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

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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

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

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

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

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

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

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?

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

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?

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)?

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

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

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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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/

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”

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

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

Image credit: https://pixabay.com/en/puzzle-last-particles-piece-654957/
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
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
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
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
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
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
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

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

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)

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)

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).

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).

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

Principle 6: Higher Education Cannot Afford to Not Use Data

• 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)

• 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.)

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?

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

(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

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

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