Presentation on 27 October 2016 at an Ethics Symposium as part of the Siyaphumelela Project, Kopanong Hotel & Conference Centre, Johannesburg, South Africa
2. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/
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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The goals of the Siyaphumelala Project are to:
• Improve capacity to collect student data and integrate it with Institutional
Research, Information and Communication Technology (ICT), academic
development, planning, student support and academic divisions.
• Create South African models of universities using successful data analytics to
improve student outcomes.
• Create a greater awareness and support for data use to improve student
success in South Africa (collaborating with existing and new South African
national initiatives wherever possible).
• Create and highlight a shared vocabulary and consensus on especially effective
practices to improve student success.
• Enlarge the cadre of experienced data analytics professionals supporting
student success.
For more information see http://www.siyaphumelela.org.za/about.php
4. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/
Overview of the presentation
• Introduction: Balancing between ethics, risk and care
• What does a contextualised, South African perspective
on the ethical collection, analysis and use of student
data entail?
• Mapping the current dilemma of considering the ethical
implications in/of learning analytics
• Possible lenses – eg deontological/teleological
• Some considerations – Knox (2010), Slade & Prinsloo
(2013), and the Open University (2014)
• Mapping a possible way forward
• (In)conclusions
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The purpose of this presentation is to provide a
tentative, broad conceptual map of different
aspects to take into consideration in developing
institutional operational and policy responses
pertaining to the ethical collection, analysis and
use of student data, in the specific context of
South African higher education
8. Imagecredit:https://pixabay.com/en/stones-pebbles-stack-pile-zen-801756/
Balancing between risk and care…
We need to ensure the
sustainability of higher
education in the light of
• funding constraints
• increased competition
• the socioeconomic
downturn
• student needs and risks
• increased need for
efficiency/effectiveness
• audit & quality
assurance regimes
• student protests
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
• critical interrogation of our assumptions
about learning, merit, data, our data
collection methods, those who do the
analyses, and the way we use and keep the
data
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
10. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/
Can we ignore the way colonialism
• Stole the dignity and lives of millions based on arbitrary
criteria and beliefs about meritocracy supported by
asymmetries of power
• Extracted value in exchange for bare survival
• Objectified humans as mere data points and
information in the global, colonial imaginary
• Controlled the movement of millions based on
arbitrary criteria such as race, cultural grouping and
risk of subversion?
Image credit: https://en.wikipedia.org/wiki/Xhosa_Wars
15. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/
Image credit: https://pixabay.com/en/prison-fence-razor-ribbon-wire-218456/
A contextualised approach to the ethical
collection, analysis and use of student data …
• Acknowledges the lasting, inter-generational effects of
colonialism and apartheid
• Collects, analyses and use student data with the aim of
addressing these effects and historical and arising tensions
between ensuring quality, sustainability and success
• Critically engages with the assumptions surrounding data,
identity, proxies, consequences and accountability
• Responds to institutional character, context and vision
• Considers the ethical implications of the purpose, the processes,
the tools, the staff involved, the governance and the results of
the collection, analysis and use of student data
17. See: 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 http://link.springer.com/article/10.1007/s11423-016-9463-4
When the collection, analysis and
use of student data have an
external focus
• Reporting to a range of
stakeholders, e.g. government,
industry, etc., and for a range of
purposes, e.g., funding
• Conference presentations
• Journal articles
• Monographs & edited volumes
• Popular press
• Marketing
When the collection, analysis and
use of student data have an
internal focus
• Departmental/institutional
reports & planning
• Scholarship of teaching and
learning
• Provide appropriate and
effective student support
• Allocation of staff/resources
18. Institutional Research
• Often located in a
designated department
• Staffed by data
scientists, analysts
• Inform strategy and
policy
• Use student data
already ‘gifted’ during
application/
registration process and
from Learning
Management System
(LMS)
• Specific data collection
• Often blanket ethical
clearance
Research (capital ‘R’)
• Mostly faculty, but
increasingly support and
professional staff
• Varying skills and
understanding
• Chasing outputs, h-index,
citations
• Results mostly not used to
inform teaching and
learning
• Use primary and
secondary student data
• Oversight provided by
Institutional Review
Boards (IRBs)
Emerging forms of research
• Mostly faculty, but increasingly
support and professional staff
• Varying skills and understanding
• Not produced for formal
outputs eg publication, but to
inform pedagogy, assessment,
personalisation, departmental
reports
• Often use student data already
‘gifted’ during application/
registration process and from
Learning Management System
(LMS) or personal synchronous
or asynchronous communication
• No ethical review/oversight
Academic & learning analytics
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Image credit: https://pixabay.com/en/lens-camera-photographer-photo-490806/
Possible lenses to engage with the ethical
considerations of the collection, analysis and use
of student data in learning and predictive analytics
(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).
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/
21. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/
Image credit: https://pixabay.com/en/camera-lens-photography-equipment-933148/
A deontological approach
• Based on rules, legal and regulatory frameworks, as well as
Terms and Conditions (T&Cs) that clarify the nature and scope
of the rights and responsibilities of parties to the agreement in
a particular context
• Are effective in relatively stable environments
• Necessitates agreeing on the type and choice of rules (e.g.
consent-based or contract-based)
• Based on the notion that decisions to adhere to the rules arise
from an “autonomous, objective and impartial agent”
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Image credit: https://pixabay.com/en/abstract-spot-reflection-blue-91462/
Some broad questions to consider:
(1) what are the benefits and harms, to whom, under what circumstances
and what are the alternatives?
(2) what are the rights of those affected by a course of action and which
course of action respects those rights?
(3) which course of action treats everyone the same except where there
is a morally justifiable reason not to?
(4) how will the common good be served by the action taken? and
(5) which possible action develops moral virtues?
Adapted from: 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/
25. Preliminary seven dimensions of surveillance
(Knox 2010) and their ethical implications
1. Automation
2. Visibility
3. Directionality
4. Assemblage
5. Temporality
6. Sorting
7. Structuring
26. 1. Automation
Key questions Dimensional intensity
What is the timing of the
collection?
Intermittently/
infrequently
Continuous
Locus of control? Human Machine
Can it be turned on and
off (and by whom?)
All the
monitoring can
be turned
on/off
None of the
monitoring can be
turned off
27. 2. Visibility
Key questions Dimensional intensity
Is the surveillance
apparent and
transparent?
All parts
(collection,
storage,
processing and
viewing) are
visible
None of the
monitoring is visible
Ratio of subject-to-
surveillant knowledge?
Subject knows
everything the
surveillant
knows
Subject does not
know anything that
the surveillant knows
28. 3. Directionality
Key questions Dimensional intensity
What is the relative
power of surveillant to
the subject?
Subjects hold all
the power
Surveillant holds all
the power
Who has access to
monitoring/recording/
broadcasting functions?
Subject Surveillant
29. 4. Assemblage
Key questions Dimensional intensity
Medium of surveillance Single medium
(e.g. text)
Multimedia
Are the data stored? No Yes
Who stores the data? Subject or
collector
Third party
30. 5. Temporality
Key questions Dimensional intensity
When does the monitoring
occur?
Confined to the
present
Combines the present
with the past
How long is the monitoring
frame?
One, isolated,
relatively short frame
(e.g. test)
Long periods, or
indefinitely
Does the system attempt to
predict future
behavior/outcomes
No – only
assessment of the
present
Present + past used to
predict the future
When are the data available? All of the data
available only after
event is completed
Available in real-time and
experienced as
instantaneous
31. 6. Sorting
Key questions Dimensional intensity
Are subjects’ data
compared with other
data – other individuals/
groups/ abstract
configurations/ state
mandates?
None Other data are used
as basis for
comparison
32. 7. Structuring
Key questions Dimensional intensity
Are data used to alter the
environment (i.e.
treatment, experience,
etc.)?
Not used Used to alter the
environment of all
subjects
Are data used to target
the subject for different
treatment that they
would otherwise receive?
No data are used
as basis for
differing
treatment
Based on data,
treatment is
prescribed
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Toward an Ethical Framework (Slade & Prinsloo, 2013)
Principle 1: Learning Analytics as Moral Practice
“Evidence-based education seems to favour a technocratic model in which
it is assumed that the only relevant research questions are about the
effectiveness of educational means and techniques, forgetting, among
other things, that what counts as “effective” crucially depends on
judgments about what is educationally desirable” (Biesta, 2007, p. 5)
“Learning analytics should not only focus on what is effective, but also aim
to provide relevant pointers to decide what is appropriate and morally
necessary” (Slade & Prinsloo, 2013, p. 1519)
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Principle 2: Students as Agents
Students are situated, constrained agents and not the passive recipients of
services (Subotzky & Prinsloo, 2011).
“In stark contrast to seeing students as producers and sources of data,
learning analytics should engage students as collaborators and not as mere
recipients of interventions and services (Buchanan, 2011; Kruse &
Pongsajapan, 2012)” (Slade & Prinsloo, 2013, p. 1519; emphasis added)
Moving from an “intervention-centric,” approach to learning analytics to a
“student-centric” model – the student as “as a co-interpreter of his own
data—and perhaps even as a participant in the identification and gathering of
that data. In this scenario, the student becomes aware of his own actions in
the system and uses that data to reflect on and potentially alter his
behaviour” (Kruse and Pongsajapan, 2012, pp. 4-5)
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Principle 3: Student Identity and Performance Are Temporal
Dynamic Constructs
“Integral in learning analytics is the notion of student identity. It is crucial to
see student identity as a combination of permanent and dynamic attributes.
During students’ enrolment, their identities are in continuous flux, and as
such they find themselves in a “Third Space” where their identities and
competencies are in a permanent liminal state (Prinsloo, Slade, & Galpin,
2012)” (Slade & Prinsloo, 2013, p. 1520).
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• Principle 1: Learning analytics is an ethical practice that
should align with core organisational principles, such as
open entry to undergraduate level study.
• Principle 2: The OU has a responsibility to all stakeholders
to use and extract meaning from student data for the
benefit of students where feasible.
• Principle 3: Students should not be wholly defined by their
visible data or our interpretation of that data.
• Principle 4: The purpose and the boundaries regarding the
use of learning analytics should be well defined and visible.
• Principle 5: The University is transparent regarding data
collection, and will provide students with the opportunity to
update their own data and consent agreements at regular
intervals.
Policy on Ethical use of Student Data for
Learning Analytics (Open University, 2014)
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• Principle 6: Students should be engaged as active agents in
the implementation of learning analytics (e.g. informed
consent, personalised learning paths, interventions).
• Principle 7: Modelling and interventions based on analysis
of data should be sound and free from bias.
• Principle 8: Adoption of learning analytics within the OU
requires broad acceptance of the values and benefits
(organisational culture) and the development of appropriate
skills across the organisation.
Policy on Ethical use of Student Data for
Learning Analytics (Open University, 2014)(cont.)
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A possible way forward
• Why do we need a policy/framework for the ethical
collection, analysis and use of student data? (Purpose)
• What are the realities and our assumptions about data,
student data, the sources & quality of student data, the
processes of collecting and analysing data, the tools we
use, the people/algorithms who do the collection, analysis
and who responds, who need access to this data and our
assumptions about learning?
• What are the ethical issues in each of the above?
• How will we ensure accountability, transparency?
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Getting practical – towards policy
formulation
Introduction/
Purpose
(context and
intended
impact)
Assumptions re
(student)
data
Sources
Quality of
data
Processes
Tools
People
Governance
(access &
storage)
Realities re
(student)
data
Sources
Quality of
data
Processes
Tools
People
Governance
(access &
storage)
Possible
issues/
principles
Accountability
and
transparency
Harm/
unintended
consequences
44. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/
(In)conclusions: Towards a contextualised
approach to the ethical collection, analysis and
use of student data …
• Acknowledges the lasting, inter-generational effects of colonialism and
apartheid
• Collects, analyses and use student data with the aim of addressing
these effects and historical and arising tensions between ensuring
quality, sustainability and success
• Critically engages with the assumptions surrounding data, identity,
proxies, consequences and accountability
• Responds to institutional character, context and vision
• Considers the ethical implications of the purpose, the processes, the
tools, the staff involved, the governance and the results of the
collection, analysis and use of student data
45. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/
Thank you. Ke a leboga. Baie dankie
Paul Prinsloo
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)
prinsp@unisa.ac.za
Personal blog:
http://opendistanceteachingandlearning.wordpress.com
Twitter profile: @14prinsp
46. Bibliography and additional reading
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-x.
Blackmore, J. (2001). Universities in crisis? Knowledge economies, emancipatory pedagogies, and the critical
intellectual. Educational Theory, 51(3), 353-370
Booth, M. (2012, July 18). Learning analytics: the new black. EDUCAUSEreview, [online]. Retrieved from
http://www.educause.edu/ero/article/learning-analytics-new-black
Chamayou, G. (n.d.). Every move will be recorded. [Web log post]. Retrieved from https://www.mpiwg-
berlin.mpg.de/en/news/features/feature14
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/
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
47. Brock, A. (2015). Deeper data: a response to boyd and Crawford. Media, Culture & Society, 37(7), 1084-1088.
Chamayou, G. (n.d.). Every move will be recorded. [Web log post]. Retrieved from https://www.mpiwg-
berlin.mpg.de/en/news/features/feature14
Citron, D.K., & Pasquale, F. (2014). The scored society: Due process for automated predictions.
http://ssrn.com/abstract=2376209
Coates, T-N. (2014, May 22). The case for reparations: an intellectual autopsy. [Web log post]. Retrieved from
http://www.theatlantic.com/business/archive/2014/05/the-case-for-reparations-an-intellectual-
autopsy/371125/
Coates, T-N. (2015). Between the world and me. Melbourne: Text Publishing.
Crawford, K. (2013, April 1). The hidden biases in Big Data. Harvard Business Review. Retrieved from
https://hbr.org/2013/04/the-hidden-biases-in-big-data/
Crawford, K. (2014, May 30). The anxieties of Big Data. The New Inquiry. Retrieved from
http://thenewinquiry.com/essays/the-anxieties-of-big-data
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
Deleuze. G. (1992). Postscript on the societies of control. October, 59 pp. 3-7.
de Oliveira Andreotti, V., Stein, S., Ahenakew, C., & Hunt, D. (2015). Mapping interpretations of decolonization in
the context of higher education. Decolonization: Indigeneity, Education & Society, 4(1), 21-40.
Diefenbach, T. (2007). The managerialistic ideology of organisational change management. Journal of
Organisational Change Management, 20(1), 126-144.
.
Bibliography and additional reading (cont.)
48. 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.
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
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.
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/
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, 173-191
Johnson, J.A. (2015, October 7). How data does political things: The processes of encoding and decoding data
are never neutral. [Web log post]. Retrieved from
http://blogs.lse.ac.uk/impactofsocialsciences/2015/10/07/how-data-does-political-things/
Kitchen, R. (2013). Big data and human geography: opportunities, challenges and risks. Dialogues in Human
Geography, 3, 262-267. SOI: 10.1177/2043820613513388
Kitchen, R. (2014). The data revolution. London, UK: SAGE.
Bibliography and additional reading (cont.)
49. Kitchen, R., & McArdle, G. (2016). What makes Big Data, Big Data? Exploring the ontological characteristics of
26 datasets. Big Data & Society, January-June, 1-10. DOI: 10.1177/2053951716631130
Knox, D. (2010). Spies in the house of learning: a typology of surveillance in online learning environments.
Paper presented at Edge, Memorial University of Newfoundland, Canada, 12-15 October.
Lagoze, C. (2014). Big Data, data integrity, and the fracturing of the control zone. Big Data & Society (July-
December), 1-11.
Leonhard, G. (2014, February 25). How tech is creating data "cravability," to make us digitally obese. Retrieved
from http://www.fastcoexist.com/3026862/how-tech-is-creating-data-cravability-to-make-us-digitally-
obese?utm_content=buffer643a8&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer
Lupton, D. (2015). The thirteen Ps of Big Data. This Sociological Life. Retrieved from
https://www.researchgate.net/profile/Deborah_Lupton/publication/276207564_The_Thirteen_Ps_of_Big_
Data/links/5552c2d808ae6fd2d81d5f20.pdf
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.
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.
Morozov, E. (2013). To save everything, click here. London, UK: Penguin Books.
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
Bibliography and additional reading (cont.)
50. Bibliography and additional reading (cont.)
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
Open University. (2014). Policy on ethical use of student data for learning analytics. Retrieved from
http://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
Pasquale, F. (2015, October 14). Scores of scores: how companies are reducing consumers to single numbers.
The Atlantic. Retrieved from http://www.theatlantic.com/business/archive/2015/10/credit-
scores/410350/
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.
Prinsloo, P. (2015, September 3). The ethics of (not) knowing our students. Presentation at the University of
South Africa, Pretoria. Retrieved from http://www.slideshare.net/prinsp/the-ethics-of-not-knowing-our-
students-52373670
Prinsloo, P. (2016). Curricula as contested and contesting spaces: Geographies of identity, resistance and desire.
Presentation at Transforming the Curriculum: South African Imperatives and 21st Century Possibilities
University of Pretoria, 28 January 2016. Retrieved from http://www.slideshare.net/prinsp/curricula-as-
contested-and-contesting-spaces-geographies-of-identity-resistance-and-desire
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).
51. Bibliography and additional reading (cont.)
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
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. (2017, under review). An elephant in the learning analytics room – the obligation to act.
Submission to LAK17, Vancouver, Canada)
Rosen, J. (2010, July 21). The web means the end of forgetting. New York Times [Online].
Selwyn, N. (2014). Distrusting educational technology. Critical questions for changing times. New York, NY:
Routledge
Scharmer, O. (2014, July 18). From Big Data to deep data. Huffington Post. Retrieved from
http://www.huffingtonpost.com/otto-scharmer/from-big-data-to-deep-dat_b_5599267.html
Shacklett, M. (2015, January 6). Thick data closes the gaps in big data analytics. TechRepublic. Retrieved from
http://www.techrepublic.com/article/thick-data-closes-the-gaps-in-big-data-analytics/
Slade, S., & Prinsloo, P. (2013). Learning Analytics: Ethical Issues and Dilemmas. American Behavioral Scientist
57(1) ,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
52. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/
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
Uprichard, E. (2013). Big data, little questions. Discover Society, 1 October. Retrieved from
http://discoversociety.org/2013/10/01/focus-big-data-little-questions/
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/
Wagner, D., & Ice, P. (2012, July 18). Data changes everything: delivering on the promise of learning analytics in
higher education. EDUCAUSEreview, [online]. Retrieved from http://www.educause.edu/ero/article/data-
changes-everything-delivering-promise-learning-analytics-higher-education
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
White, C. 92016). Decolonising edtech. [Web log post]. Retrieved from http://decolonizingedtech.xyz/
Wigan, M.R., & Clarke, R. (2013). Big data’s big unintended consequences. Computer,(June), 46-53.
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 http://link.springer.com/article/10.1007/s11423-016-9463-4
Bibliography and additional reading (cont.)