Creating Wealth for the 21st Century

Certainly, the way Arboleda defines the OLPC program and the distribution of teaching machines as a way of “creating wealth for the 21st century” in the form of intellectual property obtained from properly prepared human capital emphasizes that this would be achieved by allowing the emergence of a large number of creators of intellectual property, that is, engineers, scientists and innovators capable of generating intellectual property, in Latin American classrooms. He makes little effort to explain the implications of educational systems focusing their efforts on training creators of intellectual property rather than citizens. Nor does he ask “whether spending our limited educational funds on supporting technology will bring us closer to the democratic purposes that are the heart and soul of public education.” And he also makes little effort to address what has already become a heated issue among educators and users of educational technology in America: the actual means through which the digital interactions of current teaching technologies are managed in order to generate new forms of informational goods and intellectual property within educational markets. In other words, the collection and extraction of data on teacher and student performance, and the quantification of learning assessments made possible by massive digital interactions. Such calculations in contemporary educational markets now make possible powerful tools that allow popularization decisions to be carried out with microscopic precision, indicating where investments should be made and where they should be avoided. Aware that these new trends have begun to define educational technology markets even before being perceived by the vast majority of students, teachers and the public they will affect, historians of education such as Audrey Watters have produced work emphasizing how, increasingly, “educational technology has become something related to control, surveillance and data extraction” from participating actors, a technology modeled in the image of new informational ideals.

In other words, the human capital cultivated through the possibilities of contemporary educational technology environments can be optimized through strategies for obtaining value from future human workers. This would be achieved through contemporary educational technology investments and architectures, as well as strategies for extracting immediate value, carried out through instant data collection and calculations based on the interactions of current students, and future workers, with those digital architectures. As Watters points out: “Increasingly, educational technology works in concert with efforts, partly required by educational policies, to obtain more data. We hear claims that more data and more analysis will open the ‘black box’ of learning.” Among the companies making these claims most openly, and leading the extraction of educational data, the author identifies Knewton, a company associated with educational institutions and textbook publishers to create “adaptive” and “personalized” content for students. With millions of supposedly “actionable” data points coming daily from millions of students around the world, CEO José Ferreira says that education is “the most data-mineable industry in the world to date.” Ferreira boasted about this action in the United States in 2012, saying: “We have data about… we have more data about our students than anyone…” Literally, we know everything about what they know and how they learn.

Ferreira was only one of the 150 leading entrepreneurs and developers of educational technology software, whose sales the previous year reached nearly $8 billion, according to the Software and Information Industry Association, gathered at Datapalooza. Like other vendors of educational products present, Ferreira can avoid referring to serious privacy concerns thanks to his ability to take advantage of the legibility of emerging informational ideals among his audience. In other words, his audience is capable of recognizing the logic and promise of a new massive flow of “actionable” data that future technologies promise to unleash, and of new digital solutions that would make content “adaptive” and “personalized” not only for different student populations, but also for the different governing authorities seeking to optimize the future economic performance of human capital and to make the necessary calculations and investments, or disinvestments, accordingly. That expectation was defined in relation to another message, one could say a more foundational message, which opened the event itself: the serious current “reality” now facing educators. As Secretary of Education Arne Duncan stated after describing the quality of education as desperately mediocre: “The factory model for education is the wrong model for the 21st century; schools must […] do much more to personalize instruction.” And he had no hesitation in telling the audience that current methods and resources, including textbooks, “would become obsolete,” as he later noted, “as soon as we can make them obsolete.” They can be rapidly introduced into the economy and converted into calculations of educational technologies. And although it is still something that is not mentioned much, now the issue of students also becomes central.