"The NSA has nothing on the monitoring tools that education technologists have developed in to 'personalize' and 'adapt' learning for students in public school districts across the United States"
Jesse Irwin, Model View Culture
Jesse Irwin, Model View Culture
The
state-finance matrix defined: Influenced by David Harvey's notion of the
state-finance nexus, the state-finance matrix is a highly
disciplined neoliberal landscape where state power structures and technologies
facilitate and protect the activities and interests of finance capitalism over
all else. This matrix provides an insulated environment for financialization
via securitization, which simply described, is a process where financial
institutions bundle together (illiquid) financial assets - primarily loans -
and transform them into (liquid) tradable securities that can be expeditiously
bought and sold in secondary financial markets. Within
this globalized environment, digital securities trading - including
“fictitious” trading, hedging and speculating in derivative markets - generates
“phantom wealth”; whereby the exchange of capital, money and currency
is detached from material or labor value. In the twenty-first century,
debt is the new global currency and is a primary source of (intangible) wealth
accumulation.
Rebooting the System for a New Age
Writing
in Forbes Magazine in 2013, technology entrepreneur Naveen Jain made an
assessment of the historical origins of mass public education by pointing out
that, “Our education system was developed for an industrial era.” Jain went on
to explain that the U.S. education system,
…today uses the mass production style manufacturing process of standardization. This process requires raw material that is grouped together based on a specific criteria. Those raw materials are then moved from one station to another station where an expert makes a small modification given the small amount of time given to complete their task. At the end of the assembly line, these assembled goods are standardized tested to see if they meet certain criteria before they are moved to the next advanced assembly line.
Jain
makes this point not as a critique of education serving the interests of
capitalism through the application of the scientific management model of
production (Taylorism) to schooling. On the contrary, he does so to make a case
that current education reform policies are a continuation of the original
mission of U.S. public education as an instrument of social control, yet only
being modernized to bolster financialized capitalism. As Jain puts it, “Our education system is not broken, it has just
become obsolete.” He goes on to explain:
When I think of all the tremendous, seemingly impossible feats made possible by entrepreneurs, I am amazed that more has not been done to reinvent our education system. I want all entrepreneurs to take notice that this is a multi-hundred billion dollar opportunity that’s ripe for disruption.
The
means by which such financial “opportunities” reside by “reinventing” education
are made more explicit when Jain goes on to claim, “Rethinking education starts
with embracing our individuality…[j]ust think of the opportunities we can
unlock by making education as addictive as a video game” by flipping the
current model on its head and use “technology to focus on our learners.” Using the same historical context
that Jain does to support this argument, the superintendent of Miami-Dade
County Public Schools (and rising star in the education reform industry),
Alberto Carvalho decreed in 2015, “Unfortunately, for most American
students the old factory model of education still applies. This is a recipe for
failure and frustration. We cannot address Digital Age needs with Industrial
Age education.” Carvalho goes on to claim:
We must leave behind us the days of sorting students by age and instruction by subject. More and more, our 8th-graders are studying alongside 6th-graders of similar ability, interests and readiness. After all, we aren’t grouped by age in the employment marketplace. No one told Mark Zuckerberg he couldn’t be CEO of Facebook because he wasn’t born the same year as Bill Gates.
Jain
and Carvalho’s edicts are an integral part of the education technology (EdTech)
industry’s marketing narrative, as a driving force and beneficiary of the
financialization of public education. Be it venture philanthropists, federal
and state policymakers or EdTech executives, the current mission of education
reform is to “reinvent” education, propelled by a narrative of benevolent
intent and remedied by meeting the needs of financial markets through embracing
education technologies. In doing so, the EdTech industry promotes its products
as being student-centered, competency-based “anytime-anywhere learning” or more
specifically as “personalized learning.” According to its advocates,
personalized learning simply means the differentiation of digitized coursework
for students based on their different skill levels that allows them to engage
in learning activities at their own pace through the use of digital tools.
Accordingly, the Gates Foundation claims on its Personalized Learning page, “In
personalized learning, the student is the leader, and the teacher is the
activator and the advisor.” On its Digital Tools and Content page, the
foundation goes on to report that personalized learning “technology is not just
a way for students to pursue their interests; it is way for them to discover
their interests.” Thus, personalized learning promises to revolutionize
American education and positions EdTech to be the vanguard in liberating
students to take control of their learning. As marketing and communications
professional Jesse Irwin puts it,
Since 2011, billions of dollars of venture capital investment have poured into public education through private, for-profit technologies that promise to revolutionize education… these tools promise to remedy the many, many societal ills facing public education with… technological advancements.
Like
a visionary leader of a social movement, Superintendent Carvalho calls us to
action by proclaiming, “Now is the time for transformation, but we must do more
than reboot the system; we must redesign it for the demands of a new age,
reaching and teaching each student in the ways he learns best. It’s that
simple, and that hard. All we need is the will, skill and belief to change.”
Ultimately,
personalized learning entails immersing students in digitized software and is
at the forefront of facilitating the disruption and replacement of traditional
public schooling, yet in even more officious and imperious ways. To understand
this better, we must ask ourselves: how is personalized learning personal? Contextualizing EdTech within the larger technological landscape is important to
truly get to the root of the answer to this question; as well as how it fits
into schooling as a function of social control within the 21st century cultural political economy. To
answer this, I will first take a step back and widen the scope before
I focus more deeply on this fundemental question.
Big Data, Surveillance and the State-Finance Matrix
The
21st century is an age
where massive quantities of digital information (data) is being captured,
stored, tracked, analyzed and bought and sold by private firms and government
agencies. Enormous amounts of data are collected every minute of every day from
online activities via computers, tablets, mobile devices, smart phone apps and
smart machines. This includes web server logs and clickstream data (every click
made), social media content and social network activity, shopping and credit
card use, text from emails and survey responses, mobile-phone call records, and
more. Mobile devices track travel patterns and driving speed. Everything that
is or becomes digital is collected, and contributes to an ever accumulating
behavioral data profile for everyone. This personal profile also includes medical,
mental health, employment, education and government records, including the U.S.
Census.
This
mass accumulation of digital data is the basis for what is called “Big Data.”
According to data systems expert Rohit Rai, “Big Data relates to data creation,
storage, retrieval and analysis that is remarkable” in terms of volume (how
much data), velocity (how fast data is processed), and variety (the various
types of data). It was the symbiotic relationship among Google, Yahoo,
Facebook, Twitter, LinkedIn, Amazon, Netflix and other large Internet companies
that propelled Big Data early on, all of which were heavy users as well as
creators of fundamental Big Data technologies. These are the companies that
established industry standards in creating the “culture of analytics" that
pervades every aspect of their business. Big data is a fundamental structure of
the financialized economy that is propelled by the Internet, cloud computing,
mobile devices and social media, intended to create generations of hyper-connected
consumers.
Big
Data begins with data collection, which feeds into the data mining pipeline, a
process which encompasses three intertwined scientific disciplines: the numeric
study of data relationships (statistics); human-like intelligence displayed by
software and/or machines (artificial intelligence); and algorithms that can
learn from data to make predictions (machine learning). According Skylads, a
digital software company, Artificial Intelligence refers to computers, machines
and systems that are capable of “natural language processing (i.e. communicate
with no trouble on a given language); automated reasoning (using stored
information to answer questions and draw new conclusions) and machine learning
(the ability to adapt to new circumstances and detect patterns).”
Machine
Learning has been fundamental in the development of artificial intelligence,
enabling machines to learn and adapt when exposed to massive amounts of data.
Historically, machine learning enabled a system to acquire knowledge, but only
through human supervised learning experiences. Currently, machine learning is
innovating into “Deep Learning” systems, which enables more general, powerful,
and faster machine learning. Deep learning empowers machines with perceptual
learning capabilities - unsupervised by humans - to react to real-world visual,
auditory and natural language data; then responds in intelligent ways.
According to the deep learning company Leverton, “Deep learning technology… is
based on the idea of programming algorithms to imitate functions of neurons in
the human brain.” Data analytics are essential to the advancement of machine
learning and deep learning systems. Data analytics involves the confluence of
four distinct types of analytics: Descriptive Analytics (what has happened or
what is happening); Diagnostic Analytics (why did it happen); Predictive
Analytics (what is likely to happen) and Prescriptive Analytics (what should
happen to influence future outcomes). Descriptive analytics is the starting
point and as more detailed and contextual data is gathered over time, this
allows for more sophisticated deep learning algorithms to be applied and for
the three subsequent types of analytics. Although these algorithms are
invisible to us, Michael Evans of Dartmouth College explains that with
analytics:
We see their output as recommendations about what we should do, or about what should be done to us. Netflix suggests your next TV show. Your car reminds you it’s time for an oil change. Siri tells you about a nearby restaurant. Machine-learning algorithms monitor information about what you do, find patterns in that data, and make informed guesses about what you want to do next. Without you, there’s no data, and there’s nothing for machine learning to learn.
According
to deep learning scientist, Michael Wu, predictive analytics does not predict
one potential future, but "multiple futures" centered on a
decision-maker's preferred actions. Wu contends that, "[s]ince a
prescriptive model is able to predict the possible consequences based on
different choice of action, it can also recommend the best course of action for
any pre-specified outcome.”
Social
media has always been a commercial venture and it’s primary purpose as a profit
generator quickly became about data mining, particularly in terms of sentiment
mining for predictive and prescriptive analysis. Sentiment analysis (opinion
mining) is a subset of predictive analysis and determines if online expressions
– text, “likes", emoticons, etc. - are positive, negative or neutral as
means to determine how people feel about specific topics. Sentiment analysis
gathering software scans across all social media conversations like Facebook,
Twitter blogs, news, forums, videos, reviews, images, etc., collecting data
streams for analysis via deep learning algorithms that classify and derive
meaning. According to Sandeep Raut, the Director for Digital Transformation at
Syntel:
Nestle, via their Digital Acceleration Team, tracks the sentiments of their 2000+ brands to know what their customers think and to deliver products that they want and to prevent crisis’s from happening. Coca-Cola, the brand that built its marketing message around happiness and sharing, has built vending machines which sets the price of a can based on how positive your tweets are. Consumers are always on their smartphones leaving the trails of their feelings in the digital world.
There
is an abundance of data across various vertical markets in banking, financial
services, insurance, healthcare, life sciences, retail, consumer goods,
manufacturing, travel and hospitality, IT, telecommunication, media,
entertainment, government, and more. This boon is driving demand for the most
current and innovative deep learning and analytics related products. The
financialized global economy thrives on high speed information processing on
many levels. Big Data has become the essential infrastructure of it. The three
v’s (volume, velocity and variety) of Big Data mining is not enough to support
investors and finance professionals in their activities of high frequency
trading, fund management, exploitation of markets and management of risk
exposure. Thus, the industry demands two additional v’s – veracity (accuracy)
and value (market value) that comes with the innovations of AI’s deep learning
systems, specifically predictive and sentiment analytics.
Working
alongside data scientists, financial experts are automating the extraction of
sentiment from a rapidly expanding array of sources to better understand the
personalized reactions of individuals and groups (investors and consumers) to
specific and real time information. Data attained from sources such as news
wires, economic announcements, social media, micro blogs, twitter, online
search engines, Wikipedia, etc., are invaluable instruments of this Business
Intelligence (BI) apparatus. According to a publication put out by TCS' Global
Consulting titled: Tuning in to the Emotions of the Capital Markets
with Sentiment Analysis, “real-time social data about customers’
family situation, business interests, passions, behavior patterns and
decisions, along with data from other systems…provides a deeper understanding
of customers.” The customer analytics company Buxton, goes on to explain how
companies and financial firms that couple customer analytics and predictive
analytics software to their data mining activities,
…can unlock who exactly your best customers are – looking at more than just demographics, but actually understanding what lifestyle characteristics your best customers have, including how they spend their money and live their lives. Once we understand the attributes of your best customers, we are able to show where everyone who looks just like those best customers lives - down to the household level - anywhere in your operating areas… More importantly, we’re able to tell you the value that each of those potential customers is worth…
Social
media has become a primary data mining source for the retail industry (flush
with private equity investors, while rapidly becoming an impact investment offering),
due to its capacity to obtain instant product and service feedback via social
networking sites and blogs.
Big
Data is also integrating machine-generated data that is automatically captured
(without human intervention) by sensors connected to the Internet of Things
(IoT). According to Internet Society, the IoT’s describes:
...scenarios in which network connectivity and computing capability extends to a constellation of objects, devices, sensors, and everyday items that are not ordinarily considered to be “computers’’; this allows the devices to generate, exchange, and consume data, often with minimal human intervention.
As
Eran Levy from the business analytics company Sisense reported in 2014, we live
in a world where everything will soon be equipped with an IP address, “from
your bicycle to your pens to your washing machine. All these things will be
linked and reported. Most importantly, they will be generating tons of data…
everything you do can be recorded and analyze." According to the
University of Phoenix Research Institute:
Every object, every interaction, everything we come into contact with will be converted into data. Once we decode the world around us and start seeing it through the lens of data, we will increasingly focus on manipulating the data to achieve desired outcomes. Thus we will usher in an era of “everything is programmable.
Basically,
IoT means that everything everywhere is being technologized, connected to a
vast network that feeds the Business Intelligence and state intelligence
ecosystem that is Big Data. In essence quantitative data – largely our own personal
data – will increasingly be used to “manipulate” us and “program” our
environments according to the demands of powerful interests.
As
the Chicago Tribune reported in 2016, a rapidly growing component of this vast
ecosystem is the biometrics data market, which is projected to be worth $21.9
Billion by 2020. As part of this data market, physiological biometrics involves
technologies that labels and describes individuals and groups through
physiological characteristics, largely for identification and authentication
(access control) purposes. Physical identifiers include, but are not limited
to, fingerprints, voice, face, ear, iris and retina recognition, DNA, vein
patterns, palm prints, hand geometry and scent. Behavioral biometrics uses data
gathering technology that builds a unique behavior profile on individual users
of devices, based on keystroke and mouse movement analysis and voice and gait
recognition (the way people walk). Writing in the financial services
publication CFO in 2015, Neuburger claimed, “Biometrics is the practice of
using a digital representation of a person’s individual’s physical
characteristics as a means to identify that specific person ‘out of a crowd.’”
Additionally,
biosensor enabled mobile, wearable, indigestible, implanted, tattooed and
contact lens devices monitor, track, compile and transmit data about our
overall health status, lifestyle and performance levels. This information can
be remotely detected and monitored in real time and then integrated into the
larger Big Data infrastructure. According to PSFK labs, "the world’s
leading provider of innovation insights," embedded sensory and display
technologies will soon be commonplace, outwardly conveying “information about
the wearer and his/her reaction to the surrounding environment. Responding to
everything from an individual’s emotional state to their interactions with
others with light, color and opacity, these adaptive materials create a novel
communication stream that informs both the wearer and those around them.” Biosensor
technology can also detect drug and alcohol use and stress/anxiety levels. When
attached to analytic programs, biometric data is used for predictive purposes
in terms of medical and mental health diagnosis and intervention. Biometrics is
already being used to link human behavior and physiological data to workforce
performance, a topic that requires an entire book to itself.
Video
analytic technology is developing and increasingly being integrated into the
Big Data and IoT ecosystem. Writing in Wired Magazine, Sean Verah describes
how sophisticated digital video recording devices using computer vision
algorithms that automatically analyze video in real time and over time are
currently being utilized in various ways by business and government. Very soon this
technology will have the capacity to survey every location on the planet from
land, sea, air and space; identifying hundreds of people (with gait, facial and
other recognition abilities) and objects within any given scene, while tracking
their movements and behavior. Writing in Information Week, Lisa Morgan claims,
“the Internet of Things is gaining momentum” whereby “sensors are now small and
cheap enough to embed in all kinds of devices, and more companies are
leveraging the vast data generated.
Data
expert Phil Harvey tells us, “Consider your world. It is data now. Data is in
everything we do. Especially in business. Writing in the Harvard
Business Review, Randy Bean reports how Big Data has become firmly
established within Fortune 1000 firms, especially in the financial industry,
“where data is plentiful and data investments are substantial.” The reliance on
Big Data in the financial industry is rapidly growing, where an increasing
majority of top firms are investing heavily in Big Data technologies, while it
is also critically important to the operations of their firms. Big Data has
become the new “corporate standard,” whereby the outcomes it produces and the
business proficiencies it enables is prioritized. Due to its ever expanding
demand and value, Big Data as a service market (BDaaS) is also rapidly growing
and involves the outsourcing of the wide variety of end-to-end Big Data mining
functions within the cloud as well as ongoing support services. As reported by
Forbes in 2015, it is estimated that the global Big Data market will be worth
$88 billion by 2021, while its auxiliary BDaaS market could be worth $30
billion. According to PriceWaterhouseCooper, venture capital investing is
booming within the software industry, with most of the money being poured into
big data analytics. According to industry insider Christopher Aderyeri,
“Financial services businesses, including the investment banks, generate and
store more data than any other business in any other sector…” As banking
giant Goldman Sachs put it in 2015,
We believe the Data Revolution is here to stay, and that investors should recognize its potential to reshape the economic landscape. We believe the changes wrought by the Data Revolution will continue to ripple across industries–separating winners from losers, based on those who can best use data as an advantage–including in the world of investment management.
Fundamentally,
Big Data serves a risk detecting and reduction function for investment banks.
It enables their data analysts to instantly assess the impact of potential of
escalating geopolitical risk on their assets and securities markets. With Big
Data, banks now have built-in systems that map out market-shaping past events
as a means to identify future patterns and risk.
Customer
Relationship Management (CRM), also referred to customer intelligence or
customer analytics, pioneered the “personalization”
and customer-centered approach to consumer engagement in industry and financial
markets. In doing so, according to technology company Invoca, CRM disseminates
the narrative that “a better customer experience is driven by data.” Shannon
Gerard, a technology company marketing manager, explains to industry insiders,
…customers are telling you what they want with every click, like, share, download, and call. Marketers have access to huge volumes and varieties of data. There are digital marketing channel data points (like web conversion rates, click-through-rates, open rates, online visits, keyword searches), transactional data (like credit card information and purchase value), and customer data (like region or city, age, gender, phone number, and phone type). With every marketing activity you have the opportunity to capture almost limitless data.
CRM’s
personalized marketing and customer-centered business model requires an
enhanced 360-degree (or complete) view of individual and groups of customers in
very intimate ways. This means mining all available data from all available
sources about customer’s behaviors, and employment and personal lives as a
means to shape long-term customer loyalty to increase market share (profits).
To do so, CRM systems seek to capture customer data inside and outside of a
given company and apply descriptive, predictive, diagnostic and prescriptive
analytics that generate demographic, behavioral and psychographic insights. In
doing so, a complete and complex profile of a customer's ecosystem and spheres
of influence are created by identifying customer’s social communities, family,
friends and coworkers; employment history; lifestyles; social activities;
political views; personal tastes and interests; group memberships, etc.
Advanced analytics applied to social media and other forums are also being used
to identify users that are “thought leaders” (or influencers) and users that
are followers, while also determining the relative strength of the leader on a
particular topic or site. In the world of CRM, this allows businesses to both
glean marketing trends from leaders as well as to target them more specifically
with marketing campaigns. Outside of CRM purposes, identifying and targeting
“thought leaders” can clearly serve more authoritarian purposes.
When
writing about the advantages of personalization and CRM, industry insider Ramon
Ray cautioned his industry peers that the associated privacy invasions can be
perceived as “creepy.”
In
the same vein, FinTech, according to Deutsche Bank, “is a term that defines the
digitization of the financial sector and is a catchall term used for advanced
internet- and cloud-based technologies in the financial sector.” Built into
this, and most relevantly, FinTech describes small and large financial firms
use and investments in innovative Big Data analytic technology to “personalize”
their customer engagement, trading and risk management activities. According to
Matt Turner of Business Insider, “Goldman Sachs is
going big on big data.” Turner goes on to report that both Goldman Sachs and JP
Morgan are investing “deeply” in artificial intelligence and deep learning.
Quoting Goldman Sach’s Don Duet, Turner reports, "It's a very important
both technological strategy for the firm as well as business strategy and
helping us move to a better degree of data-driven businesses as well as really
deriving expertise, content, and knowledge of information."
Lars
Hamberg, a portfolio manager at AFAM Funds, points out that financial firms
have used data to inform their decisions for quite a while, yet the tipping
point came with a breakthrough “when computers started learning how to read.”
Hamberg pointed to early financial industry experiments in using sentiment
analysis with social media, with “so-called Twitter hedge funds,” which were
not successful and caused many within the world of finance to “give up” on
exploiting data in financial markets. As Lumley went on to report, Hamberg
asks, "Why is it that big media companies like Google are the frontrunners
in behavioral analytics and big data? Banks know everything about their
customers. The financial sector has been filing away info on us for years and
yet they do nothing with it." Hamberg’s rhetorical question was speaking
to how technology giants like Google, Apple, Facebook, Amazon, Alibaba and a
host of new start-ups took the lead (post 2008 crisis) in redefining the
finance industry and its customer engagement practices with data mining
technology.
According
to the powerful global management consulting firm, McKinsey & Company, “In
a world where more than 90% of data has been created in the last two years,
FinTech data experiments hold promise for new products and services, delivered
in new ways.” To do so, McKinsey claims that Fintech offers “fully
personalized” real time customer engagement via smartphones and tablets armed
with applications that have access to unprecedented amounts of personal data.
FinTech startups, large consumer technology ecosystems like Facebook, Google,
Apple, Amazon, Netflix, etc. and innovating long existing financial firms
powered by Big Data analytics are, as McKinsey reports, “opening up new
[market] battlegrounds in areas like customer acquisition, customer servicing,
credit provision, relationship deepening through cross-sell, and customer
retention and loyalty.” More broadly, and as with CRM, this means FinTech is:
Building a comprehensive data ecosystem to access customer data from within and beyond the bank; creating a 360-degree view of customer activities; creating a robust analytics and data infrastructure; and leveraging these to drive scientific (versus case law-based) decisions across a broad range of activities from customer acquisition to servicing to crossselling to collections - all are critical to a bank’s future success (McKinsey & Company).
As
Steven Ramirez from the tech firm Beyond the Arc exuberantly exclaims, “Think
about all that text-based data available from customers’ social media comments,
postings on support forums, call center notes, chat sessions, complaints, and
in-app feedback.”
In
the financialized global economy, securitized debt is the new currency and
generator of mass wealth. As part of the vast Big Data and IoT ecosystem,
FinTech promises to more efficiently exploit debt-based services via: equity
platforms for crowd funding; platforms that connect lenders with borrowers;
data visualization tools that assist in following companies, suppliers and
clients; and a range of debt payment systems based in mobile and cloud
technologies. According to McKinsey & Comany, the strategy that enable
these activities are readily in place:
Two iPhone 6s have more memory capacity than the International Space Station. As one FinTech entrepreneur said… “I can scale a business on the public cloud. There has also been a significant demographic shift… 85 million Millennials, all digital natives, are coming of age, and they are considerably more open… to considering a new financial services provider that is not their parents’ bank.
Big
Data analytics is also empowering the financial industry with the opportunity
to predict “next best actions” in terms of “customer needs” and investment
strategies that expedite securitization of debt. McKinsey goes on to report,
“the most exciting area of FinTech innovation is the use of data” to innovate
lending practices, especially “with new credit scoring approaches - ranging
from looking at college attended and majors for… students with thin or no
credit files to trust scores based on social network data.” With the ability to
analyze an endless sea of data, FinTech ensures that the financial industry has
more information, and therefore more “personalized” control over the indebted
masses (“customers”).
Big
Data is also at the heart of the marriage between state and private security
and surveillance systems and high tech weaponry, which can be readily activated
to either pacify, coopt or violently suppress resistance movements. Just one
dimension of this apparatus was revealed in 2013 when Edward Snowden exposed
the U.S. National Security Agency’s PRISM program, which entailed Google,
Yahoo, Apple, Facebook, Microsoft, Skype and others giving the NSA access to
their customer’s activities, including search histories, posts, emails, file
transfers and video and audio chats. Since this revelation, the same companies
have waged a PR campaign to clear their reputations, while still appearing to
quietly work to participate in the same practices. Current surveillance debates
are focused on encryption, where federal law enforcement is demanding that
technology corporations build “backdoors” into their products so that state and
federal investigators can read and listen to “criminal suspects” encrypted
communications.
In
April 2016, it was reported that the use of audio and video recording
technology is increasing being uses on public and private bus and train systems
throughout the U.S., funded by the federal government and subsidizing the
private security industry. According to National Public Radio, “It's not clear
how many… transit agencies are doing this. But the answer seems to be a lot.
The cost of surveillance systems can run into the millions of dollars, which is
often covered by the Department of Homeland Security.”
Along
those lines, Edward Snowden and others have also revealed how Big Data is being
used by governments and the private sector for familiar purposes - to
specifically monitor and track activities deemed to be dissident in nature
(such as Black Lives Matter activism). The difference now is that it is
happening in more comprehensive and "personalized" ways.
Currently
local, state and federal agencies are using complex data software to identify
everything from suspicious Internet addresses and metadata associated with
fraudulent tax filings to automatically gathering traffic data via driver
smartphone apps through formal partnerships between google and city
governments. Yet the volume, velocity, variety and veracity of these
data-driven strategies are much more ominous. In 2008 Mike German and Jay
Stanley of the American Civil Liberties Union (ACLU) wrote:
If the federal government announced it was creating a new domestic intelligence agency made up of over 800,000 operatives dispersed throughout every American city and town, filing reports on even the most common everyday behaviors, Americans would revolt.
In
the wake of 9/11 in 2003, as the U.S. was invading Iraq and ramping up the
never ending ‘War on Terror,” the federal government established such a
strategy, which was updated and outlined by the Secretary of Homeland Security
Janet Napolitano in 2013, which in part reads:
We have learned as a Nation that we must maintain a constant, capable, and vigilant posture to protect ourselves against new threats and evolving hazards. Ensuring all of those who protect the Homeland have and share the necessary information to execute our missions… [o]ver the past two years, the Department has been working diligently with our homeland security partners to build a new architecture to execute our missions. The four essential elements of the distributed homeland security architecture-The National Network of Fusion Centers, the Nationwide Suspicious Activity Reporting Initiative, the National Terrorism Advisory System, and the "If you See Something, Say Something™" campaign-learn from and build on each other.
Within
this solidifying “architecture” Fusion Centers are on the front line of mining
and sharing the private data of millions of U.S. citizens and residents within
all realms of the state-finance matrix; making them a centerpiece and powerful
hub of the Big Data and Internet of Things ecosystem (ACLU, 2016). In their
role, according to the ACLU, Fusion Centers were designed to consolidate,
…localized domestic intelligence gathering into an integrated system that can distribute data both horizontally across a network of fusion centers and vertically, down to local law enforcement and up to the federal intelligence community. These centers can employ officials from federal, state and local law enforcement and homeland security agencies, as well as other state and local government entities, the federal intelligence community, the military and even private companies, to spy on Americans in virtually complete secrecy.
According
to the U.S. Department of Justice, Fusion Centers also exchange data with
“foreign partners.”
The
ACLU goes on to point out that within the context of “the nation’s long history
of abuse with regard to domestic ‘intelligence’ gathering at all levels of
government,” Fusion Centers are characterized by ambiguous and unaccountable
chains of command, extreme secrecy, “troubling private-sector and military
participation, and an apparent bent toward suspicionless information collection
and data mining.” While portrayed as necessary in keeping law abiding citizens
safe from terrorists and violent criminals, these strategies fundamentally
serve as a highly sophisticated authoritarian infrastructure.
Big
Data is also rapidly changing political analysis and communication, whereby
rich records about our lives - polls, voter registration, credit-card data and
much more - assist lobbyists and campaign managers to effectively target those
of us who will donate and show up to vote. As Phil Howard reported in Politico,
Big Data also enables party strategists to do in-house research and
experimentation on the “mid-spectrum, undecided or ideologically ‘soft voters’
to see what kinds of contacts and content will attract new supporters.” Phil
Howard takes it further, claiming:
[The] Internet of Things will be the most powerful political tool we've ever created. For democracies, the Internet of Things will transform how we as voters affect government — and how government touches (and tracks) our lives. Authoritarian governments will have their own uses for it, some of which are already appearing. And for everyone, both citizens and leaders, it's important to realize where it could head long before we get there.
Mining the “Solopreneurs” of Tomorrow
Understanding
all that encompasses Big Data is essential to recognizing how its associated
technology serves as surveillance infrastructure; intended to shape how humans
think, feel and behave as neoliberal subjects, to safeguard financial markets
and further enrich elite investors and to preserve the existing state-finance
social order. Returning to my earlier question concerning the infusion of
personalized learning into education in which I asked, “how is personalized
learning personal?” The answer: Big Data defines personalized learning and Big
Data’s “Deep Learning” analytics ensures that all personal information about a
student is known and to be exploited.
While social control is often considered to be one of the
primary purposes of schooling, in the age of neoliberal financialization, this
purpose is being taken to new heights through the instruments of education
technology (EdTech) as part of the Big Data infrastructure. Fundamentally, the
primary function of EdTech within this landscape is intended to build and
reinforce schooling as a structure of social control as part of the all
encompassing Big Data/Internet of Things surveillance ecosystem. To do this,
digital education software products on tablets, laptops, mobile devices,
wearable technology and more enable deep learning analytics and artificial
intelligence systems. Within this environment, teachers function as highly
disciplined data technicians tasked to monitor student behavior and compliance.
The revolution in education that the EdTech industry and education reformers
promise will allegedly empower students and teachers while remedying social
inequities through the use of technology that, according to marketing and
communications specialist Jesse Irwin,
...is being used to track and record every move students make in the classroom, grooming students for a lifetime of surveillance and turning education into one of the most data-intensive industries on the face of the earth. The NSA has nothing on the monitoring tools that education technologists have developed in to “personalize” and “adapt” learning for students in public school districts across the United States.
The
revolutionary venture philanthropist Bill Gates has advanced a $1.1
million-plus biometric sensor project that would equip children with Galvanic
Skin Response (GSR) bracelets as a means to measure student engagement. As
captured in a folksy TED Talk called "Teachers Need Real Feedback",
Gates is also advancing a $5 billion project to install video cameras in all
classroom to record teachers for the purposes of evaluating their performance.
The recordings would then be evaluated by distant contracted evaluators using a
check list of teaching skills to check off as they watch.
The
imposition of EdTech products throughout education are also reinforced by well
worn education reform narratives, a principal one being that increased
competition in global labor markets, coupled with an inequitable “skills gap,”
can only be addressed through a "digitally rich” social efficiency model
of education. Within this workforce development model of education, narrow
standards of competency are prescribed by, and serve the interests of,
financialized capitalism; thus rationalizing neoliberal reforms to instill the
“21st century skills” that are required of students as future
workers and consumer citizens. These are the interests by which education is
being realigned via EdTech to fulfill its original mission, marketed as the
determinant of success based on self-determined vocational choices, which
define student achievement, the value of credentials and employment
opportunities. A glaring example of this comes from the National Network
of Business and Industry Associations, a trade organization
that represents major industry sectors and is sponsored by the Business
Roundtable. Its members include the manufacturing, retail, health care, energy,
construction, hospitality, transportation and information technology sectors;
as well as venture philanthropists, including the Walmart Foundation. A 2014
policy publication put out by the Network titled, “Common Employability Skills:
A Foundation for Success in the Workplace: The Skills All Employees Need, No
Matter Where They Work,” proclaimed:
Today, employers in every industry sector emphasize the need for employees with certain foundational skills. This model can take its place as the foundation for all industries to map skill requirements to credentials and to career paths. In doing so, this model allows employees to understand the skills that all industries believe prepare individuals to succeed. Educators and other learning providers will also have an industry-defined road map for what foundational skills to teach, providing individuals the added benefit of being able to evaluate educational programs to ensure they will in fact learn skills that employers value.
These
"industry-defined" skills include “applied skills” grounded in the
disciplines of science, technology, engineering and math (STEM); along with
basic reading and writing skills. This includes the capacity for critical
thinking, similar to how a scientist or mechanic can hypothesize and work
through concrete problem solving steps. As the National Network of Business and
Industry Associations describes it, industry is also seeking “personal” and
“people” skills that are akin to being a soldier, through training that fosters
loyalty and discipline to a mission, where “integrity, initiative,
dependability, adaptability, professionalism, teamwork, communication and
respect” are ingrained. Workplace skills are naturally important too in terms
of planning and “organization, decision making, business fundamentals, customer
focus and working with tools and technology.” According to the company New
World of Work, the development of these skills via “personalized learning”
promises to efficiently determine which students will be “the solopreneurs
of tomorrow” with the understanding that:
Gone are the days of the 40-year career with a guaranteed pension. The workplace of today and tomorrow is not necessarily a place at all. It is a virtual matrix of collaborators across the globe with varied projects; requiring different skill sets at different times. Tomorrow’s workers will need to be agile, financially savvy, entrepreneurial in their approach to work and how to market themselves to the world, resilient, and comfortable in their own self-understanding.
This
vision of “tomorrow’s” workforce is not intended for everyone of course, only
those who will “add value” to the cultural political economy of the
state-finance matrix. Within this landscape, the deceptive market-based
empowerment discourse of personalization, self-determination and choice are
deeply embedded. Yet this model is insidiously akin to students being mice
within a Skinnerian lab’s maze, forced to find their own way to one
predetermined exit, while being monitored and evaluated the entire way. Those
who have the right “hard” and “soft” skills to make it through the maze are
deemed to be superior and allowed to live, while those who do not are allowed
to perish. Ultimately, within the digitized personalized and
competency-based model of education, the immense capacity for tracking and
sorting students would
make early social control theorist Edward Ross and social efficiency guru John
Franklin Bobbitt burst with envy. Especially in that the ideologies of Social
Darwinism and Eugenics are fundamentally embedded throughout.
As
far back as 2000, a Bloomberg posting titled The
Explosion in E Learning claimed,
“Dozens of new companies are springing up to serve the emerging K-12 market for
digital learning. Investors have poured nearly $1 billion into these companies
since the beginning of 1999, estimates Merrill Lynch.” In 2005 a
national Data Summit was convened by the Council of Chief State School Officers
and the US Department of Education to kick off a Data Quality Campaign, a
concerted national strategy “to improve the quality, accessibility and use of
data in education.” Supported by the Bill & Melinda Gates Foundation and
managed by the National Center for Educational Accountability (a pioneering
education reform data company), the summit was attended by a who’s who of
private sector education reform companies, who committed to “working together to…
encourage and support state policymakers to: ‘Improve the collection,
availability and use of high-quality education data, and Implement state
longitudinal data systems to improve student achievement.’”
This
long-term effort has since resulted in the federal government mandating every
state to collect personal student information in longitudinal databases, known
as the Student Longitudinal Data Systems (SLDS). As reported in the Washington
Post in 2015, with the SLDS,
...the personal information for each child is compiled and tracked from birth or preschool onwards, including medical information, survey data, and data from many state agencies such as the criminal justice system, child services, and health departments… their data more easily shared with vendors, other governmental agencies, across states, and with organizations or individuals engaged in education-related “research” or evaluation — all without parental knowledge or consent.
More
recently federal grants are being extended to states to expand these efforts,
including making it easier to share data through multi-state data exchanges. In
fact, according to the Washington Post, the federal grants require recipient
“states to collect and share early childhood data, match students and teachers
for the purpose of teacher evaluation, and promote inter-operability across
institutions, agencies, and states.”
This
unleashing of the EdTech industry – along with other financializing and
privatizing mandates - on U.S. public education have largely been facilitated
by federal policy and enacted by state legislatures. The first was the 2002 No
Child Left Behind Act (NCLB) and was largely implemented by states under the
threat of withholding federal funds intended for impoverished families. NCLB
was followed by the 2010 Race to the Top (RTTT) competition, which further
unleashed data-driven surveillance systems into public schools. RTTT’s
digitized Common Core curriculum and its associated online tests are well known
for accumulating huge amounts of personal student data across state borders and
sharing it with third parties, including the financial industry. Immediately
following the 2008 financial crisis, RTTT offered large grants to debt ridden
states contingent upon them passing an array of punitive education reform
policies. Drafted by industry and venture philanthropist, NCLB, RTTT and other
polices are also enacted by state governments at the behest of industry demands
and lobbying. More recently the U.S. Department of Education began to encourage
states and school districts to adopt deep learning (“personalized learning”)
systems by offering waivers from rigid NCLB rules.
The
National Education Policy Center reports that in 2010, the Foundation for
Excellence in Education convened the Digital Learning Council (a group
comprised of over one hundred leaders in the education reform industry), which
included “government, philanthropy, business, technology and members of policy
think tanks led by Co- Chairmen Jeb Bush, and West Virginia Governor Bob
Wise.” Following an American Legislative Exchange Council (ALEC)
template, the group drafted the 10 Elements of High Quality Digital Learning, a
comprehensive outline of policies and actions for state legislatures to follow
in integrating EdTech into K12 public education. In 2015 Congress revised NCLB
by passing the Every Student Succeeds Act (ESSA), which advances funding
for EdTech generally and personalized learning specifically.
The
ongoing ushering in of personalized learning into schools – via the deeply
intrusive capacities of Adaptive Learning Systems - is being positioned to
replace the current use of state mandated tests as student, teacher and school
accountability systems (outcomes-based education) with an even more insidious
competency-based education (CBE) model. Within this model, high stakes
assessments occur every day throughout the day, promising to undermine current
efforts by public education activists to center a resistance movement on
parents and students “Opting Out” of education reform mandated tests. Alarmed
by this data landscape, progressive education author Alfie Kohn claims:
Still more worrisome are the variants of ed tech that [are] putting grades online (thereby increasing their salience and their damaging effects), using computers to administer tests and score essays, and setting up “embedded” assessment that’s marketed as “competency-based”... [using] dystopian devices that basically test kids (and collect and store data about them) continuously… “to do in nanoseconds things that we shouldn’t be doing at all."
The
competencies of CBE within personalized learning are not earned by credit hours
completed, but instead by students working independently to complete a sequence
of digitized and tracked exercises that lead to a “badge of completion.” Once
such badge (a product of the multinational corporation Pearson) is the “Grit
Badge” that assesses “Growth, Resilience, Instinct, and Tenacity.” As Pearson
describes it, Grit Badges are an instrument that “demonstrates a strong
correlation of GRIT and several key success factors” including “desire to
improve one’s station in life, effort, employability, goal completion, goal
magnitude and income.” This grit narrative is embedded within a larger
education reform storyline that reinforces the myth of American meritocracy; is
largely used in reference to Black and Brown boys and implicitly attached to a
deficit label that reinforces the ideology of Eugenics. In the world of
personalized learning, these (merit) “badges” are the new credential for the
self-reliant “solo worker” in the so called “gig economy” (yes, like a musician
doing a gig). The gig economy is intrinsic to neoliberal financialization, in
which the drive to reduce labor costs as a means to maximize profits results in
greater worker insecurity and reduced wages and benefits within a society void
of social safety nets. This “liberates” workers to become temporary “solo”
workers and “independent contractors” within highly profitable companies that
make up the digitized “sharing” economy (Uber, Airbnb, TaskRabbit, etc).
According to a recent study, by 2020 forty percent of U.S. workers will be
independent solo workers attempting to piece together a series of “gigs” to
survive. As the Pearson corporation frames it:
Alternative learning credentials including college coursework, self-directed learning experiences, career training, and continuing education programs can play a powerful role in defining and articulating solo workers’ capabilities. Already badges that represent these credentials are serving an important purpose in fostering trust between solo workers, employers, and project teams because they convey skill transparency and deliver seamless verification of capabilities.
True
to the American tradition of myth making in the service of ideology,
Competency-Based Education and its personalized learning narrative is
compelling. Particularly since it plays on the fundamental American values of
individualism, meritocracy and grit, while offering hope of providing greater
opportunities for employment and freedom from the tyranny of bosses within the
bleak landscape of austerity. As such, to be a winner within this dog-eat-dog
“Wild West” economy, students as future solo workers are expected to show “true
grit” and have the “right stuff” in order to endure an unforgiving
financialized world.
Personalized
learning is also (conveniently) confused with the empowering pedagogical
practices associated with traditional theories of personal and student-centered
learning, which are deeply relational, actively collaborative, humanistic,
creative and based in intellectual discovery and critical inquiry. Instead,
personalized learning and its competency-based model relies on prefabricated
skills-based exercises based on a student’s data “profiled” competencies as
determined by adaptive learning analytic software. As Canadian scholar Philip
McRae points out, personalized learning does “not build more resilient,
creative, entrepreneurial or empathetic citizens through their individualized,
linear and mechanical software algorithms… [and instead] are reductionist and
primarily attend to those things that can be easily digitized and tested.”
A
Learning Management System (LMS) is the web-based education platform, which
functions as an essential part of EdTEch infrastructure and oversees the
integration of curriculum, instructional resources and assessment strategies in
both K12 and higher education. As Phillipo and Krongard claim in their
marketing publication, Learning Management System (LMS): The Missing Link
and Great Enabler, LMS’s “tie together contemporary education
reforms with effective and creative uses of technology." More importantly,
LMS’s facilitate learning analytics and data mining systems that profile,
track, monitor and shape behavior relating to student performance, teacher
productively and institutional success related to predetermined learning
outcomes. There are currently hundreds of LMS platforms to choose from, most of
which are integrating with major social networking sites and are increasingly
cloud-based. Data mining generally, as well as through EdTech, uses
machine/deep learning analytics to build user profiles based on the continuous
collection of data that describes individual users’ background, needs,
preferences and interests. Learning analytics is built into LMS systems and
borrows analytic technology intended to profile and analyze consumer
activities, identify trends, and predict consumer behavior. According to the
technology industry association, the New Media Consortium:
Education is embarking on a similar pursuit… learning analytics is already starting to provide crucial insights into student progress and interaction with online texts, courseware, and learning environments used to deliver instruction... [through] mobile and online platforms that track data to create responsive, personalized learning experiences.
Learning
analytics enables user modeling and is a fundamental component of Adaptive
Learning Systems, or “the new teaching machines.” According to a 2012 U.S.
Department of Education brief, user modeling analytics through EdTech cohere
with surveillance-based accountability systems within education reform by
encompassing,
…what a learner knows, what a learner’s behavior and motivation are, what the user experience is like… At the simplest level, analytics can detect when a student in an online course is going astray and nudge him or her on to a course correction. At the most complex, they hold promise of detecting boredom from patterns of key clicks and redirecting the student’s attention. Because these data are gathered in real time, there is a real possibility of continuous improvement via multiple feedback loops that operate at different time scales—immediate to the student for the next problem, daily to the teacher for the next day’s teaching, monthly to the principal for judging progress, and annually to the district and state administrators for overall school improvement.
As
with all EdTech products, the marketing of adaptive learning software is
replete with terms like “algorithms” and “predictive analytics” that promise to
roll in an equitable education utopia through the disruption of outdated
teaching practices. Yet, as is pervasive in the EdTech and education reform
industry, there is no evidence to support their claims (as I will document
later). Furthermore, its products are proprietary and therefore lack
transparency and are attached to fine-grained and commodified data mining
scheme that is brimming with privacy violations.
Intelligent
Tutor software, according to EdTech industry insider Barbara Kurshan, is an
Adaptive Learning System that is able to track the “mental steps” of learners
when they are engaged in problem-solving tasks as a means to diagnose
“misconceptions” so as to evaluate learners understanding of subject matter.
Kurshan also notes how Intelligent Tutor Systems offer “timely guidance,
feedback and explanations to the learner and can promote productive learning
behaviors, such as self-regulation, self-monitoring, and
self-explanation." It then prescribes content (curriculum) and learning
activities (pedagogy) based on a learner’s diagnosed level of difficulty.
According to Kurshan, “[t]hese systems are also able to mimic the benefits of
one-to-one tutoring, and some of these systems outperform untrained tutors in
specific topics and can approach the effectiveness of expert tutors.” Philip
McRae warns how the “adaptive learning system crusade” in education is highly
organized and is gaining momentum, driven by venture capitalists, private
equity investors and multinational corporations such as Pearson, which invested
over $3.5 billion into EdTech companies in the U.S. alone in 2014.
Adaptive
Learning Systems are integrated into the comprehensive data mining capacities
of LMS’s which are also being integrated with Student Information Systems
(SIS’s). SIS’s gathers digitized data concerning demographic information
(including income level, race and ethnicity), student records (including
grades, test scores, disabilities and Individual Education Plans), medical and
mental health history, attendance, disciplinary records and more. SIS’s
generate a wealth of longitudinal data that was previously difficult to gather
and consolidate. All together, these technologies have brought about a dramatic
growth in computational power and storage capacities that allow for the
gathering and housing of unprecedented amounts of data; intended to identify
behavioral connections and patterns of students (and teachers) and allowing
decision making engines to operate in real time learning systems.
According
to education technology researchers Castro, Nebot & Mugica, the
digitization of education via EdTech LMN’s has constructed an educational
infrastructure that is based on massive amounts of information about teaching
and learning interactions that are “endlessly generated and ubiquitously
available.” In their study about the popular LMS program Moodle; Romero,
Ventura & Garcia claim, “all this information provides a gold mine of
educational data. As Leonie Haimson and Cheri Kiesecker reported in the
Washington Post in 2015, “Remember that ominous threat from your
childhood, This will go down on your permanent record? Well, your
children’s permanent record is a whole lot bigger today and it may be
permanent. Information about your children’s behavior and nearly everything
else that a school or state agency knows about them is being tracked, profiled
and potentially shared.”
As
if channeling Ayn Rand, the notorious champion of free-market individualism ,
EdTech industry insiders market personalized learning by prioritizing the
learning needs of individuals over concerns for the common good. Accordingly,
and referring to personalized learning, Austin Martin of the EdTech company
Mindflash claims “the time has come” for education leaders “to look at the
individual rather than the organization as a whole.” Disturbingly, Martin goes
on to explain:
Getting personal with learning content and delivery begins with gaining a better understanding of the learner’s needs, interests, aspirations, and goals. Companies and organizations now are taking a deeper dive into data and analytics in order to assess, provide feedback, and determine personalized content and delivery methods. The rise of Big Data and the ability to analyze learning patterns and trends all the way to the individual learner by combing through mountains or terabytes of data is the new way to go as each learner’s “digital trail or footprint” can leave critical clues as to what works, what doesn’t, and how to create specific personal content.
Martin
goes on to back up this assertion by referencing the 2016 U.S. Department of
Education brief titled: Enhancing Teaching and Learning, Through
Educational Data Mining and Learning Analytics. The brief
references the DOE’s 2010 National Education Technology Plan, which extols the
virtues of the EdTech industry’s personalized learning mission:
When students are learning online, there are multiple opportunities to exploit the power of technology for formative assessment. The same technology that supports learning activities gathers data in the course of learning that can be used for assessment. As students work, the system can capture their inputs and collect evidence of their problem-solving sequences, knowledge, and strategy use, as reflected by the information each student selects or inputs, the number of attempts the student makes, the number of hints and feedback given, and the time allocation across parts of the problem.
As
in all aspects of the larger digital world of Big Data and Internet of Things;
the intention of personalized learning is all about comprehensive surveillance
intended to penetrate deeply into all aspects of students’ lives (as future
neoliberal subjects) to serve the interests of global financial markets. This
model of personalization is facilitated by the EdTEch industry via the
increasing integration of Adaptive Learning Systems (user modeling and
Intelligent Tutoring Systems), Learning Management Systems, Student Information
Systems; as well as MOOCS, Open Educational Resources, Flipped Classrooms,
Clickers and all that falls under what is called “blended learning.” According
to the multinational publishing corporation, Pearson:
Increasing student engagement is a goal in every school, and online and blended learning… allows schools to hold students accountable while keeping them engaged and motivated. Successful programs do much more than place technology in the classroom or students’ homes. Rather, flexible online and blended learning options allow districts to restructure traditional school models and provide data-driven and personalized instruction to improve learner outcomes.
As
Philip McRae explains, “Children and youth should not be treated like automated
teller machines or retail loyalty cards from which companies can extract
valuable data.” In essence, the EdTech industry and financial firms have
positioned themselves to have a reliable and extraordinary profit stream from
the state in the name of “educating our children.” It begins with the
continuous purchasing of the EdTech infrastructure, that ultimately leads to
collected, stored, processed, analyzed, and “personalized” data being resold
throughout the global finance industry.
With
the capacity to significantly increase the volume, velocity, variety, veracity
and value of data mining within schools, a highbred personalized learning
platform known as Learning Relationship Management (LRM) is being positioned to
fully-integrate student data from all possible sources. In doing so, LRM will
replace LMS and SIS systems and further integrate student data across domains.
By doing so, LRM seeks to reduce potential risk factors in terms of student
progress, even at the front end when it comes to student admission decisions in
selective K-12 schools (like charter schools) and in higher education. On
message with other leading personalized learning “revolutionaries,” marketing
research firm Wainhouse Research, claims that LRM’s will expedite the
disruption of the "'averagarian’ architecture of the existing system into
one that values the individual student” through “granting credentials, not
diplomas… replacing grades with a focus on mastering competencies; and… letting
students determine their educational pathways." LRM is also being
marketed as facilitating community engagement, mentoring, coaching, career and
alumni engagement functions byway of “productive” digitized relationships.
Borrowing from the conceptual framework of Customer Relationship Managements
systems, Wainhouse goes on to explain how LRM software offers “the ability to
make data-driven decisions based on ongoing metrics that serve as meta-views
into the school’s performance and micro-views into each learner’s progress.”
According to the research firm Eduventures, LRM also provides,
...the utility of a central and scalable repository for learning, but also robust records management and an analytics engine capable of tracking individual learner progress, staging interventions when necessary, and mapping student progress to learning objectives and career outcomes. In other words, LRM offers a holistic student success solution that the education world has never before experienced.
A
review of the marketing material of Fishtree, one of the leading Learning Relationship
Management software companies, is illuminating. Combining adaptive learning
with “the most incredible insight into student learning” through its “powerful
performance analytics,” the Fishtree LRM system promises to make teaching more
efficient and meaningful by providing a personalized learning experience that
creates “the ultimate in digital instruction.” According to Fishtree, their LRM
is the “ideal solution” for providing blended, flipped and project-based
learning using online curriculum, open education resources and real-time
content, while aligning them all with personal competencies and standards,
including Common Core. Fishtree’s LRM claims to allow educators to “adapt to
each learner’s needs with one click!” Fishtree
guarantees teachers that it will also help them “differentiate and personalize”
teaching with one “click of a button.” How so? Their LRM system makes
…the personalization process as easy as possible. Through our recommendation and personalization engines, each student using our system is offered resources adapted to suit his/her individual needs. This means every lesson and every assignment can be tailored to the needs of every single student. A teacher can then simply view student progress, and intervene at will. Personalized instruction has never been so easy!
Fishtree’s
“time-saving platform” creates and delivers dynamic, personalized lessons so
that teachers can “collaborate and interact with students safely and easily,
monitor student progress consistently, and access all of this using any device,
anywhere, at any time.” Furthermore, “Fishtree’s multitasking learning
platform allows teachers to keep track of student progress easily and
effectively” whereby “a teacher can simply assign activities at the click of a
button, assess without having to intervene in any way, and track progress
easily by viewing student performance through clear, informative graphs and
charts.” Through Fishtree’s powerful analytics systems, teachers can see “if a
student is not reaching the specified learning objectives, a teacher can
intervene and reassess at will, with one click. This ensures all students reach
their learning objectives at their own pace, while giving the teacher more
control and making the reassessment process as simple as possible.”
Additionally, as part of learning how to work as part of a collaborative team,
Fishtree’s social stream feature, facilitates cooperation between students
outside of the classroom, in real time, through their social media-based
application, while giving teachers the ability to monitor all student activity.
The Proof is in the “Data”
Ultimately,
industry interests peddle personalized learning as being “disruptive
innovation.” Critics point out that disruptive serves as code for “dismantle”
in that the mission of EdTech and personalized learning is to completely
destroy public education and replace it with a thoroughly financialized
authoritarian system. Within this system, education will be a privately
operated, yet state subsidized (see charter schools) sector of the Big
Data/Artificial Intelligence industry as an extension of the global financial
industry. This (de)personalized model of education is a vastly controlled
environment, void of meaningful human interaction, where children spend most of
their time seated alone (often in cubicles) interfacing with devices that
monitor and “adapts” digital materials based on the inputs clicked in by the
child.
The
EdTech industry’s profit making efforts to “reinvent” education is perpetually
being propelled by a massive marketing and public relations campaign that
permeates deep into society and is framed as an effort to forge a new era of
enlightenment. A 2015 Market Data Retrieval (MDR) report titled State
of the K-12 Market speaks to the inevitability of this era in
that fifty percent of curriculum directors nationwide expect extensive
“print-to-digital conversion” within the next three years, while over half of
all school districts are now administering online assessments within their
schools. MDR went on to claim, “These two intertwined aspects of education,
linked by more rigorous Common Core Standards throughout the country, are
reinforcing each other in this shift.” Accordingly, the Software &
Information Industry Association, a major EdTech trade association, tells us
that it is the efficacy of EdTech products that is driving this sanctified
mission:
The evidence is strong that technology and eLearning are powerful tools for revitalizing education and preparing students for the world beyond the classroom. Pioneering schools have already shown what is possible when good education and good technology come together. Technology has repeatedly proven its power to energize and improve learning outcomes.
When
one uncritically reads the majority of online publications about digital
education associated with EdTech, the overall impression is that it is the
inevitable magic bullet for improving student learning outcomes, college and
career readiness and in closing the “achievement gap” (a term intended to
ignore the existence of structural inequities). Questioning the effectiveness
of EdTech products as the driving force of the EdTech market in the The
Atlantic, Angela Chen reported, “every few months, a new study
claims that gadgets in the classroom don’t improve learning—but that hasn’t
stopped the educational technology market’s steady upward climb.” A review of
the literature supports Chen’s claims in that there is very little, if any,
credible evidence that EdTech products improve learning outcomes, according to
any standards. More importantly, there is however mounting evidence that
digitized technologies not only hinders learning in some areas, but is also
significantly detrimental to child development. In fact, when claims are
made that digital learning results in preferable or effective learning
outcomes, it is often without credible supporting evidence or only supported by
anecdotal evidence. Many of these claims are also advanced by studies that
appear to be neutral institutional research scholars, yet in almost all of
these studies, when digging a little deeper; institutional connections to the
EdTech industry and/or education reform advocacy groups were found.
One
example of this is a 2014 brief put out by the Alliance for Excellent Education
and Stanford Center for Opportunity Policy in Education, which begins by
acknowledging how “the introduction of technology into classrooms has failed to
meet the grand expectations proponents anticipated.” The brief, titled Using
Technology to Support At-Risk Students’ Learning, attempts to take
a middle-ground while also advancing the interests of industry. It promotes the
use of technology based on keeping teachers as trained professionals, yet
training them to be active facilitators of diverse digital learning methods.
Ultimately it promotes the EdTech industry having full access to the teaching,
learning and assessment of “at risk” students. The “funders” and “supporters”
of the Alliance for Excellent Education and Stanford Center for Opportunity
Policy in Education are a “who’s who” of education reform venture
philanthropists and industry trade associations. They are those who stand to
profit from EdTech’s full takeover of schools, particularly in the most
subordinated communities. The lead author of the report and founder of the
Stanford Center for Opportunity Policy in Education is Linda Darling Hammond, a
prominent education policy leader who is at once known to a be an advocate of
teachers, while also being an active proponent of education reform policies,
including Common Core State Standards. She was also a developer of one of its
aligned tests - Smarter Balanced. Alfie Kohn goes on to point out:
Two corresponding groups of educators seem particularly enamored with EdTech, “those who are awed by anything that emanates from the private sector, including books about leadership whose examples are drawn from Fortune 500 companies and filled with declarations about the need to "leverage strategic cultures for transformational disruption”; and those who experience excitement that borders on sexual arousal from anything involving technology—even though much of what falls under the heading “ed tech” is, to put it charitably, of scant educational value.
Recent
and more rigorous international studies report that reading comprehension and
assessment performance is encumbered when student learners use digital text
(via computers, tablets and smartphones) compared to paper text. Many of these
studies also report that subjects have a preference for readings text on paper.
According
to a 2015 global study sponsored by the Organisation for Economic Co-operation
and Development (OECD), in countries where students commonly use EdTech for
schoolwork, students’ reading performance declined. In countries that invest
heavily in EdTech for education, the results concluded there is no noticeable
improvement in student achievement in either math or science. The study, which
took into account social background and student demographics, concluded that
technology does not close the “achievement gap” between privileged and
impoverished students. The findings also report that students who spend
significant amount of time online are prone to feelings of loneliness.
In
a 2016 study, researchers from Carnegie Mellon University and Dartmouth College
found that reading on computers, tablets and smartphones significantly reduces
reading comprehension, and causes people “to ‘retreat’ to the less
cognitively-demanding lower end of the concrete-abstract continuum.” Or as
James Titcomb describes it in The Telegraph, this technology makes
“people unable to fully understand what they are reading as our brains retreat
into focusing on small details rather than meanings.”
A
2016 study whose subjects were high performing cadets at WestPoint, researchers
at the Massachusetts Institute of Technology concluded that the use of
electronic devices in classrooms “have a substantial negative effect on academic
performance.” A 2015 study by the Georgia Institute of
Technology found that “participants who read text on paper tended to take
more notes and spend more time studying than those who read from a screen.”
A
2015 study titled “Growing Up Digital (GUD) Alberta” was conducted by
researchers from the Alberta Teachers’ Association, the University of Alberta,
Boston Children’s Hospital, and Harvard Medical School. The purpose of the
study was to gain a better understanding of the scope of physical, mental and
social consequences of digital technologies on child development, specifically
in the realms of exercise, homework, identity formation, distraction,
cognition, learning, nutrition, and sleep quality and quantity. Researchers
conducted a stratified random sample of 3,600 teachers and principals across
Alberta Canada, resulting in over 2, 200 participants that generated a highly
representative sample of Alberta’s teaching population, which corresponds with
the profession’s demographics. The findings of the study are alarming.
Correlating with the increased use of digital technology in Alberta schools,
respondents reported that student learning has been in steady decline.
According to the study’s authors:
There is a strong sense among a majority of teaching professionals within this sample that over the past 3-5 years students across all grades are increasingly having a more difficult time focusing on educational tasks (76%), are coming to school tired (66%), and are less able to bounce back from adversity (ie lacking resilience) (62%). Concurrent to this, 44% of teachers note a decrease in student empathy, and over half of the sample (56%), reported an increase in the number of students who have discussed with them incidents of online harassment and/or cyberbullying. When asked how the number of students with “diagnosed” health issues has changed in their classrooms, the following three conditions were reported by a majority of teachers to have increased: anxiety disorders (85%), Attention Deficit Disorder and Attention Deficit Hyperactive Disorder (75%), and mood disorders such as depression (73%).
In
summary, this all encompassing Big Data surveillance infrastructure is the
engine by which finance capitalism further commodifies our lives, undermines
our labor power and intensifies social inequity and economic inequality. As essential components of this landscape, the EdTech industry and education reform policies are rapidly
redesigning schooling to only serve these nefarious interests. These dynamics,
combined with the nation's longstanding culture of domination, provide fertile
ground for the authoritarian society that the United States promises to become in
the coming decades, if not already.
*A MORE COMPREHENSIVE VERSION OF THIS ARTICLE CAN BE FOUND HERE
*A MORE COMPREHENSIVE VERSION OF THIS ARTICLE CAN BE FOUND HERE
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