONDER ON… Revisited 1. What are some of the dangers in learning analytics? 2. Is “raw data” an oxymoron? 3. Should students be allowed to opt-out of having their personal digital footprints harvested and analysed? 4. To what extent should students have access to the content of their digital dossiers, who have access to these dossiers, and what it is used for? 5. How complete and permanent a picture do our data provide about students?
- 42. SOME QUESTIONS TO PONDER ON… (cont.) 6. To what extent do we provide students the option to update their digital dossiers and provide extra (possibly qualitative) data? 7. Do students have the right to request that their digital dossiers be deleted on graduation? 8. If we outsource the collection (and analysis) of student digital data to companies, do students need to give consent? *Who owns a student’s data?+ 9. Is bigger data sets always better or provide more complete pictures? 10.What responsibility comes with ‘knowing’?
- 43. (In)conclusions Referring to his set of 29 questions, Marx (1998) states that the questions will not satisfy philosophers and practitioners who “lust after a Rosetta stone of clear and consistent justifications” but the questions strive to serve as “an imperfect compass than a detailed map” (Marx, 1998, p. 182).
- 44. A detailed map “can lead to the erroneous conclusion that ethical directions can be easily reached or to a statement so far in the stratosphere that only angels can see and apply it” (Marx, 1998, p. 182).
- 45. Thank you for sharing my personal journey to make sense of ethics in learning analytics as a Faustian pact, caught between Orwell, Huxley, Kafka and the deep blue sea…
- 46. Thank you. Baie dankie. Ke a leboga Paul Prinsloo TVW4-69 P O Box 392 Unisa 0003 prinsp@unisa.ac.za http://opendistanceteachingandlearning.wordpress.com Twitter: 14prinsp +27124293683 +27823954113
- 47. REFERENCES boyd, d., & Crawford, K. (2013). Six provocations for Big Data. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1926431 Code of fair information practices. (1973). Retrieved from http://simson.net/ref/2004/csg357/handouts/01_fips.pdf Crawford, K. 2013, April 1. The hidden biases in big data. [Web log post]. Harvard Business Review. Retrieved from http://blogs.hbr.org/cs/2013/04/the_hidden_biases_in_big_data.html Gray, J. 2004. Heresies. Against progress and other illusions. London, UK: Granta Books. Marx, G.T. 1998. Ethics for the new surveillance. The Information Society: An International Journal, 14(3), 171-185. DOI: org/10.1080/019722498128809 Marx, G.T. 2001. Murky conceptual waters: the public and the private. Ethics and Information Technology, 3, 157-169. Marx, G.T., & Muschert, G.W. 2007. Personal information, borders, and the new surveillance. Annual Review of Law and Social Science, 3: 375-95. Pounder, C.N.M. 2008. Nine principles for assessing whether privacy is protected in a surveillance society. Identity in the Information Society, 1, 1-22. DOI 10.1007/s12394- 008-0002-2.
- 48. REFERENCES (CONT.) Prinsloo, P., & Slade, S. 2013. An evaluation of policy frameworks for addressing ethical considerations in learning analytics. LAK '13 Proceedings of the Third International Conference on Learning Analytics and Knowledge (LAK13), pp. 240-244. Retrieved from http://dl.acm.org/citation.cfm?id=2460344 Silver, N. 2012. The signal and the noise. The art and science of prediction. London, UK: Allen Lane. Slade, S., & Prinsloo, P. 2013. Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, XX(X), 1-20. DOI: 10.1177/0002764213479366 Solove, D.J. 2004. The digital person. Technology and privacy in the information age. New York, NY: New York University Press. Stoddart, E. (2012). a. A surveillance of care. Evaluating surveillance ethically. In K. Ball, K.D. Haggerty, and D. Lyon, (eds.), Routledge handbook of surveillance studies. London, UK: Routledge, pp. 369 – 376. Willis, J.E.III, Campbell, J.P., & Pistilli, M.D. 2013, May 6. Ethics, big data, and analytics: a model for application. EDUCAUSEreview online. Retrieved from http://www.educause.edu/ero/article/ethics-big-data-and-analytics-model- application