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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 ProjectKopanong Hotel & Conference Centre, Johannesburg, South Africa

Paul PrinslooUniversity 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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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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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

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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?

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/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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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?

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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)?

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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?

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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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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

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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/

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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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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. Automation2. Visibility3. Directionality4. Assemblage5. Temporality6. Sorting7. 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. VisibilityKey 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. DirectionalityKey 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. AssemblageKey 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. StructuringKey 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 data2. Informed consent, privacy, and the de-identification of data3. 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 inthe 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 MultidimensionalPhenomenon

Student success is the result of “mostly non-linear, multidimensional, interdependent interactions at different phases

in the nexus between student, institution and broadersocietal 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

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(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

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/Thank you. Ke a leboga. Baie dankiePaul PrinslooResearch Professor in Open Distance Learning (ODL)College of Economic and Management Sciences, Office number 3-15, Club 1, Hazelwood, P O Box 392Unisa, 0003, Republic of South AfricaT: +27 (0) 12 433 4719 (office)prinsp@unisa.ac.za

Personal blog: http://opendistanceteachingandlearning.wordpress.comTwitter profile: @14prinsp

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/

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