b2o: boundary 2 online

  • Audrey Watters – The Best Way to Predict the Future is to Issue a Press Release

    Audrey Watters – The Best Way to Predict the Future is to Issue a Press Release

    By Audrey Watters

    ~

    This talk was delivered at Virginia Commonwealth University today as part of a seminar co-sponsored by the Departments of English and Sociology and the Media, Art, and Text PhD Program. The slides are also available here.

    Thank you very much for inviting me here to speak today. I’m particularly pleased to be speaking to those from Sociology and those from the English and those from the Media, Art, and Text departments, and I hope my talk can walk the line between and among disciplines and methods – or piss everyone off in equal measure. Either way.

    This is the last public talk I’ll deliver in 2016, and I confess I am relieved (I am exhausted!) as well as honored to be here. But when I finish this talk, my work for the year isn’t done. No rest for the wicked – ever, but particularly in the freelance economy.

    As I have done for the past six years, I will spend the rest of November and December publishing my review of what I deem the “Top Ed-Tech Trends” of the year. It’s an intense research project that usually tops out at about 75,000 words, written over the course of four to six weeks. I pick ten trends and themes in order to closely at the recent past, the near-term history of education technology. Because of the amount of information that is published about ed-tech – the amount of information, its irrelevance, its incoherence, its lack of context – it can be quite challenging to keep up with what is really happening in ed-tech. And just as importantly, what is not happening.

    So that’s what I try to do. And I’ll boast right here – no shame in that – no one else does as in-depth or thorough job as me, certainly no one who is entirely independent from venture capital, corporate or institutional backing, or philanthropic funding. (Of course, if you look for those education technology writers who are independent from venture capital, corporate or institutional backing, or philanthropic funding, there is pretty much only me.)

    The stories that I write about the “Top Ed-Tech Trends” are the antithesis of most articles you’ll see about education technology that invoke “top” and “trends.” For me, still framing my work that way – “top trends” – is a purposeful rhetorical move to shed light, to subvert, to offer a sly commentary of sorts on the shallowness of what passes as journalism, criticism, analysis. I’m not interested in making quickly thrown-together lists and bullet points. I’m not interested in publishing clickbait. I am interested nevertheless in the stories – shallow or sweeping – that we tell and spread about technology and education technology, about the future of education technology, about our technological future.

    Let me be clear, I am not a futurist – even though I’m often described as “ed-tech’s Cassandra.” The tagline of my website is “the history of the future of education,” and I’m much more interested in chronicling the predictions that others make, have made about the future of education than I am writing predictions of my own.

    One of my favorites: “Books will soon be obsolete in schools,” Thomas Edison said in 1913. Any day now. Any day now.

    Here are a couple of more recent predictions:

    “In fifty years, there will be only ten institutions in the world delivering higher education and Udacity has a shot at being one of them.” – that’s Sebastian Thrun, best known perhaps for his work at Google on the self-driving car and as a co-founder of the MOOC (massive open online course) startup Udacity. The quotation is from 2012.

    And from 2013, by Harvard Business School professor, author of the book The Innovator’s Dilemma, and popularizer of the phrase “disruptive innovation,” Clayton Christensen: “In fifteen years from now, half of US universities may be in bankruptcy. In the end I’m excited to see that happen. So pray for Harvard Business School if you wouldn’t mind.”

    Pray for Harvard Business School. No. I don’t think so.

    Both of these predictions are fantasy. Nightmarish, yes. But fantasy. Fantasy about a future of education. It’s a powerful story, but not a prediction made based on data or modeling or quantitative research into the growing (or shrinking) higher education sector. Indeed, according to the latest statistics from the Department of Education – now granted, this is from the 2012–2013 academic year – there are 4726 degree-granting postsecondary institutions in the United States. A 46% increase since 1980. There are, according to another source (non-governmental and less reliable, I think), over 25,000 universities in the world. This number is increasing year-over-year as well. So to predict that the vast vast majority of these schools (save Harvard, of course) will go away in the next decade or so or that they’ll be bankrupt or replaced by Silicon Valley’s version of online training is simply wishful thinking – dangerous, wishful thinking from two prominent figures who will benefit greatly if this particular fantasy comes true (and not just because they’ll get to claim that they predicted this future).

    Here’s my “take home” point: if you repeat this fantasy, these predictions often enough, if you repeat it in front of powerful investors, university administrators, politicians, journalists, then the fantasy becomes factualized. (Not factual. Not true. But “truthy,” to borrow from Stephen Colbert’s notion of “truthiness.”) So you repeat the fantasy in order to direct and to control the future. Because this is key: the fantasy then becomes the basis for decision-making.

    Fantasy. Fortune-telling. Or as capitalism prefers to call it “market research.”

    “Market research” involves fantastic stories of future markets. These predictions are often accompanied with a press release touting the size that this or that market will soon grow to – how many billions of dollars schools will spend on computers by 2020, how many billions of dollars of virtual reality gear schools will buy by 2025, how many billions of dollars of schools will spend on robot tutors by 2030, how many billions of dollars will companies spend on online training by 2035, how big will coding bootcamp market will be by 2040, and so on. The markets, according to the press releases, are always growing. Fantasy.

    In 2011, the analyst firm Gartner predicted that annual tablet shipments would exceed 300 million units by 2015. Half of those, the firm said, would be iPads. IDC estimates that the total number of shipments in 2015 was actually around 207 million units. Apple sold just 50 million iPads. That’s not even the best worst Gartner prediction. In October of 2006, Gartner said that Apple’s “best bet for long-term success is to quit the hardware business and license the Mac to Dell.” Less than three months later, Apple introduced the iPhone. The very next day, Apple shares hit $97.80, an all-time high for the company. By 2012 – yes, thanks to its hardware business – Apple’s stock had risen to the point that the company was worth a record-breaking $624 billion.

    But somehow, folks – including many, many in education and education technology – still pay attention to Gartner. They still pay Gartner a lot of money for consulting and forecasting services.

    People find comfort in these predictions, in these fantasies. Why?

    Gartner is perhaps best known for its “Hype Cycle,” a proprietary graphic presentation that claims to show how emerging technologies will be adopted.

    According to Gartner, technologies go through five stages: first, there is a “technology trigger.” As the new technology emerges, a lot of attention is paid to it in the press. Eventually it reaches the second stage: the “peak of inflated expectations.” So many promises have been made about this technological breakthrough. Then, the third stage: the “trough of disillusionment.” Interest wanes. Experiments fail. Promises are broken. As the technology matures, the hype picks up again, more slowly – this is the “slope of enlightenment.” Eventually the new technology becomes mainstream – the “plateau of productivity.”

    It’s not that hard to identify significant problems with the Hype Cycle, least of which being it’s not a cycle. It’s a curve. It’s not a particularly scientific model. It demands that technologies always move forward along it.

    Gartner says its methodology is proprietary – which is code for “hidden from scrutiny.” Gartner says, rather vaguely, that it relies on scenarios and surveys and pattern recognition to place technologies on the line. But most of the time when Gartner uses the word “methodology,” it is trying to signify “science,” and what it really means is “expensive reports you should buy to help you make better business decisions.”

    Can it really help you make better business decisions? It’s just a curve with some technologies plotted along it. The Hype Cycle doesn’t help explain why technologies move from one stage to another. It doesn’t account for technological precursors – new technologies rarely appear out of nowhere – or political or social changes that might prompt or preclude adoption. And at the end it is simply too optimistic, unreasonably so, I’d argue. No matter how dumb or useless a new technology is, according to the Hype Cycle at least, it will eventually become widely adopted. Where would you plot the Segway, for example? (In 2008, ever hopeful, Gartner insisted that “This thing certainly isn’t dead and maybe it will yet blossom.” Maybe it will, Gartner. Maybe it will.)

    And maybe this gets to the heart as to why I’m not a futurist. I don’t share this belief in an increasingly technological future; I don’t believe that more technology means the world gets “more better.” I don’t believe that more technology means that education gets “more better.”

    Every year since 2004, the New Media Consortium, a non-profit organization that advocates for new media and new technologies in education, has issued its own forecasting report, the Horizon Report, naming a handful of technologies that, as the name suggests, it contends are “on the horizon.”

    Unlike Gartner, the New Media Consortium is fairly transparent about how this process works. The organization invites various “experts” to participate in the advisory board that, throughout the course of each year, works on assembling its list of emerging technologies. The process relies on the Delphi method, whittling down a long list of trends and technologies by a process of ranking and voting until six key trends, six emerging technologies remain.

    Disclosure/disclaimer: I am a folklorist by training. The last time I took a class on “methods” was, like, 1998. And admittedly I never learned about the Delphi method – what the New Media Consortium uses for this research project – until I became a scholar of education technology looking into the Horizon Report. As a folklorist, of course, I did catch the reference to the Oracle of Delphi.

    Like so much of computer technology, the roots of the Delphi method are in the military, developed during the Cold War to forecast technological developments that the military might use and that the military might have to respond to. The military wanted better predictive capabilities. But – and here’s the catch – it wanted to identify technology trends without being caught up in theory. It wanted to identify technology trends without developing models. How do you do that? You gather experts. You get those experts to consensus.

    So here is the consensus from the past twelve years of the Horizon Report for higher education. These are the technologies it has identified that are between one and five years from mainstream adoption:

    It’s pretty easy, as with the Gartner Hype Cycle, to look at these predictions and note that they are almost all wrong in some way or another.

    Some are wrong because, say, the timeline is a bit off. The Horizon Report said in 2010 that “open content” was less than a year away from widespread adoption. I think we’re still inching towards that goal – admittedly “open textbooks” have seen a big push at the federal and at some state levels in the last year or so.

    Some of these predictions are just plain wrong. Virtual worlds in 2007, for example.

    And some are wrong because, to borrow a phrase from the theoretical physicist Wolfgang Pauli, they’re “not even wrong.” Take “collaborative learning,” for example, which this year’s K–12 report posits as a mid-term trend. Like, how would you argue against “collaborative learning” as occurring – now or some day – in classrooms? As a prediction about the future, it is not even wrong.

    But wrong or right – that’s not really the problem. Or rather, it’s not the only problem even if it is the easiest critique to make. I’m not terribly concerned about the accuracy of the predictions about the future of education technology that the Horizon Report has made over the last decade. But I do wonder how these stories influence decision-making across campuses.

    What might these predictions – this history of the future – tell us about the wishful thinking surrounding education technology and about the direction that the people the New Media Consortium views as “experts” want the future to take. What can we learn about the future by looking at the history of our imagining about education’s future. What role does powerful ed-tech storytelling (also known as marketing) play in shaping that future? Because remember: to predict the future is to control it – to attempt to control the story, to attempt to control what comes to pass.

    It’s both convenient and troubling then these forward-looking reports act as though they have no history of their own; they purposefully minimize or erase their own past. Each year – and I think this is what irks me most – the NMC fails to looks back at what it had predicted just the year before. It never revisits older predictions. It never mentions that they even exist. Gartner too removes technologies from the Hype Cycle each year with no explanation for what happened, no explanation as to why trends suddenly appear and disappear and reappear. These reports only look forward, with no history to ground their direction in.

    I understand why these sorts of reports exist, I do. I recognize that they are rhetorically useful to certain people in certain positions making certain claims about “what to do” in the future. You can write in a proposal that, “According to Gartner… blah blah blah.” Or “The Horizon Reports indicates that this is one of the most important trends in coming years, and that is why we need to commit significant resources – money and staff – to this initiative.” But then, let’s be honest, these reports aren’t about forecasting a future. They’re about justifying expenditures.

    “The best way to predict the future is to invent it,” computer scientist Alan Kay once famously said. I’d wager that the easiest way is just to make stuff up and issue a press release. I mean, really. You don’t even need the pretense of a methodology. Nobody is going to remember what you predicted. Nobody is going to remember if your prediction was right or wrong. Nobody – certainly not the technology press, which is often painfully unaware of any history, near-term or long ago – is going to call you to task. This is particularly true if you make your prediction vague – like “within our lifetime” – or set your target date just far enough in the future – “In fifty years, there will be only ten institutions in the world delivering higher education and Udacity has a shot at being one of them.”

    Let’s consider: is there something about the field of computer science in particular – and its ideological underpinnings – that makes it more prone to encourage, embrace, espouse these sorts of predictions? Is there something about Americans’ faith in science and technology, about our belief in technological progress as a signal of socio-economic or political progress, that makes us more susceptible to take these predictions at face value? Is there something about our fears and uncertainties – and not just now, days before this Presidential Election where we are obsessed with polls, refreshing Nate Silver’s website obsessively – that makes us prone to seek comfort, reassurance, certainty from those who can claim that they know what the future will hold?

    “Software is eating the world,” investor Marc Andreessen pronounced in a Wall Street Journal op-ed in 2011. “Over the next 10 years,” he wrote, “I expect many more industries to be disrupted by software, with new world-beating Silicon Valley companies doing the disruption in more cases than not.” Buy stock in technology companies was really the underlying message of Andreessen’s op-ed; this isn’t another tech bubble, he wanted to reinsure investors. But many in Silicon Valley have interpreted this pronouncement – “software is eating the world” – as an affirmation and an inevitability. I hear it repeated all the time – “software is eating the world” – as though, once again, repeating things makes them true or makes them profound.

    If we believe that, indeed, “software is eating the world,” that we are living in a moment of extraordinary technological change, that we must – according to Gartner or the Horizon Report – be ever-vigilant about emerging technologies, that these technologies are contributing to uncertainty, to disruption, then it seems likely that we will demand a change in turn to our educational institutions (to lots of institutions, but let’s just focus on education). This is why this sort of forecasting is so important for us to scrutinize – to do so quantitatively and qualitatively, to look at methods and at theory, to ask who’s telling the story and who’s spreading the story, to listen for counter-narratives.

    This technological change, according to some of the most popular stories, is happening faster than ever before. It is creating an unprecedented explosion in the production of information. New information technologies, so we’re told, must therefore change how we learn – change what we need to know, how we know, how we create and share knowledge. Because of the pace of change and the scale of change and the locus of change (that is, “Silicon Valley” not “The Ivory Tower”) – again, so we’re told – our institutions, our public institutions can no longer keep up. These institutions will soon be outmoded, irrelevant. Again – “In fifty years, there will be only ten institutions in the world delivering higher education and Udacity has a shot at being one of them.”

    These forecasting reports, these predictions about the future make themselves necessary through this powerful refrain, insisting that technological change is creating so much uncertainty that decision-makers need to be ever vigilant, ever attentive to new products.

    As Neil Postman and others have cautioned us, technologies tend to become mythic – unassailable, God-given, natural, irrefutable, absolute. So it is predicted. So it is written. Techno-scripture, to which we hand over a certain level of control – to the technologies themselves, sure, but just as importantly to the industries and the ideologies behind them. Take, for example, the founding editor of the technology trade magazine Wired, Kevin Kelly. His 2010 book was called What Technology Wants, as though technology is a living being with desires and drives; the title of his 2016 book, The Inevitable. We humans, in this framework, have no choice. The future – a certain flavor of technological future – is pre-ordained. Inevitable.

    I’ll repeat: I am not a futurist. I don’t make predictions. But I can look at the past and at the present in order to dissect stories about the future.

    So is the pace of technological change accelerating? Is society adopting technologies faster than it’s ever done before? Perhaps it feels like it. It certainly makes for a good headline, a good stump speech, a good keynote, a good marketing claim, a good myth. But the claim starts to fall apart under scrutiny.

    This graph comes from an article in the online publication Vox that includes a couple of those darling made-to-go-viral videos of young children using “old” technologies like rotary phones and portable cassette players – highly clickable, highly sharable stuff. The visual argument in the graph: the number of years it takes for one quarter of the US population to adopt a new technology has been shrinking with each new innovation.

    But the data is flawed. Some of the dates given for these inventions are questionable at best, if not outright inaccurate. If nothing else, it’s not so easy to pinpoint the exact moment, the exact year when a new technology came into being. There often are competing claims as to who invented a technology and when, for example, and there are early prototypes that may or may not “count.” James Clerk Maxwell did publish A Treatise on Electricity and Magnetism in 1873. Alexander Graham Bell made his famous telephone call to his assistant in 1876. Guglielmo Marconi did file his patent for radio in 1897. John Logie Baird demonstrated a working television system in 1926. The MITS Altair 8800, an early personal computer that came as a kit you had to assemble, was released in 1975. But Martin Cooper, a Motorola exec, made the first mobile telephone call in 1973, not 1983. And the Internet? The first ARPANET link was established between UCLA and the Stanford Research Institute in 1969. The Internet was not invented in 1991.

    So we can reorganize the bar graph. But it’s still got problems.

    The Internet did become more privatized, more commercialized around that date – 1991 – and thanks to companies like AOL, a version of it became more accessible to more people. But if you’re looking at when technologies became accessible to people, you can’t use 1873 as your date for electricity, you can’t use 1876 as your year for the telephone, and you can’t use 1926 as your year for the television. It took years for the infrastructure of electricity and telephony to be built, for access to become widespread; and subsequent technologies, let’s remember, have simply piggy-backed on these existing networks. Our Internet service providers today are likely telephone and TV companies; our houses are already wired for new WiFi-enabled products and predictions.

    Economic historians who are interested in these sorts of comparisons of technologies and their effects typically set the threshold at 50% – that is, how long does it take after a technology is commercialized (not simply “invented”) for half the population to adopt it. This way, you’re not only looking at the economic behaviors of the wealthy, the early-adopters, the city-dwellers, and so on (but to be clear, you are still looking at a particular demographic – the privileged half.)

    And that changes the graph again:

    How many years do you think it’ll be before half of US households have a smart watch? A drone? A 3D printer? Virtual reality goggles? A self-driving car? Will they? Will it be fewer years than 9? I mean, it would have to be if, indeed, “technology” is speeding up and we are adopting new technologies faster than ever before.

    Some of us might adopt technology products quickly, to be sure. Some of us might eagerly buy every new Apple gadget that’s released. But we can’t claim that the pace of technological change is speeding up just because we personally go out and buy a new iPhone every time Apple tells us the old model is obsolete. Removing the headphone jack from the latest iPhone does not mean “technology changing faster than ever,” nor does showing how headphones have changed since the 1970s. None of this is really a reflection of the pace of change; it’s a reflection of our disposable income and a ideology of obsolescence.

    Some economic historians like Robert J. Gordon actually contend that we’re not in a period of great technological innovation at all; instead, we find ourselves in a period of technological stagnation. The changes brought about by the development of information technologies in the last 40 years or so pale in comparison, Gordon argues (and this is from his recent book The Rise and Fall of American Growth: The US Standard of Living Since the Civil War), to those “great inventions” that powered massive economic growth and tremendous social change in the period from 1870 to 1970 – namely electricity, sanitation, chemicals and pharmaceuticals, the internal combustion engine, and mass communication. But that doesn’t jibe with “software is eating the world,” does it?

    Let’s return briefly to those Horizon Report predictions again. They certainly reflect this belief that technology must be speeding up. Every year, there’s something new. There has to be. That’s the purpose of the report. The horizon is always “out there,” off in the distance.

    But if you squint, you can see each year’s report also reflects a decided lack of technological change. Every year, something is repeated – perhaps rephrased. And look at the predictions about mobile computing:

    • 2006 – the phones in their pockets
    • 2007 – the phones in their pockets
    • 2008 – oh crap, we don’t have enough bandwidth for the phones in their pockets
    • 2009 – the phones in their pockets
    • 2010 – the phones in their pockets
    • 2011 – the phones in their pockets
    • 2012 – the phones too big for their pockets
    • 2013 – the apps on the phones too big for their pockets
    • 2015 – the phones in their pockets
    • 2016 – the phones in their pockets

    This hardly makes the case for technological speeding up, for technology changing faster than it’s ever changed before. But that’s the story that people tell nevertheless. Why?

    I pay attention to this story, as someone who studies education and education technology, because I think these sorts of predictions, these assessments about the present and the future, frequently serve to define, disrupt, destabilize our institutions. This is particularly pertinent to our schools which are already caught between a boundedness to the past – replicating scholarship, cultural capital, for example – and the demands they bend to the future – preparing students for civic, economic, social relations yet to be determined.

    But I also pay attention to these sorts of stories because there’s that part of me that is horrified at the stuff – predictions – that people pass off as true or as inevitable.

    “65% of today’s students will be employed in jobs that don’t exist yet.” I hear this statistic cited all the time. And it’s important, rhetorically, that it’s a statistic – that gives the appearance of being scientific. Why 65%? Why not 72% or 53%? How could we even know such a thing? Some people cite this as a figure from the Department of Labor. It is not. I can’t find its origin – but it must be true: a futurist said it in a keynote, and the video was posted to the Internet.

    The statistic is particularly amusing when quoted alongside one of the many predictions we’ve been inundated with lately about the coming automation of work. In 2014, The Economist asserted that “nearly half of American jobs could be automated in a decade or two.”“Before the end of this century,” Wired Magazine’s Kevin Kelly announced earlier this year, “70 percent of today’s occupations will be replaced by automation.”

    Therefore the task for schools – and I hope you can start to see where these different predictions start to converge – is to prepare students for a highly technological future, a future that has been almost entirely severed from the systems and processes and practices and institutions of the past. And if schools cannot conform to this particular future, then “In fifty years, there will be only ten institutions in the world delivering higher education and Udacity has a shot at being one of them.”

    Now, I don’t believe that there’s anything inevitable about the future. I don’t believe that Moore’s Law – that the number of transistors on an integrated circuit doubles every two years and therefore computers are always exponentially smaller and faster – is actually a law. I don’t believe that robots will take, let alone need take, all our jobs. I don’t believe that YouTube has been rendered school irrevocably out-of-date. I don’t believe that technologies are changing so quickly that we should hand over our institutions to entrepreneurs, privatize our public sphere for techno-plutocrats.

    I don’t believe that we should cheer Elon Musk’s plans to abandon this planet and colonize Mars – he’s predicted he’ll do so by 2026. I believe we stay and we fight. I believe we need to recognize this as an ego-driven escapist evangelism.

    I believe we need to recognize that predicting the future is a form of evangelism as well. Sure gets couched in terms of science, it is underwritten by global capitalism. But it’s a story – a story that then takes on these mythic proportions, insisting that it is unassailable, unverifiable, but true.

    The best way to invent the future is to issue a press release. The best way to resist this future is to recognize that, once you poke at the methodology and the ideology that underpins it, a press release is all that it is.

    Image credits: 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28. And a special thanks to Tressie McMillan Cottom and David Golumbia for organizing this talk. And to Mike Caulfield for always helping me hash out these ideas.
    _____

    Audrey Watters is a writer who focuses on education technology – the relationship between politics, pedagogy, business, culture, and ed-tech. She has worked in the education field for over 15 years: teaching, researching, organizing, and project-managing. Although she was two chapters into her dissertation (on a topic completely unrelated to ed-tech), she decided to abandon academia, and she now happily fulfills the one job recommended to her by a junior high aptitude test: freelance writer. Her stories have appeared on NPR/KQED’s education technology blog MindShift, in the data section of O’Reilly Radar, on Inside Higher Ed, in The School Library Journal, in The Atlantic, on ReadWriteWeb, and Edutopia. She is the author of the recent book The Monsters of Education Technology (Smashwords, 2014) and working on a book called Teaching Machines. She maintains the widely-read Hack Education blog, and writes frequently for The b2 Review Digital Studies magazine on digital technology and education.

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  • Audrey Watters – Public Education Is Not Responsible for Tech’s Diversity Problem

    Audrey Watters – Public Education Is Not Responsible for Tech’s Diversity Problem

    By Audrey Watters

    ~

    On July 14, Facebook released its latest “diversity report,” claiming that it has “shown progress” in hiring a more diverse staff. Roughly 90% of its US employees are white or Asian; 83% of those in technical positions at the company are men. (That’s about a 1% improvement from last year’s stats.) Black people still make up just 2% of the workforce at Facebook, and 1% of the technical staff. Those are the same percentages as 2015, when Facebook boasted that it had hired 7 Black people. “Progress.”

    In this year’s report, Facebook blamed the public education system its inability to hire more people of color. I mean, whose fault could it be?! Surely not Facebook’s! To address its diversity problems, Facebook said it would give $15 million to Code.org in order to expand CS education, news that was dutifully reported by the ed-tech press without any skepticism about Facebook’s claims about its hiring practices or about the availability of diverse tech talent.

    The “pipeline” problem, writes Dare Obasanjo, is a “big lie.” “The reality is that tech companies shape the ethnic make up of their employees based on what schools & cities they choose to hire from and where they locate engineering offices.” There is diverse technical talent, ready to be hired; the tech sector, blinded by white, male privilege, does not recognize it, does not see it. See the hashtag #FBNoExcuses which features more smart POC in tech than work at Facebook and Twitter combined, I bet.

    Facebook’s decision to “blame schools” is pretty familiar schtick by now, I suppose, but it’s still fairly noteworthy coming from a company whose founder and CEO is increasingly active in ed-tech investing. More broadly, Silicon Valley continues to try to shape the future of education – mostly by defining that future as an “engineering” or “platform” problem and then selling schools and parents and students a product in return. As the tech industry utterly fails to address diversity within its own ranks, what can we expect from its vision for ed-tech?!

    My fear: ed-tech will ignore inequalities. Ed-tech will expand inequalities. Ed-tech will, as Edsurge demonstrated this week, simply co-opt the words of people of color in order to continue to sell its products to schools. (José Vilson has more to say about this particular appropriation in this week’s #educolor newsletter.)

    And/or: ed-tech will, as I argued this week in the keynote I delivered at the Digital Pedagogy Institute in PEI, confuse consumption with “innovation.” “Gotta catch ’em all” may be the perfect slogan for consumer capitalism; but it’s hardly a mantra I’m comfortable chanting to push for education transformation. You cannot buy your way to progress.

    All of the “Pokémon GO will revolutionize education” claims have made me incredibly angry, even though it’s a claim that’s made about every single new product that ed-tech’s early adopters find exciting (and clickbait-worthy). I realize there are many folks who seem to find a great deal of enjoyment in the mobile game. Hoorah. But there are some significant issues with the game’s security, privacy, its Terms of Service, its business model, and its crowd-sourced data model – a data model that reflects the demographics of those who played an early version of the game and one that means that there are far fewer “pokestops” in Black neighborhoods. All this matters for Pokémon GO; all this matters for ed-tech.

    Pokémon GO.
    Pokémon GO

    Pokémon GO is just the latest example of digital redlining, re-inscribing racist material policies and practices into new, digital spaces. So when ed-tech leaders suggest that we shouldn’t criticize Pokémon GO, I despair. I really do. Who is served by being silent!? Who is served by enforced enthusiasm? How does ed-tech, which has its own problems with diversity, serve to re-inscribe racist policies and practices because its loudest proponents have little interest in examining their own privileges, unless, as José points out, it gets them clicks?

    Sigh.
    _____

    Audrey Watters is a writer who focuses on education technology – the relationship between politics, pedagogy, business, culture, and ed-tech. She has worked in the education field for over 15 years: teaching, researching, organizing, and project-managing. Although she was two chapters into her dissertation (on a topic completely unrelated to ed-tech), she decided to abandon academia, and she now happily fulfills the one job recommended to her by a junior high aptitude test: freelance writer. Her stories have appeared on NPR/KQED’s education technology blog MindShift, in the data section of O’Reilly Radar, on Inside Higher Ed, in The School Library Journal, in The Atlantic, on ReadWriteWeb, and Edutopia. She is the author of the recent book The Monsters of Education Technology (Smashwords, 2014) and working on a book called Teaching Machines. She maintains the widely-read Hack Education blog, and writes frequently for The b2 Review Digital Studies magazine on digital technology and education.

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  • Data and Desire in Academic Life

    Data and Desire in Academic Life

    a review of Erez Aiden and Jean-Baptiste Michel, Uncharted: Big Data as a Lens on Human Culture (Riverhead Books, reprint edition, 2014)
    by Benjamin Haber
    ~

    On a recent visit to San Francisco, I found myself trying to purchase groceries when my credit card was declined. As the cashier is telling me this news, and before I really had time to feel any particular way about it, my leg vibrates. I’ve received a text: “Chase Fraud-Did you use card ending in 1234 for $100.40 at a grocery store on 07/01/2015? If YES reply 1, NO reply 2.” After replying “yes” (which was recognized even though I failed to follow instructions), I swiped my card again and was out the door with my food. Many have probably had a similar experience: most if not all credit card companies automatically track purchases for a variety of reasons, including fraud prevention, the tracking of illegal activity, and to offer tailored financial products and services. As I walked out of the store, for a moment, I felt the power of “big data,” how real-time consumer information can be read as be a predictor of a stolen card in less time than I had to consider why my card had been declined. It was a too rare moment of reflection on those networks of activity that modulate our life chances and capacities, mostly below and above our conscious awareness.

    And then I remembered: didn’t I buy my plane ticket with the points from that very credit card? And in fact, hadn’t I used that card on multiple occasions in San Francisco for purchases not much less than the amount my groceries cost. While the near-instantaneous text provided reassurance before I could consciously recognize my anxiety, the automatic card decline was likely not a sophisticated real-time data-enabled prescience, but a rather blunt instrument, flagging the transaction on the basis of two data points: distance from home and amount of purchase. In fact, there is plenty of evidence to suggest that the gap between data collection and processing, between metadata and content and between current reality of data and its speculative future is still quite large. While Target’s pregnancy predicting algorithm was a journalistic sensation, the more mundane computational confusion that has Gmail constantly serving me advertisements for trade and business schools shows the striking gap between the possibilities of what is collected and the current landscape of computationally prodded behavior. The text from Chase, your Klout score, the vibration of your FitBit, or the probabilistic genetic information from 23 and me are all primarily affective investments in mobilizing a desire for data’s future promise. These companies and others are opening of new ground for discourse via affect, creating networked infrastructures for modulating the body and social life.

    I was thinking about this while reading Uncharted: Big Data as a Lens on Human Culture, a love letter to the power and utility of algorithmic processing of the words in books. Though ostensibly about the Google Ngram Viewer, a neat if one-dimensional tool to visualize the word frequency of a portion of the books scanned by Google, Uncharted is also unquestionably involved in the mobilization of desire for quantification. Though about the academy rather than financialization, medicine, sports or any other field being “revolutionized” by big data, its breathless boosterism and obligatory cautions are emblematic of the emergent datafied spirit of capitalism, a celebratory “coming out” of the quantifying systems that constitute the emergent infrastructures of sociality.

    While published fairly recently, in 2013, Uncharted already feels dated in its strangely muted engagement with the variety of serious objections to sprawling corporate and state run data systems in the post-Snowden, post-Target, post-Ashley Madison era (a list that will always be in need of update). There is still the dazzlement about the sheer magnificent size of this potential new suitor—“If you wrote out all five zettabytes that humans produce every year by hand, you would reach the core of the Milky Way” (11)—all the more impressive when explicitly compared to the dusty old technologies of ink and paper. Authors Erez Aiden and Jean-Baptiste Michel are floating in a world of “simple and beautiful” formulas (45), “strange, fascinating and addictive” methods (22), producing “intriguing, perplexing and even fun” conclusions (119) in their drive to colonize the “uncharted continent” (76) that is the English language. The almost erotic desire for this bounty is made more explicit in their tongue-in-cheek characterization of their meetings with Google employees as an “irresistible… mating dance” (22):

    Scholars and scientists approach engineers, product managers, and even high-level executives about getting access to their companies’ data. Sometimes the initial conversation goes well. They go out for coffee. One thing leads to another, and a year later, a brand-new person enters the picture. Unfortunately this person is usually a lawyer. (22)

    There is a lot to unpack in these metaphors, the recasting of academic dependence on data systems designed and controlled by corporate entities as a sexy new opportunity for scholars and scientists. There are important conversations to be had about these circulations of quantified desire; about who gets access to this kind of data, the ethics of working with companies who have an existential interest in profit and shareholder return and the cultural significance of wrapping business transactions in the language of heterosexual coupling. Here however I am mostly interested in the real allure that this passage and others speaks to, and the attendant fear that mostly whispers, at least in a book written by Harvard PhDs with Ted talks to give.

    For most academics in the social sciences and the humanities “big data” is a term more likely to get caught in the throat than inspire butterflies in the stomach. While Aiden and Michel certainly acknowledge that old-fashion textual analysis (50) and theory (20) will have a place in this brave new world of charts and numbers, they provide a number of contrasts to suggest the relative poverty of even the most brilliant scholar in the face of big data. One hypothetical in particular, that is not directly answered but is strongly implied, spoke to my discipline specifically:

    Consider the following question: Which would help you more if your quest was to learn about contemporary human society—unfettered access to a leading university’s department of sociology, packed with experts on how societies function, or unfettered access to Facebook, a company whose goal is to help mediate human social relationships online? (12)

    The existential threat at the heart of this question was catalyzed for many people in Roger Burrows and Mike Savage’s 2007 “The Coming Crisis of Empirical Sociology,” an early canary singing the worry of what Nigel Thrift has called “knowing capitalism” (2005). Knowing capitalism speaks to the ways that capitalism has begun to take seriously the task of “thinking the everyday” (1) by embedding information technologies within “circuits of practice” (5). For Burrows and Savage these practices can and should be seen as a largely unrecognized world of sophisticated and profit-minded sociology that makes the quantitative tools of academics look like “a very poor instrument” in comparison (2007: 891).

    Indeed, as Burrows and Savage note, the now ubiquitous social survey is a technology invented by social scientists, folks who were once seen as strikingly innovative methodologists (888). Despite ever more sophisticated statistical treatments however, the now over 40 year old social survey remains the heart of social scientific quantitative methodology in a radically changed context. And while declining response rates, a constraining nation-based framing and competition from privately-funded surveys have all decreased the efficacy of academic survey research (890), nothing has threatened the discipline like the embedded and “passive” collecting technologies that fuel big data. And with these methodological changes come profound epistemological ones: questions of how, when, why and what we know of the world. These methods are inspiring changing ideas of generalizability and new expectations around the temporality of research. Does it matter, for example, that studies have questioned the accuracy of the FitBit? The growing popularity of these devices suggests at the very least that sociologists should not count on empirical rigor to save them from irrelevance.

    As academia reorganizes around the speculative potential of digital technologies, there is an increasing pile of capital available to those academics able to translate between the discourses of data capitalism and a variety of disciplinary traditions. And the lure of this capital is perhaps strongest in the humanities, whose scholars have been disproportionately affected by state economic retrenchment on education spending that has increasingly prioritized quantitative, instrumental, and skill-based majors. The increasing urgency in the humanities to use bigger and faster tools is reflected in the surprisingly minimal hand wringing over the politics of working with companies like Facebook, Twitter and Google. If there is trepidation in the N-grams project recounted in Uncharted, it is mostly coming from Google, whose lawyers and engineers have little incentive to bother themselves with the politically fraught, theory-driven, Institutional Review Board slow lane of academic production. The power imbalance of this courtship leaves those academics who decide to partner with these companies at the mercy of their epistemological priorities and, as Uncharted demonstrates, the cultural aesthetics of corporate tech.

    This is a vision of the public humanities refracted through the language of public relations and the “measurable outcomes” culture of the American technology industry. Uncharted has taken to heart the power of (re)branding to change the valence of your work: Aiden and Michel would like you to call their big data inflected historical research “culturomics” (22). In addition to a hopeful attempt to coin a buzzy new work about the digital, culturomics linguistically brings the humanities closer to the supposed precision, determination and quantifiability of economics. And lest you think this multivalent bringing of culture to capital—or rather the renegotiation of “the relationship between commerce and the ivory tower” (8)—is unseemly, Aiden and Michel provide an origin story to show how futile this separation has been.

    But the desire for written records has always accompanied economic activity, since transactions are meaningless unless you can clearly keep track of who owns what. As such, early human writing is dominated by wheeling and dealing: a menagerie of bets, chits, and contracts. Long before we had the writings of prophets, we had the writing of profits. (9)

    And no doubt this is true: culture is always already bound up with economy. But the full-throated embrace of culturomics is not a vision of interrogating and reimagining the relationship between economic systems, culture and everyday life; [1] rather it signals the acceptance of the idea of culture as transactional business model. While Google has long imagined itself as a company with a social mission, they are a publicly held company who will be punished by investors if they neglect their bottom line of increasing the engagement of eyeballs on advertisements. The N-gram Viewer does not make Google money, but it perhaps increases public support for their larger book-scanning initiative, which Google clearly sees as a valuable enough project to invest many years of labor and millions of dollars to defend in court.

    This vision of the humanities is transactionary in another way as well. While much of Uncharted is an attempt to demonstrate the profound, game-changing implications of the N-gram viewer, there is a distinctly small-questions, cocktail-party-conversation feel to this type of inquiry that seems ironically most useful in preparing ABD humanities and social science PhDs for jobs in the service industry than in training them for the future of academia. It might be more precise to say that the N-gram viewer is architecturally designed for small answers rather than small questions. All is resolved through linear projection, a winner and a loser or stasis. This is a vision of research where the precise nature of the mediation (what books have been excluded? what is the effect of treating all books as equally revealing of human culture? what about those humans whose voices have been systematically excluded from the written record?) is ignored, and where the actual analysis of books, and indeed the books themselves, are black-boxed from the researcher.

    Uncharted speaks to perils of doing research under the cloud of existential erasure and to the failure of academics to lead with a different vision of the possibilities of quantification. Collaborating with the wealthy corporate titans of data collection requires an acceptance of these companies own existential mandate: make tons of money by monetizing a dizzying array of human activities while speculatively reimagining the future to attempt to maintain that cash flow. For Google, this is a vision where all activities, not just “googling” are collected and analyzed in a seamlessly updating centralized system. Cars, thermostats, video games, photos, businesses are integrated not for the public benefit but because of the power of scale to sell or rent or advertise products. Data is promised as a deterministic balm for the unknowability of life and Google’s participation in academic research gives them the credibility to be your corporate (sen.se) mother. What, might we imagine, are the speculative possibilities of networked data not beholden to shareholder value?
    _____

    Benjamin Haber is a PhD candidate in Sociology at CUNY Graduate Center and a Digital Fellow at The Center for the Humanities. His current research is a cultural and material exploration of emergent infrastructures of corporeal data through a queer theoretical framework. He is organizing a conference called “Queer Circuits in Archival Times: Experimentation and Critique of Networked Data” to be held in New York City in May 2016.

    Back to the essay

    _____

    Notes

    [1] A project desperately needed in academia, where terms like “neoliberalism,” “biopolitics” and “late capitalism” more often than not are used briefly at end of a short section on implications rather than being given the critical attention and nuanced intentionality that they deserve.

    Works Cited

    Savage, Mike, and Roger Burrows. 2007. “The Coming Crisis of Empirical Sociology.” Sociology 41 (5): 885–99.

    Thrift, Nigel. 2005. Knowing Capitalism. London: SAGE.

  • Coding Bootcamps and the New For-Profit Higher Ed

    Coding Bootcamps and the New For-Profit Higher Ed

    By Audrey Watters
    ~
    After decades of explosive growth, the future of for-profit higher education might not be so bright. Or, depending on where you look, it just might be…

    In recent years, there have been a number of investigations – in the media, by the government – into the for-profit college sector and questions about these schools’ ability to effectively and affordably educate their students. Sure, advertising for for-profits is still plastered all over the Web, the airwaves, and public transportation, but as a result of journalistic and legal pressures, the lure of these schools may well be a lot less powerful. If nothing else, enrollment and profits at many for-profit institutions are down.

    Despite the massive amounts of money spent by the industry to prop it up – not just on ads but on lobbying and legal efforts, the Obama Administration has made cracking down on for-profits a centerpiece of its higher education policy efforts, accusing these schools of luring students with misleading and overblown promises, often leaving them with low-status degrees sneered at by employers and with loans students can’t afford to pay back.

    But the Obama Administration has also just launched an initiative that will make federal financial aid available to newcomers in the for-profit education sector: ed-tech experiments like “coding bootcamps” and MOOCs. Why are these particular for-profit experiments deemed acceptable? What do they do differently from the much-maligned for-profit universities?

    School as “Skills Training”

    In many ways, coding bootcamps do share the justification for their existence with for-profit universities. That is, they were founded in order to help to meet the (purported) demands of the job market: training people with certain technical skills, particularly those skills that meet the short-term needs of employers. Whether they meet students’ long-term goals remains to be seen.

    I write “purported” here even though it’s quite common to hear claims that the economy is facing a “STEM crisis” – that too few people have studied science, technology, engineering, or math and employers cannot find enough skilled workers to fill jobs in those fields. But claims about a shortage of technical workers are debatable, and lots of data would indicate otherwise: wages in STEM fields have remained flat, for example, and many who graduate with STEM degrees cannot find work in their field. In other words, the crisis may be “a myth.”

    But it’s a powerful myth, and one that isn’t terribly new, dating back at least to the launch of the Sputnik satellite in 1957 and subsequent hand-wringing over the Soviets’ technological capabilities and technical education as compared to the US system.

    There are actually a number of narratives – some of them competing narratives – at play here in the recent push for coding bootcamps, MOOCs, and other ed-tech initiatives: that everyone should go to college; that college is too expensive – “a bubble” in the Silicon Valley lexicon; that alternate forms of credentialing will be developed (by the technology sector, naturally); that the tech sector is itself a meritocracy, and college degrees do not really matter; that earning a degree in the humanities will leave you unemployed and burdened by student loan debt; that everyone should learn to code. Much like that supposed STEM crisis and skill shortage, these narratives might be powerful, but they too are hardly provable.

    Nor is the promotion of a more business-focused education that new either.

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    Career Colleges: A History

    Foster’s Commercial School of Boston, founded in 1832 by Benjamin Franklin Foster, is often recognized as the first school established in the United States for the specific purpose of teaching “commerce.” Many other commercial schools opened on its heels, most located in the Atlantic region in major trading centers like Philadelphia, Boston, New York, and Charleston. As the country expanded westward, so did these schools. Bryant & Stratton College was founded in Cleveland in 1854, for example, and it established a chain of schools, promising to open a branch in every American city with a population of more than 10,000. By 1864, it had opened more than 50, and the chain is still in operation today with 18 campuses in New York, Ohio, Virginia, and Wisconsin.

    The curriculum of these commercial colleges was largely based around the demands of local employers alongside an economy that was changing due to the Industrial Revolution. Schools offered courses in bookkeeping, accounting, penmanship, surveying, and stenography. This was in marketed contrast to those universities built on a European model, which tended to teach topics like theology, philosophy, and classical language and literature. If these universities were “elitist,” the commercial colleges were “popular” – there were over 70,000 students enrolled in them in 1897, compared to just 5800 in colleges and universities – something that highlights what’s a familiar refrain still today: that traditional higher ed institutions do not meet everyone’s needs.

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    The existence of the commercial colleges became intertwined in many success stories of the nineteenth century: Andrew Carnegie attended night school in Pittsburgh to learn bookkeeping, and John D. Rockefeller studied banking and accounting at Folsom’s Commercial College in Cleveland. The type of education offered at these schools was promoted as a path to become a “self-made man.”

    That’s the story that still gets told: these sorts of classes open up opportunities for anyone to gain the skills (and perhaps the certification) that will enable upward mobility.

    It’s a story echoed in the ones told about (and by) John Sperling as well. Born into a working class family, Sperling worked as a merchant marine, then attended community college during the day and worked as a gas station attendant at night. He later transferred to Reed College, went on to UC Berkeley, and completed his doctorate at Cambridge University. But Sperling felt as though these prestigious colleges catered to privileged students; he wanted a better way for working adults to be able to complete their degrees. In 1976, he founded the University of Phoenix, one of the largest for-profit colleges in the US which at its peak in 2010 enrolled almost 600,000 students.

    Other well-known names in the business of for-profit higher education: Walden University (founded in 1970), Capella University (founded in 1993), Laureate Education (founded in 1999), Devry University (founded in 1931), Education Management Corporation (founded in 1962), Strayer University (founded in 1892), Kaplan University (founded in 1937 as The American Institute of Commerce), and Corinthian Colleges (founded in 1995 and defunct in 2015).

    It’s important to recognize the connection of these for-profit universities to older career colleges, and it would be a mistake to see these organizations as distinct from the more recent development of MOOCs and coding bootcamps. Kaplan, for example, acquired the code school Dev Bootcamp in 2014. Laureate Education is an investor in the MOOC provider Coursera. The Apollo Education Group, the University of Phoenix’s parent company, is an investor in the coding bootcamp The Iron Yard.

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    Promises, Promises

    Much like the worries about today’s for-profit universities, even the earliest commercial colleges were frequently accused of being “purely business speculations” – “diploma mills” – mishandled by administrators who put the bottom line over the needs of students. There were concerns about the quality of instruction and about the value of the education students were receiving.

    That’s part of the apprehension about for-profit universities’ (almost most) recent manifestations too: that these schools are charging a lot of money for a certification that, at the end of the day, means little. But at least the nineteenth century commercial colleges were affordable, UC Berkley history professor Caitlin Rosenthal argues in a 2012 op-ed in Bloomberg,

    The most common form of tuition at these early schools was the “life scholarship.” Students paid a lump sum in exchange for unlimited instruction at any of the college’s branches – $40 for men and $30 for women in 1864. This was a considerable fee, but much less than tuition at most universities. And it was within reach of most workers – common laborers earned about $1 per day and clerks’ wages averaged $50 per month.

    Many of these “life scholarships” promised that students who enrolled would land a job – and if they didn’t, they could always continue their studies. That’s quite different than the tuition at today’s colleges – for-profit or not-for-profit – which comes with no such guarantee.

    Interestingly, several coding bootcamps do make this promise. A 48-week online program at Bloc will run you $24,000, for example. But if you don’t find a job that pays $60,000 after four months, your tuition will be refunded, the startup has pledged.

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    According to a recent survey of coding bootcamp alumni, 66% of graduates do say they’ve found employment (63% of them full-time) in a job that requires the skills they learned in the program. 89% of respondents say they found a job within 120 days of completing the bootcamp. Yet 21% say they’re unemployed – a number that seems quite high, particularly in light of that supposed shortage of programming talent.

    For-Profit Higher Ed: Who’s Being Served?

    The gulf between for-profit higher ed’s promise of improved job prospects and the realities of graduates’ employment, along with the price tag on its tuition rates, is one of the reasons that the Obama Administration has advocated for “gainful employment” rules. These would measure and monitor the debt-to-earnings ratio of graduates from career colleges and in turn penalize those schools whose graduates had annual loan payments more than 8% of their wages or 20% of their discretionary earnings. (The gainful employment rules only apply to those schools that are eligible for Title IV federal financial aid.)

    The data is still murky about how much debt attendees at coding bootcamps accrue and how “worth it” these programs really might be. According to the aforementioned survey, the average tuition at these programs is $11,852. This figure might be a bit deceiving as the price tag and the length of bootcamps vary greatly. Moreover, many programs, such as App Academy, offer their program for free (well, plus a $5000 deposit) but then require that graduates repay up to 20% of their first year’s salary back to the school. So while the tuition might appear to be low in some cases, the indebtedness might actually be quite high.

    According to Course Report’s survey, 49% of graduates say that they paid tuition out of their own pockets, 21% say they received help from family, and just 1.7% say that their employer paid (or helped with) the tuition bill. Almost 25% took out a loan.

    That percentage – those going into debt for a coding bootcamp program – has increased quite dramatically over the last few years. (Less than 4% of graduates in the 2013 survey said that they had taken out a loan). In part, that’s due to the rapid expansion of the private loan industry geared towards serving this particular student population. (Incidentally, the two ed-tech companies which have raised the most money in 2015 are both loan providers: SoFi and Earnest. The former has raised $1.2 billion in venture capital this year; the latter $245 million.)

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    The Obama Administration’s newly proposed “EQUIP” experiment will open up federal financial aid to some coding bootcamps and other ed-tech providers (like MOOC platforms), but it’s important to underscore some of the key differences here between federal loans and private-sector loans: federal student loans don’t have to be repaid until you graduate or leave school; federal student loans offer forbearance and deferment if you’re struggling to make payments; federal student loans have a fixed interest rate, often lower than private loans; federal student loans can be forgiven if you work in public service; federal student loans (with the exception of PLUS loans) do not require a credit check. The latter in particular might help to explain the demographics of those who are currently attending coding bootcamps: if they’re having to pay out-of-pocket or take loans, students are much less likely to be low-income. Indeed, according to Course Report’s survey, the cost of the bootcamps and whether or not they offered a scholarship was one of the least important factors when students chose a program.

    Here’s a look at some coding bootcamp graduates’ demographic data (as self-reported):

    Age
    Mean Age 30.95
    Gender
    Female 36.3%
    Male 63.1%
    Ethnicity
    American Indian 1.0%
    Asian American 14.0%
    Black 5.0%
    Other 17.2%
    White 62.8%
    Hispanic Origin
    Yes 20.3%
    No 79.7%
    Citizenship
    Yes, born in the US 78.2%
    Yes, naturalized 9.7%
    No 12.2%
    Education
    High school dropout 0.2%
    High school graduate 2.6%
    Some college 14.2%
    Associate’s degree 4.1%
    Bachelor’s degree 62.1%
    Master’s degree 14.2%
    Professional degree 1.5%
    Doctorate degree 1.1%

    (According to several surveys of MOOC enrollees, these students also tend to be overwhelmingly male from more affluent neighborhoods, and MOOC students also tend to already possess Bachelor’s degrees. The median age of MITx registrants is 27.)

    It’s worth considering how the demographics of students in MOOCs and coding bootcamps may (or may not) be similar to those enrolled at other for-profit post-secondary institutions, particularly since all of these programs tend to invoke the rhetoric about “democratizing education” and “expanding access.” Access for whom?

    Some two million students were enrolled in for-profit colleges in 2010, up from 400,000 a decade earlier. These students are disproportionately older, African American, and female when compared to the entire higher ed student population. While one in 20 of all students are enrolled in a for-profit college, 1 in 10 African American students, 1 in 14 Latino students, and 1 in 14 first-generation college students are enrolled at a for-profit. Students at for-profits are more likely to be single parents. They’re less likely to enter with a high school diploma. Dependent students in for-profits have about half as much family income as students in not-for-profit schools. (This demographic data is drawn from the NCES and from Harvard University researchers David Deming, Claudia Goldin, and Lawrence Katz in their 2013 study on for-profit colleges.)

    Deming, Goldin, and Katz argue that

    The snippets of available evidence suggest that the economic returns to students who attend for-profit colleges are lower than those for public and nonprofit colleges. Moreover, default rates on student loans for proprietary schools far exceed those of other higher-education institutions.

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    According to one 2010 report, just 22% of first- and full-time students pursuing Bachelor’s degrees at for-profit colleges in 2008 graduated, compared to 55% and 65% of students at public and private non-profit universities respectively. Of the more than 5000 career programs that the Department of Education tracks, 72% of those offered by for-profit institutions produce graduates who earn less than high school dropouts.

    For their part, today’s MOOCs and coding bootcamps also boast that their students will find great success on the job market. Coursera, for example, recently surveyed its students who’d completed one of its online courses and 72% who responded said they had experienced “career benefits.” But without the mandated reporting that comes with federal financial aid, a lot of what we know about their student population and student outcomes remains pretty speculative.

    What kind of students benefit from coding bootcamps and MOOC programs, the new for-profit education? We don’t really know… although based on the history of higher education and employment, we can guess.

    EQUIP and the New For-Profit Higher Ed

    On October 14, the Obama Administration announced a new initiative, the Educational Quality through Innovative Partnerships (EQUIP) program, which will provide a pathway for unaccredited education programs like coding bootcamps and MOOCs to become eligible for federal financial aid. According to the Department of Education, EQUIP is meant to open up “new models of education and training” to low income students. In a press release, it argues that “Some of these new models may provide more flexible and more affordable credentials and educational options than those offered by traditional higher institutions, and are showing promise in preparing students with the training and education needed for better, in-demand jobs.”

    The EQUIP initiative will partner accredited institutions with third-party providers, loosening the “50% rule” that prohibits accredited schools from outsourcing more than 50% of an accredited program. Since bootcamps and MOOC providers “are not within the purview of traditional accrediting agencies,” the Department of Education says, “we have no generally accepted means of gauging their quality.” So those organizations that apply for the experiment will have to provide an outside “quality assurance entity,” which will help assess “student outcomes” like learning and employment.

    By making financial aid available for bootcamps and MOOCs, one does have to wonder if the Obama Administration is not simply opening the doors for more of precisely the sort of practices that the for-profit education industry has long been accused of: expanding rapidly, lowering the quality of instruction, focusing on marketing to certain populations (such as veterans), and profiting off of taxpayer dollars.

    Who benefits from the availability of aid? And who benefits from its absence? (“Who” here refers to students and to schools.)

    Shawna Scott argues in “The Code School-Industrial Complex” that without oversight, coding bootcamps re-inscribe the dominant beliefs and practices of the tech industry. Despite all the talk of “democratization,” this is a new form of gatekeeping.

    Before students are even accepted, school admission officers often select for easily marketable students, which often translates to students with the most privileged characteristics. Whether through intentionally targeting those traits because it’s easier to ensure graduates will be hired, or because of unconscious bias, is difficult to discern. Because schools’ graduation and employment rates are their main marketing tool, they have a financial stake in only admitting students who are at low risk of long-term unemployment. In addition, many schools take cues from their professional developer founders and run admissions like they hire for their startups. Students may be subjected to long and intensive questionnaires, phone or in-person interviews, or be required to submit a ‘creative’ application, such as a video. These requirements are often onerous for anyone working at a paid job or as a caretaker for others. Rarely do schools proactively provide information on alternative application processes for people of disparate ability. The stereotypical programmer is once again the assumed default.

    And so, despite the recent moves to sanction certain ed-tech experiments, some in the tech sector have been quite vocal in their opposition to more regulations governing coding schools. It’s not just EQUIP either; there was much outcry last year after several states, including California, “cracked down” on bootcamps. Many others have framed the entire accreditation system as a “cabal” that stifles innovation. “Innovation” in this case implies alternate certificate programs – not simply Associate’s or Bachelor’s degrees – in timely, technical topics demanded by local/industry employers.

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    The Forgotten Tech Ed: Community Colleges

    Of course, there is an institution that’s long offered alternate certificate programs in timely, technical topics demanded by local/industry employers, and that’s the community college system.

    Vox’s Libby Nelson observed that “The NYT wrote more about Harvard last year than all community colleges combined,” and certainly the conversations in the media (and elsewhere) often ignore that community colleges exist at all, even though these schools educate almost half of all undergraduates in the US.

    Like much of public higher education, community colleges have seen their funding shrink in recent decades and have been tasked to do more with less. For community colleges, it’s a lot more with a lot less. Open enrollment, for example, means that these schools educate students who require more remediation. Yet despite many community colleges students being “high need,” community colleges spend far less per pupil than do four-year institutions. Deep budget cuts have also meant that even with their open enrollment policies, community colleges are having to restrict admissions. In 2012, some 470,000 students in California were on waiting lists, unable to get into the courses they need.

    This is what we know from history: as the funding for public higher ed decreased – for two- and four-year schools alike, for-profit higher ed expanded, promising precisely what today’s MOOCs and coding bootcamps now insist they’re the first and the only schools to do: to offer innovative programs, training students in the kinds of skills that will lead to good jobs. History tells us otherwise…
    _____

    Audrey Watters is a writer who focuses on education technology – the relationship between politics, pedagogy, business, culture, and ed-tech. She has worked in the education field for over 15 years: teaching, researching, organizing, and project-managing. Although she was two chapters into her dissertation (on a topic completely unrelated to ed-tech), she decided to abandon academia, and she now happily fulfills the one job recommended to her by a junior high aptitude test: freelance writer. Her stories have appeared on NPR/KQED’s education technology blog MindShift, in the data section of O’Reilly Radar, on Inside Higher Ed, in The School Library Journal, in The Atlantic, on ReadWriteWeb, and Edutopia. She is the author of the recent book The Monsters of Education Technology (Smashwords, 2014) and working on a book called Teaching Machines. She maintains the widely-read Hack Education blog, on which an earlier version of this essay first appeared, and writes frequently for The b2 Review Digital Studies magazine on digital technology and education.

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  • How We Think About Technology (Without Thinking About Politics)

    How We Think About Technology (Without Thinking About Politics)

    N. Katherine Hayles, How We Think: Digital Media and Contemporary Technogenesis (Chicago, 2012)a review of N. Katherine Hayles, How We Think: Digital Media and Contemporary Technogenesis (Chicago, 2012)
    by R. Joshua Scannell

    ~

    In How We Think, N Katherine Hayles addresses a number of increasingly urgent problems facing both the humanities in general and scholars of digital culture in particular. In keeping with the research interests she has explored at least since 2002’s Writing Machines (MIT Press), Hayles examines the intersection of digital technologies and humanities practice to argue that contemporary transformations in the orientation of the University (and elsewhere) are attributable to shifts that ubiquitous digital culture have engendered in embodied cognition. She calls this process of mutual evolution between the computer and the human technogenesis (a term that is mostly widely associated with the work of Bernard Stiegler, although Hayles’s theories often aim in a different direction from Stiegler’s). Hayles argues that technogenesis is the basis for the reorientation of the academy, including students, away from established humanistic practices like close reading. Put another way, not only have we become posthuman (as Hayles discusses in her landmark 1999 University of Chicago Press book, How We Became Posthuman: Virtual Bodies in Cybernetics, Literature, and Informatics), but our brains have begun to evolve to think with computers specifically and digital media generally. Rather than a rearguard eulogy for the humanities that was, Hayles advocates for an opening of the humanities to digital dromology; she sees the Digital Humanities as a particularly fertile ground from which to reimagine the humanities generally.

    Hayles is an exceptional scholar, and while her theory of technogenesis is not particularly novel, she articulates it with a clarity and elegance that are welcome and useful in a field that is often cluttered with good ideas, unintelligibly argued. Her close engagement with work across a range of disciplines – from Hegelian philosophy of mind (Catherine Malabou) to theories of semiosis and new media (Lev Manovich) to experimental literary production – grounds an argument about the necessity of transmedial engagement in an effective praxis. Moreover, she ably shifts generic gears over the course of a relatively short manuscript, moving from quasi-ethnographic engagement with University administrators, to media archaeology a la Friedrich Kittler, to contemporary literary theory, with grace. Her critique of the humanities that is, therefore, doubles as a praxis: she is actually producing the discipline-flouting work that she calls on her colleagues to pursue.

    The debate about the death and/or future of the humanities is weather worn, but Hayles’s theory of technogenesis as a platform for engaging in it is a welcome change. For Hayles, the technogenetic argument centers on temporality, and the multiple temporalities embedded in computer processing and human experience. She envisions this relation as cybernetic, in which computer and human are integrated as a system through the feedback loops of their coemergent temporalities. So, computers speed up human responses, which lag behind innovations, which prompt beta test cycles at quicker rates, which demand humans to behave affectively, nonconsciously. The recursive relationship between human duration and machine temporality effectively mutates both. Humanities professors might complain that their students cannot read “closely” like they used to, but for Hayles this is a fault of those disciplines to imagine methods in step with technological changes. Instead of digital media making us “dumber” by reducing our attention spans, as Nicholas Carr argues, Hayles claims that the movement towards what she calls “hyper reading” is an ontological and biological fact of embodied cognition in the age of digital media. If “how we think” were posed as a question, the answer would be: bodily, quickly, cursorily, affectively, non-consciously.

    Hayles argues that this doesn’t imply an eliminative teleology of human capacity, but rather an opportunity to think through novel, expansive interventions into this cyborg loop. We may be thinking (and feeling, and experiencing) differently than we used to, but this remains a fact of human existence. Digital media has shifted the ontics of our technogenetic reality, but it has not fundamentally altered its ontology. Morphological biology, in fact, entails ontological stability. To be human, and to think like one, is to be with machines, and to think with them. The kids, in other words, are all right.

    This sort of quasi-Derridean or Stieglerian Hegelianism is obviously not uncommon in media theory. As Hayles deploys it, this disposition provides a powerful framework for thinking through the relationship of humans and machines without ontological reductivism on either end. Moreover, she engages this theory in a resolutely material fashion, evading the enervating tendency of many theorists in the humanities to reduce actually existing material processes to metaphor and semiosis. Her engagement with Malabou’s work on brain plasticity is particularly useful here. Malabou has argued that the choice facing the intellectual in the age of contemporary capitalism is between plasticity and self-fashioning. Plasticity is a quintessential demand of contemporary capitalism, whereas self-fashioning opens up radical possibilities for intervention. The distinction between these two potentialities, however, is unclear – and therefore demands an ideological commitment to the latter. Hayles is right to point out that this dialectic insufficiently accounts for the myriad ways in which we are engaged with media, and are in fact produced, bodily, by it.

    But while Hayles’ critique is compelling, the responses she posits may be less so. Against what she sees as Malabou’s snide rejection of the potential of media, she argues

    It is precisely because contemporary technogenesis posits a strong connection between ongoing dynamic adaptation of technics and humans that multiple points of intervention open up. These include making new media…adapting present media to subversive ends…using digital media to reenvision academic practices, environments and strategies…and crafting reflexive representations of media self fashionings…that call attention to their own status as media, in the process raising our awareness of both the possibilities and dangers of such self-fashioning. (83)

    With the exception of the ambiguous labor done by the word “subversive,” this reads like a catalog of demands made by administrators seeking to offload ever-greater numbers of students into MOOCs. This is unfortunately indicative of what is, throughout the book, a basic failure to engage with the political economics of “digital media and contemporary technogenesis.” Not every book must explicitly be political, and there is little more ponderous than the obligatory, token consideration of “the political” that so many media scholars feel compelled to make. And yet, this is a text that claims to explain “how” “we” “think” under post-industrial, cognitive capitalism, and so the lack of this engagement cannot help but show.

    Universities across the country are collapsing due to lack of funding, students are practically reduced to debt bondage to cope with the costs of a desperately near-compulsory higher education that fails to deliver economic promises, “disruptive” deployment of digital media has conjured teratic corporate behemoths that all presume to “make the world a better place” on the backs of extraordinarily exploited workforces. There is no way for an account of the relationship between the human and the digital in this capitalist context not to be political. Given the general failure of the book to take these issues seriously, it is unsurprising that two of Hayles’ central suggestions for addressing the crisis in the humanities are 1) to use voluntary, hobbyist labor to do the intensive research that will serve as the data pool for digital humanities scholars and 2) to increasingly develop University partnerships with major digital conglomerates like Google.

    This reads like a cost-cutting administrator’s fever dream because, in the chapter in which Hayles promulgates novel (one might say “disruptive”) ideas for how best to move the humanities forward, she only speaks to administrators. There is no consideration of labor in this call for the reformation of the humanities. Given the enormous amount of writing that has been done on affective capitalism (Clough 2008), digital labor (Scholz 2012), emotional labor (Van Kleaf 2015), and so many other iterations of exploitation under digital capitalism, it boggles the mind a bit to see an embrace of the Mechanical Turk as a model for the future university.

    While it may be true that humanities education is in crisis – that it lacks funding, that its methods don’t connect with students, that it increasingly must justify its existence on economic grounds – it is unclear that any of these aspects of the crisis are attributable to a lack of engagement with the potentials of digital media, or the recognition that humans are evolving with our computers. All of these crises are just as plausibly attributable to what, among many others, Chandra Mohanty identified ten years ago as the emergence of the corporate university, and the concomitant transformation of the mission of the university from one of fostering democratic discourse to one of maximizing capital (Mohanty 2003). In other words, we might as easily attribute the crisis to the tightening command that contemporary capitalist institutions have over the logic of the university.

    Humanities departments are underfunded precisely because they cannot – almost by definition – justify their existence on monetary grounds. When students are not only acculturated, but are compelled by financial realities and debt, to understand the university as a credentialing institution capable of guaranteeing certain baseline waged occupations – then it is no surprise that they are uninterested in “close reading” of texts. Or, rather, it might be true that students’ “hyperreading” is a consequence of their cognitive evolution with machines. But it is also just as plausibly a consequence of the fact that students often are working full time jobs while taking on full time (or more) course loads. They do not have the time or inclination to read long, difficult texts closely. They do not have the time or inclination because of the consolidating paradigm around what labor, and particularly their labor, is worth. Why pay for a researcher when you can get a hobbyist to do it for free? Why pay for a humanities line when Google and Wikipedia can deliver everything an institution might need to know?

    In a political economy in which Amazon’s reduction of human employees to algorithmically-managed meat wagons is increasingly diagrammatic and “innovative” in industries from service to criminal justice to education, the proposals Hayles is making to ensure the future of the university seem more fifth columnary that emancipatory.

    This stance also evacuates much-needed context from what are otherwise thoroughly interesting, well-crafted arguments. This is particularly true of How We Think’s engagement with Lev Manovich’s claims regarding narrative and database. Speaking reductively, in The Language of New Media (MIT Press, 2001), Manovich argued that under there are two major communicative forms: narrative and database. Narrative, in his telling, is more or less linear, and dependent on human agency to be sensible. Novels and films, despite many modernist efforts to subvert this, tend toward narrative. The database, as opposed to the narrative, arranges information according to patterns, and does not depend on a diachronic point-to-point communicative flow to be intelligible. Rather, the database exists in multiple temporalities, with the accumulation of data for rhizomatic recall of seemingly unrelated information producing improbable patterns of knowledge production. Historically, he argues, narrative has dominated. But with the increasing digitization of cultural output, the database will more and more replace narrative.

    Manovich’s dichotomy of media has been both influential and roundly criticized (not least by Manovich himself in Software Takes Command, Bloomsbury 2013) Hayles convincingly takes it to task for being reductive and instituting a teleology of cultural forms that isn’t borne out by cultural practice. Narrative, obviously, hasn’t gone anywhere. Hayles extends this critique by considering the distinctive ways space and time are mobilized by database and narrative formations. Databases, she argues, depend on interoperability between different software platforms that need to access the stored information. In the case of geographical information services and global positioning services, this interoperability depends on some sort of universal standard against which all information can be measured. Thus, Cartesian space and time are inevitably inserted into database logics, depriving them of the capacity for liveliness. That is to say that the need to standardize the units that measure space and time in machine-readable databases imposes a conceptual grid on the world that is creatively limiting. Narrative, on the other hand, does not depend on interoperability, and therefore does not have an absolute referent against which it must make itself intelligible. Given this, it is capable of complex and variegated temporalities not available to databases. Databases, she concludes, can only operate within spatial parameters, while narrative can represent time in different, more creative ways.

    As an expansion and corrective to Manovich, this argument is compelling. Displacing his teleology and infusing it with a critique of the spatio-temporal work of database technologies and their organization of cultural knowledge is crucial. Hayles bases her claim on a detailed and fascinating comparison between the coding requirements of relational databanks and object-oriented databanks. But, somewhat surprisingly, she takes these different programming language models and metonymizes them as social realities. Temporality in the construction of objects transmutes into temporality as a philosophical category. It’s unclear how this leap holds without an attendant sociopolitical critique. But it is impossible to talk about the cultural logic of computation without talking about the social context in which this computation emerges. In other words, it is absolutely true that the “spatializing” techniques of coders (like clustering) render data points as spatial within the context of the data bank. But it is not an immediately logical leap to then claim that therefore databases as a cultural form are spatial and not temporal.

    Further, in the context of contemporary data science, Hayles’s claims about interoperability are at least somewhat puzzling. Interoperability and standardized referents might be a theoretical necessity for databases to be useful, but the ever-inflating markets around “big data,” data analytics, insights, overcoming data siloing, edge computing, etc, demonstrate quite categorically that interoperability-in-general is not only non-existent, but is productively non-existent. That is to say, there are enormous industries that have developed precisely around efforts to synthesize information generated and stored across non-interoperable datasets. Moreover, data analytics companies provide insights almost entirely based on their capacity to track improbably data patterns and resonances across unlikely temporalities.

    Far from a Cartesian world of absolute space and time, contemporary data science is a quite posthuman enterprise in committing machine learning to stretch, bend and strobe space and time in order to generate the possibility of bankable information. This is both theoretically true in the sense of setting algorithms to work sorting, sifting and analyzing truly incomprehensible amounts of data and materially true in the sense of the massive amount of capital and labor that is invested in building, powering, cooling, staffing and securing data centers. Moreover, the amount of data “in the cloud” has become so massive that analytics companies have quite literally reterritorialized information– particularly trades specializing in high frequency trading, which practice “co- location,” locating data centers geographically closer   the sites from which they will be accessed in order to maximize processing speed.

    Data science functions much like financial derivatives do (Martin 2015). Value in the present is hedged against the probable future spatiotemporal organization of software and material infrastructures capable of rendering a possibly profitable bundling of information in the immediate future. That may not be narrative, but it is certainly temporal. It is a temporality spurred by the queer fluxes of capital.

    All of which circles back to the title of the book. Hayles sets out to explain How We Think. A scholar with such an impeccable track record for pathbreaking analyses of the relationship of the human to technology is setting a high bar for herself with such a goal. In an era in which (in no small part due to her work) it is increasingly unclear who we are, what thinking is or how it happens, it may be an impossible bar to meet. Hayles does an admirable job of trying to inject new paradigms into a narrow academic debate about the future of the humanities. Ultimately, however, there is more resting on the question than the book can account for, not least the livelihoods and futures of her current and future colleagues.
    _____

    R Joshua Scannell is a PhD candidate in sociology at the CUNY Graduate Center. His current research looks at the political economic relations between predictive policing programs and urban informatics systems in New York City. He is the author of Cities: Unauthorized Resistance and Uncertain Sovereignty in the Urban World (Paradigm/Routledge, 2012).

    Back to the essay
    _____

    Patricia T. Clough. 2008. “The Affective Turn.” Theory Culture and Society 25(1) 1-22

    N. Katherine Hayles. 2002. Writing Machines. Cambridge: MIT Press

    N. Katherine Hayles. 1999. How We Became Posthuman: Virtual Bodies in Cybernetics, Literature, and Informatics. Chicago: University of Chicago Press

    Catherine Malabou. 2008. What Should We Do with Our Brain? New York: Fordham University Press

    Lev Manovich. 2001. The Language of New Media. Cambridge: MIT Press.

    Lev Manovich. 2009. Software Takes Command. London: Bloomsbury

    Randy Martin. 2015. Knowledge LTD: Toward a Social Logic of the Derivative. Philadelphia: Temple University Press

    Chandra Mohanty. 2003. Feminism Without Borders: Decolonizing Theory, Practicing Solidarity. Durham: Duke University Press.

    Trebor Scholz, ed. 2012. Digital Labor: The Internet as Playground and Factory. New York: Routledge

    Bernard Stiegler. 1998. Technics and Time, 1: The Fault of Epimetheus. Palo Alto: Stanford University Press

    Kara Van Cleaf. 2015. “Of Woman Born to Mommy Blogged: The Journey from the Personal as Political to the Personal as Commodity.” Women’s Studies Quarterly 43(3/4) 247-265

    Back to the essay

  • Men (Still) Explain Technology to Me: Gender and Education Technology

    Men (Still) Explain Technology to Me: Gender and Education Technology

    By Audrey Watters
    ~

    Late last year, I gave a similarly titled talk—“Men Explain Technology to Me”—at the University of Mary Washington. (I should note here that the slides for that talk were based on a couple of blog posts by Mallory Ortberg that I found particularly funny, “Women Listening to Men in Art History” and “Western Art History: 500 Years of Women Ignoring Men.” I wanted to do something similar with my slides today: find historical photos of men explaining computers to women. Mostly I found pictures of men or women working separately, working in isolation. Mostly pictures of men and computers.)

    Men Explain Technology

    So that University of Mary Washington talk: It was the last talk I delivered in 2014, and I did so with a sigh of relief, but also more than a twinge of frightened nausea—nausea that wasn’t nerves from speaking in public. I’d had more than a year full of public speaking under my belt—exhausting enough as I always try to write new talks for each event, but a year that had become complicated quite frighteningly in part by an ongoing campaign of harassment against women on the Internet, particularly those who worked in video game development.

    Known as “GamerGate,” this campaign had reached a crescendo of sorts in the lead-up to my talk at UMW, some of its hate aimed at me because I’d written about the subject, demanding that those in ed-tech pay attention and speak out. So no surprise, all this colored how I shaped that talk about gender and education technology, because, of course, my gender shapes how I experience working in and working with education technology. As I discussed then at the University of Mary Washington, I have been on the receiving end of threats and harassment for stories I’ve written about ed-tech—almost all the women I know who have a significant online profile have in some form or another experienced something similar. According to a Pew Research survey last year, one in 5 Internet users reports being harassed online. But GamerGate felt—feels—particularly unhinged. The death threats to Anita Sarkeesian, Zoe Quinn, Brianna Wu, and others were—are—particularly real.

    I don’t really want to rehash all of that here today, particularly my experiences being on the receiving end of the harassment; I really don’t. You can read a copy of that talk from last November on my website. I will say this: GamerGate supporters continue to argue that their efforts are really about “ethics in journalism” not about misogyny, but it’s quite apparent that they have sought to terrorize feminists and chase women game developers out of the industry. Insisting that video games and video game culture retain a certain puerile machismo, GamerGate supporters often chastise those who seek to change the content of videos games, change the culture to reflect the actual demographics of video game players. After all, a recent industry survey found women 18 and older represent a significantly greater portion of the game-playing population (36%) than boys age 18 or younger (17%). Just over half of all games are men (52%); that means just under half are women. Yet those who want video games to reflect these demographics are dismissed by GamerGate as “social justice warriors.” Dismissed. Harassed. Shouted down. Chased out.

    And yes, more mildly perhaps, the verb that grew out of Rebecca Solnit’s wonderful essay “Men Explain Things to Me” and the inspiration for the title to this talk, mansplained.

    Solnit first wrote that essay back in 2008 to describe her experiences as an author—and as such, an expert on certain subjects—whereby men would presume she was in need of their enlightenment and information—in her words “in some sort of obscene impregnation metaphor, an empty vessel to be filled with their wisdom and knowledge.” She related several incidents in which men explained to her topics on which she’d published books. She knew things, but the presumption was that she was uninformed. Since her essay was first published the term “mansplaining” has become quite ubiquitous, used to describe the particular online version of this—of men explaining things to women.

    I experience this a lot. And while the threats and harassment in my case are rare but debilitating, the mansplaining is more insidious. It is overpowering in a different way. “Mansplaining” is a micro-aggression, a practice of undermining women’s intelligence, their contributions, their voice, their experiences, their knowledge, their expertise; and frankly once these pile up, these mansplaining micro-aggressions, they undermine women’s feelings of self-worth. Women begin to doubt what they know, doubt what they’ve experienced. And then, in turn, women decide not to say anything, not to speak.

    I speak from experience. On Twitter, I have almost 28,000 followers, most of whom follow me, I’d wager, because from time to time I say smart things about education technology. Yet regularly, men—strangers, typically, but not always—jump into my “@-mentions” to explain education technology to me. To explain open source licenses or open data or open education or MOOCs to me. Men explain learning management systems to me. Men explain the history of education technology to me. Men explain privacy and education data to me. Men explain venture capital funding of education startups to me. Men explain the business of education technology to me. Men explain blogging and journalism and writing to me. Men explain online harassment to me.

    The problem isn’t just that men explain technology to me. It isn’t just that a handful of men explain technology to the rest of us. It’s that this explanation tends to foreclose questions we might have about the shape of things. We can’t ask because if we show the slightest intellectual vulnerability, our questions—we ourselves—lose a sort of validity.

    Yet we are living in a moment, I would contend, when we must ask better questions of technology. We neglect to do so at our own peril.

    Last year when I gave my talk on gender and education technology, I was particularly frustrated by the mansplaining to be sure, but I was also frustrated that those of us who work in the field had remained silent about GamerGate, and more broadly about all sorts of issues relating to equity and social justice. Of course, I do know firsthand that it can difficult if not dangerous to speak out, to talk critically and write critically about GamerGate, for example. But refusing to look at some of the most egregious acts easily means often ignoring some of the more subtle ways in which marginalized voices are made to feel uncomfortable, unwelcome online. Because GamerGate is really just one manifestation of deeper issues—structural issues—with society, culture, technology. It’s wrong to focus on just a few individual bad actors or on a terrible Twitter hashtag and ignore the systemic problems. We must consider who else is being chased out and silenced, not simply from the video game industry but from the technology industry and a technological world writ large.

    I know I have to come right out and say it, because very few people in education technology will: there is a problem with computers. Culturally. Ideologically. There’s a problem with the internet. Largely designed by men from the developed world, it is built for men of the developed world. Men of science. Men of industry. Military men. Venture capitalists. Despite all the hype and hope about revolution and access and opportunity that these new technologies will provide us, they do not negate hierarchy, history, privilege, power. They reflect those. They channel it. They concentrate it, in new ways and in old.

    I want us to consider these bodies, their ideologies and how all of this shapes not only how we experience technology but how it gets designed and developed as well.

    There’s that very famous New Yorker cartoon: “On the internet, nobody knows you’re a dog.” The cartoon was first published in 1993, and it demonstrates this sense that we have long had that the Internet offers privacy and anonymity, that we can experiment with identities online in ways that are severed from our bodies, from our material selves and that, potentially at least, the internet can allow online participation for those denied it offline.

    Perhaps, yes.

    But sometimes when folks on the internet discover “you’re a dog,” they do everything in their power to put you back in your place, to remind you of your body. To punish you for being there. To hurt you. To threaten you. To destroy you. Online and offline.

    Neither the internet nor computer technology writ large are places where we can escape the materiality of our physical worlds—bodies, institutions, systems—as much as that New Yorker cartoon joked that we might. In fact, I want to argue quite the opposite: that computer and Internet technologies actually re-inscribe our material bodies, the power and the ideology of gender and race and sexual identity and national identity. They purport to be ideology-free and identity-less, but they are not. If identity is unmarked it’s because there’s a presumption of maleness, whiteness, and perhaps even a certain California-ness. As my friend Tressie McMillan Cottom writes, in ed-tech we’re all supposed to be “roaming autodidacts”: happy with school, happy with learning, happy and capable and motivated and well-networked, with functioning computers and WiFi that works.

    By and large, all of this reflects who is driving the conversation about, if not the development of these technology. Who is seen as building technologies. Who some think should build them; who some think have always built them.

    And that right there is already a process of erasure, a different sort of mansplaining one might say.

    Last year, when Walter Isaacson was doing the publicity circuit for his latest book, The Innovators: How a Group of Hackers, Geniuses, and Geeks Created the Digital Revolution (2014), he’d often relate of how his teenage daughter had written an essay about Ada Lovelace, a figure whom Isaacson admitted that he’d never heard of before. Sure, he’d written biographies of Steve Jobs and Albert Einstein and Benjamin Franklin and other important male figures in science and technology, but the name and the contributions of this woman were entirely unknown to him. Ada Lovelace, daughter of Lord Byron and the woman whose notes on Charles Babbage’s proto-computer the Analytical Engine are now recognized as making her the world’s first computer programmer. Ada Lovelace, the author of the world’s first computer algorithm. Ada Lovelace, the person at the very beginning of the field of computer science.

    Ada Lovelace
    Augusta Ada King, Countess of Lovelace, now popularly known as Ada Lovelace, in a painting by Alfred Edward Chalon (image source: Wikipedia)

    “Ada Lovelace defined the digital age,” Isaacson said in an interview with The New York Times. “Yet she, along with all these other women, was ignored or forgotten.” (Actually, the world has been celebrating Ada Lovelace Day since 2009.)

    Isaacson’s book describes Lovelace like this: “Ada was never the great mathematician that her canonizers claim…” and “Ada believed she possessed special, even supernatural abilities, what she called ‘an intuitive perception of hidden things.’ Her exalted view of her talents led her to pursue aspirations that were unusual for an aristocratic woman and mother in the early Victorian age.” The implication: she was a bit of an interloper.

    A few other women populate Isaacson’s The Innovators: Grace Hopper, who invented the first computer compiler and who developed the programming language COBOL. Isaacson describes her as “spunky,” not an adjective that I imagine would be applied to a male engineer. He also talks about the six women who helped program the ENIAC computer, the first electronic general-purpose computer. Their names, because we need to say these things out loud more often: Jean Jennings, Marilyn Wescoff, Ruth Lichterman, Betty Snyder, Frances Bilas, Kay McNulty. (I say that having visited Bletchley Park where civilian women’s involvement has been erased, as they were forbidden, thanks to classified government secrets, from talking about their involvement in the cryptography and computing efforts there).

    In the end, it’s hard not to read Isaacson’s book without coming away thinking that, other than a few notable exceptions, the history of computing is the history of men, white men. The book mentions education Seymour Papert in passing, for example, but assigns the development of Logo, a programming language for children, to him alone. No mention of the others involved: Daniel Bobrow, Wally Feurzeig, and Cynthia Solomon.

    Even a book that purports to reintroduce the contributions of those forgotten “innovators,” that says it wants to complicate the story of a few male inventors of technology by looking at collaborators and groups, still in the end tells a story that ignores if not undermines women. Men explain the history of computing, if you will. As such it tells a story too that depicts and reflects a culture that doesn’t simply forget but systematically alienates women. Women are a rediscovery project, always having to be reintroduced, found, rescued. There’s been very little reflection upon that fact—in Isaacson’s book or in the tech industry writ large.

    This matters not just for the history of technology but for technology today. And it matters for ed-tech as well. (Unless otherwise noted, the following data comes from diversity self-reports issued by the companies in 2014.)

    • Currently, fewer than 20% of computer science degrees in the US are awarded to women. (I don’t know if it’s different in the UK.) It’s a number that’s actually fallen over the past few decades from a high in 1983 of 37%. Computer science is the only field in science, engineering, and mathematics in which the number of women receiving bachelor’s degrees has fallen in recent years. And when it comes to the employment not just the education of women in the tech sector, the statistics are not much better. (source: NPR)
    • 70% of Google employees are male. 61% are white and 30% Asian. Of Google’s “technical” employees. 83% are male. 60% of those are white and 34% are Asian.
    • 70% of Apple employees are male. 55% are white and 15% are Asian. 80% of Apple’s “technical” employees are male.
    • 69% of Facebook employees are male. 57% are white and 34% are Asian. 85% of Facebook’s “technical” employees are male.
    • 70% of Twitter employees are male. 59% are white and 29% are Asian. 90% of Twitter’s “technical” employees are male.
    • Only 2.7% of startups that received venture capital funding between 2011 and 2013 had women CEOs, according to one survey.
    • And of course, Silicon Valley was recently embroiled in the middle of a sexual discrimination trial involving the storied VC firm Kleiner, Smith, Perkins, and Caulfield filed by former executive Ellen Pao who claimed that men at the firm were paid more and promoted more easily than women. Welcome neither as investors nor entrepreneurs nor engineers, it’s hardly a surprise that, as The Los Angeles Times recently reported, women are leaving the tech industry “in droves.”

    This doesn’t just matter because computer science leads to “good jobs” or that tech startups lead to “good money.” It matters because the tech sector has an increasingly powerful reach in how we live and work and communicate and learn. It matters ideologically. If the tech sector drives out women, if it excludes people of color, that matters for jobs, sure. But it matters in terms of the projects undertaken, the problems tackled, the “solutions” designed and developed.

    So it’s probably worth asking what the demographics look like for education technology companies. What percentage of those building ed-tech software are men, for example? What percentage are white? What percentage of ed-tech startup engineers are men? Across the field, what percentage of education technologists—instructional designers, campus IT, sysadmins, CTOs, CIOs—are men? What percentage of “education technology leaders” are men? What percentage of education technology consultants? What percentage of those on the education technology speaking circuit? What percentage of those developing not just implementing these tools?

    And how do these bodies shape what gets built? How do they shape how the “problem” of education gets “fixed”? How do privileges, ideologies, expectations, values get hard-coded into ed-tech? I’d argue that they do in ways that are both subtle and overt.

    That word “privilege,” for example, has an interesting dual meaning. We use it to refer to the advantages that are are afforded to some people and not to others: male privilege, white privilege. But when it comes to tech, we make that advantage explicit. We actually embed that status into the software’s processes. “Privileges” in tech refer to whomever has the ability to use or control certain features of a piece of software. Administrator privileges. Teacher privileges. (Students rarely have privileges in ed-tech. Food for thought.)

    Or take how discussion forums operate. Discussion forums, now quite common in ed-tech tools—in learning management systems (VLEs as you call them), in MOOCs, for example—often trace their history back to the earliest Internet bulletin boards. But even before then, education technologies like PLATO, a programmed instruction system built by the University of Illinois in the 1970s, offered chat and messaging functionality. (How education technology’s contributions to tech are erased from tech history is, alas, a different talk.)

    One of the new features that many discussion forums boast: the ability to vote up or vote down certain topics. Ostensibly this means that “the best” ideas surface to the top—the best ideas, the best questions, the best answers. What it means in practice often is something else entirely. In part this is because the voting power on these sites is concentrated in the hands of the few, the most active, the most engaged. And no surprise, “the few” here is overwhelmingly male. Reddit, which calls itself “the front page of the Internet” and is the model for this sort of voting process, is roughly 84% male. I’m not sure that MOOCs, who’ve adopted Reddit’s model of voting on comments, can boast a much better ratio of male to female participation.

    What happens when the most important topics—based on up-voting—are decided by a small group? As D. A. Banks has written about this issue,

    Sites like Reddit will remain structurally incapable of producing non-hegemonic content because the “crowd” is still subject to structural oppression. You might choose to stay within the safe confines of your familiar subreddit, but the site as a whole will never feel like yours. The site promotes mundanity and repetition over experimentation and diversity by presenting the user with a too-accurate picture of what appeals to the entrenched user base. As long as the “wisdom of the crowds” is treated as colorblind and gender neutral, the white guy is always going to be the loudest.

    How much does education technology treat its users similarly? Whose questions surface to the top of discussion forums in the LMS (the VLE), in the MOOC? Who is the loudest? Who is explaining things in MOOC forums?

    Ironically—bitterly ironically, I’d say, many pieces of software today increasingly promise “personalization,” but in reality, they present us with a very restricted, restrictive set of choices of who we “can be” and how we can interact, both with our own data and content and with other people. Gender, for example, is often a drop down menu where one can choose either “male” or “female.” Software might ask for a first and last name, something that is complicated if you have multiple family names (as some Spanish-speaking people do) or your family name is your first name (as names in China are ordered). Your name is presented how the software engineers and designers deemed fit: sometimes first name, sometimes title and last name, typically with a profile picture. Changing your username—after marriage or divorce, for example—is often incredibly challenging, if not impossible.

    You get to interact with others, similarly, based on the processes that the engineers have determined and designed. On Twitter, you cannot direct message people, for example, that do not follow you. All interactions must be 140 characters or less.

    This restriction of the presentation and performance of one’s identity online is what “cyborg anthropologist” Amber Case calls the “templated self.” She defines this as “a self or identity that is produced through various participation architectures, the act of producing a virtual or digital representation of self by filling out a user interface with personal information.”

    Case provides some examples of templated selves:

    Facebook and Twitter are examples of the templated self. The shape of a space affects how one can move, what one does and how one interacts with someone else. It also defines how influential and what constraints there are to that identity. A more flexible, but still templated space is WordPress. A hand-built site is much less templated, as one is free to fully create their digital self in any way possible. Those in Second Life play with and modify templated selves into increasingly unique online identities. MySpace pages are templates, but the lack of constraints can lead to spaces that are considered irritating to others.

    As we—all of us, but particularly teachers and students—move to spend more and more time and effort performing our identities online, being forced to use preordained templates constrains us, rather than—as we have often been told about the Internet—lets us be anyone or say anything online. On the Internet no one knows you’re a dog unless the signup process demanded you give proof of your breed. This seems particularly important to keep in mind when we think about students’ identity development. How are their identities being templated?

    While Case’s examples point to mostly “social” technologies, education technologies are also “participation architectures.” Similarly they produce and restrict a digital representation of the learner’s self.

    Who is building the template? Who is engineering the template? Who is there to demand the template be cracked open? What will the template look like if we’ve chased women and people of color out of programming?

    It’s far too simplistic to say “everyone learn to code” is the best response to the questions I’ve raised here. “Change the ratio.” “Fix the leaky pipeline.” Nonetheless, I’m speaking to a group of educators here. I’m probably supposed to say something about what we can do, right, to make ed-tech more just not just condemn the narratives that lead us down a path that makes ed-tech less son. What we can do to resist all this hard-coding? What we can do to subvert that hard-coding? What we can do to make technologies that our students—all our students, all of us—can wield? What we can do to make sure that when we say “your assignment involves the Internet” that we haven’t triggered half the class with fears of abuse, harassment, exposure, rape, death? What can we do to make sure that when we ask our students to discuss things online, that the very infrastructure of the technology that we use privileges certain voices in certain ways?

    The answer can’t simply be to tell women to not use their real name online, although as someone who started her career blogging under a pseudonym, I do sometimes miss those days. But if part of the argument for participating in the open Web is that students and educators are building a digital portfolio, are building a professional network, are contributing to scholarship, then we have to really think about whether or not promoting pseudonyms is a sufficient or an equitable solution.

    The answer can’t be simply be “don’t blog on the open Web.” Or “keep everything inside the ‘safety’ of the walled garden, the learning management system.” If nothing else, this presumes that what happens inside siloed, online spaces is necessarily “safe.” I know I’ve seen plenty of horrible behavior on closed forums, for example, from professors and students alike. I’ve seen heavy-handed moderation, where marginalized voices find their input are deleted. I’ve seen zero-moderation, where marginalized voices are mobbed. We recently learned, for example, that Walter Lewin, emeritus professor at MIT, one of the original rockstar professors of YouTube—millions have watched the demonstrations from his physics lectures, has been accused of sexually harassing women in his edX MOOC.

    The answer can’t simply be “just don’t read the comments.” I would say that it might be worth rethinking “comments” on student blogs altogether—or rather the expectation that they host them, moderate them, respond to them. See, if we give students the opportunity to “own their own domain,” to have their own websites, their own space on the Web, we really shouldn’t require them to let anyone that can create a user account into that space. It’s perfectly acceptable to say to someone who wants to comment on a blog post, “Respond on your own site. Link to me. But I am under no obligation to host your thoughts in my domain.”

    And see, that starts to hint at what I think the answer here to this question about the unpleasantness—by design—of technology. It starts to get at what any sort of “solution” or “alternative” has to look like: it has to be both social and technical. It also needs to recognize there’s a history that might help us understand what’s done now and why. If, as I’ve argued, the current shape of education technologies has been shaped by certain ideologies and certain bodies, we should recognize that we aren’t stuck with those. We don’t have to “do” tech as it’s been done in the last few years or decades. We can design differently. We can design around. We can use differently. We can use around.

    One interesting example of this dual approach that combines both social and technical—outside the realm of ed-tech, I recognize—are the tools that Twitter users have built in order to address harassment on the platform. Having grown weary of Twitter’s refusal to address the ways in which it is utilized to harass people (remember, its engineering team is 90% male), a group of feminist developers wrote The Block Bot, an application that lets you block, en masse, a large list of Twitter accounts who are known for being serial harassers. That list of blocked accounts is updated and maintained collaboratively. Similarly, Block Together lets users subscribe to others’ block lists. Good Game Autoblocker, a tool that blocks the “ringleaders” of GamerGate.

    That gets, just a bit, at what I think we can do in order to make education technology habitable, sustainable, and healthy. We have to rethink the technology. And not simply as some nostalgia for a “Web we lost,” for example, but as a move forward to a Web we’ve yet to ever see. It isn’t simply, as Isaacson would posit it, rediscovering innovators that have been erased, it’s about rethinking how these erasures happen all throughout technology’s history and continue today—not just in storytelling, but in code.

    Educators should want ed-tech that is inclusive and equitable. Perhaps education needs reminding of this: we don’t have to adopt tools that serve business goals or administrative purposes, particularly when they are to the detriment of scholarship and/or student agency—technologies that surveil and control and restrict, for example, under the guise of “safety”—that gets trotted out from time to time—but that have never ever been about students’ needs at all. We don’t have to accept that technology needs to extract value from us. We don’t have to accept that technology puts us at risk. We don’t have to accept that the architecture, the infrastructure of these tools make it easy for harassment to occur without any consequences. We can build different and better technologies. And we can build them with and for communities, communities of scholars and communities of learners. We don’t have to be paternalistic as we do so. We don’t have to “protect students from the Internet,” and rehash all the arguments about stranger danger and predators and pedophiles. But we should recognize that if we want education to be online, if we want education to be immersed in technologies, information, and networks, that we can’t really throw students out there alone. We need to be braver and more compassionate and we need to build that into ed-tech. Like Blockbot or Block Together, this should be a collaborative effort, one that blends our cultural values with technology we build.

    Because here’s the thing. The answer to all of this—to harassment online, to the male domination of the technology industry, the Silicon Valley domination of ed-tech—is not silence. And the answer is not to let our concerns be explained away. That is after all, as Rebecca Solnit reminds us, one of the goals of mansplaining: to get us to cower, to hesitate, to doubt ourselves and our stories and our needs, to step back, to shut up. Now more than ever, I think we need to be louder and clearer about what we want education technology to do—for us and with us, not simply to us.
    _____

    Audrey Watters is a writer who focuses on education technology – the relationship between politics, pedagogy, business, culture, and ed-tech. She has worked in the education field for over 15 years: teaching, researching, organizing, and project-managing. Although she was two chapters into her dissertation (on a topic completely unrelated to ed-tech), she decided to abandon academia, and she now happily fulfills the one job recommended to her by a junior high aptitude test: freelance writer. Her stories have appeared on NPR/KQED’s education technology blog MindShift, in the data section of O’Reilly Radar, on Inside Higher Ed, in The School Library Journal, in The Atlantic, on ReadWriteWeb, and Edutopia. She is the author of the recent book The Monsters of Education Technology (Smashwords, 2014) and working on a book called Teaching Machines. She maintains the widely-read Hack Education blog, on which an earlier version of this review first appeared, and writes frequently for The b2 Review Digital Studies magazine on digital technology and education.

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  • The Automatic Teacher

    The Automatic Teacher

    By Audrey Watters
    ~

    “For a number of years the writer has had it in mind that a simple machine for automatic testing of intelligence or information was entirely within the realm of possibility. The modern objective test, with its definite systemization of procedure and objectivity of scoring, naturally suggests such a development. Further, even with the modern objective test the burden of scoring (with the present very extensive use of such tests) is nevertheless great enough to make insistent the need for labor-saving devices in such work” – Sidney Pressey, “A Simple Apparatus Which Gives Tests and Scores – And Teaches,” School and Society, 1926

    Ohio State University professor Sidney Pressey first displayed the prototype of his “automatic intelligence testing machine” at the 1924 American Psychological Association meeting. Two years later, he submitted a patent for the device and spent the next decade or so trying to market it (to manufacturers and investors, as well as to schools).

    It wasn’t Pressey’s first commercial move. In 1922 he and his wife Luella Cole published Introduction to the Use of Standard Tests, a “practical” and “non-technical” guide meant “as an introductory handbook in the use of tests” aimed to meet the needs of “the busy teacher, principal or superintendent.” By the mid–1920s, the two had over a dozen different proprietary standardized tests on the market, selling a couple of hundred thousand copies a year, along with some two million test blanks.

    Although standardized testing had become commonplace in the classroom by the 1920s, they were already placing a significant burden upon those teachers and clerks tasked with scoring them. Hoping to capitalize yet again on the test-taking industry, Pressey argued that automation could “free the teacher from much of the present-day drudgery of paper-grading drill, and information-fixing – should free her for real teaching of the inspirational.”

    pressey_machines

    The Automatic Teacher

    Here’s how Pressey described the machine, which he branded as the Automatic Teacher in his 1926 School and Society article:

    The apparatus is about the size of an ordinary portable typewriter – though much simpler. …The person who is using the machine finds presented to him in a little window a typewritten or mimeographed question of the ordinary selective-answer type – for instance:

    To help the poor debtors of England, James Oglethorpe founded the colony of (1) Connecticut, (2) Delaware, (3) Maryland, (4) Georgia.

    To one side of the apparatus are four keys. Suppose now that the person taking the test considers Answer 4 to be the correct answer. He then presses Key 4 and so indicates his reply to the question. The pressing of the key operates to turn up a new question, to which the subject responds in the same fashion. The apparatus counts the number of his correct responses on a little counter to the back of the machine…. All the person taking the test has to do, then, is to read each question as it appears and press a key to indicate his answer. And the labor of the person giving and scoring the test is confined simply to slipping the test sheet into the device at the beginning (this is done exactly as one slips a sheet of paper into a typewriter), and noting on the counter the total score, after the subject has finished.

    The above paragraph describes the operation of the apparatus if it is being used simply to test. If it is to be used also to teach then a little lever to the back is raised. This automatically shifts the mechanism so that a new question is not rolled up until the correct answer to the question to which the subject is responding is found. However, the counter counts all tries.

    It should be emphasized that, for most purposes, this second set is by all odds the most valuable and interesting. With this second set the device is exceptionally valuable for testing, since it is possible for the subject to make more than one mistake on a question – a feature which is, so far as the writer knows, entirely unique and which appears decidedly to increase the significance of the score. However, in the way in which it functions at the same time as an ‘automatic teacher’ the device is still more unusual. It tells the subject at once when he makes a mistake (there is no waiting several days, until a corrected paper is returned, before he knows where he is right and where wrong). It keeps each question on which he makes an error before him until he finds the right answer; he must get the correct answer to each question before he can go on to the next. When he does give the right answer, the apparatus informs him immediately to that effect. If he runs the material through the little machine again, it measures for him his progress in mastery of the topics dealt with. In short the apparatus provides in very interesting ways for efficient learning.

    A video from 1964 shows Pressey demonstrating his “teaching machine,” including the “reward dial” feature that could be set to dispense a candy once a certain number of correct answers were given:

    [youtube https://www.youtube.com/watch?v=n7OfEXWuulg?rel=0]

    Market Failure

    UBC’s Stephen Petrina documents the commercial failure of the Automatic Teacher in his 2004 article “Sidney Pressey and the Automation of Education, 1924–1934.” According to Petrina, Pressey started looking for investors for his machine in December 1925, “first among publishers and manufacturers of typewriters, adding machines, and mimeo- graph machines, and later, in the spring of 1926, extending his search to scientific instrument makers.” He approached at least six Midwestern manufacturers in 1926, but no one was interested.

    In 1929, Pressey finally signed a contract with the W. M. Welch Manufacturing Company, a Chicago-based company that produced scientific instruments.

    Petrina writes that,

    After so many disappointments, Pressey was impatient: he offered to forgo royalties on two hundred machines if Welch could keep the price per copy at five dollars, and he himself submitted an order for thirty machines to be used in a summer course he taught school administrators. A few months later he offered to put up twelve hundred dollars to cover tooling costs. Medard W. Welch, sales manager of Welch Manufacturing, however, advised a “slower, more conservative approach.” Fifteen dollars per machine was a more realistic price, he thought, and he offered to refund Pressey fifteen dollars per machine sold until Pressey recouped his twelve-hundred-dollar investment. Drawing on nearly fifty years experience selling to schools, Welch was reluctant to rush into any project that depended on classroom reforms. He preferred to send out circulars advertising the Automatic Teacher, solicit orders, and then proceed with production if a demand materialized.

    ad_pressey

    The demand never really materialized, and even if it had, the manufacturing process – getting the device to market – was plagued with problems, caused in part by Pressey’s constant demands to redefine and retool the machines.

    The stress from the development of the Automatic Teacher took an enormous toll on Pressey’s health, and he had a breakdown in late 1929. (He was still teaching, supervising courses, and advising graduate students at Ohio State University.)

    The devices did finally ship in April 1930. But that original sales price was cost-prohibitive. $15 was, as Petrina notes, “more than half the annual cost ($29.27) of educating a student in the United States in 1930.” Welch could not sell the machines and ceased production with 69 of the original run of 250 devices still in stock.

    Pressey admitted defeat. In a 1932 School and Society article, he wrote “The writer is regretfully dropping further work on these problems. But he hopes that enough has been done to stimulate other workers.”

    But Pressey didn’t really abandon the teaching machine. He continued to present on his research at APA meetings. But he did write in a 1964 article “Teaching Machines (And Learning Theory) Crisis” that “Much seems very wrong about current attempts at auto-instruction.”

    Indeed.

    Automation and Individualization

    In his article “Toward the Coming ‘Industrial Revolution’ in Education (1932), Pressey wrote that

    “Education is the one major activity in this country which is still in a crude handicraft stage. But the economic depression may here work beneficially, in that it may force the consideration of efficiency and the need for laborsaving devices in education. Education is a large-scale industry; it should use quantity production methods. This does not mean, in any unfortunate sense, the mechanization of education. It does mean freeing the teacher from the drudgeries of her work so that she may do more real teaching, giving the pupil more adequate guidance in his learning. There may well be an ‘industrial revolution’ in education. The ultimate results should be highly beneficial. Perhaps only by such means can universal education be made effective.”

    Pressey intended for his automated teaching and testing machines to individualize education. It’s an argument that’s made about teaching machines today too. These devices will allow students to move at their own pace through the curriculum. They will free up teachers’ time to work more closely with individual students.

    But as Pretina argues, “the effect of automation was control and standardization.”

    The Automatic Teacher was a technology of normalization, but it was at the same time a product of liberality. The Automatic Teacher provided for self- instruction and self-regulated, therapeutic treatment. It was designed to provide the right kind and amount of treatment for individual, scholastic deficiencies; thus, it was individualizing. Pressey articulated this liberal rationale during the 1920s and 1930s, and again in the 1950s and 1960s. Although intended as an act of freedom, the self-instruction provided by an Automatic Teacher also habituated learners to the authoritative norms underwriting self-regulation and self-governance. They not only learned to think in and about school subjects (arithmetic, geography, history), but also how to discipline themselves within this imposed structure. They were regulated not only through the knowledge and power embedded in the school subjects but also through the self-governance of their moral conduct. Both knowledge and personality were normalized in the minutiae of individualization and in the machinations of mass education. Freedom from the confines of mass education proved to be a contradictory project and, if Pressey’s case is representative, one more easily automated than commercialized.

    The massive influx of venture capital into today’s teaching machines, of course, would like to see otherwise…
    _____

    Audrey Watters is a writer who focuses on education technology – the relationship between politics, pedagogy, business, culture, and ed-tech. She has worked in the education field for over 15 years: teaching, researching, organizing, and project-managing. Although she was two chapters into her dissertation (on a topic completely unrelated to ed-tech), she decided to abandon academia, and she now happily fulfills the one job recommended to her by a junior high aptitude test: freelance writer. Her stories have appeared on NPR/KQED’s education technology blog MindShift, in the data section of O’Reilly Radar, on Inside Higher Ed, in The School Library Journal, in The Atlantic, on ReadWriteWeb, and Edutopia. She is the author of the recent book The Monsters of Education Technology (Smashwords, 2014) and working on a book called Teaching Machines. She maintains the widely-read Hack Education blog, on which an earlier version of this review first appeared.

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  • Teacher Wars and Teaching Machines

    Teacher Wars and Teaching Machines

    teacher warsa review of Dana Goldstein, The Teacher Wars: A History of America’s Most Embattled Profession (Doubleday, 2014)
    by Audrey Watters
    ~

    Teaching is, according to the subtitle of education journalist Dana Goldstein’s new book, “America’s Most Embattled Profession.” “No other profession,” she argues, ”operates under this level of political scrutiny, not even those, like policing or social work, that are also tasked with public welfare and are paid for with public funds.”

    That political scrutiny is not new. Goldstein’s book The Teacher Wars chronicles the history of teaching at (what has become) the K–12 level, from the early nineteenth century and “common schools” — that is, before before compulsory education and public school as we know it today — through the latest Obama Administration education policies. It’s an incredibly well-researched book that moves from the feminization of the teaching profession to the recent push for more data-driven teacher evaluation, observing how all along the way, teachers have been deemed ineffectual in some way or another — failing to fulfill whatever (political) goals the public education system has demanded be met, be those goals be economic, civic, or academic.

    As Goldstein describes it, public education is a labor issue; and it has been, it’s important to note, since well before the advent of teacher unions.

    The Teacher Wars and Teaching Machines

    To frame education this way — around teachers and by extension, around labor — has important implications for ed-tech. What happens if we examine the history of teaching alongside the history of teaching machines? As I’ve argued before, the history of public education in the US, particularly in the 20th century, is deeply intertwined with various education technologies – film, TV, radio, computers, the Internet – devices that are often promoted as improving access or as making an outmoded system more “modern.” But ed-tech is frequently touted too as “labor-saving” and as a corrective to teachers’ inadequacies and inefficiencies.

    It’s hardly surprising, in this light, that teachers have long looked with suspicion at new education technologies. With their profession constantly under attack, many teacher are worried no doubt that new tools are poised to replace them. Much is said to quiet these fears, with education reformers and technologists insisting again and again that replacing teachers with tech is not the intention.

    And yet the sentiment of science fiction writer Arthur C. Clarke probably does resonate with a lot of people, as a line from his 1980 Omni Magazine article on computer-assisted instruction is echoed by all sorts of pundits and politicians: “Any teacher who can be replaced by a machine should be.”

    Of course, you do find people like former Washington DC mayor Adrian Fenty – best known arguably via his school chancellor Michelle Rhee – who’ll come right out and say to a crowd of entrepreneurs and investors, “If we fire more teachers, we can use that money for more technology.”

    So it’s hard to ignore the role that technology increasingly plays in contemporary education (labor) policies – as Goldstein describes them, the weakening of teachers’ tenure protections alongside an expansion of standardized testing to measure “student learning,” all in the service finding and firing “bad teachers.” The growing data collection and analysis enabled by schools’ adoption of ed-tech feeds into the politics and practices of employee surveillance.

    Just as Goldstein discovered in the course of writing her book that the current “teacher wars” have a lengthy history, so too does ed-tech’s role in the fight.

    As Sidney Pressey, the man often credited with developing the first teaching machine, wrote in 1933 (from a period Goldstein links to “patriotic moral panics” and concerns about teachers’ political leanings),

    There must be an “industrial revolution” in education, in which educational science and the ingenuity of educational technology combine to modernize the grossly inefficient and clumsy procedures of conventional education. Work in the schools of the school will be marvelously though simply organized, so as to adjust almost automatically to individual differences and the characteristics of the learning process. There will be many labor-saving schemes and devices, and even machines — not at all for the mechanizing of education but for the freeing of teacher and pupil from the educational drudgery and incompetence.

    Or as B. F. Skinner, the man most associated with the development of teaching machines, wrote in 1953 (one year before the landmark Brown v Board of Education),

    Will machines replace teachers? On the contrary, they are capital equipment to be used by teachers to save time and labor. In assigning certain mechanizable functions to machines, the teacher emerges in his proper role as an indispensable human being. He may teach more students than heretofore — this is probably inevitable if the world-wide demand for education is to be satisfied — but he will do so in fewer hours and with fewer burdensome chores.

    These quotations highlight the longstanding hopes and fears about teaching labor and teaching machines; they hint too at some of the ways in which the work of Pressey and Skinner and others coincides with what Goldstein’s book describes: the ongoing concerns about teachers’ politics and competencies.

    The Drudgery of School

    One of the things that’s striking about Skinner and Pressey’s remarks on teaching machines, I think, is that they recognize the “drudgery” of much of teachers’ work. But rather than fundamentally change school – rather than ask why so much of the job of teaching entails “burdensome chores” – education technology seems more likely to offload that drudgery to machines. (One of the best contemporary examples of this perhaps: automated essay grading.)

    This has powerful implications for students, who – let’s be honest – suffer through this drudgery as well.

    Goldstein’s book doesn’t really address students’ experiences. Her history of public education is focused on teacher labor more than on student learning. As a result, student labor is missing from her analysis. This isn’t a criticism of the book; and it’s not just Goldstein that does this. Student labor in the history of public education remains largely under-theorized and certainly underrepresented. Cue AFT president Al Shanker’s famous statement: “Listen, I don’t represent children. I represent the teachers.”

    But this question of student labor seems to be incredibly important to consider, particularly with the growing adoption of education technologies. Students’ labor – students’ test results, students’ content, students’ data – feeds the measurements used to reward or punish teachers. Students’ labor feeds the algorithms – algorithms that further this larger narrative about teacher inadequacies, sure, and that serve to financially benefit technology, testing, and textbook companies, the makers of today’s “teaching machines.”

    Teaching Machines and the Future of Collective Action

    The promise of teaching machines has long been to allow students to move “at their own pace” through the curriculum. “Personalized learning,” it’s often called today (although the phrase often refers only to “personalization” in terms of the pace, not in terms of the topics of inquiry). This means, supposedly, that instead of whole class instruction, the “work” of teaching changes: in the words of one education reformer, “with the software taking up chores like grading math quizzes and flagging bad grammar, teachers are freed to do what they do best: guide, engage, and inspire.”

    Again, it’s not clear how this changes the work of students.

    So what are the implications – not just pedagogically but politically – of students, their headphones on staring at their individual computer screens working alone through various exercises? Because let’s remember: teaching machines and all education technologies are ideological. What are the implications – not just pedagogically but politically – of these technologies’ emphasis on individualism, self-management, personal responsibility, and autonomy?

    What happens to discussion and debate, for example, in a classroom of teaching machines and “personalized learning”? What happens, in a world of schools catered to individual student achievement, to the community development that schools (at their best, at least) are also tasked to support?

    What happens to organizing? What happens to collective action? And by collectivity here, let’s be clear, I don’t mean simply “what happens to teachers’ unions”? If we think about The Teacher Wars and teaching machines side-by-side, we should recognize our analysis of (our actions surrounding) the labor issues of school need to go much deeper and more farther than that.

    _____

    Audrey Watters is a writer who focuses on education technology – the relationship between politics, pedagogy, business, culture, and ed-tech. She has worked in the education field for over 15 years: teaching, researching, organizing, and project-managing. Although she was two chapters into her dissertation (on a topic completely unrelated to ed-tech), she decided to abandon academia, and she now happily fulfills the one job recommended to her by a junior high aptitude test: freelance writer. Her stories have appeared on NPR/KQED’s education technology blog MindShift, in the data section of O’Reilly Radar, on Inside Higher Ed, in The School Library Journal, in The Atlantic, on ReadWriteWeb, and Edutopia. She is the author of the recent book The Monsters of Education Technology (Smashwords, 2014) and working on a book called Teaching Machines. She maintains the widely-read Hack Education blog, on which an earlier version of this review first appeared.

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