boundary 2

Tag: educational labor

  • 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).

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    _____

    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

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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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