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Mapping the ethical implications of
using student data – A South African
contextualised view
Presentation at an Ethics Symposium as part of the Siyaphumelela Project
Kopanong Hotel & Conference Centre, Johannesburg, South Africa
Paul Prinsloo
University of South Africa (Unisa)
@14prinsp
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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
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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
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Image credit: https://pixabay.com/en/frog-butterfly-pond-mirroring-540812/
Some of the debates
regarding mapping the
ethical issues in the
collection, analysis and
use of student data
focus on identifying
and mitigating risk –
for students and for
the institution
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Image credit: https://pixabay.com/en/structure-sea-beach-gumyeonghwan-1426309/
While there are also voices that emphasise our
duty to save the drowning, referring to higher
education’s fiduciary duty to care for those we
allowed into the system and/or those who were
entrusted to our care
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
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What does a contextualised, South
African perspective on the ethical
collection, analysis and use of
student data entail?
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
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Image credit: http://avpixlat.info/2016/05/16/prestigefyllda-praktiktjanster-endast-for-rasifierade/
Can we ignore how data were used during Apartheid to
classify humans according to those worthy of humanity
and dignity and those who were , somehow, less
human, less worthy, and of lesser merit?
Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/
Image credit: http://za.geoview.info/apartheid_museum_entrance,83879989p
Can we ignore the fact that data collection, analysis and
use are political acts and serve declared and hidden
assumptions about the purpose of higher education and
the masters it serves (Apple, 2004, 2007; Grimmelman,
2013; Watters, 2015)?
Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/
Image credit: http://www.citylab.com/housing/2016/06/apartheids-urban-legacy-in-striking-aerial-photographs-south-africa-cities-
architecture-racism/487808/
How do we collect, analyse
and use student data
recognising that their data are
not indicators of their
potential, merit or even
necessarily engagement but
the results of the inter-
generational impact of the
skewed allocation of value
and resources based on race,
gender and culture?
Imagecredit:http://connect.citizen.co.za/wp-content/uploads/sites/25/2015/10/C2.jpg?81cf05
https://c2.staticflickr.com/8/7213/6914441342_605f947885_z.jpg
What do the ethical collection, analysis and use of
student data look like and what purposes do the
data serve in the light of, inter alia
• Systematic and increasing defunding by the state
• Massification of higher education
• A dysfunctional vocational post-school sector
• Underprepared students and staff
• Increasing outsourcing of teaching
• Institutional character and vision
• Protection of Personal Information
Act 4 of 2013
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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
Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/
Image credit: https://pixabay.com/en/puzzle-last-particles-piece-654957/
Mapping the current dilemma of
considering the ethical implications
in/of learning analytics
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
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/
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Image credit: https://pixabay.com/en/lens-camera-photographer-photo-490806/
Another approach is to look at a
deontological and/versus a
teleological approach
Adapted from Prinsloo, P., & Slade, S. (2017, under review). An elephant in the learning analytics
room – the obligation to act. Submission to LAK17, Vancouver, Canada)
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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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A teleological approach
• Considers the potential for harm, the scope of consent and
recourses/appeal in cases of unintended harm are negotiated
and agreed upon
• Focuses moving beyond a rule-based approach to also consider
the potential vulnerabilities of those affected by the
intervention or opportunity
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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/
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Illustrating the need
for considering the
ethical implications
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
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
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
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
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
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
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
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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Slade and Prinsloo (2013) cluster the ethical
issues in three broad, overlapping categories:
1. The location and interpretation of data
2. Informed consent, privacy, and the de-identification of
data
3. The management, classification, and storage of data
Student Identity as Transient Construct
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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 4: Student Success Is a Complex and Multidimensional
Phenomenon
Student success is the result of “mostly non-linear,
multidimensional, interdependent interactions at different phases
in the nexus between student, institution and broader
societal factors” (Prinsloo, 2012).
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Principle 5: Transparency
Students have a right to know what data are collected, by
whom, when, for what purposes, how they can verify the
data, how long the data will be kept and who will have access
to the data for which purposes
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Principle 6: Higher Education Cannot Afford to Not Use Data
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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
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
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
Bibliography and additional reading
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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
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from http://philosophicaldisquisitions.blogspot.com/2014/01/rule-by-algorithm-big-data-and-threat.html
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Diefenbach, T. (2007). The managerialistic ideology of organisational change management. Journal of
Organisational Change Management, 20(1), 126-144.
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Bibliography and additional reading (cont.)
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
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Bibliography and additional reading (cont.)

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Mapping the ethical implications of using student data – A South African contextualised view

  • 1. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/ Mapping the ethical implications of using student data – A South African contextualised view Presentation at an Ethics Symposium as part of the Siyaphumelela Project Kopanong Hotel & Conference Centre, Johannesburg, South Africa Paul Prinsloo University of South Africa (Unisa) @14prinsp
  • 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
  • 3. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/ 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
  • 5. Imagecredit:https://pixabay.com/en/earth-continents-all-space-cosmos-1391673/ 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
  • 6. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/ Image credit: https://pixabay.com/en/frog-butterfly-pond-mirroring-540812/ Some of the debates regarding mapping the ethical issues in the collection, analysis and use of student data focus on identifying and mitigating risk – for students and for the institution
  • 7. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/ Image credit: https://pixabay.com/en/structure-sea-beach-gumyeonghwan-1426309/ While there are also voices that emphasise our duty to save the drowning, referring to higher education’s fiduciary duty to care for those we allowed into the system and/or those who were entrusted to our care
  • 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
  • 9. Imagecredit:https://pixabay.com/en/south-africa-flag-national-flag-1184103/ What does a contextualised, South African perspective on the ethical collection, analysis and use of student data entail?
  • 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
  • 11. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/ Image credit: http://avpixlat.info/2016/05/16/prestigefyllda-praktiktjanster-endast-for-rasifierade/ Can we ignore how data were used during Apartheid to classify humans according to those worthy of humanity and dignity and those who were , somehow, less human, less worthy, and of lesser merit?
  • 12. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/ Image credit: http://za.geoview.info/apartheid_museum_entrance,83879989p Can we ignore the fact that data collection, analysis and use are political acts and serve declared and hidden assumptions about the purpose of higher education and the masters it serves (Apple, 2004, 2007; Grimmelman, 2013; Watters, 2015)?
  • 13. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/ Image credit: http://www.citylab.com/housing/2016/06/apartheids-urban-legacy-in-striking-aerial-photographs-south-africa-cities- architecture-racism/487808/ How do we collect, analyse and use student data recognising that their data are not indicators of their potential, merit or even necessarily engagement but the results of the inter- generational impact of the skewed allocation of value and resources based on race, gender and culture?
  • 14. Imagecredit:http://connect.citizen.co.za/wp-content/uploads/sites/25/2015/10/C2.jpg?81cf05 https://c2.staticflickr.com/8/7213/6914441342_605f947885_z.jpg What do the ethical collection, analysis and use of student data look like and what purposes do the data serve in the light of, inter alia • Systematic and increasing defunding by the state • Massification of higher education • A dysfunctional vocational post-school sector • Underprepared students and staff • Increasing outsourcing of teaching • Institutional character and vision • Protection of Personal Information Act 4 of 2013
  • 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
  • 19. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/ 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/
  • 20. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/ Image credit: https://pixabay.com/en/lens-camera-photographer-photo-490806/ Another approach is to look at a deontological and/versus a teleological approach Adapted from Prinsloo, P., & Slade, S. (2017, under review). An elephant in the learning analytics room – the obligation to act. Submission to LAK17, Vancouver, Canada)
  • 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”
  • 22. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/ Image credit: https://pixabay.com/en/camera-lens-photography-equipment-933148/ A teleological approach • Considers the potential for harm, the scope of consent and recourses/appeal in cases of unintended harm are negotiated and agreed upon • Focuses moving beyond a rule-based approach to also consider the potential vulnerabilities of those affected by the intervention or opportunity
  • 23. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/ 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
  • 33. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/ Slade and Prinsloo (2013) cluster the ethical issues in three broad, overlapping categories: 1. The location and interpretation of data 2. Informed consent, privacy, and the de-identification of data 3. The management, classification, and storage of data Student Identity as Transient Construct
  • 34. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/ 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)
  • 35. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/ 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)
  • 36. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/ 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).
  • 37. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/ Principle 4: Student Success Is a Complex and Multidimensional Phenomenon Student success is the result of “mostly non-linear, multidimensional, interdependent interactions at different phases in the nexus between student, institution and broader societal factors” (Prinsloo, 2012).
  • 38. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/ Principle 5: Transparency Students have a right to know what data are collected, by whom, when, for what purposes, how they can verify the data, how long the data will be kept and who will have access to the data for which purposes
  • 40. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/ • 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)
  • 41. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/ • 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.)
  • 42. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/ 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?
  • 43. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/ 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.)