This article first appeared on Points, a Data & Society publication in February 2017
That inBloom might exist as a cautionary tale in the annals of ed-tech is rather remarkable, if for no other reason than ed-tech – at least its manifestation as a current blend of venture capital exuberance, Silicon Valley hype, philanthropic dollars, and ed-reform policy-making – tends to avoid annals. That is to say, ed-tech today has very little sense of its own history. Everything is “new” and “innovative” and “disruptive.” It’s always forward-facing, with barely a glance over its should at the past – at the history of education or the history of technology. No one had ever thought about using computers in the classroom – or so you might glean if you only read the latest marketing about apps and analytics – until this current batch of philanthropists and entrepreneurs and investors and politicians suddenly stumbled upon the idea circa 2010.
Perhaps that very deliberate dismissal of history helped doom inBloom from the start. Those who worked on the initiative seemed to ignore the legacy of the expensive and largely underutilized ARIS (Achievement Reporting and Innovation System) system that had been built for New York City schools, for example, hiring many of ARIS’s staff and soliciting the company in charge of building it, Wireless Generation, to engineer the inBloom product.
While those making sweeping promises about data collection and data analytics wanted to suggest that, thanks to digital technologies, InBloom offered a unique opportunity to glean insights from data from the classroom, many parents and educators likely had a different sense – a deeper history –of what data had already done or undone, of what data could do or undo. They certainly had a different sense of risk.
The compulsion to gather more and more data is hardly new, although certainly new technologies facilitate it, generating more and more data in turn. In 1962, Raymond Callahan published Education and the Cult of Efficiency, tracing to the early twentieth century the eagerness of school leaders to adopt the language and the practices of business management in the hopes that schools might be run more efficiently and more “scientifically.”
There’s something quite compelling about those hopes, it seems, as they underlie much of the push for education reform and education technology in schools still today. Indeed, this belief in efficiency and science helped to justify inBloom, as Data & Society’s new report on the history of the $100 million data infrastructure initiative demonstrates.
That belief is evident in the testimonies from various politicians, administrators, entrepreneurs, and technologists involved in the project. Data collection – facilitated by inBloom – was meant to be “the game-changer,” in the words of the CEO of the Data Quality Campaign, providing a way to “actually use individual student information to guide teaching and learning and to really leverage the power of this information to help teachers tailor learning to every single child in their class. That’s what made inBloom revolutionary.” “The promise was that [inBloom] was supposed to be adaptive differentiated instruction for individual students, based on test results and other data that the states had. InBloom was going to provide different resources based on those results,” according to the superintendent of a New York school district.
But this promise of a data-driven educational “revolution” was – and still is – mostly that: a promise. The claims about “personalized learning” attainable through more data collection and data analysis remain primarily marketing hype. Indeed, “personalized learning” is itself a rather nebulous concept. As Data & Society observed in a 2016 report on the topic,
Description of personalized learning encompass such a broad range of possibilities – from customized interfaces to adaptive tutors, from student-centered classrooms to learning management systems – that expectations run high for their potential to revolutionize learning. Less clear from these descriptions are what personalized learning systems actually offer and whether they improve the learning experiences and outcomes for students.
So while “personalized learning” might be a powerful slogan for the ed-tech industry and its funders, the sweeping claims about its benefits are largely unproven by educational research.
But it sounds like science. With all the requisite high-tech gadgetry and data dashboards, it looks like science. It signifies science, and that signification is, in the end, the justification that inBloom largely relied upon. I’m someone who tried to get the startup to clarify “what inBloom will gather, how long it will store it, and what recourse parents have who want to opt out,” and I remember clearly that there was nevertheless much more hand-waving and hype than there ever was a clear explanation (“scientific” or otherwise) of “how” or “why” it would work.
No surprise then, there was pushback, primarily from parents, educators, and a handful of high profile NYC education activists who opposed InBloom’s data collection, storage, and sharing practices. But as the Data & Society report details, “instead of seeking to build trust at the district level with teachers and parents, many interview participants observed that inBloom and the Gates Foundation responded to what were very emotional concerns with complex technical descriptions or legal defenses.”
This juxtaposition of parents as “emotional” and inBloom and the project’s supporters as “scientific” and “technical” runs throughout the report, which really serves to undermine and belittle the fears of inBloom opponents. (This was also evident in many media reports at the time of inBloom’s demise that tended to describe parents as “hysterical” or that patronized them by contending the issues were “understandably obscure to the average PTA mom.”) The opposition to inBloom is described in the Data & Society report as a “visceral, fervently negative response to student data collection,” for example, while the data collection itself is repeatedly framed in terms of its “great promise.” While the report does point to the failure of inBloom officials to build parents’ trust, many of the interviewees repeatedly dismiss the mistrust as irrational. “The activism about InBloom felt like anti-vaccination activism. Just fear,” said one participant. “I don’t know how else to put it,” said another. “It was not rational.”
But inBloom opponents did have reason – many perfectly rational reasons – for concern. As the report chronicles, there were a number of concurrent events that prompted many people to be highly suspicious of plans for the data infrastructure initiative – its motivations and its security. These included inBloom’s connection to the proponents of the Common Core and other education reform policies; the growing concern about the Gates Foundation’s role in shaping these very policies; Edward Snowden’s revelations about NSA surveillance; several high profile data breaches, including credit card information of some 70 million Target customers; the role of News Corp’s subsidiary Wireless Generation in building the inBloom infrastructure, coinciding with News Corp’s phone hacking scandal in the UK, as well as its decision to hire Joel Klein, the former NYC schools chancellor who’d commissioned the failed ARIS system, to head News Corp’s new education efforts. As the report notes, “The general atmosphere of data mistrust combined with earlier education reform movements that already characterized educational data as a means of harsh accountability.”
In the face of this long list of concerns, the public’s “low tolerance for uncertainty and risk” surrounding student data is hardly irrational. Indeed, I’d argue it serves as a perfectly reasonable challenge to a technocratic ideology that increasingly argues that “the unreasonable effectiveness of data” will supplant theory and politics and will solve all manner of problems, including the challenge of “improving teaching” and “personalizing learning.” There really isn’t any “proof” that more data collection and analysis will do this – mostly just the insistence that this is “science” and therefore must be “the future.”
History – the history of inBloom, the history of ed-tech more generally – might suggest otherwise.