The cloud is a largely invisible, background presence in education, despite playing an increasingly significant role in many technical and institutional processes and practices. As recent relevant scholarship on the cloud has indicated, cloud computing arrangements are significantly affecting and reshaping a range of industries and sectors. The cloud represents an expansion of corporate big tech power into sectors like education, introducing new economic models, platform ecosystem arrangements, and AIOps capacities of automated governance.
As my colleague John Potter pointed out in response to the Google marketing, magic often refers to the ‘skill of misdirection,’ a certain sleight of hand that indicates to the audience to ‘Look at this magical stuff over here… (but don’t look at what’s happening over there).’ But ‘over there’ is precisely where educational attention needs to be directed, at the technical things, even the boring things like privacy policies and user agreements, that are reshaping teaching and learning in schools. Google may be opening up exciting new directions for schooling, but it may also be misdirecting education towards a future of ever-increasing automation and corporate control of the classroom.
As the three examples I’ve sketchily outlined here indicate, learning loss can’t be understood as a ‘whole’ without disaggregating it into its disparate elements and the various measurement practices they rely on. I’ve counted only three ways of measuring learning loss here—the original psychometric studies; testing companies’ assessments of reading and numeracy; and econometric calculations of ‘hysteresis effects’ in the economy—but even these are made of multiple parts, and are based on longer histories of measurement that are contested, incompatible with one another, sometimes contradictory, and incoherent when bundled together.
Thread by @BenPatrickWill: The key takeaway of this is even though algorithmic approaches to predicting and awarding grades have been spectacularly terrible, biased, and badly affected students’ lives, data science……
Here’s a list the lovely people on Twitter suggested of edtech sci-fi texts, TV, and film. Three were even suggestions of existing compilations of edtech sci-fi: a 2015 piece by Audrey Watters on Education in Science Fiction, a collection by Stephen Heppell, and an entry on Education in SF at the Encyclopedia of Science Fiction. Check those out too. I’ve alphabetized the list but nothing more. Some people added short descriptions, which I’ve paraphrased, and others links, which you’ll have to mine the replies to find, I’m afraid.
Overall, the project has revealed a particular set of mutations in the global education industry during the Covid-19 pandemic. It has documented some ways in which privatization of education has expanded – through increasing participation of private actors in public education – and of how commercialization of education has developed through the creation, marketing and sale of education goods and services to schools (and parents) by external providers. We understand this as a particularly intense instantiation of fast policy involving multisector actors and networks, and as an accelerated realization of sociotechnical imaginaries of a highly digitalized future of education. The shifting landscape of commercialization and privatization in education we have surveyed will require sustained attention by educators, unions and researchers to ensure that all stakeholders, and not just private or commercial organizations, can participate democratically in imagining the post-Covid future of public education.
The current pandemic is being used as an experimental opportunity for edtech to demonstrate its benefits not just in an emergency, but as a normal mode of education into the future.
The Chan Zuckerberg Initiative may not yet have the reach and influence of the Gates Foundation, but it is fast becoming one of the most significant funders of educational technology development and scientific research into learning and child development. This positions it to become a powerful source of authority in the shaping of education in multiple ways.
Through support for Summit and other charter school operations it is continuing the longstanding project of philanthropic advocacy for alternatives to public education, albeit now in the for-profit mode of disruptive philanthropy. Its personalized learning projects are extending adaptive, data-driven software beyond the charter chains where they have been developed and tested and out into schools and colleges at very large scale. And by funding research and development in learning science and learning engineering, CZI is advancing experimental new understandings of the human brain and cognition into applied teaching practices. It is in other words championing a new model of personalized, precision education that brings together the Silicon Valley culture of disruption, commercial technology, personalized learning advocacy, and new scientific practices modelled on those of precision medicine.
By creating CZI as an LLC, Chan and Zuckerberg also maintain powerful control over their spending and the direction of the organization. This gives them unprecedented power to shape the direction of research and development in education, by selecting and investing in programs that fit their personal vision. These efforts amount to an attempt to experiment on and re-engineer education into the form that Mark Zuckerberg and his networks find desirable, and that they believe can and ought to be pursued and attained. CZI is re-engineering education at scale.
All in all, Amazon Ignite is encouraging teachers to see themselves as empowered and branded-up personal edubusinesses operating inside Amazon’s commerce platform. It is easy to see the attraction in the context of underfunded schools and low teacher pay. But it also brings teachers into the precarious conditions of the gig economy. These educators are gig workers and small-scale edu-startup businesses who will need to compete to turn a profit. Rather than making select teachers into brand ambassadors for its platform, Amazon is bringing teacher-producers and education startups on to its platform as content producers doing the labour of making, uploading and marketing resources for royalty payments. It expands platform capitalism to the production, circulation and provision of classroom resources, and positions Amazon as an intermediary between the producers and consumers in a new educational market.
Overall, what I’ve tried to show in the article is that SEL is a policy field in-the-making and that it remains inchoate and in some ways incoherent. We can understand it as a policy infrastructure that is being assembled from highly diverse elements, and that is centrally focused on the production of ‘psychodata’. In fact, the potential of a SEL policy infrastructure depends to a great extent on the creation of the data infrastructure required to produce policy-relevant knowledge. In other words, the generation of psycho-economic calculations is at the very core of current international policy interest in social-emotional learning, which is already relaying into classroom practices globally, governing teachers’ practices, and shaping the priorities of education systems to be focused on the enumeration of student emotions.
- SEL needs to be understood as the product of a ‘psycho-economic’ fusion of psychological and economics expertise
- There are sets of moving relations among think tanks, philanthropies and campaigning coalitions which have been central to establishing SEL as an emerging policy field
- SEL is a site of considerable movement of money
- A huge industry of SEL products, consultancy and technologies has emerged, which has allowed SEL practices to proliferate through schools
- SELs enactment is contingent on local, regional and national priorities
- The OECD overtly brings together psychology and economics with their new test positioned as a way of calculating the contribution of social-emotional skills to ‘human capital
This has me rethinking the book Counting what Counts and my reflections:
It feels like the real question in need of answering isn’t what needs to be counted, but why? Although it might be useful to measure how interested we may be or our global awareness, what seems more important is what purpose does this actually achieve. In an age when counting seems to be a given and we only care about what we can count, the book it at least offers a vision about what we can measure.
If these are signals of an emerging edtechlash, then educators, decision-makers and the edtech industry would benefit from being engaged in the key issues that are now emerging, namely that:
- private sector influence and outsourcing is perceived to be detrimental to public education
- lack of edtech diversity may reproduce the pedagogic assumptions of engineers
- student distrust of engineering solutions and continuing trust in human interactions as central to education
- there may be bad science behind positive industry and investor PR
- new data protection regulations question how easily student ‘consent’ can be assumed when the balance of power is unequal
- algorithmic ‘accuracy’ is being exposed as deeply flawed and full of biases
- algorithmic flaws can lead to devastating consequences at huge costs to individuals, the public, and institutions
- increasingly invasive surveillance proposals raise new ethical and human rights issues that are likely to be acted upon in coming years.
We should not and cannot ignore these tensions and challenges. They are early signals of resistance ahead for edtech which need to be engaged with before they turn to public outrage. By paying attention to and acting on edtech resistances it may be possible to create education systems, curricula and practices that are fair and trustworthy. It is important not to allow edtech resistance to metamorphose into resistance to education itself.
ClassDojo has positioned itself as a ‘technical fix’ for the ‘engineering problems’ of classroom behaviour, discipline and more. Behaviour monitoring, content distribution, parent communication, teacher tools, social networking, pedagogic thinking, even relationships between parents and their children have become ClassDojo-fied as part of its Silicon Valley-backed expansion. As such, the expansion of ed-tech products and markets represents the clear commercialisation of public education.
Turnitin is the clear market-leader to solve the essay mills problem that the department has now called on universities to tackle. Its technical solution, however, does not address the wider reasons—social, institutional, psychological, financial or pedagogic—for student cheating, or encourage universities to work proactively with students to resolve them. Instead, it acts as a kind of automated ‘plagiarism police force’ to enforce academic integrity, which at the same time is also set to further disadvantage young people in countries such as Kenya where preparing academic texts for UK and US students is seen as a legitimate and lucrative service by students and graduates.
Turnitin is making massive money from manufacturing mistrust between students & staff, while using its global database of student writing to train its 'ghostwriter' detection algorithm & profit from political demands for HE to tackle essay mills https://t.co/0fgukGne0y pic.twitter.com/vkaFxhsQy5
— Ben Williamson (@BenPatrickWill) June 28, 2019
Ben Williamson Surveillance capitalism combines data analytics, business strategy, and human behavioural experimentation. Image: “Fraction collector” by proteinbiochemist ‘Surveil…
1) Cultures of computational learning
2) Human-machine learning confluences
3) Programmable policies
By learning lessons from past controversies with data in education, and anticipating the controversies to come, we can ensure we have good answers to these hard questions. We can also ensure that good, ethical data practices are built in to educational technologies, hopefully preventing problems before they become full-blown public data controversies.
- Software can’t ‘solve’ educational ‘problems’
- Global edtech influence raises public concern
- Data leaks break public trust
- Algorithmic mistakes & encoded politics cause social consequences
- Transparency, not algorithmic opacity, is key to building trust with users
- Psychological surveillance raises fears of emotional manipulation
- ‘Reading the brain’ poses risks to human rights
- Genetic datafication could lead to dangerous ‘Eugenics2.0’
This is a good introduction to Williamson’s book on the same topic, which unpacks these issues in more detail. Along with Audrey Watters year in review, these posts provide a useful snapshot of educational technology in 2018. You can also watch the talk.
HE is encompassed in the sprawling networks of actors and technologies of metric power. The data infrastructure of higher education is an accomplishment of a mobile policy network of sector agencies along with a whole host of other organizations and experts from the governmental, commercial and nonprofit sectors. A form of mobile, networked fast policy is propelling metrics across the sector, and increasingly prompting changes in organizational and individual behaviours that will transform the higher education sector to see and act upon itself as a market.
The tech elite now making a power-grab for public education probably has little to fear from FBI warnings about education technology. The FBI is primarily concerned with potentially malicious uses of sensitive student information by cybercriminals. There’s nothing criminal about creating Montessori-inspired preschool networks, using ClassDojo as a vehicle to build a liberal society, reimagining high school as personalized learning, or reshaping universities as AI-enhanced factories for producing labour market outcomes–unless you consider all of this a kind of theft of public education for private commercial advantage and influence.