Collecting, analysing & using student data: Breaking or serving cycles of inequality & injustice?By Paul Prinsloo (University of South Africa)

Presentation at the 14 – 15 May 2015 Launch Conference of the Siyaphumelela Programme

GARDEN COURT OR TAMBO INTERNATIONAL AIRPORT HOTEL

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ACKNOWLEDGEMENTSThis presentation forms part of a collaborative research project with Prof Laura Czerniewicz (University of Cape Town)

I do not own the copyright of any of the images in this presentation. I hereby acknowledge the original copyright and licensing regime of every image and reference used. All the images used in this presentation have been sourced from Google and were labeled for non-commercial reuse.

This work (excluding the images) is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Overview of the presentation• Education, social justice, equality and other myths• Always remember, never forget…• Mapping a conceptual framework for understanding the role of the

collection, analysis and use of student data to break cycles of inequality and injustice

• Diversity, inequality and injustice 101• The collection, analysis and use of students’ digital data • Some pointers for consideration• Problematising student success and retention• What are the implications for the collection, analysis and use of student

(digital) data? • (In)conclusions

Audrey Watters (2014) – Social justice - http://hackeducation.com/2014/12/18/top-ed-tech-trends-2014-justice/

Education, social justice, equality and other myths• “Education is the civil rights issue of our time” (Watters, 2014) versus “things

that the ‘education gospel cannot fix’” (McMillan Cottom, 2014)• Evidence suggests that inequalities and injustice are on the increase and

that, somehow, education does not (on its own) address inter-generational and structural injustice/inequality

• Knowledge, education and science cannot (and will not) “end the conflicts in history. It is an instrument that humans use to achieve their goals, whether winning wars or curing the sick, alleviating poverty or committing genocide” (Gray,2004, p. 70)

• Data, data collection and analysis are neither neutral or an unqualified good (Henman, 2003; Gitelman, 2013; Morozov, 2013)

• Techno-solutionism - To save everything, click here (Morozov, 2013)

Our collection, analyses & use of student data: Will it break or serve cycles of inequality & injustice?

Image credit: http://dallten.deviantart.com/art/Always-remember-never-forget-281408542

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

• 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

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 neighbours 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)

The Paperholder – “le serre papiers” (1749)

http://iconicphotos.wordpress.com/2010/07/29/the-great-ivy-league-photo-scandal/

“… 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)

The great Ivy League photo scandal 1940-

1970

Image credit: http://www.preventgenocide.org/edu/pastgenocides/rwanda/indangamuntu.htm

When data goes wrong…

Image credit: http://www.genocidearchiverwanda.org.rw/index.php/Category:Identity_Documents

Diversity, inequality and injustice

The collection, analysis and use of (student) data

Collecting, analysing and using student data to break

cycles of inequality and

injustice

Broad overview of the presentation

Diversity, nequality and injustice 101

• Diversity ≠ necessarily inequality• Inequality should be understood as a plurality, as inequalities• “How, through what processes, are inequalities actually

produced, increased, or reduced?” (Therborn, 2006, p. xiii)• In knowledge economies/knowledge societies, knowledge and

access to knowledge are crucial resources and can be used to challenge or maintain inequalities

• How is the collection and analysis of data used to break or perpetuate cycles of inequality and injustice?

Vital inequality – based on a moral conception of fundamental human equality

Life expectancy, health expectancy (expected years of life without serious illness), etc.

Existential inequality

Access to opportunitiesThe impact of patriarchy, slavery, caste, class, racism, & sexism on social mobility/opportunities for a dignified life

Resource inequality Differentiated access to resources and their ability to act. Important to note that networks not only include but also exclude

(Therborn, 2006)

Rethinking the mechanisms of inequality

• “…what is called ‘achievement’ is in fact largely dependent on systemic game construction and reward structuration” (Therborn, 2006, p. 11)

• The ideological blind spot of achievement – “it is blind to everything but the achieving actor, telling us nothing about her relations to others, or about the contexts of opportunities and rewards (Therborn, 2006, p. 12)

• “…‘equality of opportunity’ is no more than a fleeting moment in the overall process of inequality” (Therborn, 2006, p. 11)

Image credit: http://commons.wikimedia.org/wiki/File:Gears.JPG

Image credit: http://commons.wikimedia.org/wiki/File:Gears.JPG

• Distantiation: what is regarded as capital, by whom, what are the criteria of success, what are the rewards & penalities

• Hierarchisation: the structuration of privilege &power, membership

• Exclusion: barring the advance or access to resources – who is worthy and who is not

• Exploitation: groups of superiors and inferiors, abuse

These four mechanisms are cumulative

Four mechanisms of inequality (Therborn, 2006)

The reality of cycles of inequality and injustice in South African higher education• Despite substantial government funding incentives,

numerous policy initiatives and well-intentioned institutional efforts, retention and success rates are notoriously poor

• Higher education institutions are as ill-prepared for underprepared students as vice versa…

• The legacy of colonialism and apartheid, inter-generational cycles of injustice, poverty and inequality

• The revolving door and equal opportunities(Subotzky & Prinsloo, 2011)

Image credit: http://i.imgur.com/r99mO5b.jpg

Moving from equality to justice

How will the collection, analyses, and use of student data assist us to move from

equality to justice?

Image credit: http://commons.wikimedia.org/wiki/File:DARPA_Big_Data.jpg

The collection, analysis and use of students’ digital data in the context of…• Claims that Big Data in higher education will change everything &

that student data are “the new black” (Booth, 2012) & “the new oil” (Watters, 2013)

• Ever-increasing concerns about surveillance, and new forms of “societies of control” (Deleuze, 1992)

• The “algorithmic turn” and the “alogorithm as institution” (Napoli, 2013)

• A possible “gnoseological turning point” where our belief about what constitutes knowledge is changing and where individuals are reduced to classes and numbers (Totaro & Ninno, 2014)

• Claims that “Privacy is dead. Get over it” (Rambam, 2008)

(Big) data is…

…not an unqualified good (Boyd and Crawford, 2011) and “raw data is an oxymoron” (Gitelman, 2013)(Also see Kitchen, 2013)

Technology and specifically the use of data have been and will always be ideological (Henman, 2004; Selwyn, 2014) and embedded in relations of power (Apple, 2010; Bauman, 2012)

Points of departure (1)

If we accept that

“… ‘educational technology’ needs to be understood as a knot of social, political, economic and cultural agendas that are riddled with complications, contradictions and conflicts”

(Selwyn, 2014, p. 6)

Points of departure (2)

…what are the implications for the collection, analysis and use of student

data?

Points of departure (3)• Students’ digital lives are but a minute part of a bigger whole –

but our collection and analysis pretend as if this minute part represents the whole (n≠ the whole)

• We create smoke and claim we see a fire – so what does the number of clicks mean? 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” (Boyd & Crawford, 2012,p. 668)

• We collect and analyse what we think matters – how sure are we that it does?

• We seldom wonder what if our algorithms are wrong, and what are the long-term implications for students?

(See Slade & Prinsloo, 2013; Prinsloo & Slade, 2015)

Using student data and student vulnerability: between the devil and the deep blue sea?

Students (some more vulnerable

than others)

Generation, harvesting and

analysis of data

Our assumptions, selection of data and algorithms

may be ill-defined

Turning ‘pathogenic’ – “a response intended to

ameliorate vulnerability has the paradoxical effect of exacerbating existing

vulnerabilities or generating new ones”

(Mackenzie et al, 2014, p. 9)

Adapted from Prinsloo, P., & Slade, S. (2015). Student vulnerability, agency and learning analytics: an exploration. Presentation at LAK15, Poughkkeepsie, NY, 16 March 2015

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

Problematising student success and retention

Image credit: http://commons.wikimedia.org/wiki/File:Revolving_door-base.jpg

If…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, 2009)

… what data should we collect, when, why and what then?

What are the implications for the collection, analysis and use of student (digital) data? 1. Students and institutions are situated agents

• In the context of he asymmetrical power relationship between institution and students the social contract and duty of fiduciary care matters

• How much choice should students be provided regarding alternative curricula, assessments and attendance of compulsory support?

• The need for algorithmic accountability, transparency, diagnosis, prognosis and outcomes

• Educational triage as moral practice requires having the best interests of students as rationale

(Prinsloo & Slade, 2014)

What are the implications …? (2)2. The need for non-maleficence – do no harm. Beneficence and non-

maleficence are sides of the same coin.

3. The notion and practice of distributive justice – difficult and complex. We cannot just assess or consider school leaving marks, or examination marks. Context and the historical and inter-generational legacies of inequality and injustice matter. We cannot and should not negate the impact of he “causal power of social structures”

(Prinsloo & Slade, 2014)

(In)conclusions

“Technology is neither good or bad; nor is it neutral… technology’s interaction with social ecology is such that technical developments frequently have environmental, social, and human consequences that go far beyond the immediate purposes of the technical devices and practices themselves”

(Melvin Kranzberg, 1986, p. 545)

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.com

Twitter profile: @14prinsp

ReferencesApple, M.W. (Ed.). (2010). Global crises, social justice, and education. New York, NY: Routledge. Bauman, Z. (2012). On education. conversations with Riccardo Mazzeo. Cambridge, UK: Polity. Booth, M. (2012, July 18). Learning analytics: the new black. EDUCAUSEreview, [online]. Retrieved from

http://www.educause.edu/ero/article/learning-analytics-new-black Boyd, D., & Crawford, K. (2013). Six provocations for Big Data. Retrieved from http://

papers.ssrn.com/sol3/papers.cfm?abstract_id=1926431 Chamayou, G. (n.d.). Every move will be recorded. [Web log post]. Retrieved from https://

www.mpiwg-berlin.mpg.de/en/news/features/feature14 Deleuze. G. (1992). Postscript on the societies of control. October, 59 pp. 3-7. Gitelman, L. (Ed.). (2013). “Raw data” is an oxymoron. London, UK: MIT Press.Gray, J. (2004). Heresies. Against progress and other illusions. London, UK: Granta Books.Kitchen, R. (2013). Big data and human geography: opportunities, challenges and risks. Dialogues in

Human Geography, 3, 262-267. SOI: 10.1177/2043820613513388 Kranzberg, M. (1986) Technology and history: Kranzberg's laws’. Technology and Culture, 27(3), 544—560.McMillan-Cottom, T. (2014). Reparations: What the education gospel cannot FixMorozov, 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

References (cont.)Prinsloo, P. (2009). Modelling throughput at Unisa: The key to the successful implementation of ODL. Retrieved

from http://uir.unisa.ac.za/handle/10500/6035 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

Rambam, S. (2008). Privacy is dead. Get over it. Retrieved from https://www.youtube.com/watch?v=Vsxxsrn2Tfs&index=1&list=PL8C71542205AA51E5

Selwyn, N. (2014). Distrusting educational technology. Critical questions for changing times. New York, NY: Routlegde.

Slade, S., & Prinsloo, P. (2013). Learning analytics: ethical issues and dilemmas. American Behavioural Scientist, 57(1) pp. 1509–1528.

Subotzky, G., & Prinsloo, P. (2011). Turning the tide: A socio-critical model and framework for improving student success in open distance learning at the University of South Africa. Distance Education, 32(2), 177-193.

Therborn, G. (ed.).(2006). Inequalities of the world. New theoretical frameworks, multiple empirical approaches. London, UK: Verso Books.

References (cont)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 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