If cutting-edge educational technologies can scientifically maximize student learning, then why do so many Silicon Valley bigwigs at Google, Apple, Hewlett-Packard, and Yahoo! send their children to the Waldorf School of the Peninsula, a school which bans computer technology from its classrooms? If high-tech computerization were such a fundamental enhancement to cognitive development, then why did Steve Jobs withhold iPads and other “screen-time” technologies from his children? Why would these tech gurus not practice for their own sons and daughters what they preach (and bankroll) for the public education system? These incongruences signal red flags that the real objective behind the accelerating push for personalized computer learning is not to boost academic outcomes, but to data-mine students for the purposes of corporate-fascist political-economic planning.
Enter US Secretary of Education, Betsy DeVos, a billionaire who has played both sides of the public-private fence as a supporter of federal Common Core data-mining as well as corporate charter schooling and privatized voucher programs. As President Trump’s Education Secretary, we can expect DeVos to transfer the government data-mining policies of Common Core into a deregulated system of public-private “school choice” partnerships, which will open loopholes for private data-mining corporations to coopt public student data for the purposes of for-profit behavioral advertising and corporate-fascist workforce planning.
By capitalizing on loosened Family Educational Rights and Privacy Act (FERPA) restrictions while blurring the regulatory lines between public education institutions and their privately contracted vendors, a DeVos federal policy of high-tech data-mining throughout a privatized national schooling system could open back doors for treasure-troves of public student data to be shared with educational technology companies, and even non-educational corporations, for fascistic market manipulation and workforce planning.
Less Privacy, More Privatization
DeVos has a decorated track record of school privatization activism as a member of numerous “school-choice” advocacy organizations such as the Alliance for School Choice, Advocates for School Choice, Choices for Children, The Education Freedom Fund, the American Education Reform Council, the American Federation for Children, Children First America, the Great Lakes Education Project, and Jeb Bush’s Foundation for Excellence in Education. She has also funded EdChoice, formerly known as the Milton Friedman Foundation for Educational Choice. (As I demonstrate in my article “The Corporatization of Education”, “school choice” is just a euphemism, coined by Friedman himself, for charter school privatization).
Furthermore, DeVos has been instrumental in funding charter school privatization throughout Michigan where she poured $5 million into lobbying efforts for pro-voucher legislation. Additionally, she pumped $1.45 million into political efforts to stop the Michigan State Legislature from passing regulations that would have set up a local commission to oversee the state’s 80% for-profit charter school system.
With Trump’s pledge to commit $20 billion in federal funding to school-choice reform, don’t expect Secretary DeVos to skip a beat as a crusader for education privatization that will eradicate locally elected school boards.
Greenlighted with presidential backing, DeVos deregulation will be ripe to capitalize on the revised federal regulations under FERPA laws which have been reinterpreted since 2011 to now permit third-party corporations to data-mine American students. Through these (de)regulatory loopholes, a DeVos Department of Education could incentivize accelerated digitalization of student FERPA data to be aggregated by educational data-cumulating corporations, such as the now-defunct inBloom, which share databases with third-party corporations that could expropriate student data sets for potentially non-educational research and development.
To be sure, such third-party corporations must be deemed to have “legitimate educational interests” in order to be granted legal access to FERPA-protected data. However, as demonstrated in my article titled “National Charter School Fascism“, the institutional lines between traditional classroom education and “cradle-to-career” community-oriented learning are being blurred by the burgeoning privatization mergers of public education, public health and human services, and “community policing” agencies guided by workforce training pedagogies under public-private P-16/20 council governance.
With these public institutions privatized together through business partnerships managed under interlocking P-16/20 governance, DeVos’ policy may broaden the terms and conditions of what constitutes a “legitimate educational interest” to encompass any service that contributes to the improvement of the labor force or any of the healthcare, welfare, or criminal justice systems as integral sub-components of America’s new P-20/cradle-to-career schooling system. By incorporating social services and criminal justice agencies as ostensible branches of the US education system, public-private P-20 mergers will set the stage for the DeVos Department of Education to justify corporate data-mining of students for R&D by any contingent business that could enhance a student’s psychosocial development from cradle to career.1
No Child Data Left Un-Mined
DeVos is also the chair of the Philanthropy Roundtable, which published Blended Learning: A Wise Giver’s Guide to Bolstering Tech-Assisted Teaching, an April 2013 guidebook advocating for educational reform through deregulated charter and voucher privatization which implements individualized online computer-learning modules that streamline Common Core testing by data-mining students in real time.
The guidebook, which hypes Common Core as “an exciting idea” (30), endorses corporate charter schools such as KIPP Empower Academy LA, KIPP Chicago, KIPP Ascend Primary School, KIPP Ascend Middle School, KIPP Create College Prep Middle School, KIPP NYC, and KIPP Washington Heights Middle School as case-study success stories of the “blended-learning” methodology: “the artful combination of computerized instruction (personalized for each student . . . ) with small-group teaching that is closer to tutoring than to traditional mass lectures” (8). In fact, at some of these KIPP charter schools, the entire curriculum is “taught,” or facilitated, through a 100% blended-learning methodology.
For implementing such computerized instruction, the guidebook promotes adaptive learning software such as Knewton and Dreambox, which data-mine students with cognitive learning algorithms that mimic the behavioral advertising algorithms used by so many web-based corporations: “[m]uch as Netflix or Amazon or Pandora are able to learn from each user’s actions to predict what that person will next need or desire, so adaptive educational software can pick up how a given student learns, and what he or she is missing. . . . The lessons presented to students begin to differ, and teachers get suggestions on which resources they might try to get through problems with that pupil, based on his particular learning history” (68).2
This Philanthropy Roundtable blueprint for federal Common Core testing managed by private adaptive-learning/data-mining corporations will likely serve as something of a handbook for DeVos’s policies as Secretary of Education under the Every Student Succeeds Act. Indeed, ESSA stipulates provisions for school districts to implement an “‘innovative assessment system’ . . . that may include—(1) competency-based assessments . . . or performance-based assessments that combine into an annual summative determination for a student, which may be administered through computer adaptive assessments; and (2) assessments that validate when students are ready to demonstrate mastery or proficiency and allow for differentiated student support based on individual learning needs.”
The language here in the ESSA law reads as if it were tailored to facilitate the corporate adaptive-learning/data-mining endorsed by DeVos’s Philanthropy Roundtable. Under ESSA, public-private “‘innovative assessment system[s]” of individualized adaptive-learning/data-mining software such as Knewton and Dreambox can be contracted to proctor “computer adaptive assessments” for calculating student workforce “competenc[e]” and “performance” outcomes in accordance with ESSA’s “career readiness” clauses.
Such a policy of deregulated public-private data-mining for workforce development could also open back doors for corporations to coopt student data for behavioral advertising and corporate-fascist political-economic planning under the guise of “legitimate educational interest.”
If you think that a DeVos policy of public-private education data-mining won’t be ripe for abuse, then consider how privacy activists at the Electronic Frontier Foundation (EFF) have put the Google Corporation under fire from a complaint filed with the Federal Trade Commission. The EFF is charging Google with breaching its “K-12 School Service Provider Pledge to Safeguard Student Privacy” because it allegedly assimilated student data collected from Google Apps for Education (GAFE) and then expropriated that data for non-educational behavioral advertising. Similarly, Google is also being sued by University of California-Berkley students, along with hundreds of class-action litigants from twenty-one other states, because GAFE allegedly excavated their student emails to extract data for non-educational market R&D.
The verdicts are still out on both cases. In the meantime, Google’s loose privacy practices are setting the precedent for obfuscating the lines between government-restricted public student data and commercially tradable consumer data. Now, consider within these blurred lines the possibility of a DeVos resurrection of a national education data bank like inBloom, which aspired to digitally aggregate all student data across the nation—until it was shut down as a result of outcries from privacy advocates.
Originally called the Shared Learning Collaborative, the educational data-mining company known as inBloom was created with funding from the Bill and Melinda Gates Foundation and the Carnegie Corporation of New York. From 2011 to 2014, inBloom programs tabulated for each individual student copious data points in over “400 data fields” sequenced into three categorical data sets: personally identifiable information (PII), student information system (SIS) data, and “user interaction information” (UII).3
Charting these individualized and demographic data hubs with an operating system from the NewsCorp subsidiary Wireless Generation, inBloom stored this massive data aggregate in a web cloud managed by the Amazon Corporation, which has also built for the Central Intelligence Agency a similar web-based computing cloud that streamlines CIA data dossiers. Once uploaded to the Amazon cloud, this PII-SIS-UII data warehouse became accessible to educational institutions and other third-party vendors including for-profit corporations.
In 2014, after fighting against testimony from the Electronic Privacy Information Center (EPIC), and after battling a lawsuit filed with the New York State Supreme Court, inBloom caved in to mounting public backlash against its third-party data-sharing practices by terminating all of its contracts and shutting down all operations.
But don’t worry; DeVos could contract with a new Carnegie-created data-mining enterprise that is picking up where inBloom left off. Funded by a $5 million grant from the federal government through the National Science Foundation, Carnegie Mellon University has launched Learnsphere, a not-for-profit digital repository for sharing student data with third parties including for-profit vendors. Professor of Human Computer Interaction and Psychology at Carnegie Mellon University, Ken Koedinger, who heads up the Big Data project, explains that Learnsphere is basically a rendition of the infamous inBloom: “There certainly are some similarities,” stated Koedinger.
To be sure, Koedinger also says that “[i]n some ways, it’s a deep philosophical difference.” In particular, to mitigate public resistance from privacy activists, Learnsphere will not be lumping together all of its educational data into a single online cloud like inBloom. Instead, Learnsphere utilizes a “distributed infrastructure” which allows educational institutions and learning software companies to store student data on their own servers while creating proxy links between the servers and the central Learnsphere network. Also, Koedinger pledges that Learnsphere will collect zero PII.
Nevertheless, despite this “decentralized” IT infrastructure, Learnsphere is still sharing student data with third parties and for-profit vendors. Furthermore, Koedinger admits that Learnsphere must employ a monitor to scan databanks to filter out any “accidental” PII. Moreover, Learnsphere does collect certain SIS demographics such as the percentage of free and reduced-price lunch recipients in a given school.
For instance, the Learnsphere website contains a shared network of inter-collaborative databanks, including MITx and HarvardX Dataverse, which is a data hub that contains a file titled “Socioeconomic Status Indicators of HarvardX and MITx Participants 2012-2014” with the following description:
[t]his dataset includes the home mailing addresses of all participants . . . in MITx and HarvardX courses. For U.S. residents, These mailing addresses can be parsed and geo-matched with data from the US Census to develop a suite of socioeconomic status indicators, including median neighborhood income and neighborhood level of education. We also include self-reported survey data about parental level of education, and we include an indicator for whether or not the participant earned a certificate.
With such vast pools of socioeconomic SIS data cross-referenced with Learnsphere’s dossiers of cognitive-behavioral UII, third-party corporations under DeVos deregulation could commandeer these student data for epidemiological behavioral advertising and corporate-fascist workforce planning by computing real-time consumer behavior patterns and workforce development trends across various social groups and geopolitical regions.
National Standards for National Socialism
Of course, a DeVos policy of student data-mining for corporate-fascist workforce planning would be nothing new to America. Learnsphere and inBloom are only two of the most recent educational data-tabulating projects to be spawned from a long historical web of corporate-fascist Carnegie institutions.
Although the US Department of Education was not established until 1979, America’s progression toward a corporate-fascist national education policy can be traced at least as far back as the publication of the Carnegie-funded Conclusions and Recommendations for the Social Studies by the American Historical Association in 1934. The document reads:
[The United States] is embarking upon vast experiments in social planning and control which call for large scale cooperation on the part of the people . . . [T]he age of laissez faire in economy and government is closing and a new age of collectivism is emerging . . . The implications of education are clear and imperative: (a) the efficient functioning of the emerging economy and the full utilization of its potentialities require profound changes in the attitudes and outlook of the American people (qtd. in Iserbyt 40).
To measure progress toward the achievement of these corporate-fascist reeducation goals, twelve years later, the Carnegie Corporation of New York endowed the founding of Educational Testing Services (ETS) with a $750,000 grant in 1946 (Iserbyt 55). Ever since then, ETS has played a central role in calculating student metrics for the standardization of national learning benchmarks, including corporate-fascist workforce training goals. In 1964, the Carnegie Corporation put together the Committee on Assessing the Progress of Education, which in 1969 evolved into the National Assessment of Educational Progress (NAEP) (Iserbyt 89). Originally, NAEP stats were configured by the Education Commission of the States, which was likewise founded by a grant from the Carnegie Corporation (Iserbyt 91). But since 1983, the NAEP has been proctoring all of its “Nation’s Report Card” evaluations through ETS (Iserbyt 110).4
It should be noted that these national Carnegie metrics for corporate-fascist workforce schooling are rooted in the Hegelian philosophy of the first President of the Carnegie Institution, Skull-and-Bonesman Daniel Coit Gilman, an academic who was the first President of Johns Hopkins University and the first President of the University of California (62). According to former Research Fellow at Stanford University’s Hoover Institution, Antony C. Sutton, Gilman’s Hegelianism at the Carnegie Institution was carried on by “other members of The Order [of Skull and Bones] [who] have been on Carnegie boards since the turn of the century” (27).5 It should also be noted that DeVos’s colleague on the Trump Cabinet is Bonesman Stephen Mnuchin (Sutton 195, 299), who has been confirmed as US Treasury Secretary.
Will DeVos carry the Carnegie torch for corporate-fascist workforce planning by pushing Big Data privatization of education?
You don’t need a terabyte of data to forecast this prediction.
References:6
- The Bill and Melinda Gates Foundation donated $1,221,800 to the College for All Texans Foundation “to fund strategic planning and research support aimed at improving of P-20 data infrastructure and data use among policy makers and practitioners.”
- The founder, president, CEO, and Chairman of the Board of Netflix, Reed Hastings, sits on the KIPP Board of Directors, and he is also a former member of the California State Board of Education who has lobbied aggressively to usurp locally elected schoolboards with the “self-perpetuating governance” of corporate charter school councils.
- PII is data that identifies a specific student: name, address, Social Security number. SIS data tracks the institutional demographics of a school’s academic and behavioral outcomes: attendance and suspension rates; course grades, course levels, and class sizes; standardized test scores; learning disability and IEP classifications; free/reduced-price lunch distribution; ethnic population ratios. In addition, SIS data contains certain health information such as absences due to illness or other physical or mental health complication; and SIS can also contain criminal justice records pertaining to in-school arrests and other school code violations. UII is the psycho-behavioral data collected by adaptive learning software like Dreambox, Knewton, and Khan Academy; this real-time data tracks screen time and keystrokes per assessment module while mapping student-response feedback loops to algorithmically chart a student’s cognitive-behavioral efficacy.
- ETS has likewise played a crucial role in the data analyses of No Child Left Behind (NCLB) and Race to the Top testing. Specifically, ETS published two comprehensive reviews of NCLB testing entitled National Education Standards: Getting Beneath the Surface and Education Issues 2007. In addition, ETS conducted actuaries to contribute to a 2010 joint report by the US Department of Education and the RAND Corporation titled State and Local Implementation of the No Child Left Behind Act (Volume IX—Accountability under NCLB: Final Report). After the initiation of the Race to the Top program, ETS contracted with the Partnership for Assessment of Readiness for College and Careers (PARCC) to crunch the numbers for Race to the Top data-mining.
- For a detailed analysis of how The Order inculcated American education with a Hegelian philosophy that evolved into the cradle-to-career charter school system of fascistic workforce development, see my article entitled “Corporate-Fascist Workforce Training for the Hegelian State”.
- Iserbyt, Charlotte Thomson. The Deliberate Dumbing Down of America: A Chronological Paper Trail. Revised and Abridged Ed. Parkman, OH: Conscience Press, 2011. Print; Sutton, Antony C. America’s Secret Establishment: An Introduction to the Order of Skull and Bones. Updated Reprint. Walterville, OR: Trine Day, 2002. Print.