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Building the learning analytics curriculum: Should we teach (a code of) ethics?


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LAK17 Workshop | Simon Fraser University | Vancouver, BC, Canada |March 13, 2017 - Authors: Paul Prinsloo (Unisa) and Sharon Slade (OU)

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Building the learning analytics curriculum: Should we teach (a code of) ethics?

  1. 1. By Paul Prinsloo (University of South Africa) @14prinsp Sharon Slade (Open University, UK) @SharonSlade LAK17 Workshop | Simon Fraser University | Vancouver, BC, Canada |March 13, 2017 Building the learning analytics curriculum: Should we teach (a code of) ethics? Image credit:
  2. 2. ACKNOWLEDGEMENTS The presenters do not own the copyright of any of the images in this presentation. We hereby acknowledge the original copyright and licensing regime of every image and reference used. All the images used in this presentation have been sourced from Google labeled for non-commercial reuse This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
  3. 3. Why bother? Learning analytics is a structuring device, not neutral, informed by current beliefs about what counts as knowledge and learning, coloured by assumptions about gender/race/class/capital/literacy and in service of and perpetuating existing or new power relations Prinsloo, P. 2015, May 28. A brave new world Image credit:
  4. 4. Image credit: (A code of) ethics in learning analytics (should) include… How we see data, what we see as data, our reasons and processes for collecting data, our analyses, those who do the analyses, how we verify our analyses, how/why we share the analyses, and whether we have the resources to ethically respond to identified needs/gaps
  5. 5. • Explore the question of “should we teach (a code of) ethics?” against the background of our beliefs regarding data scientists, data analysis, data and, in particular, student data • Whose ethics? Whose code? Different approaches/choices • What difference do we hope that any teaching of (a code of) ethics would make? • Under what conditions might this actually make a difference? • (In)conclusions Overview of the presentation
  6. 6. So what are (some of) our beliefs and assumptions about… • data scientists • data analysis • data • student data and • Codes of Ethics? Image credit:
  7. 7. Data scientists Image credit:
  8. 8. Web page credit:
  9. 9. Web page credit: of-the-tech-world
  10. 10. Web page credit: “…computation seems almost a theological process. It takes as its fodder the primeval choice between yes or no, the fundamental state of 1 or 0.”
  11. 11. Web page credit:
  12. 12. Web page credit: scientists-1407543088
  13. 13. Credit: Retrieved from
  14. 14. Harris, H., Murphy, S., & Vaisman, M. (2013). Analyzing the analyzers: An introspective survey of Data Scientists and their work. O'Reilly Media, Inc.". How do data scientists think about themselves and their career?
  15. 15. “What skills do you bring to your work? What are your primary areas of expertise?”
  16. 16. Gods, rock stars, game changers or … fallible humans with biases? They are ‘mere’ humans who “interpret meaning from data in different ways. Data scientists can be shown the same sets of data and reasonably come to different conclusions. Naked and hidden biases in selecting, collecting, structuring and analysing data present serious risks. How we decide to slice and dice data and what elements to emphasise or ignore influences the types and quality of measurements” Walker, M. A. (2015). The professionalisation of data science. International Journal of Data Science, 1(1), 7-16
  17. 17. Image credit: 77940.jpg Data analysis is an “art” (Ibrahim, 2013) and a “black art” (Floridi, 2012). These descriptions create the impression of data analysis providing access to ‘hidden’ knowledge, not normally accessible to mere mortals - knowledge that can only be accessed through an interlocutor Data analysis With power comes responsibility and accountability
  18. 18. Image credit:
  19. 19. Some of our beliefs about data… • Data are neutral • Represents ‘the Truth’ – you can’t argue with data • We talk about data as “raw”, “cooked”, “corrupted”, “cleaned”, “scraped” “mined” and “processed” (Gitelman & Jackson, 2013) • Data are self explanatory (Mayer-Schönberger & Cukier, 2013) • We believe that n=all, and that knowing ‘what’ is happening erases the need to know ‘why’ something is happening • Big(ger) data are better data • We can distinguish between the signal and the noise (Silver, 2012)
  20. 20. Data are not neutral, raw, objective and pre-analytic but 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) Contrary to our beliefs... When a fact is proven false, it stops being accepted as a fact. When data are false, it remains data…
  21. 21. The relation between data, information, knowledge, evidence and wisdom is more complex and contested than we may be comfortable with… Data are political in nature – loaded, shaped and limited with the values, interests and assumptions of those who collect, frame and use the data (Selwyn, 2014) What are the implications for learning analytics as ethical practice when...
  22. 22. Imagecredit: Apophenia – “seeing patterns where none actually exist, simply because enormous quantities of data can offer connections that radiate in all directions” (boyd & Crawford, 2012, p. 668) What are the implications for learning analytics as ethical practice when... Seeing Jesus in toast: Irreverent ideas on some of the claims pertaining to learning analytics (Prinsloo, 2016) – https://opendistanceteachingandlearning.wor toast-irreverent-ideas-on-some-of-the-claims- pertaining-to-learning-analytics/ @Jesus_H_Toast
  23. 23. Imagecredit: Student data as the ‘new black”, as oil, as a resource to be mined Image credit: world-energy-outlook.jpg
  24. 24. We believe student (digital) data … • Represent the whole picture • Whose interests are really at stake? • The data belong to us • Students don’t need access, and they don’t need to know what we collect, the reasons for the collection, how we analyse the data, how long we keep the data, who has access to the data, and who we share the data with….
  25. 25. Whose ethics? Whose values? Image credit:
  26. 26. Imagecredit: Whose values? It depends… Neoliberal CriticalLiberal Prinsloo, P. (2016, October 19). A social cartography of student data: Moving beyond #StudentsAsDataObjects –
  27. 27. Teleological • The potential for harm • The scope of consent and • recourse in cases of unintended harm are negotiated and agreed upon Deontological • Basis for legal and regulatory frameworks • Terms and Conditions • By consent and/or contract • Works well in stable environments Two traditional categories of ethical approaches
  28. 28. (1) a utilitarian approach (deciding on an action that “provides the greatest balance of good over evil”); (2) a rights approach (referring to basic, universal rights such as the right to privacy, not to be injured); (3) a fairness or justice approach; (4) the common-good approach (where the welfare of the individual is linked to the welfare of the community); and (5) the virtue approach (based on the aspiration towards certain shared ideals) An alternative framework Velasquez, M., Andre, C., Shanks, T.S.J., & Meyer, M.J. (2015, August 1). Thinking ethically. Retrieved from
  29. 29. But… will it make a difference? What type of ‘ethics’ teaching will make a difference? Image credit:
  30. 30. Web page credit:
  31. 31. Ethics in learning analytics: Selected examples 2013-2017
  32. 32. “Ethics are the mirror in which we evaluate ourselves and hold ourselves accountable” (emphasis added). Holding actors and humans accountable still works “better than every single other system ever tried” (Brin, 2016) The way forward: some considerations
  33. 33. So the question is not “should we teach (a code of) ethics as part of a learning analytics curriculum?” but … under what conditions might this make a difference? Image credit:
  34. 34. THANK YOU Paul Prinsloo (Prof) Research Professor in Open Distance Learning (ODL) College of Economic and Management Sciences, Office number 3-15, Club 1, Hazelwood, P O Box 392 Unisa, 0003, Republic of South Africa T: +27 (0) 12 433 4719 (office) T: +27 (0) 82 3954 113 (mobile) Skype: paul.prinsloo59 Personal blog: http://opendistanceteachingandlearning.wordpres Twitter profile: @14prinsp Sharon Slade (Dr) Senior Lecturer Faculty of Business and Law The Open University, Walton Hall, Milton Keynes, MK7 6AA, United Kingdom T: +44 (0) 1865 486250 Personal blog: Twitter profile: @SharonSlade