Some indigenous people believed that a photograph could steal their soul. Modern human believes that data are something about them; a digital twin.
Digitalization as a promise
There is a huge potential in digitalization and automation. They have spread to all walks of life. As a disruptive force, they create new innovations, employment and business opportunities but also make the future more uncertain than ever. Therefore, we need new knowledge, attitudes, and competences in the future. Datafication and predictive algorithms can support evidence-based decision making for various organizations.
Digitalization as a betrayal
The main narrative of the potential of digitalization is overly optimistic, technologically deterministic and dominated by neoliberal dictionary and oppressive discursive rules that limit genuine discussion. Its innovation is merely surveillance capitalism cloaked as freedom. It is a paradox: it disrupts the future but also claims to be the way to make it more certain. Furthermore, especially in the form of datafication and predictive algorithms, digitalization is about management, prediction, and control of life itself.
Digitalization as [ ]
Digitalization creates new impossibilities. It has contracted to nowhere of life. As such, it recreates past ways of work and business. Therefore, the past is less certain than never. Because of this, we need old ways to unsure that past generations can obtain the wrong skills and knowledge in the past. Datafication and predictive algorithms = evidence-based thinking = the quest for uncertainty.
Last Saturday I, Mark Curcher and CARDE research group student researchers Ana Paula, Charlotte and David attended a Silver Lining for Learning podcast hosted by Curtis Bonk, Punya Mishra, Yong Zhao and Chris Dede. We had been invited to discuss Digitalization and Datafication of Education. I really enjoyed the conversation and feel that stimulating and important questions were raised during the podcast. Naturally, time is scarce during such an event, so I couldn’t address many of them. In addition, I wanted to give room for others to talk more on their research and work they do in Brazil and Dubai. That said, some of the discussions and questions stayed with me over the weekend and I want to address some of them here.
At some point Chris Dede posed a question “What automated forms of analyzing data about students do you support, if any? How do these approaches avoid the issues you raise about other types of automated data analysis?” I don’t think I gave a really good answer, because the question really opens a whole variety of things and a thorough reply would’ve taken too much time. Still, I want to at least partially try to answer that question here.
First of all, what we have noticed in our research is that language in itself is a powerful technology that sometimes commands instead of invites for a discussion, but more often, restricts and directs what can be discussed and in what terms. In addition, different actors (people, but also objects such as reports) have more power to speak depending on their status. As an example, we have witnessed how an OECD consultant thinking paper, a Microsoft report or an “EdTech enthusiast” blog post that claims “there is a huge potential in datafication/learning analytics/digitalization/ MOOCs/gamification/virtual reality/e-portfolios/LMSs (I presume this is historically accurate chronological order) often yields more power than research – even if research shows that digital technologies do not always (and actually quite rarely) make learning results “better” in themselves.
Furthermore, discussing only about using systems for benefit is discussing inside topical restrictions which exclude important root questions such as why all of a sudden education seems to be “broken” (almost globally) and to be fixed with EdTech, even in Finland with awesome PISA results where the government at the same time claims we have the best teachers in the world? This narrative is already before Covid-19. Of course it has amplified it and promoted the benefits discussion with the idea of access, even if the pandemic has at the same highlighted inequalities and shown that benefits are benefits in a certain context for certain people.
Even before Covid-19, everyone from governments to “the working life” seem to be targeting education as something that needs development or needs to “be brought to the 21st century”. It is also argued that teachers need new skills, but especially training in “attitude” towards accepting and using technology. Also, everywhere seems to be spreading a statement that educational institutions should be more closely connected “to the working life” or “the society” (as if education was ever disconnected from these). Naturally, the proposed solution is technology in its various current forms.
There are at least two problems in this narrative, and I say problems because I do not see them as “challenges” as challenges imply something that we can/should try to solve – I actually consider this challenges thinking often as part of the problems. These problems could be summarized as technological “solutionism” and “educationalizing”: in short, the former does not ask the “why” of technology but only the “how” while the latter is using language to move complex societal problems for the educational institutions to solve.
The current discourse with digitalization and datafication of education tells us that technological “solutions” can solve existing, often vaguely described problems in education (which as written, are restricted to education alone). At the same time, the lack of/cutting funding in public education, in addition to other societal problems, go unaddressed.
We have seen this in our research with teachers who have worked 20-30 years in the field. Something shifted in the 90’s and beginning of 2000’s. EdTech has increasingly become an unquestionable end, instead even a means to an end. A current narrative also wants to pose teachers as someone who are somehow against technology, and claim it as the reason preventing adoption of technology in education. Interestingly, all the teachers in our study were actually eager to try and develop technology in various forms in their work. Problems seem to rise when teachers ask questions such as “why this technology” or show they don’t actually work so well in practice. Then they were claimed to be against digitalization or “too critical”.
To me, this seem like we are excluding a voice from people who are experts, and note, often highly trained and competent professionals, in education, pedagogy, teaching and learning – naturally this varies globally, but we appear to treat all teachers the same in relation to technology. Instead, oftentimes we are replacing their expert assessment with simple EdTech market talk. It is as if we do not realize that EdTech products are marketed products in the same way than a car for example: naturally, also EdTech companies want to make their products appear appealing and as “the latest thing”.
This also connects to the trend of discussing or brainstorming technology future utopias and dystopias (https://learningfutures.education.asu.edu/resource-collection/future-tense-fiction-learning-futures). We also have a research project on implementing social science fiction methodology to “rethink” the future of digitalization of education. This type of thinking seems rather innovative and solution-oriented, right? One thing that we do not seem to notice though, is that the current potential speech of digitalization, datafication and EdTech in general, is already utopian but we treat it as true. One can witness it in the abstract level of reasoning and logic, for example in simple justifications such as “students can see their progress” and “there is a huge potential in big data/analytics/insert another tech here”. Such statements that hold yield discursive power can be found even in governmental reports, often with no research to back them up.
At the same time, when some of these technologies are put into practice, new paradoxes, tensions and discontinuities emerge, but we “just need to deal with them”. Technology is always inserted into a complex web of practice and human relations – it is actually inherently part of them. As a real life example, one work hour report software when put into practice created a lot of confusion as it did not allow marking work hours the way one did them but the way it was expected by the administration. This made staff quite confused. But, instead of protesting for example due to ethical concerns (which actually many had), they asked for more information or a policy. Still, they were not given these, as basically it would’ve said they need to lie (which, it appears, you cannot explicitly write in a policy documents).
So, this brings us back to the question from Chris Dede on automated student systems. In one way, technology is like a rock that you throw in a pond: it sends ripples everywhere but the rock itself sinks in the bottom and becomes invisible, as eventually do the ripples. If we think automated systems only from the perspective of “benefits”, we are missing the big picture. Such systems will create so kinds of ripples that go through the whole educational system and most likely beyond it, even after the system itself is gone. More importantly, they currently shift attention from various important issues, for example, that we are reducing the amount of teachers and increasing the number of students. Technology solutionism also seems to be ignoring research, such as listening to students who often in their feedback say that what they feel important is the teacher (this was one of the results in our national level learning analytics research and development project funded by the Finnish Ministry of Education and Culture as it was in the University of Wollongong study that we did back in 2013 on students’ sense belonging to enhance participation, success and retention in online programs).
As a perhaps slightly simplified conclusion, and a matter that should be addressed: One of the paradoxes of current discussions and developments around education seems to be that we claim to be developing “self-directed 21st century learners” for “the working life” (not so much for the society anymore these days) but we seem to want to do at least two things that sort of go against the idea and needs of a self-directed person: 1) we increase student surveillance, organization and control with data, analytics and algorithms, and 2) ignore their feedback, needs and research results if it goes against our EdTech solutionism type of thinking, when sometimes instead of new bells and whistles, the students simply appear to need a teacher to support their learning.