This presentation discusses the need to teach ethics as part of a learning analytics curriculum. It explores beliefs about data scientists, data analysis, and student data to understand different perspectives on ethics. The presentation considers whose ethics and values should be taught, and under what conditions teaching ethics could make a meaningful difference. It concludes that simply teaching a code of ethics may not be enough, and that holding people and systems accountable is also important.

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: https://pixabay.com/en/maze-blue-futuristic-assembly-1706853/

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. 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: https://pixabay.com/en/structure-beams-engineering-839656/

4. Image credit: https://pixabay.com/en/water-drop-liquid-splash-wet-1759703/
(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. • 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. So what are (some of) our beliefs
and assumptions about…
• data scientists
• data analysis
• data
• student data and
• Codes of Ethics?
Image credit: https://pixabay.com/en/audience-concert-music-868074/

7. Data scientists
Image credit: https://pixabay.com/en/hacker-attack-mask-internet-1872291/

8. Web page credit: https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century

9. Web page credit: https://www.techopedia.com/2/28526/it-business/it-careers/data-scientists-the-new-rock-stars-
of-the-tech-world

10. Web page credit: https://www.wired.com/2002/12/holytech/
“…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. Web page credit: https://blogs.ischool.utexas.edu/digitalcuration/2012/10/03/data-scientists-or-data-gods/

12. Web page credit: https://www.wsj.com/articles/academic-researchers-find-lucrative-work-as-big-data-
scientists-1407543088

13. Credit: Retrieved from http://www.oreilly.com/data/free/files/analyzing-the-analyzers.pdf

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. “What skills do you bring to
your work? What are your
primary areas of expertise?”

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. Image credit: http://maxpixel.freegreatpicture.com/static/photo/1x/Metal-Fence-Iron-Old-
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. Image credit: https://www.flickr.com/photos/bionicteaching/2920562020

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. 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. 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. Imagecredit:https://www.flickr.com/photos/spam/3355824586
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
dpress.com/2015/12/07/seeing-jesus-in-
toast-irreverent-ideas-on-some-of-the-claims-
pertaining-to-learning-analytics/
@Jesus_H_Toast

23. 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

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. Whose ethics?
Whose values?
Image credit: https://pixabay.com/en/empty-abandoned-messy-grunge-scene-863118/

26. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/
Whose values? It depends…
Neoliberal
CriticalLiberal
Prinsloo, P. (2016, October 19). A social cartography of student data: Moving beyond #StudentsAsDataObjects –
https://www.slideshare.net/prinsp/a-social-cartography-of-student-data-moving-beyond-studentsasdataobjects

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. (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
https://www.scu.edu/ethics/ethics-resources/ethical-decision-making/thinking-ethically/

29. But…
will it make a difference?
What type of ‘ethics’
teaching will make a
difference?
Image credit: https://pixabay.com/en/empty-abandoned-messy-grunge-scene-863118/

30. Web page credit: https://www.physicsforums.com/threads/whose-ethics.105227/

32. Ethics in learning analytics: Selected examples
2013-2017

33. “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

34. 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: https://pixabay.com/en/stairs-empty-grey-concrete-996638/

35. 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)
prinsp@unisa.ac.za
Skype: paul.prinsloo59
Personal blog:
http://opendistanceteachingandlearning.wordpres
s.com
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
Sharon.slade@open.ac.uk
Personal blog:
http://odlsharonslade.wordpress.com/
Twitter profile: @SharonSlade