5 Decolonising the Collection, Analyses and Use of Student Data: A Tentative Exploration/Proposal

Paul Prinsloo

Originally published on November 14, 2016
Old map of Cape Colony

Voices from the Global South* (*I know the term is contentious) increasingly demand to not only be recognised in the extremely uneven and skewed terrain of knowledge production and dissemination, but to actively take part and contest and reshape knowledge claims. I would like to use this blog to tentatively interrogate the potential of a decolonising lens on the collection, analyses and use of student data.

Disclaimer 1: I am intensely aware of the impact of my race and gender in thinking about student data through a decolonising lens. My race, gender and the fact that I write this blog in English should make me uncomfortable and I am. Whether my inherent complicity in notions of white superiority precludes me in taking part in the debate is for you, as reader, to decide. I constantly grapple with the intersectionalities of my gender, race and settler identity as an African. In the field of learning analytics, as the measurement, collection, analysis and use of student data, this blog is a fundamentally and intentionally incomplete attempt to map a decolonising lens on learning analytics.

Disclaimer 2: I acknowledge that notions of post colonialism, decoloniality and coloniality are subjects of serious intellectual pursuits and my grasp of the different overlaps and differences/nuances is, for now, basic. I do accept, however, that coloniality is a reality and that we need to “better understand the nexus of knowledge, power, and being that sustain an endless war on specific bodies, cultures, knowledges, nature and peoples” (Maldonado-Torres, Outline of ten theses on coloniality and decoloniality, 26 October, 2016).

Disclaimer 3: I have a suspicion that the collection, analysis and use of student data overlaps with other discourses and practices of surveillance and digital redlining. As such a decolonising lens on learning analytics overlaps with and needs to take into account these discourses.

A month ago at the annual conference of the South African Association for Institutional Research (SAAIR) researchers from the Southern African region reflected on the role of institutional research in the extremely volatile South African higher education context with its increasing student demands for free higher education (#FeesMustFall) and demands to decolonise curricula. In my presentation I asked “How is it possible that the #FeesMustFall #RhodesMustFall campaigns caught higher education institutions relatively (or totally?) unprepared despite everything that we already know about our students?” (emphasis added); “Is it possible that the writing was on the wall but that we, for whatever reason, decided to ignore the message? Or did not understand the message?” and “What did we not know that would have prepared us for the disruption and destruction we faced over the last 18 months?”

Excursus: A lot of my research focused and still focuses on the ethical and privacy implications in learning analytics and in my preparation for this conference it started to deem on me how our collection, analysis and use of student data are informed by particular ideological and political agendas. This was the beginning of my discomfort and reflection.

I had (and still have) the nagging thought that the our samples, variables and the tools we use to  collect, analyse and use student data in higher education are shaped by the liberal and neoliberal social imaginaries of higher education, of the ‘educated subject.’ If we accept 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, what are the implications for learning analytics? In a follow-up discussion during that conference I became aware of my increasing discomfort with our uncritical if not blasé approach to the collection, analysis and use of student data – without ever questioning the social imaginary informing our choice of variables, the hidden assumptions informing the proxies we use to define ‘effective’ teaching and learning, our emphasis on what our students lack and their deficiencies that prevent them from fitting in and our seeming nonchalant responses to the collateral damage of our analytics and interventions.  During the conference I raised the question: “What does a decolonised and decolonising collection, analysis and use of student data look like?”  Following the question there were a few awkward laughs, one or two responses that implied that I may have lost my senses or don’t I know that data are raw and the collection of data is neutral…

I could not sleep that night as I wrestled with the thought of what a decolonised and decolonising approach to the collection, analysis and use would look like? Already in the said presentation did I think aloud on how our collection and use of student data seem to disregard the entrenched, inter-generational structural inequalities in South African society.  We collect student data as if students start their studies with a clean slate, a tabula rasa, and as if they have not been impacted upon by generations of discrimination and disenfranchisement. We seem to blatantly disregard the fact that most of our students have limited loci of control over where they study, where and how long they can access the Internet, how many prescribed books they can buy. We ignore the epistemic violence integral to much of our curricula. We somehow believe that (more) grit and a growth mind set are the answer to their pathogenic vulnerability. And when you add to this the belief by government that education, on its own can rectify generations of injustice and inequality, then higher education institutions select and collect data that provide us with information on how to move students quicker through the system to increase our return-on-investment.

As my thoughts on what a decolonised/decolonising approach to the collection, analysis and use of student data were taking shape, I was forced to reflect on the question “how does a South African perspective differ from other perspectives in the world? What difference does a postcolonial and post-apartheid context make in how we view the ethical implications of the collection, analysis and use of student data?”

In the South African context we’ve been down the road before during Apartheid where individuals were classified according to some arbitrary classifications of race – white, black, coloured, and Indian. Four categories. Categories based on the curliness of your hair. The shape of your nose. The colour of your skin. There were also many people that somehow did not fit clearly into one category but who were categorised regardless of their ‘ill-fit’.

These classifications had immense consequences for many generations since.

Your category determined where you were allowed to live. What schools you had access to. The age at which you were allowed to start school. The curricula prescribed for the schools. The universities you had access to. The job opportunities. The loans and insurance you had access to.  Your risk profile for defaulting on loans, for getting HIV, for being in possession of drugs, for having friends and family who are in jail.

All based on you fitting into an arbitrary category. Categories that were informed by white superiority. Categories that were needed to ensure that we protect racial purity (WTF). Categories that ensured that education for white kids received much more funding, had access to better resources and better curricula and better job opportunities and better loan schemes and better universities and better lives.  And I was part of this. I was white.

The effects of these classifications have been felt and will be felt for many generations to come. Many of the assumptions and effects of these classifications became institutionalised and formed the basis for a massive set of laws and regulations. While many of these laws and institutionalised forms of racism and discrimination have been changed, it will take generations to address the effects of these structural inequalities and injustices. And yet we continue to use students’ home addresses and school experiences as variables if not determinants for access to higher education? We still charge a one-size-fits-all registration fee? We use variables such as number of logins, and contributions to discussion forums where the language of tuition is a settler language as variables to predict their success. WTF.

In the broader discourses on the collection, analysis and use of data – those who are on the receiving end of discriminatory practices and bias are often unheard, redlined and often excluded from access to the criteria being used to make decisions. The sources used to collect the data, the biases and assumptions of those who collected and analysed the data, the algorithms and decisions made in the analyses of the data – all of these disappear into a ‘black box’ – inaccessible, and not accountable to anyone, not even the user of the analysis at a particular moment in time.

So a contextualised view on the ethical implications on the collection, analysis and use of student data has to account for addressing the structural inequalities of the past, and ensuring that issues of race, gender, home addresses, credit records, criminal records, school completion marks are not used to predict potential and/or to exclude individuals from reaching their potential.

A decolonising lens on the collection, analysis and use of student data cannot ignore how 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?

How dare we collect data like schooling backgrounds, and home addresses, and parental income as if there is not history to these data?

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?

A decolonising lens on the collection, analysis and use of student data therefore has to

  • Acknowledge the lasting, inter-generational effects of colonialism and apartheid
  • Collect, analyse and use student data with the aim of addressing these effects and historical and arising tensions between ensuring quality, sustainability and success
  • Critically engage with the assumptions surrounding data, identity, proxies, consequences and accountability
  • Respond to institutional character, context and vision
  • Consider 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


I acknowledged that this blog is a fundamentally and intentionally incomplete attempt to map a decolonising lens on learning analytics. I acknowledged my complicity and my own discomfort in attempting to take part in this discourse.  How our the purpose of our collection, analysis and use of student data, our tools, our samples, our variables still informed by a colonial social imaginary of control and ‘the educated subject’?

I hope this blog starts a conversation.

I close with a poem by Abhay Xaxa –

I am not your data, nor am I your vote bank,

I am not your project, or any exotic museum object,

I am not the soul waiting to be harvested,

Nor am I the lab where your theories are tested,

I am not your cannon fodder, or the invisible worker,

or your entertainment at India habitat centre,

I am not your field, your crowd, your history,

your help, your guilt, medallions of your victory,

I refuse, reject, resist your labels,

your judgments, documents, definitions,

your models, leaders and patrons,

because they deny me my existence, my vision, my space,

your words, maps, figures, indicators,

they all create illusions and put you on pedestal,

from where you look down upon me,

So I draw my own picture, and invent my own grammar,

I make my own tools to fight my own battle,

For me, my people, my world, and my Adivasi self!

About the Author

Paul Prinsloo is a Research Professor in Open and Distance Learning (ODL) in the Department of Business Management, in the College of Economic and Management Sciences, University of South Africa (Unisa). Since 2015, he is also a Visiting Professor at the Carl von Ossietzky University of Oldenburg, Germany. Paul is an internationally recognised speaker, scholar and researcher and has published numerous articles in the fields of teaching and learning, student success in distance education contexts, learning analytics, and curriculum development. His current research focuses on the collection, analysis and use of student data in learning analytics, graduate supervision and digital identity. He blogs at https://opendistanceteachingandlearning.wordpress.com/ and his Twitter alias is @14prinsp

Other works:

Prinsloo, P. (2020). Of ‘black boxes’ and algorithmic decision-making in (higher) education – A commentary. Big Data & Society. https://doi.org/10.1177/2053951720933994

Prinsloo, P. (2019). A social cartography of student data as performative politics. British Journal of Educational Technology (BJET), 50(6), 2810-2823. doi:10.1111/bjet.12872