In Conversation with Paul Prinsloo, Research Professor at University of South Africa (UNISA)
By Dr David Lim

Paul Prinsloo is a Research Professor in Open and Distance Learning (ODL) in the Department of Business Management, College of Economic and Management Sciences, University of South Africa (Unisa). He is a Visiting Professor at the National Open University of Nigeria (NOUN), a Research Associate for Contact North/Contact Nord (Canada), a member of the Center for Open Education Research at the Carl von Ossietzky University of Oldenburg, Germany, a Fellow of the European Distance and E-Learning Network (EDEN), a member of the Executive Committee for the Society of Learning Analytics Research (SoLAR), and serves on several editorial boards. Paul is an internationally recognised scholar and has published numerous articles in the fields of teaching and learning, student success in distributed learning contexts, the ethical collection, analysis, and use of student data in learning analytics (LA), and digital identities.

Departing from Cultural Exclusivity and Meeting Distance Education on the Way

Dr David Lim [DL]: Thank you, Prof Paul Prinsloo, for agreeing to engage in a conversation with inspired. I first encountered your work in the form of a journal article you published in 2017 intriguingly titled “Fleeing from Frankenstein’s Monster and Meeting Kafka On the Way: Algorithmic Decision-Making in Higher Education”. What immediately caught my eye then were the intertexts found in the title. Mainstream education studies, being conventionally staid, almost never make literary references to the likes of the monster created by Frankenstein in Mary Shelley’s classic novel, or the surreal, illogical, and nightmarish world of Franz Kafka’s fiction. For me, this first encounter with your work extends beyond itself, opening up as it does to a range of intertwined issues I am hoping we could discuss, including algorithmic decision-making and the ethics of care particularly in relation to their application in the developing world, the notions of the “flesh electric” and postdigital learning analytics (LA) you wrote about recently, critical research as a paradigm confronting resurgent positivism in the broad field of education, and the thin line between technological optimism and technological solutionism in the contexts of open, distance, and digital education (ODDE) and open universities (OUs).

While my own journey into academia was rather unplanned, and in many ways unexpected, my innate curiosity and disciplinary rebelliousness were, most probably, evident from an early age.

But let’s start with the subject of your academic background and intellectual trajectory. You have formal education in the Arts, Art History, Theology, Divinity, Management, Religious Studies, and various aspects of Education, including online and distance education, internationalizing the curriculum, and online learning and teaching. Your academic journey towards educational studies in general and ODDE in particular is simultaneously unique and representative of the multilayered or non-linear journey of some scholars working in OU and ODDE circles. Please could you tell us more about your multidisciplinary academic background and your long service at the 150-year-old University of South Africa (Unisa), how these have shaped your intellectual perspectives on educational matters, and how you came to develop an interest in critical studies of LA as a key research focus?

The potential of learning analytics connected with my own experiences as student advisor, tutor and learning designer, as well as my own research into factors shaping students’ failure or success.

Prof Paul Prinsloo [PP]: Thank you so much, David. On the one hand I wish I had a more traditional journey into academia and into research – journeys we often hear about and journeys that are often portrayed or perceived as ‘preferred’ or less ‘contaminated’ by other disciplinary influences – starting from getting a PhD, moving into a postdoctoral position and then into a tenured position in one particular discipline. While my own journey into academia was rather unplanned, and in many ways unexpected, my innate curiosity and disciplinary rebelliousness were, most probably, evident from an early age.

I grew up in a small mining village during apartheid, and amid, or despite the claimed cultural exclusivity (and lager mentality) embraced by my race and culture, the town’s library provided me with ample space for discovery, curiosity, and transgression. I quickly devoured the books in the Afrikaans (my home language) children’s section before I conquered the English children’s section. Somehow, after discovering the adult fiction section in Afrikaans and English, I quickly grew bored and moved to the non-fiction section which proved to change my life, and my curiosity, possibly forever. Considering the immense censure apparatus of the apartheid regime and dedicated moral police that ensured that young curious minds like mine were not exposed to immoral art and the truth about racial oppression, the library provided a haven into which I could disappear.

My initial academic and professional journey after school (in Christian theology) turned out to have been misguided and a rather naïve choice and, at the age of 35, I started afresh at the University of South Africa (Unisa) in 1995 as a student advisor and tutor at a regional office up in the northern province of South Africa. It was a tumultuous time in my life of coming to terms with loss and new beginnings. Somehow my own journey coincided with major changes in South Africa after the first democratic elections, as the country faced the horrors of apartheid through the testimonies heard by the Truth and Reconciliation Commission. It was a time of new beginnings, irrevocably shaped by the past. As student advisor and tutor, I had the opportunity to come face-to-face with the reality faced by distance education students who tried to find a balance between their studies and their personal and professional lives. I became intrigued by the many factors impacting on students’ chances of success and how student failure, dropout or success was a puzzle for which I was looking for the missing pieces.

My academic curiosity to understand student failure, combined with my daily exposure to students’ journeys, led me to document student experiences and read whatever I could lay my hands on that could assist in my understanding of student success and/or failure.

These early years, however, were not only shaped by my innate curiosity, and personal interests (e.g., in art and art history), but also by a need to formally find a new career in post-apartheid South Africa. I did a postgraduate diploma in management and realised that a formal career as manager in the early years of post-apartheid South Africa was most probably not the most appropriate choice for a white male in his late 30s. I started to pursue a law degree. Halfway through my law degree I applied for a position of learning designer at Unisa, and was successful and moved to Pretoria.

Without boring you with the details, during my time as a learning designer I was able to operationalise many of my insights and readings into the factors impacting on student success and failure. I published my first scholarly article on rural students’ learning journeys, and was encouraged to complete my PhD (in Religious Studies) which resulted in my dropping out from pursuing a law degree. I realised that a new identity (on both personal and scholarly levels) was slowly but surely taking shape.

I don’t think it is very helpful to ask whether learning analytics ‘works’ or not, but rather under what conditions may learning analytics impact on teaching and assessment strategies, institutional efficiencies, and students’ approaches to learning.

In my role as a learning designer, I was constantly faced with the need to make sense of students’ learning journeys in new disciplinary contexts. I read and explored literature as if my life was dependent on it and I started to co-publish with academics on improving the success of their students’ learning. Gradually a new identity as educational researcher started to take shape, an identity and role that somehow provided me with a (new) sense of purpose.

In 2007 I had the opportunity to visit the Open University of the United Kingdom, and meet Dr Sharon Slade and her colleague, Fenella Galpin, both from the Open University Business School. Not only did I find the Open University to be an amazingly stimulating environment in the context of research into student success, my collaboration with Sharon and Fenella provided me with a professional and research focus I may have been looking for.

Inspired by the unsolved riddle of student success, and encouraged by an amazing scholar and friend, the late Professor George Subotzky, we developed a first socio-critical model on student success from the context of the Global South (2011). In this model we proposed that student failure or success needed to be understood as emerging from the interplay of multiple, often mutually constitutive factors found in the intersections of students’ personal circumstances, prior learning experiences, loci of control and self-efficacy; disciplinary and institutional cultures, efficiencies and loci of control; and macro-societal factors that are often outside of the control of both institutions and students but that shape students’ learning in often profound ways.

Also, in 2011 the field of LA as research focus and practice emerged, and in 2012 Sharon invited me to co-author an article with her on the ethical implications of LA. The potential of LA connected with my own experiences as student advisor, tutor and learning designer, as well as my own research into factors shaping students’ failure or success. Sharon and I grappled with mapping the privacy and ethical implications of the collection, analysis, and use of student data. While other researchers in the LA community recognised that there were ethical and privacy concerns, no one, at that stage, had attempted to map the tensions between the moral and fiduciary duty of educational institutions to provide effective and appropriate learning experiences and support for students, and the possible encroachment on students’ privacy by collecting and analysing their data.

Since this first foray into the ethical and privacy implications of LA, the field of LA and more recently, algorithmic decision-making systems, have opened many new questions that need to be reflected upon and researched.

There were many definitive moments in my life as human and researcher but my collaboration with George Subotzky and Sharon Slade changed my academic life and research trajectory dramatically.

But interestingly, I often still feel like the young boy venturing into a row of shelves filled with books where I am not supposed to be. And yet I am where I am supposed and want to be.

Learning Analytics at the Intersections of Ed-Tech

DL: LA is widely understood as “the measurement, collection, analysis, and reporting of data about learners and their contexts, for the purposes of understanding and optimizing learning and the environments in which it occurs”, to borrow the definition cited in George Siemens’ article, “Learning Analytics: The Emergence of a Discipline” (2013). Beyond a tenuous grasp of what LA might be, many, including academics in OU and ODDE circles, would not, arguably, be able to say with any certainty how LA actually works, how advanced LA has or has not become since the field emerged in 2011, and how extensively LA has been applied on the ground in OUs generally and OUs in the developing world specifically, let alone how LA intersects with algorithmic decision-making, artificial intelligence (AI), and personalised learning. A fair number, I’d say, would not even have guessed that, at its most basic, algorithmic decision-making (that draws and acts on data harvested by LA) may be at work when students receive an automated personalised email with a message of support after failing to log onto the course site for a week. Would this be a fair assessment, in your view? Briefly, how would you help our readers obtain a better understanding of these facets of LA at the intersections of digital EdTech?

PP: Oh wow, this is quite a question. Of course, you are right that we have not seen wide-spread and large-scale institutional adoption of LA and the implementation of LA in (higher) education is currently located in institutions in the Global North with a few scattered examples (e.g., South Africa and Brazil) in the Global South. Combined with an uneven adoption, until recently there have also been concerns about the seemingly scant evidence that LA really changes students’ trajectories pertaining to their risk of failing.

If one considers, in following the work of Gert Biesta, that education is an open recursive system with a multitude of intersecting and mutually constitutive factors at play as I mentioned earlier, then one should not be surprised about the seeming lack of evidence of the positive impact of LA on students’ probability of failing. The impact of LA, or as some may claim, the lack of impact, presumes that LA functions in isolation from, among others, institutional (in)efficiencies, administrative systems and exam timetables, the complexity of the lives of our staff and students, and macro-societal factors. I don’t think it is very helpful to ask whether LA ‘works’ or not, but rather under what conditions may LA impact on teaching and assessment strategies, institutional efficiencies, and students’ approaches to learning.

We have to ask to what extent the platformisation and datafication of teaching and learning constitute a brutal colonisation of teaching and learning.

While research at the Open University, as one example, shows that LA does assist in designing fairer and more appropriate pedagogical strategies and assist student progression, there is, however, also research showing that many faculty (tenured and untenured) feel excluded from decisions to implement LA or feel that they just don’t have the capacity to act on insights provided from dashboards.

Without underestimating the intuition and insights most teachers would have regarding which students may be at risk of failing, large classes, especially in the contexts of ODDE and OU, do make it more difficult, if not impossible, for faculty and support staff to notice whether students are falling behind or have not engaged recently with either their peers or with the learning resources. In large-scale ODDE and OUs, LA, and specifically, the appropriate and ethical design and use of algorithmic decision-making systems do offer an opportunity to deliver responsive and appropriate student support – whether administrative, cognitive, or psychosocial. While there may be examples of, and calls for, taking humans out of the loop in the design of curricula, pedagogical strategies, assessment, and evaluation of learning, I think a much more fruitful and ethical approach is to consider how to design and work with algorithmic decision-making systems on a spectrum where such systems are allowed to act unsupervised to where humans have the final responsibility to act, or to not use these algorithmic decision-making systems at all.

Amid the hype surrounding the deployment of algorithmic decision-making systems in education, it is simply neither true nor helpful to think that algorithmic decision-making systems are ‘objective’ and ‘error-free’, while human decision-making is biased, slower, and often messy; or, for that matter, that algorithmic decision-making systems cannot assist in ethical and appropriate ways.

So, coming back to your question: I do think that, often, when LA systems are implemented, students and staff are excluded from discussions about the purpose and deployment of LA, the use of algorithmic decision-making systems and how these systems will impact on faculty workloads, scope of responsibility and, of course, student privacy and data sovereignty. LA cannot, on its own, realise its potential to contribute to better teaching and learning if the responsibility of responding to the insights arising from LA is not integrated into the whole system.

The Simultaneous Pursuit of Algorithmic Decision-Making and an Ethics of Care

DL: In “Fleeing from Frankenstein’s Monster and Meeting Kafka On the Way”, you make a case for “student-centred learning analytics” (emphasis added), noting that the questions that ought to be asked in pursuit of it include:

What data do students currently have access to about their learning and about our choices pertaining to their learning? What data do students not currently have, but we have, that will help them to plan their time and resources in order to maximize their chances of success? What student data that we don’t have, do we need in order to teach better, allocate resources, and support students? Is this data available, under what conditions will we be able to access it, how will we govern its – storage, combination with other sources of data, who will have access to it and under what conditions?

At the same time, you are acutely aware of “the technocratic and neoliberal logic in which institutional profits and institutional sustainability is often conflated with rhetoric of how algorithmic decision-making will help institutions to ‘personalise’ learning and student support”. This, to me, reads like a variant of the Frankenstein/Kafka dilemma that is arguably worsened if the institution is so depleted of human and financial resources in setting up what is at best a semi-reliable small-scale working system that it hardly has any resource left to consult and involve students to create a student-centred system – this being a case of simultaneity in which one cannot not pursue algorithmic decision-making (for profits and sustainability under the guise of pedagogical advancement) and cannot afford an ethics of care. OUs in the developing world seem especially vulnerable in this respect. If they cannot afford to simultaneously pursue algorithmic decision-making and an ethics of care that is genuine and not merely gestural, should they opt out of the former altogether to avoid harm?

PP: You are right, it is almost impossible to disregard the technocratic and neoliberal logic informing much of the adoption of technology in (higher) education – in service of whatever concept or phrase is en vogue at any present moment – whether it is effectiveness and evidence-based management (see, for example the work of Biesta), or data-driven teaching. There is ample evidence of the platformisation of education where we increasingly teach on platforms neither designed nor owned by us, and the commercialisation and colonisation of students’ data lives as data frontier to be mined and conquered. Historical colonialisation provides ample evidence of how colonisation plundered and exported resources and humans, changed the values and languages of those in the colonies and how the colonial administrative, legal, and educational systems normalised and naturalised colonisation. We have to ask to what extent the platformisation and datafication of teaching and learning constitute a brutal colonisation of teaching and learning by those whose main interest may be to profit from learning platforms-as-service and learning platforms as the commodification and commercialisation of teaching and learning.

Student data is therefore, in its essence, digital-material, electric/digital and flesh.

When data analysis and platform providers promise institutions speedy, accessible, revealing, panoramic, prophetic and smart solutions (see David Beer’s The Data Gaze, published in 2019), the precondition for realising this promise is the successful and irreversible colonisation of the digital lifeblood, data-flows and data infrastructure(s) of the institution. And making it look normal and natural.

Within this Kafkaesque maze from which there is increasingly no escape, and very little resistance is possible, we have to continue to find ways to live, teach, and learn.

I hope that I do not sound cynical, but I respond to this Frankenstein/Kafka dilemma in two ways: Firstly, I recognise how the layout, design and inherent features of the ‘room’ in which I teach, in the ‘building’ not owned by the institution, shape my role as teacher, the type of teaching and learning it values and enables, and the type of teaching and learning it renders impossible. Even in the pre-digital era, the designs and features of the rooms and lecture halls in which we taught and learned shaped the roles and features of teaching and learning. The design and features of the spaces we taught and learned in, made some forms of teaching and learning possible, and excluded alternative forms of teaching and learning. As we are now teaching in ‘rooms’ on a platform neither designed nor owned by our institution, we need to understand not only how it shapes our teaching and students’ learning, but also how it changes the way we speak about teaching and learning. Knowing how the ‘room’ shapes my teaching and students’ learning is the start of my own transgression – of finding ways to break out of windows, of creating safe spaces, of pushing back where it concerns the language used when I speak about students, their learning, and my teaching. Possibly, in these circumstances, an ethics of care resembles an ethics of resistance, disobedience, and activism.

Secondly, our students have a right to know how the ‘rooms’ shape their learning, which algorithmic decision-making systems collect what type of data and how these systems are used to categorise them according to their potential or lack of, and, in future, how these systems will personalise their learning – acting as filter bubbles based on a non-human assessment of their potential, based on categories informed by models trained on previous years’ students. Students have a right to not only know what data are collected and by whom, but also who (human and non-human) has access to their data, under what conditions, and to be used for what purposes.

An ethics of care also has to account for not using students’ data. Much of the research – both empirical and theoretical – on the ethical implications of the collection, analysis and use of student data do not necessarily consider the ethical implications of not using student data to support and inform their learning. An ethics of care has to encompass both the implications of using and not using.

Postdigital Learning Analytics and the Flesh Electric

DL: Your concern for an ethics of care is picked up again and developed further in your recently published book chapter titled “Postdigital Student Bodies: Mapping the Flesh Electric” (2023). The chapter reiterates how, although educational institutions have always collected, analysed, and used student data, LA – being a product of the digitalization and datafication of higher education – is different due to the “greater volumes, varieties, velocity of the flow and processing of data, and granularity of students’ digital data.” One problem with LA is that it uses only digital and digitized data it has access to in order to explain and predict student experiences. LA does not factor in the wealth of data it has no access to but treats what it has as the basis to narrate the totality of the student’s learning journey. That totality, then, becomes a kind of an “iron cage” that reduces students to what they “did not do, lack, or don’t have, or don’t have enough of, or don’t do enough of.” As you underscore in the chapter, the problem with “deficit understandings of student identity and learning” is that, when combined with or used to inform the design of LA, they not only provide “legitimacy of such deficit understandings, but potentially become self-fulfilling prophecies.” To circumvent this, you advocate what you term “postdigital learning analytics”, which you premise on the concept of the “flesh electric”. In layperson’s terms, please could you explain what these two terms mean and how they build upon and expand the notion of student-centred LA proposed in your earlier work?

PP: Another great question. Where shall I start? From the earliest forms of education, teachers (or whoever assumed the role) made sense of whatever data presented to them to make sense of students’ progress and understanding. Facial expressions, class attendance, completion of tasks, participation in learning activities, and final summative assessment counted as examples of data. Based on a range of data points, teachers made normative judgments about students’ competencies and understandings. As I indicate in the said chapter, a lot has changed over the years but the basic premise stands – making sense of data (often as proxy) for students’ progress and learning. As teaching and learning moved online and became datafied, institutions had access to not only more (volume) data, but also a greater velocity, variety, and granularity of digital data. And in online and ODDE environments, digital data was often the only data institutions, teachers, and student support staff had.

We need to invite theoretical discussions not only on new advances in technology, but also discussions on enriching or critiquing existing theory’s ability to explain current educational issues and/or technological advances.

Resulting from this abundance of data, four misconceptions became normalised – firstly, the belief that the digital data we had access to presents a truthful, holistic, and objective version of students’ learning journeys, and secondly, that we know what these data points meant. In our sensemaking of these data points a third and fourth misconception became entrenched. We considered the categories we operationalised – e.g., gender, zip codes, home language, nationality, prior learning experiences, etc., as separate, stable, and un-linked to each other.

This results not only in a flattening of the student experience and a quantified ‘voice-over’, it also perpetuates a particular understanding of student agency or, as we see in the deficit discourses, lack of agency. Educational research, grey literature and possibly even policies are awash with focusing on what students don’t have, forgetting, as I indicated earlier in our conversation, that their capabilities, efficacy, and locus of control are but a part of a bigger and dynamic ecology of intersecting institutional, disciplinary, pedagogical, and macro-societal factors.

So how do we understand the potential and the limitations of students’ data to help us not only to understand their learning journeys in more holistic and ethical ways, but also to see student data as an invitation for a conversation with students on what their data mean?

In “Postdigital Student Bodies: Mapping the Flesh Electric” (2023), I attempt to map a different, possibly alternative understanding of student data by firstly pointing to the fact that it is increasingly impossible to separate the physical from the digital and that the digital data we collect from students are much more than digital for they are sociomaterially entangled in the physical life worlds of students. A student’s address on our registration system may allow us to categorise the neighbourhood according to socioeconomic income criteria, but what is ‘just’ an address and a proxy to us of (possibly) socioeconomic income, is so much more. What the address does not tell us are, for example, details such as with how many other individuals the student shares not only the address, but also his/her room, the number of rooms at the address, the nature of privacy and access to (quiet) dedicated spaces, and so forth. As another example, the digital data point (and/or category) of being ‘single’ or ‘married’ does not speak of the history and entanglements of this data point with the responsibilities (financial, psychosocial, or legal), the trauma or wellness, the fears, or joys. A digital data point is much more than a ‘just’ a data point, but is, indeed, deeply material, historical, economic, social, legal, philosophical, and technological. Our students’ digital data journeys are deeply entangled in the messy, joyful, smelly, clean, fluid, traumatic, ugly, for-sale alleys, neighbourhoods, and suburbs. Student data is therefore, in its essence, digital-material, electric/digital and flesh. When we collect and analyse students’ digital data we should not cut apart the digital from its intensely material entanglements.

In the chapter I proposed that “Using (only) digital and digitized data to explain, and predict student experiences, does not only result in and depend on classifications of vulnerability and risks but also in skewed and incomplete accounts of students’ learning.” We therefore have the responsibility to ask ourselves not only what the data means but what the data does not tell us. We therefore need to “consider datafied as well as non-datafied understandings of student learning as part of a broader educational deeply material ecology consisting of human and increasing, autonomous nonhuman actors.”

Bringing Critical Research to Bear on ODDE

DL: The particular variant of intellectual discourse you engage with and produce in relation to matters of education is aligned with what has been variously described as critical research, immanent critique, and critical studies, and counterposed against positivist-based empirical discourse that takes truth and knowledge as existing independently of the observer. The knowledge-problematizing discourse, with its interventionist agenda, of critical research is something I am at home with, given my background in postfoundationalist literary and cultural studies. In education, it is a thriving discourse that has found home in such cutting-edge journals as Postdigital Science and Education and Learning, Media and Technology. It is also, however, entirely alien to the majority steeped in conventional (read: positivist) education studies constituting the mainstream. And it is virtually absent in mainstream ODDE discourse, despite the fact that many, if not most, practitioners of ODDE, including those based in OUs, were trained in wide-ranging non-education disciplines. Even if the critical approach were to be applied to read ODDE, it is likely to be looked upon by a majority with incomprehension or as a method that is somehow less than legitimate and acceptable, especially given today’s (resurgent) positivism that is considered by many, including the gatekeeping powers that be, as the gold standard of scholarship. This, to me, seems like wasted opportunity, for I see great potential in the critical approach to radicalise ODDE as a discursive field, a mode, a practice, and a community. What are your thoughts on this?

Personally, I suspect that much of the madness and obsession with the quantification of educational research rushes to produce answers and solutions without considering, critically, the questions.

PP: Your concern is shared by many in the educational research community – from the early concerns expressed by Thomas C. Reeves and, more recently, Junhong Xiao and others. The seeming lack of critical orientations in educational research or even LA can most probably be attributed to a range of factors such as, but not limited to, the immense pressures to publish – pressures that do not allow for careful and slow contemplation and deep interrogation of how to understand a particular phenomenon. The recent advances of generative artificial intelligence may most probably worsen the ‘shallowing’ of thinking about and researching education. We can now, with the press of “ENTER”, ask ChatGPT, Bard, or Bing to summarise articles, formulate an argument, provide us with examples, create outlines and, most promising, write the article for us.

Another question to consider is the following: To what extent can one ascribe the lack of a critical orientation to a lack of interest or to disinterest in educational theory? That is open for discussion. From my experience as reviewer and participant in numerous academic conferences, there is, possibly, on the one hand, a general disinterest in educational theory as a point of departure for looking at data, or to develop theory based on the analysis of data; and, on the other hand, a possible laziness to explore different theoretical perspectives on a particular phenomenon in learning. Some educational researchers may believe, like Chris Anderson proposed in 2008 in an article published in Wired magazine, that data speaks for itself, and we don’t need to understand why people do things if we can show the trends of people doing things. In the context of the increasing datafication of teaching and learning, and life in general, what is the role of theory? Currently, the most dominant theory in LA research is self-regulated learning (SRL) and one has to ask, is this the only way or even the most appropriate way to understand student learning?

A refreshing initiative is the recent special issue in the British Journal for Educational Technology on “Advancing theory in the age of artificial intelligence” (2023) edited by Shane Dawson, Srecko Joksimovic, Caitlin Mills, Dragan Gašević and George Siemens. In their editorial, the guest editors state: “Theory provides the guard rails to ensure that principles, values and trusted constructs shape the use of AI in educational settings, ensuring that values, existing research, concerns of multiple stakeholders and ongoing contributions to science remain centre stage.” I think this approach needs to be applauded. We need to invite theoretical discussions not only on new advances in technology, but also discussions on enriching or critiquing existing theory’s ability to explain current educational issues and/or technological advances.

Lastly, I think we need to differentiate between critique and criticism. Not only is there a difference between the two, but critique also requires a slower process of engaging and reflection. While “criticism asserts that something is wrong” and critics “unmask in order to judge”, critique brings an ethical dimension to bear and “aspires less to unmask falsehood than to compel its audience to see matters in a different – but not necessarily truer – light”, to quote Webb Keane in his article “For the Slow Work of Critique in Critical Times” (2020). In the same article, Keane also suggests that critique can be slow and indirect, and often raises more questions than it answers. The aim of critique is “to open up what we can imagine” (original italics).

The distinction between criticism and critique reminds me of the comment by Randall Nichols and Vanessa Allen-Brown in their chapter titled “Critical Theory and Educational Technology” (2004): “Note that critical is not meant to indicate a theory that examines only the negative. Critical theories seek to reveal the contradictions, social inequalities, and dominances; to this extent they can be called negative. However, it might be more accurate to say that because critical theories run contrary to that which oppresses people, the theories usually are positive and hopeful” (italics added).

Personally, I suspect that much of the madness and obsession with the quantification of educational research rushes to produce answers and solutions without considering, critically, the questions. I agree with Neil Selwyn who suggests in his book Distrusting Educational Technology (2014) that we should “deliberately slow down the pace of our discussions in the face of the fast-moving, rapidly changing and often ephemeral nature of the topic.”

In the specific context of ODDE research, there are two additional aspects that I think we need to consider. Firstly, most of the documentation of the history and theoretical development of distance education and the later different forms of ODDE have been told by the Global North, and predominantly, by white male scholars (the latter characteristic of me). Where are the other voices? Where are the other histories of distance education and ODDE from the Global South? We cannot ignore the asymmetries in knowledge production between the Global North and Global South. Secondly, while part of the asymmetry is attributable to the composition of editorial boards and exclusionary and biased practices, the Global South also has to own up to our complicity in not producing original, rigorous critical research but parroting theoretical models and understandings that emerged from and are sustained by the Global North.

Techno-Optimism, Techno-Solutionism, and the Conditions of Possibility for Technology to Provide Hope

DL: Conversations about the place of educational technologies in OU contexts seem ripe for the application of the critical approach, especially at a time when more and more universities, both conventional and open, are caught up in the digitization fever, as is evident, for instance, in the ‘digital transformation’ plans they are generating and implementing at speed, and in the way they are seeking to expand market share by introducing or mainstreaming digitally-mediated forms of teaching and learning and an array of fully online programmes. In your viewpoint, how pervasive is techno-solutionism as an operating ideology in OUs? Do you see techno-solutionism as divided by a thin line from techno-optimism? Lastly, how urgent is it for OU academics of all ranks to counter techno-solutionist lines of thought in their respective institutions?

PP: This is a lovely and critical question and I hope I do justice to the question with my response.

Since Evgeny Morozov coined the term “techno-solutionism” in 2013, it has become a handy phrase to describe much of the current adoption of technology in education when we discard the checks and balances. While I do think the phrase still applies, possibly even more so now than when it was first coined, we have to understand the allure of technology against possibly another metanarrative, namely the narrative of progress. We fail to consider that the collateral damage of progress – whether on the climate, increasing inequalities, the thousands, if not millions of individuals and groups of individuals who live on the margins of society dumped as “collateral damage”, to pluck the phrase from Zygmunt Bauman’s book (2011). Bauman explains: “Casualties are dubbed ‘collateral’ in so far as they are dismissed as not important enough to justify the costs of their prevention, or simply ‘unexpected’ because the planners did not consider them worthy of inclusion among the objects of preparatory reconnoitring.” Despite scientific progress and advances of technology, there does not seem to be an appetite to solve some of the oldest dilemmas created by humankind, namely inequality and poverty.

One of my favourite authors and philosophers is John Gray who observes in his book Heresies (2004): “Human beings use the power of scientific knowledge to assert and defend the values and goals they already have. New technologies can be used to alleviate suffering and enhance freedom. They can, and will, also be used to wage war and strengthen tyranny.” He further warns that knowledge, and in the context of my answer, technology, “is not an unmixed good; it can be as much a curse as a blessing.”

So, getting back to your question on whether there is a thin line between techno-solutionism and techno-optimism, I am more on the side of John Gray who is, in my opinion, very sober with regard to his assessment of humanity which he describes as homo rapiens – a humanity whose history is as often saturated with blood as with goodwill. I think it is, possibly, more helpful to consider not whether there is a distinction between techno-solutionism and techno-optimism but rather to consider under what conditions can technology provide hope, erase inequality, and champion social justice. How many individuals or current providers of technological solutions would commit to providing hope, erasing inequalities, and championing social justice? Your guess is as good as mine.

Once again, I would like to quote Neil Selwyn who said in Distrusting Educational Technology that educational technology “needs to be understood as a knot of social, political, economic and cultural agendas that is riddled with complications, contradictions and conflicts.”

DL: You have certainly given us much food for thought, Prof Prinsloo. Thank you for taking the time to talk to us. It has been a real pleasure.