The ethics of (not) knowing our studentsPaul Prinsloo ODL Research ProfessorPresentation @ the Ethics RoundtableUniversity of South Africa (Unisa) 3 September 2015

Acknowledgement• I don’t own the copyright of any of the images

used and hereby acknowledge their original copyright and licensing regimes. All the images used in this presentation have been sourced from Google and were labeled for non-commercial reuse

• This work (excluding the licencing regimes of the images from Google) is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

• I don’t have the answers • I think we need to problematise ethics in the context of

knowing, not knowing and the (im)possibility of un-knowing• There are many possible approaches to and lenses on the

ethics of (not)knowing and I approach the ethics of (not)knowing from a social critical perspective in the broader context of surveillance studies

• This presentation further develops ideas flowing from, inter alia, my collaborative research with Dr Sharon Slade, Open University, United Kingdom

Disclaimer

Do we know our students?

What are the challenges of planning for an unknown student population?

What do we need to do to address the

problem?

A counter question: What does “knowing” look like in the context of a mega distance education institution?

Image credit: https://commons.wikimedia.org/wiki/File:BinaryData50.png

Some more counter-questions:

What responsibility comes with knowing our students? [We cannot un-know knowing…]

To know our students does not necessarily imply understanding …

Even if we knew and understood our students, do we have the will and the resources to do something about what we (think we) know?

Therefore – tread carefully…

Image credit: https://www.flickr.com/photos/timrich26/3308513067/

OVERVIEW OF THE PRESENTATION

• What we know, who knows what, and what we do about what we (think we) know…

• Responding to what we don’t know, if only we knew…

• The responsibility (and ethics) arising from knowing more…

• Towards a fiduciary duty of care…

So what do we know about our students?• Demographic details – provided on

application/registration• Registration data – qualification, number of courses• Historical data of previously registered students• Learning data – assignments (not) submitted,

learning histories – asynchronous, synchronous and (increasingly) digital

• Contact/correspondence with various actors in the institution

• Increasingly personal information

Who knows these things of our students?

• The ‘system’ – disparate databases that do not (necessarily) talk to one another

• Various stakeholders – student advisors, ICT, counsellors, academics, tutors, e-tutors, & researchers, external markers

• Other external stakeholders – employers, law enforcement agencies, data brokers, labor brokers, commercial stakeholders

• Social media platforms and networks

We also know what we don’t know…• Is s/he a “first generation” student or not?• Socio-economic circumstances?• Access, sustainability of access and cost of access

to the Internet?• Do they have access to prescribed learning

resources?• Motivation for registering for the qualification?• Reading/comprehension skills?• Support networks?• Health and parental status, etc.?

What we don’t know and may never know…

What happens in the nexus between students (and their life-worlds) and institutional (operational, academic and social) identities and processes and how do these impact and shape student success and retention as a complex, dynamic, non-linear, unfolding process consisting of mutually constitutive and often incommensurable factors…?

Processes Inter & intra-personaldomains

Modalities:• Attribution• Locus of control• Self-efficacy

Processes Modalities:• Attribution• Locus of control• Self-efficacy

Domains Academic Operational Social

TRANSFORMED INSTITUTIONAL IDENTITY & ATTRIBUTES

THE STUDENT AS AGENTIDENTITY, ATTRIBUTES, HABITUS

Success

THE INSTITUTION AS AGENTIDENTITY, ATTRIBUTES, HABITUS

SHAPING CONDITIONS: (predictable as well as uncertain)

SHAPING CONDITIONS: (predictable as well as uncertain)

Choice, Admission

Learning activities

Coursesuccess

Gradua-tion

THE STUDENT WALK Multiple, mutually constitutive interactions between student,

institution & networks

FIT

FIT

FIT

FIT

Employ-ment/

citizenship

TRANSFORMED STUDENT IDENTITY & ATTRIBUTES

FIT

FIT

FIT

FIT

FIT

FIT

FIT

FIT

Retention/Progression/Positive experience

(From Subotzky & Prinsloo, 2011)

Who acts (if we do) on what we (think we) know?

• Faculty – often, due to workloads and student: staff ratios in a generalised, one-size-fits-all way

• E-tutors• Administrators – for everyone (new) contact,

a different administrator, starting over, explaining everything again

• Tutors, counsellors, regional staff

How do we (they) verify & update what we (they) know

• Do students have access to what we know and/or think we know about them?

• How do we verify our assumptions about our students, their learning needs and trajectories?

• How do they verify and provide context to their (digital) profiles?

And… who has access to what we know, & under what conditions?

• We protect students from harm when we approve research but how do we protect students from harm when we act – change pedagogy, assessment, staff allocation?

• How do we govern student databases, for how long do we keep student data, on what conditions do we share student data, with whom?

We are stumbling through a dark room, not knowing the meaning of the noises we hear, reacting in kneejerk fashion, often in uncoordinated ways, our actions based on assumptions, hearsay, well-intended but non-empirical, context-disjointed, fragmented and possibly discipline-inappropriate ways…

Image credit: http://www.elmundodehector.com/wp-content/uploads/2015/04/door-dark.jpg

So, what are the ethical implications?

• The ethics of knowing – not only what we know, but who knows what?

• The ethics of knowing – how do we verify/test what we know? What are the implications if we are wrong?

• The ethics of knowing and not acting• The ethics of not knowing…

(Student) data as Medusa

Higher education is mesmerized and seduced by the potential of the collection, analysis and use of student data. If only we know more…

Image credit: http://en.wikipedia.org/wiki/Medusa

We therefore need to critically consider the ethical implications of …

• Knowing• Not knowing• Knowing more

The solution is not necessarily in knowing more, but ensuring that once we know, we

respond in ethical, caring, discipline and context-appropriate ways

The Paperholder – “le serre papiers” (1749)

The technology will allow the sovereign “…to know every inch of the city as well as his own house, he will know more about ordinary citizens than their own neighbors and the people who see them everyday (…) in their mass, copies of these certificates will provide him with an absolute faithful image of the city” (Chamayou, n.d)

• 1749 Jacques Francois Gaullauté proposed “le serre-papiers” – The Paperholder – to King Louis the 15th

• One of the first attempts to articulate a new technology of power – one based on traces and archives (Chamayou, nd)

• The stored documents comprised individual reports on each and every citizen of Paris

Image source: https://www.mpiwg-berlin.mpg.de/en/news/features/feature14 Copyright could not be established

The great Ivy League photo scandal 1940-1970

“… a person’s body, measured and analysed, could tell much about intelligence, moral worth, and probably future achievement… The data accumulated… will eventually lead on to proposals to ‘control and limit the production of inferior and useless organisms’”

(Rosenbaum, 1995) Image credit: http://iconicphotos.wordpress.com/2010/07/29/the-great-ivy-league-photo-scandal/

So how do we understand and

critically engage with the ethics

surrounding the increasing

surveillance of students in higher

education?

Image credit: http://graffitiwatcher.deviantart.com/art/Big-Brother-is-Watching-173890591

Understanding the collection, analysis and use of student data in the contexts of

• Broader trends in higher education

• From surveillance to sousveillance

• The discourses in data and increasingly Big Data

So what do we need to consider when thinking about what we (don’t) know about our students… (1)

1. Changes in funding regimes – funding follows performance rather than preceding it – evidence-based policy versus research led…

2. Increasing concerns regarding student retention and dropout

3. Ranking systems and the internationalization of higher education

So what do we need to consider when thinking about what we (don’t) know about our students… (2)

4. Higher education as business 5. The algorithmic turn and the quantification

fetish in higher education6. The increasing digitization of learning and

teaching – and our beliefs about the ‘evidence’7. The gospel of technosolutionism in higher

education8. The hype, promise and dangers of (Big) data

The ethics of the collection, analysis and use of student data in the context of the change from surveillance to

sousveilance

Image credit: http://commons.wikimedia.org/wiki/File:SurSousVeillanceByStephanieMannAge6.png

Jennifer Ringely – 1996-2003 – webcam Source: http://onedio.com/haber/tum-zamanlarin-en-etkili-ve-onemli-internet-videolari-36465

If I did not share it on Facebook, did it really happen?

We share more than ever before, we are watched more than ever before and we watch each other more than ever before…

Three sources of dataDirected

A digital form of surveillance wherein the “gaze of the technology is focused on a person or place by a human operator”

Volunteered“gifted by users and include interactions across social media and the crowdsourcing of data wherein users generate data” (emphasis added)

(Kitchen, 2013, pp. 262—263)

AutomatedGenerated as “an inherent, automatic function of the device or system and include traces …”

The Trinity of Big Data results in an “elaborate lattice of information networking” (Solove, 2004, p. 3) where consent and protection of privacy are and remain fragile (Prinsloo & Slade, 2015)

Image credit: http://commons.wikimedia.org/wiki/File:Red_sandstone_Lattice_piercework,_Qutb_Minar_complex.jpg

• The claim that Big Data is equavalent to “allness” (Lagoze, 2014) – n=all – providing a complete view of reality

• Big data “lessen our desire for exactitude” (Mayer-Schönberger & Cukier, 2013 in Lagoze, 2014)

• It is no longer necessary to investigate the why things happen… More important is to note what is happening – data speaks for itself…

Critical questions for (big) student data (1)

1. Big data changes the definition of knowledge – “Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity. With enough data, the numbers speak for themselves” (Anderson, 2008, in boyd & Crawford, 2012, p. 666)

2. Claims to objectivity and accuracy are misleading – “working with Big Data is still subjective, and what it quantifies does not necessarily have a closer claim on objective truth” (Boyd & Crawford, 2012, p. 667). Big Data “enables the practice of apophenia: seeing patterns where none actually exist, simply because enormous quantities of data can offer connections that radiate in all directions” (ibid., p. 668)

3. Bigger data are not (necessarily) better data

4. Taken out of context, big and more data loses its meaning – leading to context collapse & lack of contextual integrity

5. Just because it is accessible does not make it ethical – the difference in ethical review procedures and overview between research and ‘institutional research’

Critical questions for (big) student data (2)

Exploring the ethics of knowing and not knowing through the seven dimensions

of surveillance (Knox 2010)1. Automation2. Visibility3. Directionality4. Assemblage5. Temporality6. Sorting7. Structuring

AutomationKey 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

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 self-to-surveillant knowledge?

Subject knows everything the surveillant knows

Subject does not know anything that the surveillant knows

DirectionalityKey questions Dimensional intensity

What is the relative power of surveillant to subject?

Subjects hold all the power

Surveillant holds all the power

Who has access to monitoring/recording/ broadcasting functions?

Subjects Surveillant

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

TemporalityKey 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

SortingKey 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

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

Do students know/have the right to know…

• what data we harvest from them• about the assumptions that guide our actions

and algorithms• when we collect data & for what purposes• who will have access to the data (now & later)• how long we will keep the data & for what

purpose & in what format• how will we verify the data & • do they have access to confirm/enrich their

digital profiles…?Adapted from Prinsloo, P., & Slade, S. (2015). Student privacy self-management: implications for learning analytics. Presentation at LAK15, Poughkeepsie, NY, 16 March 2015 http://www.slideshare.net/prinsp/lak15-workshop-vulnerability-final

Do they know? Do they have the right to know?

Can they opt out and what are

the implications if they do/don’t?

Adapted from Prinsloo, P., & Slade, S. (2015). Student privacy self-management: implications for learning analytics. Presentation at LAK15, Poughkeepsie, NY, 16

March 2015 http://www.slideshare.net/prinsp/lak15-workshop-vulnerability-final

What are the implications for the collection, analysis and use of student (digital) data? 1. The duty of reciprocal care

• Make TOCs as accessible and understandable (the latter may mean longer…)

• Make it clear what data is collected, when, for what purpose, for how long it will be kept and who will have access and under what circumstances

• Students as stakeholders – current, correct information• Provide users access to information and data held

about them, to verify and/or question the conclusions drawn, and where necessary, provide context

• Provide access to a neutral ombudsperson

(Prinsloo & Slade, 2015)

What are the implications …? (2)2. The contextual integrity of privacy and data – ensure the

contextual integrity and lifespan of personal data. Context matters…

3. Student agency and privacy self-management• The fiduciary duty of higher education implies a social

contract of goodwill and ‘do no harm’• The asymmetrical power relationship between institution and

students necessitates transparency, accountability, access and input/collaboration

• Empower students – digital citizenship/care• The costs and benefits of sharing data with the institution

should be clear• Higher education should not accept a non-response as equal

to opting in… (Prinsloo & Slade, 2015)

What are the implications …? (3)4. Future direction and reflection

• Rethink consent and employ nudges – move away from thinking just in terms of a binary of opting in or out – but provide a range of choices in specific contexts or needs

• Develop partial privacy self-management – based on context/need/value

• Adjust privacy’s timing and focus - the downstream use of data, the importance of contextual integrity, the lifespan of data

• Moving toward substance over neutrality – blocking troublesome and immoral practices, but also soft, negotiated spaces of reciprocal care

(Prinsloo & Slade, 2015)

(In)conclusions

The gathering, analysis and use of student data act as a structuring device. It is not neutral. It is informed by current beliefs about what counts as knowledge and learning, colored by assumptions about gender/race/class/capital/literacy and in service of and perpetuating existing or new power relations.

Welcome to a brave new world…

THANK YOUPaul Prinsloo (Prof)Research Professor in Open Distance Learning (ODL)College of Economic and Management Sciences, Office number 3-15, Club 1, Hazelwood, P O Box 392Unisa, 0003, Republic of South Africa

T: +27 (0) 12 433 4719 (office)T: +27 (0) 82 3954 113 (mobile)prinsp@unisa.ac.za Skype: paul.prinsloo59

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

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